Endocrinology (part 5 – calcium and bone metabolism)

Some observations from chapter 6:

“*Osteoclasts – derived from the monocytic cells; resorb bone. *Osteoblasts – derived from the fibroblast-like cells; make bone. *Osteocytes – buried osteoblasts; sense mechanical strain in bone. […] In order to ensure that bone can undertake its mechanical and metabolic functions, it is in a constant state of turnover […] Bone is laid down rapidly during skeletal growth at puberty. Following this, there is a period of stabilization of bone mass in early adult life. After the age of ~40, there is a gradual loss of bone in both sexes. This occurs at the rate of approximately 0.5% annually. However, in ♀ after the menopause, there is a period of rapid bone loss. The accelerated loss is maximal in the first 2-5 years after the cessation of ovarian function and then gradually declines until the previous gradual rate of loss is once again established. The excess bone loss associated with the menopause is of the order of 10% of skeletal mass. This menopause-associated loss, coupled with higher peak bone mass acquisition in ♂, largely explains why osteoporosis and its associated fractures are more common in ♀.”

“The clinical utility of routine measurements of bone turnover markers is not yet established. […] Skeletal radiology[:] *Useful for: *Diagnosis of fracture. *Diagnosis of specific diseases (e.g. Paget’s disease and osteomalacia). *Identification of bone dysplasia. *Not useful for assessing bone density. […] Isotope bone scans are useful for identifying localized areas of bone disease, such as fracture, metastases, or Paget’s disease. […] Isotope bone scans are particularly useful in Paget’s disease to establish the extent and sites of skeletal involvement and the underlying disease activity. […] Bone biopsy is occasionally necessary for the diagnosis of patients with complex metabolic bone diseases. […] Bone biopsy is not indicated for the routine diagnosis of osteoporosis. It should only be undertaken in highly specialist centres with appropriate expertise. […] Measurement of 24h urinary excretion of calcium provides a measure of risk of renal stone formation or nephrocalcinosis in states of chronic hypercalcaemia. […] 250H vitamin D […] is the main storage form of vitamin D, and the measurement of ‘total vitamin D’ is the most clinically useful measure of vitamin D status. Internationally, there remains controversy around a ‘normal’ or ‘optimal’ concentration of vitamin D. Levels over 50nmol/L are generally accepted as satisfactory and values <25nmol/L representing deficiency. True osteomalacia occurs with vitamin D values <15 nmol/L. Low levels of 250HD can result from a variety of causes […] Bone mass is quoted in terms of the number of standard deviations from an expected mean. […] A reduction of one SD in bone density will approximately double the risk of fracture.”

[I should perhaps add a cautionary note here that while this variable is very useful in general, it is more useful in some contexts than in others; and in some specific disease process contexts it is quite clear that it will tend to underestimate the fracture risk. Type 1 diabetes is a clear example. For more details, see this post.]

“Hypercalcaemia is found in 5% of hospital patients and in 0.5% of the general population. […] Many different disease states can lead to hypercalcaemia. […] In asymptomatic community-dwelling subjects, the vast majority of hypercalcaemia is the result of hyperparathyroidism. […] The clinical features of hypercalcaemia are well recognized […]; unfortunately, they are non-specific […] [They include:] *Polyuria. *Polydipsia. […] *Anorexia. *Vomiting. *Constipation. *Abdominal pain. […] *Confusion. *Lethargy. *Depression. […] Clinical signs of hypercalcaemia are rare. […] the presence of bone pain or fracture and renal stones […] indicate the presence of chronic hypercalcaemia. […] Hypercalcaemia is usually a late manifestation of malignant disease, and the primary lesion is usually evident by the time hypercalcaemia is expressed (50% of patients die within 30 days).”

“Primary hyperparathyroidism [is] [p]resent in up to 1 in 500 of the general population where it is predominantly a disease of post-menopausal ♀ […] The normal physiological response to hypocalcaemia is an increase in PTH secretion. This is termed 2° hyperparathyroidism and is not pathological in as much as the PTH secretion remains under feedback control. Continued stimulation of the parathyroid glands can lead to autonomous production of PTH. This, in turn, causes hypercalcaemia which is termed tertiary hyperparathyroidism. This is usually seen in the context of renal disease […] In majority of patients [with hyperparathyroidism] without end-organ damage, disease is benign and stable. […] Investigation is, therefore, primarily aimed at determining the presence of end-organ damage from hypercalcaemia in order to determine whether operative intervention is indicated. […] It is generally accepted that all patients with symptomatic hyperparathyroidism or evidence of end-organ damage should be considered for parathyroidectomy. This would include: *Definite symptoms of hypercalcaemia. […] *Impaired renal function. *Renal stones […] *Parathyroid bone disease, especially osteitis fibrosis cystica. *Pancreatitis. […] Patients not managed with surgery require regular follow-up. […] <5% fail to become normocalcaemic [after surgery], and these should be considered for a second operation. […] Patients rendered permanently hypoparathyroid by surgery require lifelong supplements of active metabolites of vitamin D with calcium. This can lead to hypercalciuria, and the risk of stone formation may still be present in these patients. […] In hypoparathyroidism, the target serum calcium should be at the low end of the reference range. […] any attempt to raise the plasma calcium well into the normal range is likely to result in unacceptable hypercalciuria”.

“Although hypocalcaemia can result from failure of any of the mechanisms by which serum calcium concentration is maintained, it is usually the result of either failure of PTH secretion or because of the inability to release calcium from bone. […] The clinical features of hypocalcaemia are largely as a result of neuromuscular excitability. In order of  severity, these include: *Tingling – especially of fingers, toes, or lips. *Numbness – especially of fingers, toes, or lips. *Cramps. *Carpopedal spasm. *Stridor due to laryngospasm. *Seizures. […] symptoms of hypocalcaemia tend to reflect the severity and rapidity of onset of the metabolic abnormality. […] there may be clinical signs and symptoms associated with the underlying condition: *Vitamin D deficiency may be associated with generalized bone pain, fractures, or proximal myopathy […] *Hypoparathyroidism can be accompanied by mental slowing and personality disturbances […] *If hypocalcaemia is present during the development of permanent teeth, these may show areas of enamel hypoplasia. This can be a useful physical sign, indicating that the hypocalcaemia is long-standing. […] Acute symptomatic hypocalcaemia is a medical emergency and demands urgent treatment whatever the cause […] *Patients with tetany or seizures require urgent IV treatment with calcium gluconate […] Care must be taken […] as too rapid elevation of the plasma calcium can cause arrhythmias. […] *Treatment of chronic hypocalcaemia is more dependent on the cause. […] In patients with mild parathyroid dysfunction, it may be possible to achieve acceptable calcium concentrations by using calcium supplements alone. […] The majority of patients will not achieve adequate control with such treatment. In those cases, it is necessary to use vitamin D or its metabolites in pharmacological doses to maintain plasma calcium.”

“Pseudohypoparathyroidism[:] *Resistance to parathyroid hormone action. *Due to defective signalling of PTH action via cell membrane receptor. *Also affects TSH, LH, FSH, and GH signalling. […] Patients with the most common type of pseudohypoparathyroidism (type 1a) have a characteristic set of skeletal abnormalities, known as Albright’s hereditary osteodystrophy. This comprises: *Short stature. *Obesity. *Round face. *Short metacarpals. […] The principles underlying the treatment of pseudohypoparathyroidism are the same as those underlying hypoparathyroidism. *Patients with the most common form of pseudohypoparathyroidism may have resistance to the action of other hormones which rely on G protein signalling. They, therefore, need to be assessed for thyroid and gonadal dysfunction (because of defective TSH and gonadotrophin action). If these deficiencies are present, they need to be treated in the conventional manner.”

“Osteomalacia occurs when there is inadequate mineralization of mature bone. Rickets is a disorder of the growing skeleton where there is inadequate mineralization of bone as it is laid down at the epiphysis. In most instances, osteomalacia leads to build-up of excessive unmineralized osteoid within the skeleton. In rickets, there is build-up of unmineralized osteoid in the growth plate. […] These two related conditions may coexist. […] Clinical features [of osteomalacia:] *Bone pain. *Deformity. *Fracture. *Proximal myopathy. *Hypocalcaemia (in vitamin D deficiency). […] The majority of patients with osteomalacia will show no specific radiological abnormalities. *The most characteristic abnormality is the Looser’s zone or pseudofracture. If these are present, they are virtually pathognomonic of osteomalacia. […] Oncogenic osteomalacia[:] Certain tumours appear to be able to produce FGF23 which is phosphaturic. This is rare […] Clinically, such patients usually present with profound myopathy as well as bone pain and fracture. […] Complete removal of the tumour results in resolution of the biochemical and skeletal abnormalities. If this is not possible […], treatment with vitamin D metabolites and phosphate supplements […] may help the skeletal symptoms.”

Hypophosphataemia[:] Phosphate is important for normal mineralization of bone. In the absence of sufficient phosphate, osteomalacia results. […] In addition, phosphate is important in its own right for neuromuscular function, and profound hypophosphataemia can be accompanied by encephalopathy, muscle weakness, and cardiomyopathy. It must be remembered that, as phosphate is primarily an intracellular anion, a low plasma phosphate does not necessarily represent actual phosphate depletion. […] Mainstay [of treatment] is phosphate replacement […] *Long-term administration of phosphate supplements stimulates parathyroid activity. This can lead to hypercalcaemia, a further fall in phosphate, with worsening of the bone disease […] To minimize parathyroid stimulation, it is usual to give one of the active metabolites of vitamin D in conjunction with phosphate.”

“Although the term osteoporosis refers to the reduction in the amount of bony tissue within the skeleton, this is generally associated with a loss of structural integrity of the internal architecture of the bone. The combination of both these changes means that osteoporotic bone is at high risk of fracture, even after trivial injury. […] Historically, there has been a primary reliance on bone mineral density as a threshold for treatment, whereas currently there is far greater emphasis on assessing individual patients’ risk of fracture that incorporates multiple clinical risk factors as well as bone mineral density. […] Osteoporosis may arise from a failure of the body to lay down sufficient bone during growth and maturation; an earlier than usual onset of bone loss following maturity; or an rate of that loss. […] Early menopause or late puberty (in ♂ or ♀) is associated with risk of osteoporosis. […] Lifestyle factors affecting bone mass [include:] *weight-bearing exercise [increase bone mass] […] *Smoking. *Excessive alcohol. *Nulliparity. *Poor calcium nutrition. [These all decrease bone mass] […] The risk of osteoporotic fracture increases with age. Fracture rates in ♂ are approximately half of those seen in ♀ of the same age. An ♀ aged 50 has approximately a 1:2 chance [risk, surely… – US] of sustaining an osteoporotic fracture in the rest of her life. The corresponding figure for a ♂ is 1:5. […] One-fifth of hip fracture victims will die within 6 months of the injury, and only 50% will return to their previous level of independence.”

“Any fracture, other than those affecting fingers, toes, or face, which is caused by a fall from standing height or less is called a fragility (low-trauma) fracture, and underlying osteoporosis should be considered. Patients suffering such a fracture should be considered for investigation and/or treatment for osteoporosis. […] [Osteoporosis is] [u]sually clinically silent until an acute fracture. *Two-thirds of vertebral fractures do not come to clinical attention. […] Osteoporotic vertebral fractures only rarely lead to neurological impairment. Any evidence of spinal cord compression should prompt a search for malignancy or other underlying cause. […] Osteoporosis does not cause generalized skeletal pain. […] Biochemical markers of bone turnover may be helpful in the calculation of fracture risk and in judging the response to drug therapies, but they have no role in the diagnosis of osteoporosis. […] An underlying cause for osteoporosis is present in approximately 10-30% of women and up to 50% of men with osteoporosis. […] 2° causes of osteoporosis are more common in ♂ and need to be excluded in all ♂ with osteoporotic fracture. […] Glucocorticoid treatment is one of the major 2° causes of osteoporosis.”


February 22, 2018 Posted by | Books, Cancer/oncology, Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Pharmacology | Leave a comment

Endocrinology (part 4 – reproductive endocrinology)

Some observations from chapter 4 of the book below.

“*♂. The whole process of spermatogenesis takes approximately 74 days, followed by another 12-21 days for sperm transport through the epididymis. This means that events which may affect spermatogenesis may not be apparent for up to three months, and successful induction of spermatogenesis treatment may take 2 years. *♀. From primordial follicle to primary follicle, it takes about 180 days (a continuous process). It is then another 60 days to form a preantral follicle which then proceeds to ovulation three menstrual cycles later. Only the last 2-3 weeks of this process is under gonadotrophin drive, during which time the follicle grows from 2 to 20mm.”

“Hirsutism (not a diagnosis in itself) is the presence of excess hair growth in ♀ as a result of androgen production and skin sensitivity to androgens. […] In ♀, testosterone is secreted primarily by the ovaries and adrenal glands, although a significant amount is produced by the peripheral conversion of androstenedione and DHEA. Ovarian androgen production is regulated by luteinizing hormone, whereas adrenal production is ACTH-dependent. The predominant androgens produced by the ovaries are testosterone and androstenedione, and the adrenal glands are the main source of DHEA. Circulating testosterone is mainly bound to sex hormone-binding globulin (SHBG), and it is the free testosterone which is biologically active. […] Slowly progressive hirsutism following puberty suggests a benign cause, whereas rapidly progressive hirsutism of recent onset requires further immediate investigation to rule out an androgen-secreting neoplasm. [My italics, US] […] Serum testosterone should be measured in all ♀ presenting with hirsutism. If this is <5nmol/L, then the risk of a sinister cause for her hirsutism is low.”

“Polycystic ovary syndrome (PCOS) *A heterogeneous clinical syndrome characterized by hyperandrogenism, mainly of ovarian origin, menstrual irregularity, and hyperinsulinaemia, in which other causes of androgen excess have been excluded […] *A distinction is made between polycystic ovary morphology on ultrasound (PCO which also occurs in congenital adrenal hyperplasia, acromegaly, Cushing’s syndrome, and testesterone-secreting tumours) and PCOS – the syndrome. […] PCOS is the most common endocrinopathy in ♀ of reproductive age; >95% of ♀ presenting to outpatients with hirsutism have PCOS. *The estimated prevalence of PCOS ranges from 5 to 10% on clinical criteria. Polycystic ovaries on US alone are present in 20-25% of ♀ of reproductive age. […] family history of type 2 diabetes mellitus is […] more common in ♀ with PCOS. […] Approximately 70% of ♀ with PCOS are insulin-resistant, depending on the definition. […] Type 2 diabetes mellitus is 2-4 x more common in ♀ with PCOS. […] Hyperinsulinaemia is exacerbated by obesity but can also be present in lean ♀ with PCOS. […] Insulin […] inhibits SHBG synthesis by the liver, with a consequent rise in free androgen levels. […] Symptoms often begin around puberty, after weight gain, or after stopping the oral contraceptive pill […] Oligo-/amenorrhoea [is present in] 70% […] Hirsutism [is present in] 66% […] Obesity [is present in] 50% […] *Infertility (30%). PCOS accounts for 75% of cases of anovulatory infertility. The risk of spontaneous miscarriage is also thought to be higher than the general population, mainly because of obesity. […] The aims of investigations [of PCOS] are mainly to exclude serious underlying disorders and to screen for complications, as the diagnosis is primarily clinical […] Studies have uniformly shown that weight reduction in obese ♀ with PCOS will improve insulin sensitivity and significantly reduce hyperandrogenaemia. Obese ♀ are less likely to respond to antiandrogens and infertility treatment.”

“Androgen-secreting tumours [are] [r]are tumours of the ovary or adrenal gland which may be benign or malignant, which cause virilization in ♀ through androgen production. […] Virilization […] [i]ndicates severe hyperandrogenism, is associated with clitoromegaly, and is present in 98% of ♀ with androgen-producing tumours. Not usually a feature of PCOS. […] Androgen-secreting ovarian tumours[:] *75% develop before the age of 40 years. *Account for 0.4% of all ovarian tumours; 20% are malignant. *Tumours are 5-25cm in size. The larger they are, the more likely they are to be malignant. They are rarely bilateral. […] Androgen-secreting adrenal tumours[:] *50% develop before the age of 50 years. *Larger tumours […] are more likely to be malignant. *Usually with concomitant cortisol secretion as a variant of Cushing’s syndrome. […] Symptoms and signs of Cushing’s syndrome are present in many of ♀ with adrenal tumours. […] Onset of symptoms. Usually recent onset of rapidly progressive symptoms. […] Malignant ovarian and adrenal androgen-secreting tumours are usually resistant to chemotherapy and radiotherapy. […] *Adrenal tumours. 20% 5-year survival. Most have metastatic disease at the time of surgery. *Ovarian tumours. 30% disease-free survival and 40% overall survival at 5 years. […] Benign tumours. *Prognosis excellent. *Hirsutism improves post-operatively, but clitoromegaly, male pattern balding, and deep voice may persist.”

*Oligomenorrhoea is defined as the reduction in the frequency of menses to <9 periods a year. *1° amenorrhoea is the failure of menarche by the age of 16 years. Prevalence ~0.3% *2° amenorrhoea refers to the cessation of menses for >6 months in ♀ who had previously menstruated. Prevalence ~3%. […] Although the list of causes is long […], the majority of cases of secondary amenorrhoea can be accounted for by four conditions: *Polycystic ovary syndrome. *Hypothalamic amenorrhoea. *Hyperprolactinaemia. *Ovarian failure. […] PCOS is the only common endocrine cause of amenorrhoea with normal oestrogenization – all other causes are oestrogen-deficient. Women with PCOS, therefore, are at risk of endometrial hyperplasia, and all others are at risk of osteoporosis. […] Anosmia may indicate Kallman’s syndrome. […] In routine practice, a common differential diagnosis is between mild version of PCOS and hypothalamic amenorrhoea. The distinction between these conditions may require repeated testing, as a single snapshot may not discriminate. The reason to be precise is that PCOS is oestrogen-replete and will, therefore, respond to clomiphene citrate (an antioestrogen) for fertility. HA will be oestrogen-deficient and will need HRT and ovulation induction with pulsatile GnRH or hMG [human Menopausal Gonadotropins – US]. […] […] 75% of ♀ who develop 2° amenorrhoea report hot flushes, night sweats, mood changes, fatigue, or dyspareunia; symptoms may precede the onset of menstrual disturbances.”

“POI [Premature Ovarian Insufficiency] is a disorder characterized by amenorrhoea, oestrogen deficiency, and elevated gonadotrophins, developing in ♀ <40 years, as a result of loss of ovarian follicular function. […] *Incidence – 0.1% of ♀ <30 years and 1% of those <40 years. *Accounts for 10% of all cases of 2° amenorrhoea. […] POI is the result of accelerated depletion of ovarian germ cells. […] POI is usually permanent and progressive, although a remitting course is also experienced and cannot be fully predicted, so all women must know that pregnancy is possible, even though fertility treatments are not effective (often a difficult paradox to describe). Spontaneous pregnancy has been reported in 5%. […] 80% of [women with Turner’s syndrome] have POI. […] All ♀ presenting with hypergonadotrophic amenorrhoea below age 40 should be karyotyped.”

“The menopause is the permanent cessation of menstruation as a result of ovarian failure and is a retrospective diagnosis made after 12 months of amenorrhoea. The average age of at the time of the menopause is ~50 years, although smokers reach the menopause ~2 years earlier. […] Cycles gradually become increasingly anovulatory and variable in length (often shorter) from about 4 years prior to the menopause. Oligomenorrhoea often precedes permanent amenorrhoea. in 10% of ♀, menses cease abruptly, with no preceding transitional period. […] During the perimenopausal period, there is an accelerated loss of bone mineral density (BMD), rendering post-menopausal more susceptible to osteoporotic fractures. […] Post-menopausal are 2-3 x more likely to develop IHD [ischaemic heart disease] than premenopausal , even after age adjustments. The menopause is associated with an increase in risk factors for atherosclerosis, including less favourable lipid profile, insulin sensitivity, and an ↑ thrombotic tendency. […] ♀ are 2-3 x more likely to develop Alzheimer’s disease than ♂. It is suggested that oestrogen deficiency may play a role in the development of dementia. […] The aim of treatment of perimenopausal ♀ is to alleviate menopausal symptoms and optimize quality of life. The majority of women with mild symptoms require no HRT. […] There is an ↑ risk of breast cancer in HRT users which is related to the duration of use. The risk increases by 35%, following 5 years of use (over the age of 50), and falls to never-used risk 5 years after discontinuing HRT. For ♀ aged 50 not using HRT, about 45 in every 1,000 will have cancer diagnosed over the following 20 years. This number increases to 47/1,000 ♀ using HRT for 5 years, 51/1,000 using HRT for 10 years, and 57/1,000 after 15 years of use. The risk is highest in ♀ on combined HRT compared with oestradiol alone. […] Oral HRT increases the risk [of venous thromboembolism] approximately 3-fold, resulting in an extra two cases/10,000 women-years. This risk is markedly ↑ in ♀ who already have risk factors for DVT, including previous DVT, cardiovascular disease, and within 90 days of hospitalization. […] Data from >30 observational studies suggest that HRT may reduce the risk of developing CVD [cardiovascular disease] by up to 50%. However, randomized placebo-controlled trials […] have failed to show that HRT protects against IHD. Currently, HRT should not be prescribed to prevent cardiovascular disease.”

“Any chronic illness may affect testicular function, in particular chronic renal failure, liver cirrhosis, and haemochromatosis. […] 25% of  who develop mumps after puberty have associated orchitis, and 25-50% of these will develop 1° testicular failure. […] Alcohol excess will also cause 1° testicular failure. […] Cytotoxic drugs, particularly alkylating agents, are gonadotoxic. Infertility occurs in 50% of patients following chemotherapy, and a significant number of  require androgen replacement therapy because of low testosterone levels. […] Testosterone has direct anabolic effects on skeletal muscle and has been shown to increase muscle mass and strength when given to hypogonadal men. Lean body mass is also with a reduction in fat mass. […] Hypogonadism is a risk factor for osteoporosis. Testosterone inhibits bone resorption, thereby reducing bone turnover. Its administration to hypogonadal has been shown to improve bone mineral density and reduce the risk of developing osteoporosis. […] *Androgens stimulate prostatic growth, and testosterone replacement therapy may therefore induce symptoms of bladder outflow obstruction in with prostatic hypertrophy. *It is unlikely that testosterone increases the risk of developing prostrate cancer, but it may promote the growth of an existing cancer. […] Testosterone replacement therapy may cause a fall in both LDL and HDL cholesterol levels, the significance of which remains unclear. The effect of androgen replacement therapy on the risk of developing coronary artery disease is unknown.”

“Erectile dysfunction [is] [t]he consistent inability to achieve or maintain an erect penis sufficient for satisfactory sexual intercourse. Affects approximately 10% of and >50% of >70 years. […] Erectile dysfunction may […] occur as a result of several mechanisms: *Neurological damage. *Arterial insufficiency. *Venous incompetence. *Androgen deficiency. *Penile abnormalities. […] *Abrupt onset of erectile dysfunction which is intermittent is often psychogenic in origin. *Progressive and persistent dysfunction indicates an organic cause. […] Absence of morning erections suggests an organic cause of erectile dysfunction.”

“*Infertility, defined as failure of pregnancy after 1 year of unprotected regular (2 x week) sexual intercourse, affects ~10% of all couples. *Couples who fail to conceive after 1 years of regular unprotected sexual intercourse should be investigated. […] Causes[:] *♀ factors (e.g. PCOS, tubal damage) 35%. *♂ factors (idiopathic gonadal failure in 60%) 25%. *Combined factors 25%. *Unexplained infertility 15%. […] [♀] Fertility declines rapidly after the age of 36 years. […] Each episode of acute PID causes infertility in 10-15% of cases. *Trachomatis is responsible for half the cases of PID in developed countries. […] Unexplained infertility [is] [i]nfertility despite normal sexual intercourse occurring at least twice weakly, normal semen analysis, documentation of ovulation in several cycles, and normal patent tubes (by laparoscopy). […] 30-50% will become pregnant within 3 years of expectant management. If not pregnant by then, chances that spontaneous pregnancy will occur are greatly reduced, and ART should be considered. In ♀>34 years of age, then expectant management is not an option, and up to six cycles of IUI or IVF should be considered.”

February 9, 2018 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Genetics, Medicine, Pharmacology | Leave a comment

Endocrinology (part 3 – adrenal glands)

Some observations from chapter 3 below.

“The normal adrenal gland weigh 4-5g. The cortex represents 90% of the normal gland and surrounds the medulla. […] Glucocorticoid (cortisol […]) production occurs from the zona fasciculata, and adrenal androgens arise from the zona reticularis. Both of these are under the control of ACTH [see also my previous post about the book – US], which regulates both steroid synthesis and also adrenocortical growth. […] Mineralocorticoid (aldosterone […]) synthesis occurs in zona glomerulosa, predominantly under the control of the renin-angiotensin system […], although ACTH also contributes to its regulation. […] The adrenal gland […] also produces sex steroids in the form of dehydroepiandrostenedione (DHEA) and androstenedione. The synthetic pathway is under the control of ACTH. Urinary steroid profiling provides quantitative information on the biosynthetic and catabolic pathways. […] CT is the most widely used modality for imaging the adrenal glands. […] MRI can also reliably detect adrenal masses >5-10mm in diameter and, in some circumstances, provides additional information to CT […] PET can be useful in locating tumours and metastases. […] Adrenal vein sampling (AVS) […] can be useful to lateralize an adenoma or to differentiate an adenoma from bilateral hyperplasia. […] AVS is of particular value in lateralizing small aldosterone-producing adenomas that cannot easily be visualized on CT or MRI. […] The procedure should only be undertaken in patients in whom surgery is feasible and desired […] [and] should be carried out in specialist centres only; centres with <20 procedures per year have been shown to have poor success rates”.

“The majority of cases of mineralocorticoid excess are due to excess aldosterone production, […] typically associated with hypertension and hypokalemia. *Primary hyperaldosteronism is a disorder of autonomous aldosterone hypersecretion with suppressed renin levels. *Secondary hyperaldosteronism occurs when aldosterone hypersecretion occurs 2° [secondary, US] to elevated circulating renin levels. This is typical of heart failure, cirrhosis, or nephrotic syndrome but can also be due to renal artery stenosis and, occasionally, a very rare renin-producing tumour (reninoma). […] Primary hyperaldosteronism is present in around 10% of hypertensive patients. It is the most prevalent form of secondary hypertension. […] Aldosterone causes renal sodium retention and potassium loss. This results in expansion of body sodium content, leading to suppression of renal renin synthesis. The direct action of aldosterone on the distal nephron causes sodium retention and loss and hydrogen and potassium ions, resulting in a hypokalaemic alkalosis, although serum potassium […] may be normal in up to 50% of cases. Aldosterone has pathophysiological effects on a range of other tissues, causing cardiac fibrosis, vascular endothelial dysfunction, and nephrosclerosis. […] hypertension […] is often resistant to conventional therapy. […] Hypokalaemia is usually asymptomatic. […] Occasionally, the clinical syndrome of hyperaldosteronism is not associated with excess aldosterone. […] These conditions are rare.”

“Bilateral adrenal hyperplasia [make up] 60% [of cases of primary hyperaldosteronism]. […] Conn’s syndrome (aldosterone-producing adrenal adenoma) [make up] 35%. […] The pathophysiology of bilateral adrenal hyperplasia is not understood, and it is possible that it represents an extreme end of the spectrum of low renin essential hypertension. […] Aldosterone-producing carcinoma[s] [are] [r]are and usually associated with excessive secretion of other corticosteroids (cortisol, androgen, oestrogen). […] Indications [for screening include:] *Patients resistant to conventional antihypertensive medication (i.e. not controlled on three agents). *Hypertension associated with hypokalaemia […] *Hypertension developing before age of 40 years. […] Confirmation of autonomous aldosterone production is made by demonstrating failure to suppress aldosterone in face of sodium/volume loading. […] A number of tests have been described that are said to differentiate between the various subtypes of 1° [primary, US] aldosteronism […]. However, none of these are sufficiently specific to influence management decisions”.

“Laparoscopic adrenalectomy is the treatment of choice for aldosterone-secreting adenomas […] and laparoscopic adrenalectomy […] has become the procedure of choice for removal of most adrenal tumours. *Hypertension is cured in about 70%. *If it persists […], it is more amenable to medical treatment. *Overall, 50% become normotensive in 1 month and 70% within 1 year. […] Medical therapy remains an option for patients with bilateral disease and those with a solitary adrenal adenoma who are unlikely to be cured by surgery, who are unfit for operation, or who express a preference for medical management. *The mineralocorticoid receptor antagonist spironolactone […] has been used successfully for many years to treat hypertension and hypokalaemia associated with bilateral adrenal hyperplasia […] Side effects are common – particularly gynaecomastia and impotence in ♂, menstrual irregularities in ♀, and GI effects. […] Eplerenone […] is a mineralocorticoid receptor antagonist without antiandrogen effects and hence greater selectivity and less side effects than spironolactone. *Alternative drugs include the potassium-sparing diuretics amiloride and triamterene.”

“Cushing’s syndrome results from chronic excess cortisol [see also my second post in this series] […] The causes may be classified as ACTH-dependent and ACTH-independent. […] ACTH-independent Cushing’s syndrome […] is due to adrenal tumours (benign and malignant), and is responsible for 10-15% of cases of Cushing’s syndrome. […] Benign adrenocortical adenomas (ACA) are usually encapsulated and <4cm in diameter. They are usually associated with pure glucocorticoid excess. *Adrenocortical carcinomas (ACC) are usually >6cm in diameter, […] and are not infrequently associated with local invasion and metastases at the time of diagnosis. Adrenal carcinomas are characteristically associated with the excess secretion of several hormones; most frequently found is the combination of cortisol and androgen (precursors) […] ACTH-dependent Cushing’s results in bilateral adrenal hyperplasia, thus one has to firmly differentiate between ACTH-dependent and independent causes of Cushing’s before assuming bilateral adrenal hyperplasia as the primary cause of disease. […] It is important to note that, in patients with adrenal carcinoma, there may also be features related to excessive androgen production in ♀ and also a relatively more rapid time course of development of the syndrome. […] Patients with ACTH-independent Cushing’s syndrome do not suppress cortisol […] on high-dose dexamethasone testing and fail to show a rise in cortisol and ACTH following administration of CRH. […] ACTH-independent causes are adrenal in origin, and the mainstay of further investigation is adrenal imaging by CT”.

“Adrenal adenomas, which are successfully treated with surgery, have a good prognosis, and recurrence is unlikely. […] Bilateral adrenalectomy [in the context of bilateral adrenal hyperplasia] is curative. Lifelong glucocorticoid and mineralocorticoid treatment is [however] required. […] The prognosis for adrenal carcinoma is very poor despite surgery. Reports suggest a 5-year survival of 22% and median survival time of 14 months […] Treatment of adrenocortical carcinoma (ACC) should be carried out in a specialist centre, with expert surgeons, oncologists, and endocrinologists with extensive treatment in treating ACC. This improves survival.”

“Adrenal insufficiency [AI, US] is defined by the lack of cortisol, i.e. glucocorticoid deficiency, may be due to destruction of the adrenal cortex (1°, Addison’s disease and congenital adrenal hyperplasia (CAH) […] or due to disordered pituitary and hypothalamic function (2°). […] *Permanent adrenal insufficiency is found in 5 in 10,000 population. *The most frequent cause is hypothalamic-pituitary damage, which is the cause of AI in 60% of affected patients. *The remaining 40% of cases are due to primary failure of the adrenal to synthesize cortisol, almost equal prevalence of Addison’s disease (mostly of autoimmune origin, prevalence 0.9-1.4 in 10,000) and congenital adrenal hyperplasia (0.7-1.0 in 10,000). *2° adrenal insufficiency due to suppression of pituitary-hypothalamic function by exogenously administered, supraphysiological glucocorticoid doses for treatment of, for example, COPD or rheumatoid arthritis, is much more common (50-200 in 10,000 population). However, adrenal function in these patients can recover”.

“[In primary AI] [a]drenal gland destruction or dysfunction occurs due to a disease process which usually involves all three zones of the adrenal cortex, resulting in inadequate glucocorticoid, mineralocorticoid, and adrenal androgen precursor secretion. The manifestations of insufficiency do not usually appear until at least 90% of the gland has been destroyed and are usually gradual in onset […] Acute adrenal insufficiency may occur in the context of acute septicaemia […] Mineralocorticoid deficiency leads to reduced sodium retention and hyponatraemia and hypotension […] Androgen deficiency presents in ♀ with reduced axillary and pubic hair and reduced libido. (Testicular production of androgens is more important in ♂). [In secondary AI] [i]nadequate ACTH results in deficient cortisol production (and ↓ androgens in ♀). […] Mineralocorticoid secretion remains normal […] The onset is usually gradual, with partial ACTH deficiency resulting in reduced response to stress. […] Lack of stimulation of skin MC1R due to ACTH deficiency results in pale skin appearance. […] [In 1° adrenal insufficiency] hyponatraemia is present in 90% and hyperkalaemia in 65%. […] Undetectable serum cortisol is diagnostic […], but the basal cortisol is often in the normal range. A cortisol >550nmol/L precludes the diagnosis. At times of acute stress, an inappropriately low cortisol is very suggestive of the diagnosis.”

“Autoimmune adrenalitis[:] Clinical features[:] *Anorexia and weight loss (>90%). *Tiredness. *Weakness – generalized, no particular muscle groups. […] Dizziness and postural hypotension. *GI symptoms – nausea and vomiting, abdominal pain, diarrhea. *Arthralgia and myalgia. […] *Mediated by humoral and cell-mediated immune mechanisms. Autoimmune insufficiency associated with polyglandular autoimmune syndrome is more common in ♀ (70%). *Adrenal cortex antibodies are present in the majority of patients at diagnosis, and […] they are still found in approximately 70% of patients 10 years later. Up to 20% patients/year with [positive] antibodies develop adrenal insufficiency. […] *Antiadrenal antibodies are found in <2% of patients with other autoimmune endocrine disease (Hashimoto’s thyroiditis, diabetes mellitus, autoimmune hypothyroidism, hypoparathyroidism, pernicious anemia). […] antibodies to other endocrine glands are commonly found in patients with autoimmune adrenal insufficiency […] However, the presence of antibodies does not predict subsequent manifestation of organ-specific autoimmunity. […] Patients with type 1 diabetes mellitus and autoimmune thyroid disease only rarely develop autoimmune adrenal insufficiency. Approximately 60% of patients with Addison’s disease have other autoimmune or endocrine disorders. […] The adrenals are small and atrophic in chronic autoimmune adrenalitis.”

“Autoimmune polyglandular syndrome (APS) type 1[:] *Also known as autoimmune polyendocrinopathy, candidiasis, and ectodermal dystrophy (APECED). […] [C]hildhood onset. *Chronic mucocutaneous candidiasis. *Hypoparathyroidism (90%), 1° adrenal insufficiency (60%). *1° gonadal failure (41%) – usually after Addison’s diagnosis. *1° hypothyroidism. *Rarely hypopituitarism, diabetes insipidus, type 1 diabetes mellitus. […] APS type 2[:] *Adult onset. *Adrenal insufficiency (100%). 1° autoimmune thyroid disease (70%) […] Type 1 diabetes mellitus (5-20%) – often before Addison’s diagnosis. *1° gonadal failure in affected women (5-20%). […] Schmidt’s syndrome: *Addison’s disease, and *Autoimmune hypothyroidism. *Carpenter syndrome: *Addison’s disease, and *Autoimmune hypothyroidism, and/or *Type 1 diabetes mellitus.”

“An adrenal incidentaloma is an adrenal mass that is discovered incidentally upon imaging […] carried out for reasons other than a suspected adrenal pathology.  […] *Autopsy studies suggest incidence prevalence of adrenal masses of 1-6% in the general population. *Imagining studies suggest that adrenal masses are present 2-3% in the general population. Incidence increases with ageing, and 8-10% of 70-year olds harbour an adrenal mass. […] It is important to determine whether the incidentally discovered adrenal mass is: *Malignant. *Functioning and associated with excess hormonal secretion.”


January 17, 2018 Posted by | Books, Cancer/oncology, Diabetes, Epidemiology, Immunology, Medicine, Nephrology, Pharmacology | Leave a comment

Endocrinology (part 2 – pituitary)

Below I have added some observations from the second chapter of the book, which covers the pituitary gland.

“The pituitary gland is centrally located at the base of the brain in the sella turcica within the sphenoid bone. It is attached to the hypothalamus by the pituitary stalk and a fine vascular network. […] The pituitary measures around 13mm transversely, 9mm anteroposteriorly, and 6mm vertically and weighs approximately 100mg. It increases during pregnancy to almost twice its normal size, and it decreases in the elderly. *Magnetic resonance imaging (MRI) currently provides the optimal imaging of the pituitary gland. *Computed tomography (CT) scans may still be useful in demonstrating calcification in tumours […] and hyperostosis in association with meningiomas or evidence of bone destruction. […] T1– weighted images demonstrate cerebrospinal fluid (CSF) as dark grey and brain as much whiter. This imagining is useful for demonstrating anatomy clearly. […] On T1– weighted images, pituitary adenomas are of lower signal intensity than the remainder of the normal gland. […] The presence of microadenomas may be difficult to demonstrate.”

“Hypopituitarism refers to either partial or complete deficiency of anterior and/or posterior pituitary hormones and may be due to [primary] pituitary disease or to hypothalamic pathology which interferes with the hypothalamic control of the pituitary. Causes: *Pituitary tumours. *Parapituitary tumours […] *Radiotherapy […] *Pituitary infarction (apoplexy), Sheehan’s syndrome. *Infiltration of the pituitary gland […] *infection […] *Trauma […] *Subarachnoid haemorrhage. *Isolated hypothalamic-releasing hormone deficiency, e.g. Kallmann’s syndrome […] *Genetic causes [Let’s stop here: Point is, lots of things can cause pituitary problems…] […] The clinical features depend on the type and degree of hormonal deficits, and the rate of its development, in addition to whether there is intercurrent illness. In the majority of cases, the development of hypopituitarism follows a characteristic order, which secretion of GH [growth hormone, US], then gonadotrophins being affected first, followed by TSH [Thyroid-Stimulating Hormone, US] and ACTH [Adrenocorticotropic Hormone, US] secretion at a later stage. PRL [prolactin, US] deficiency is rare, except in Sheehan’s syndrome associated with failure of lactation. ADH [antidiuretic hormone, US] deficiency is virtually unheard of with pituitary adenomas but may be seen rarely with infiltrative disorders and trauma. The majority of the clinical features are similar to those occurring when there is target gland insufficiency. […] NB Houssay phenomenon. Amelioration of diabetes mellitus in patients with hypopituitarism due to reduction in counter-regulatory hormones. […] The aims of investigation of hypopituitarism are to biochemically assess the extent of pituitary hormone deficiency and also to elucidate the cause. […] Treatment involves adequate and appropriate hormone replacement […] and management of the underlying cause.”

“Apoplexy refers to infarction of the pituitary gland due to either haemorrhage or ischaemia. It occurs most commonly in patients with pituitary adenomas, usually macroadenomas […] It is a medical emergency, and rapid hydrocortisone replacement can be lifesaving. It may present with […] sudden onset headache, vomiting, meningism, visual disturbance, and cranial nerve palsy.”

“Anterior pituitary hormone replacement therapy is usually performed by replacing the target hormone rather than the pituitary or hypothalamic hormone that is actually deficient. The exceptions to this are GH replacement […] and when fertility is desired […] [In the context of thyroid hormone replacement:] In contrast to replacement in [primary] hypothyroidism, the measurement of TSH cannot be used to assess adequacy of replacment in TSH deficiency due to hypothalamo-pituitary disease. Therefore, monitoring of treatment in order to avoid under- and over-replacement should be via both clinical assessment and by measuring free thyroid hormone concentrations […] [In the context of sex hormone replacement:] Oestrogen/testosterone administration is the usual method of replacement, but gonadotrophin therapy is required if fertility is desired […] Patients with ACTH deficiency usually need glucocorticoid replacement only and do not require mineralcorticoids, in contrast to patients with Addison’s disease. […] Monitoring of replacement [is] important to avoid over-replacement which is associated with BP, elevated glucose and insulin, and reduced bone mineral density (BMD). Under-replacement leads to the non-specific symptoms, as seen in Addison’s disease […] Conventional replacement […] may overtreat patients with partial ACTH deficiency.”

“There is now a considerable amount of evidence that there are significant and specific consequences of GH deficiency (GDH) in adults and that many of these features improve with GH replacement therapy. […] It is important to differentiate between adult and childhood onset GDH. […] the commonest cause in childhood is an isolated variable deficiency of GH-releasing hormone (GHRH) which may resolve in adult life […] It is, therefore, important to retest patients with childhood onset GHD when linear growth is completed (50% recovery of this group). Adult onset. GHD usually occurs [secondarily] to a structural pituitary or parapituitary condition or due to the effects of surgical treatment or radiotherapy. Prevalence[:] *Adult onset GHD 1/10,000 *Adult GHD due to adult and childhood onset GHD 3/10,000. Benefits of GH replacement[:] *Improved QoL and psychological well-being. *Improved exercise capacity. *↑ lean body mass and reduced fat mass. *Prolonged GH replacement therapy (>12-24 months) has been shown to increase BMD, which would be expected to reduce fracture rate. *There are, as yet, no outcome studies in terms of cardiovascular mortality. However, GH replacement does lead to a reduction (~15%) in cholesterol. GH replacement also leads to improved ventricular function and ↑ left ventricular mass. […] All patients with GHD should be considered for GH replacement therapy. […] adverse effects experienced with GH replacement usually resolve with dose reduction […] GH treatment may be associated with impairment of insulin sensitivity, and therefore markers of glycemia should be monitored. […] Contraindications to GH replacement[:] *Active malignancy. *Benign intracranial hypertension. *Pre-proliferative/proliferative retinopathy in diabetes mellitus.”

“*Pituitary adenomas are the most common pituitary disease in adults and constitute 10-15% of primary brain tumours. […] *The incidence of clinically apparent pituitary disease is 1 in 10,000. *Pituitary carcinoma is very rare (<0.1% of all tumours) and is most commonly ACTH- or prolactin-secreting. […] *Microadenoma <1cm. *Macroadenoma >1cm. [In terms of the functional status of tumours, the break-down is as follows:] *Prolactinoma 35-40%. *Non-functioning 30-35%. Growth hormone (acromegaly) 10-15%. *ACTH adenoma (Cushing’s disease) 5-10% *TSH adenoma <5%. […] Pituitary disease is associated with an increased mortality, predominantly due to vascular disease. This may be due to oversecretion of GH or ACTH, hormone deficiencies or excessive replacement (e.g. of hydrocortisone).”

“*Prolactinomas are the commonest functioning pituitary tumour. […] Malignant prolactinomas are very rare […] [Clinical features of hyperprolactinaemia:] *Galactorrhoea (up to 90%♀, <10% ♂). *Disturbed gonadal function [menstrual disturbance, infertility, reduced libido, ED in ♂] […] Hyperprolactinaemia is associated with a long-term risk of BMD. […] Hypothyroidism and chronic renal failure are causes of hyperprolactinaemia. […] Antipsychotic agents are the most likely psychotrophic agents to cause hyperprolactinaemia. […] Macroadenomas are space-occupying tumours, often associated with bony erosion and/or cavernous sinus invasion. […] *Invasion of the cavernous sinus may lead to cranial nerve palsies. *Occasionally, very invasive tumours may erode bone and present with a CSF leak or [secondary] meningitis. […] Although microprolactinomas may expand in size without treatment, the vast majority do not. […] Macroprolactinomas, however, will continue to expand and lead to pressure effects. Definite treatment of the tumour is, therefore, necessary.”

“Dopamine agonist treatment […] leads to suppression of PRL in most patients [with prolactinoma], with [secondary] effects of normalization of gonadal function and termination of galactorrhoea. Tumour shrinkage occurs at a variable rate (from 24h to 6-12 months) and extent and must be carefully monitored. Continued shrinkage may occur for years. Slow chiasmal decompression will correct visual field defects in the majority of patients, and immediate surgical decompression is not necessary. […] Cabergoline is more effective in normalization of PRL in microprolactinoma […], with fewer side effects than bromocriptine. […] Tumour enlargement following initial shrinkage on treatment is usually due to non-compliance. […] Since the introduction of dopamine agonist treatment, transsphenoidal surgery is indicated only for patients who are resistant to, or intolerant of, dopamine agonist treatment. The cure rate for macroprolactinomas treated with surgery is poor (30%), and, therefore, drug treatment is first-line in tumours of all size. […] Standard pituitary irradiation leads to slow reduction (over years) of PRL in the majority of patients. […] Radiotherapy is not indicated in the management of patients with microprolactinomas. It is useful in the treatment of macroprolactinomas once the tumour has been shrunken away from the chiasm, only if the tumour is resistant.”

“Acromegaly is the clinical condition resulting from prolonged excessive GH and hence IGF-1 secretion in adults. GH secretion is characterized by blunting of pulsatile secretion and failure of GH to become undetectable during the 24h day, unlike normal controls. […] *Prevalence 40-86 cases/million population. Annual incidence of new cases in the UK is 4/million population. *Onset is insidious, and there is, therefore, often a considerable delay between onset of clinical features and diagnosis. Most cases are diagnosed at 40-60 years. […] Pituitary gigantism [is] [t]he clinical syndrome resulting from excess GH secretion in children prior to fusion of the epiphyses. […] growth velocity without premature pubertal manifestations should arouse suspicion of pituitary gigantism. […] Causes of acromegaly[:] *Pituitary adenoma (>99% of cases). Macroadenomas 60-80%, microadenomas 20-40%. […] The clinical features arise from the effects of excess GH/IGF-1, excess PRL in some (as there is co-secretion of PRL in a minority (30%) of tumours […] and the tumour mass. [Signs and symptoms:] * sweating -> 80% of patients. *Headaches […] *Tiredness and lethargy. *Joint pains. *Change in ring or shoe size. *Facial appearance. Coarse features […] enlarged nose […] prognathism […] interdental separation. […] Enlargement of hands and feet […] [Complications:] *Hypertension (40%). *Insulin resistance and impaired glucose tolerance (40%)/diabetes mellitus (20%). *Obstructive sleep apnea – due to soft tissue swelling […] Ischaemic heart disease and cerebrovascular disease.”

“Management of acromegaly[:] The management strategy depends on the individual patient and also on the tumour size. Lowering of GH is essential in all situations […] Transsphenoidal surgery […] is usually the first line for treatment in most centres. *Reported cure rates vary: 40-91% for microadenomas and 10-48% for macroadenomas, depending on surgical expertise. […] Using the definition of post-operative cure as mean GH <2.5 micrograms/L, the reported recurrence rate is low (6% at 5 years). Radiotherapy […] is usually reserved for patients following unsuccessful transsphenoidal surgery, only occasionally is it used as [primary] therapy. […] normalization of mean GH may take several years and, during this time, adjunctive medical treatment (usually with somatostatin analogues) is required. […] Radiotherapy can induce GH deficiency which may need GH therapy. […] Somatostatin analogues lead to suppresion of GH secretion in 20-60% of patients with acromegaly. […] some patients are partial responders, and although somatostatin analogues will lead to lowering of mean GH, they do not suppress to normal despite dose escalation. These drugs may be used as [primary] therapy where the tumour does not cause mass effects or in patients who have received surgery and/or radiotherapy who have elevated mean GH. […] Dopamine agonists […] lead to lowering of GH levels but, very rarely, lead to normalization of GH or IGF-1 (<30%). They may be helpful, particularly if there is coexistent secretion of PRL, and, in these cases, there may be significant tumour shrinkage. […] GH receptor antagonists [are] [i]ndicated for somatostatin non-responders.”

“Cushing’s syndrome is an illness resulting from excess cortisol secretion, which has a high mortality if left untreated. There are several causes of hypercortisolaemia which must be differentiated, and the commonest cause is iatrogenic (oral, inhaled, or topical steroids). […] ACTH-dependent Cushing’s must be differentiated from ACTH-independent disease (usually due to an adrenal adenoma, or, rarely, carcinoma […]). Once a diagnosis of ACTH-dependent disease has been established, it is important to differentiate between pituitary-dependent (Cushing’s disease) and ectopic secretion. […] [Cushing’s disease is rare;] annual incidence approximately 2/million. The vast majority of Cushing’s syndrome is due to a pituitary ACTH-secreting corticotroph microadenoma. […] The features of Cushing’s syndrome are progressive and may be present for several years prior to diagnosis. […] *Facial appearance – round plethoric complexion, acne and hirsutism, thinning of scalp hair. *Weight gain – truncal obesity, buffalo hump […] *Skin – thin and fragile […] easy bruising […] *Proximal muscle weakness. *Mood disturbance – labile, depression, insomnia, psychosis. *Menstrual disturbance. *Low libido and impotence. […] Associated features [include:] *Hypertension (>50%) due to mineralocorticoid effects of cortisol […] *Impaired glucose tolerance/diabetes mellitus (30%). *Osteopenia and osteoporosis […] *Vascular disease […] *Susceptibility to infections. […] Cushing’s is associated with a hypercoagulable state, with increased cardiovascular thrombotic risks. […] Hypercortisolism suppresses the thyroidal, gonadal, and GH axes, leading to lowered levels of TSH and thyroid hormones as well as reduced gonadotrophins, gonadal steroids, and GH.”

“Treatment of Cushing’s disease[:] Transsphenoidal surgery [is] the first-line option in most cases. […] Pituitary radiotherapy [is] usually administered as second-line treatment, following unsuccessful transsphenoidal surgery. […] Medical treatment [is] indicated during the preoperative preparation of patients or while awaiting radiotherapy to be effective or if surgery or radiotherapy are contraindicated. *Inhibitors of steroidogenesis: metyrapone is usually used first-line, but ketoconazole should be used as first-line in children […] Disadvantage of these agents inhibiting steroidogenesis is the need to increase the dose to maintain control, as ACTH secretion will increase as cortisol concentrations decrease. […] Successful treatment (surgery or radiotherapy) of Cushing’s disease leads to cortisol deficiency and, therefore, glucocorticoid replacement therapy is essential. […] *Untreated [Cushing’s] disease leads to an approximately 30-50% mortality at 5 years, owing to vascular disease and susceptibility to infections. *Treated Cushing’s syndrome has a good prognosis […] *Although the physical features and severe psychological disorders associated with Cushing’s improve or resolve within weeks or months of successful treatment, more subtle mood disturbance may persist for longer. Adults may also have impaired cognitive function. […] it is likely that there is an cardiovascular risk. *Osteoporosis will usually resolve in children but may not improve significantly in older patients. […] *Hypertension has been shown to resolve in 80% and diabetes mellitus in up to 70%. *Recent data suggests that mortality even with successful treatment of Cushing’s is increased significantly.”

“The term incidentaloma refers to an incidentally detected lesion that is unassociated with hormonal hyper- or hyposecretion and has a benign natural history. The increasingly frequent detection of these lesions with technological improvements and more widespread use of sophisticated imaging has led to a management challenge – which, if any, lesions need investigation and/or treatment, and what is the optimal follow-up strategy (if required at all)? […] *Imaging studies using MRI demonstrate pituitary microadenomas in approximately 10% of normal volunteers. […] Clinically significant pituitary tumours are present in about 1 in 1,000 patients. […] Incidentally detected microadenomas are very unlikely (<10%) to increase in size whereas larger incidentally detected meso- and macroadenomas are more likely (40-50%) to enlarge. Thus, conservative management in selected patients may be appropriate for microadenomas which are incidentally detected […]. Macroadenomas should be treated, if possible.”

“Non-functioning pituitary tumours […] are unassociated with clinical syndromes of anterior pituitary hormone excess. […] Non-functioning pituitary tumours (NFA) are the commonest pituitary macroadenoma. They represent around 28% of all pituitary tumours. […] 50% enlarge, if left untreated, at 5 years. […] Tumour behaviour is variable, with some tumours behaving in a very indolent, slow-growing manner and others invading the sphenoid and cavernous sinus. […] At diagnosis, approximately 50% of patients are gonadotrophin-deficient. […] The initial definitive management in virtually every case is surgical. This removes mass effects and may lead to some recovery of pituitary function in around 10%. […] The use of post-operative radiotherapy remains controversial. […] The regrowth rate at 10 years without radiotherapy approaches 45% […] administration of post-operative radiotherapy reduces this regrowth rate to <10%. […] however, there are sequelae to radiotherapy – with a significant long-term risk of hypopituitarism and a possible risk of visual deterioration and malignancy in the field of radiation. […] Unlike the case for GH- and PRL-secreting tumours, medical therapy for NFAs is usually unhelpful […] Gonadotrophinomas […] are tumours that arise from the gonadotroph cells of the pituitary gland and produce FSH, LH, or the α subunit. […] they are usually silent and unassociated with excess detectable secretion of LH and FSH […] [they] present in the same manner as other non-functioning pituitary tumours, with mass effects and hypopituitarism […] These tumours are managed as non-functioning tumours.”

“The posterior lobe of the pituitary gland arises from the forebrain and comprises up to 25% of the normal adult pituitary gland. It produces arginine vasopressin and oxytocin. […] Oxytoxin has no known role in ♂ […] In ♀, oxytoxin contracts the pregnant uterus and also causes breast duct smooth muscle contraction, leading to breast milk ejection during breastfeeding. […] However, oxytoxin deficiency has no known adverse effect on parturition or breastfeeding. […] Arginine vasopressin is the major determinant of renal water excretion and, therefore, fluid balance. It’s main action is to reduce free water clearance. […] Many substances modulate vasopressin secretion, including the catecholamines and opioids. *The main site of action of vasopressin is in the collecting duct and the thick ascending loop of Henle […] Diabetes Insipidus (DI) […] is defined as the passage of large volumes (>3L/24h) of dilute urine (osmolality <300mOsm/kg). [It may be] [d]ue to deficiency of circulating arginine vasopressin [or] [d]ue to renal resistance to vasopressin.” […lots of other causes as well – trauma, tumours, inflammation, infection, vascular, drugs, genetic conditions…]

Hyponatraemia […] Incidence *1-6% of hospital admissions Na<130mmol/L. *15-22% hospital admissions Na<135mmol/L. […] True clinically apparent hyponatraemia is associated with either excess water or salt deficiency. […] Features *Depend on the underlying cause and also on the rate of development of hyponatraemia. May develop once sodium reaches 115mmol/L or earlier if the fall is rapid. Level at 100mmol/L or less is life-threatening. *Features of excess water are mainly neurological because of brain injury […] They include confusion and headache, progressing to seizures and coma. […] SIADH [Syndrome of Inappropriate ADH, US] is a common cause of hyponatraemia. […] The elderly are more prone to SIADH, as they are unable to suppress ADH as efficiently […] ↑ risk of hyponatraemia with SSRIs. […] rapid overcorrection of hyponatraemia may cause central pontine myelinolysis (demyelination).”

“The hypothalamus releases hormones that act as releasing hormones at the anterior pituitary gland. […] The commonest syndrome to be associated with the hypothalamus is abnormal GnRH secretion, leading to reduced gonadotrophin secretion and hypogonadism. Common causes are stress, weight loss, and excessive exercise.”


January 14, 2018 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Epidemiology, Medicine, Nephrology, Neurology, Ophthalmology, Pharmacology | Leave a comment

Endocrinology (part I – thyroid)

Handbooks like these are difficult to blog, but I decided to try anyway. The first 100 pages or so of the book deals with the thyroid gland. Some observations of interest below.

“Biosynthesis of thyroid hormones requires iodine as substrate. […] The thyroid is the only source of T4. The thyroid secretes 20% of circulating T3; the remainder is generated in extraglandular tissues by the conversion of T4 to T3 […] In the blood, T4 and T3 are almost entirely bound to plasma proteins. […] Only the free or unbound hormone is available to tissues. The metabolic state correlates more closely with the free than the total hormone concentration in the plasma. The relatively weak binding of T3 accounts for its more rapid onset and offset of action. […] The levels of thyroid hormone in the blood are tightly controlled by feedback mechanisms involved in the hypothalamo-pituitary-thyroid (HPT) axis“.

“Annual check of thyroid function [is recommended] in the annual review of diabetic patients.”

“The term thyrotoxicosis denotes the clinical, physiological, and biochemical findings that result when the tissues are exposed to excess thyroid hormone. It can arise in a variety of ways […] It is essential to establish a specific diagnosis […] The term hyperthyroidism should be used to denote only those conditions in which hyperfunction of the thyroid leads to thyrotoxicosis. […] [Thyrotoxicosis is] 10 x more common in ♀ than in ♂ in the UK. Prevalence is approximately 2% of the ♀ population. […] Subclinical hyperthyroidism is defined as low serum thyrotropin (TSH) concentration in patients with normal levels of T4 and T3. Subtle symptoms and signs of thyrotoxicosis may be present. […] There is epidemiological evidence that subclinical hyperthyroidism is a risk factor for the development of atrial fibrillation or osteoporosis.1 Meta-analyses suggest a 41% increase in all-cause mortality.2 […] Thyroid crisis [storm] represents a rare, but life-threatening, exacerbation of the manifestations of thyrotoxicosis. […] the condition is associated with a significant mortality (30-50%, depending on series) […]. Thyroid crisis develops in hyperthyroid patients who: *Have an acute infection. *Undergo thyroidal or non-thyroidal surgery or (rarely) radioiodine treatment.”

“[Symptoms and signs of hyperthyroidism (all forms):] *Hyperactivity, irritability, altered mood, insomnia. *Heat intolerance, sweating. […] *Fatigue, weakness. *Dyspnoea. *Weight loss with appetite (weight gain in 10% of patients). *Pruritus. […] *Thirst and polyuria. *Oligomenorrhoea or amenorrhoea, loss of libido, erectile dysfunction (50% of men may have sexual dysfunction). *Warm, moist skin. […] *Hair loss. *Muscle weakness and wasting. […] Manifestations of Graves’s disease (in addition to [those factors already mentioned include:]) *Diffuse goitre. *Ophthalmopathy […] A feeling of grittiness and discomfort in the eye. *Retrobulbar pressure or pain, eyelid lag or retraction. […] *Exophthalmos (proptosis) […] Optic neuropathy.”

“Two alternative regimens are practiced for Graves’s disease: dose titration and block and replace. […] The [primary] aim [of the dose titration regime] is to achieve a euthyroid state with relatively high drug doses and then to maintain euthyroidism with a low stable dose. […] This regimen has a lower rate of side effects than the block and replace regimen. The treatment is continued for 18 months, as this appears to represent the length of therapy which is generally optimal in producing the remission rate of up to 40% at 5 years after discontinuing therapy. *Relapses are most likely to occur within the first year […] Men have a higher recurrence rate than women. *Patients with multinodular goitres and thyrotoxicosis always relapse on cessation of antithyroid medication, and definite treatment with radioiodine or surgery is usually advised. […] Block and replace regimen *After achieving a euthyroid state on carbimazole alone, carbimazole at a dose of 40mg daily, together with T4 at a dose of 100 micrograms, can be prescribed. This is usually continued for 6 months. *The main advantages are fewer hospital visits for checks of thyroid function and shorter duration of treatment.”

“Radioiodine treatment[:] Indications: *Definite treatment of multinodular goitre or adenoma. *Relapsed Graves’s disease. […] *Radioactive iodine-131 is administered orally as a capsule or a drink. *There is no universal agreement regarding the optimal dose. […] The recommendation is to administer enough radioiodine to achieve euthyroidism, with the acceptance of a moderate rate of hypothyroidism, e.g. 15-20% at 2 years. […] In general, 50-70% of patients have restored normal thyroid function within 6-8 weeks of receiving radioiodine. […] The prevalence of hypothyroidism is about 50% at 10 years and continues to increase thereafter.”

“Thyrotoxicosis occurs in about 0.2% of pregnancies. […] *Diagnosis of thyrotoxicosis during pregnancy may be difficult or delayed. *Physiological changes of pregnancy are similar to those of hyperthyroidism. […] 5-7% of ♀ develop biochemical evidence of thyroid dysfunction after delivery. An incidence is seen in patients with type I diabetes mellitus (25%) […] One-third of affected ♀ with post-partum thyroiditis develop symptoms of hypothyroidism […] There is a suggestion of an risk of post-partum depression in those with hypothyroidism. […] *The use of iodides and radioiodine is contraindicated in pregnancy. *Surgery is rarely performed in pregnancy. It is reserved for patients not responding to ATDs [antithyroid drugs, US]. […] Hyperthyroid ♀ who want to conceive should attain euthyroidism before conception since uncontrolled hyperthyroidism is associated with an an risk of congenital abnormalities (stillbirth and cranial synostosis are the most serious complications).”

“Nodular thyroid disease denotes the presence of single or multiple palpable or non-palpable nodules within the thyroid gland. […] *Clinically apparent thyroid nodules are evident in ~5% of the UK population. […] Thyroid nodules always raise the concern of cancer, but <5% are cancerous. […] clinically detectable thyroid cancer is rare. It accounts for <1% of all cancer and <0.5% of cancer deaths. […] Thyroid cancers are commonest in adults aged 40-50 and rare in children [incidence of 0.2-5 per million per year] and adolescents. […] History should concentrate on: *An enlarging thyroid mass. *A previous history of radiation […] family history of thyroid cancer. *The development of hoarseness or dysphagia. *Nodules are more likely to be malignant in patients <20 or >60 years. *Thyroid nodules are more common in ♀ but more likely to be malignant in ♂. […] Physical findings suggestive of malignancy include a firm or hard, non-tender nodule, a recent history of enlargement, fixation to adjacent tissue, and the presence of regional lymphadenopathy. […] Thyroid nodules may be described as adenomas if the follicular cell differentiation is enclosed within a capsule; adenomatous when the lesions are circumscribed but not encapsulated. *The most common benign thyroid tumours are the nodules of multinodular goitres (colloid nodules) and follicular adenomas. […] Autonomously functioning thyroid adenomas (or nodules) are benign tumours that produce thyroid hormone. Clinically, they present as a single nodule that is hyperfunctioning […], sometimes causing hyperthyroidism.”

“Inflammation of the thyroid gland often leads to a transient thyrotoxicosis followed by hypothyroidism. Overt hypothyroidism caused by autoimmunity has two main forms: Hashimoto’s (goitrous) thyroiditis and atrophic thyroiditis. […] Hashimoto’s thyroiditis [is] [c]haracterized by a painless, variable-sized goitre with rubbery consistency and an irregular surface. […] Occasionally, patients present with thyrotoxicosis in association with a thyroid gland that is unusually firm […] Atrophic thyroiditis [p]robably indicates end-stage thyroid disease. These patients do not have goitre and are antibody [positive]. […] The long-term prognosis of patients with chronic thyroiditis is good because hypothyroidism can easily be corrected with T4 and the goitre is usually not of sufficient size to cause local symptoms. […] there is an association between this condition and thyroid lymphoma (rare, but risk by a factor of 70).”

“Hypothyroidism results from a variety of abnormalities that cause insufficient secretion of thyroid hormones […] The commonest cause is autoimmune thyroid disease. Myxoedema is severe hypothyroidism [which leads to] thickening of the facial features and a doughy induration of the skin. [The clinical picture of hypothyroidism:] *Insidious, non-specific onset. *Fatigue, lethargy, constipation, cold intolerance, muscle stiffness, cramps, carpal tunnel syndrome […] *Slowing of intellectual and motor activities. *↓ appetite and weight gain. *Dry skin; hair loss. […] [The term] [s]ubclinical hypothyroidism […] is used to denote raised TSH levels in the presence of normal concentrations of free thyroid hormones. *Treatment is indicated if the biochemistry is sustained in patients with a past history of radioiodine treatment for thyrotoxicosis or [positive] thyroid antibodies as, in these situations, progression to overt hypothyroidism is almost inevitable […] There is controversy over the advantages of T4 treatment in patients with [negative] thyroid antibodies and no previous radioiodine treatment. *If treatment is not given, follow-up with annual thyroid function tests is important. *There is no generally accepted consensus of when patients should receive treatment. […] *Thyroid hormone replacement with synthetic levothyroxine remains the treatment of choice in primary hypothyroidism. […] levothyroxine has a narrow therapeutic index […] Elevated TSH despite thyroxine replacement is common, most usually due to lack of compliance.”



January 8, 2018 Posted by | Books, Cancer/oncology, Diabetes, Medicine, Ophthalmology, Pharmacology | Leave a comment

Occupational Epidemiology (III)

This will be my last post about the book.

Some observations from the final chapters:

“Often there is confusion about the difference between systematic reviews and metaanalyses. A meta-analysis is a quantitative synthesis of two or more studies […] A systematic review is a synthesis of evidence on the effects of an intervention or an exposure which may also include a meta-analysis, but this is not a prerequisite. It may be that the results of the studies which have been included in a systematic review are reported in such a way that it is impossible to synthesize them quantitatively. They can then be reported in a narrative manner.10 However, a meta-analysis always requires a systematic review of the literature. […] There is a long history of debate about the value of meta-analysis for occupational cohort studies or other occupational aetiological studies. In 1994, Shapiro argued that ‘meta-analysis of published non-experimental data should be abandoned’. He reasoned that ‘relative risks of low magnitude (say, less than 2) are virtually beyond the resolving power of the epidemiological microscope because we can seldom demonstrably eliminate all sources of bias’.13 Because the pooling of studies in a meta-analysis increases statistical power, the pooled estimate may easily become significant and thus incorrectly taken as an indication of causality, even though the biases in the included studies may not have been taken into account. Others have argued that the method of meta-analysis is important but should be applied appropriately, taking into account the biases in individual studies.14 […] We believe that the synthesis of aetiological studies should be based on the same general principles as for intervention studies, and the existing methods adapted to the particular challenges of cohort and case-control studies. […] Since 2004, there is a special entity, the Cochrane Occupational Safety and Health Review Group, that is responsible for the preparing and updating of reviews of occupational safety and health interventions […]. There were over 100 systematic reviews on these topics in the Cochrane Library in 2012.”

“The believability of a systematic review’s results depends largely on the quality of the included studies. Therefore, assessing and reporting on the quality of the included studies is important. For intervention studies, randomized trials are regarded as of higher quality than observational studies, and the conduct of the study (e.g. in terms of response rate or completeness of follow-up) also influences quality. A conclusion derived from a few high-quality studies will be more reliable than when the conclusion is based on even a large number of low-quality studies. Some form of quality assessment is nowadays commonplace in intervention reviews but is still often missing in reviews of aetiological studies. […] It is tempting to use quality scores, such as the Jadad scale for RCTs34 and the Downs and Black scale for non-RCT intervention studies35 but these, in their original format, are insensitive to variation in the importance of risk areas for a given research question. The score system may give the same value to two studies (say, 10 out of 12) when one, for example, lacked blinding and the other did not randomize, thus implying that their quality is equal. This would not be a problem if randomization and blinding were equally important for all questions in all reviews, but this is not the case. For RCTs an important development in this regard has been the Cochrane risk of bias tool.36 This is a checklist of six important domains that have been shown to be important areas of bias in RCTs: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, and selective reporting.”

“[R]isks of bias tools developed for intervention studies cannot be used for reviews of aetiological studies without relevant modification. This is because, unlike interventions, exposures are usually more complicated to assess when we want to attribute the outcome to them alone. These scales do not cover all items that may need assessment in an aetiological study, such as confounding and information bias relating to exposures. […] Surprisingly little methodological work has been done to develop validated tools for aetiological epidemiology and most tools in use are not validated,38 […] Two separate checklists, for observational studies of incidence and prevalence and for risk factor assessment, have been developed and validated recently.40 […] Publication and other reporting bias is probably a much bigger issue for aetiological studies than for intervention studies. This is because, for clinical trials, the introduction of protocol registration, coupled with the regulatory system for new medications, has helped in assessing and preventing publication and reporting bias. No such checks exist for observational studies.”

“Most ill health that arises from occupational exposures can also arise from nonoccupational exposures, and the same type of exposure can occur in occupational and non-occupational settings. With the exception of malignant mesothelioma (which is essentially only caused by exposure to asbestos), there is no way to determine which exposure caused a particular disorder, nor where the causative exposure occurred. This means that usually it is not possible to determine the burden just by counting the number of cases. Instead, approaches to estimating this burden have been developed. There are also several ways to define burden and how best to measure it.”

“The population attributable fraction (PAF) is the proportion of cases that would not have occurred in the absence of an occupational exposure. It can be estimated by combining two measures — a risk estimate (usually relative risk (RR) or odds ratio) of the disorder of interest that is associated with exposure to the substance of concern; and an estimate of the proportion of the population exposed to the substance at work (p(E)). This approach has been used in several studies, particularly for estimating cancer burden […] There are several possible equations that can be used to calculate the PAF, depending on the available data […] PAFs cannot in general be combined by summing directly because: (1) summing PAFs for overlapping exposures (i.e. agents to which the same ‘ever exposed’ workers may have been exposed) may give an overall PAF exceeding 100%, and (2) summing disjoint (not concurrently occurring) exposures also introduces upward bias. Strategies to avoid this include partitioning exposed numbers between overlapping exposures […] or estimating only for the ‘dominant’ carcinogen with the highest risk. Where multiple exposures remain, one approach is to assume that the exposures are independent and their joint effects are multiplicative. The PAFs can then be combined to give an overall PAF for that cancer using a product sum. […] Potential sources of bias for PAFs include inappropriate choice of risk estimates, imprecision in the risk estimates and estimates of proportions exposed, inaccurate risk exposure period and latency assumptions, and a lack of separate risk estimates in some cases for women and/or cancer incidence. In addition, a key decision is the choice of which diseases and exposures are to be included.”

“The British Cancer Burden study is perhaps the most detailed study of occupationally related cancers in that it includes all those relevant carcinogens classified at the end of 2008 […] In the British study the attributable fractions ranged from less than 0.01% to 95% overall, the most important cancer sites for occupational attribution being, for men, mesothelioma (97%), sinonasal (46%), lung (21.1%), bladder (7.1%), and non-melanoma skin cancer (7.1%) and, for women, mesothelioma (83%), sinonasal (20.1%), lung (5.3%), breast (4.6%), and nasopharynx (2.5%). Occupation also contributed 2% or more overall to cancers of the larynx, oesophagus, and stomach, and soft tissue sarcoma with, in addition for men, melanoma of the eye (due to welding), and non-Hodgkin lymphoma. […] The overall results from the occupational risk factors component of the Global Burden of Disease 2010 study illustrate several important aspects of burden studies.14 Of the estimated 850 000 occupationally related deaths worldwide, the top three causes were: (1) injuries (just over a half of all deaths); (2) particulate matter, gases, and fumes leading to COPD; and (3) carcinogens. When DALYs were used as the burden measure, injuries still accounted for the highest proportion (just over one-third), but ergonomic factors leading to low back pain resulted in almost as many DALYs, and both were almost an order of magnitude higher than the DALYs from carcinogens. The difference in relative contributions of the various risk factors between deaths and DALYs arises because of the varying ages of those affected, and the differing chronicity of the resulting conditions. Both measures are valid, but they represent a different aspect of the burden arising from the hazardous exposures […]. Both the British and Global Burden of Disease studies draw attention to the important issues of: (1) multiple occupational carcinogens causing specific types of cancer, for example, the British study evaluated 21 lung carcinogens; and (2) specific carcinogens causing several different cancers, for example, IARC now defines asbestos as a group 1 or 2A carcinogen for seven cancer sites. These issues require careful consideration for burden estimation and for prioritizing risk reduction strategies. […] The long latency of many cancers means that estimates of current burden are based on exposures occurring in the past, often much higher than those existing today. […] long latency [also] means that risk reduction measures taken now will take a considerable time to be reflected in reduced disease incidence.”

“Exposures and effects are linked by dynamic processes occurring across time. These processes can often be usefully decomposed into two distinct biological relationships, each with several components: 1. The exposure-dose relationship […] 2. The dose-effect relationship […] These two component relationships are sometimes represented by two different mathematical models: a toxicokinetic model […], and a disease process model […]. Depending on the information available, these models may be relatively simple or highly complex. […] Often the various steps in the disease process do not occur at the same rate, some of these processes are ‘fast’, such as cell killing, while others are ‘slow’, such as damage repair. Frequently a few slow steps in a process become limiting to the overall rate, which sets the temporal pattern for the entire exposure-response relationship. […] It is not necessary to know the full mechanism of effects to guide selection of an exposure-response model or exposure metric. Because of the strong influence of the rate-limiting steps, often it is only necessary to have observations on the approximate time course of effects. This is true whether the effects appear to be reversible or irreversible, and whether damage progresses proportionately with each unit of exposure (actually dose) or instead occurs suddenly, and seemingly without regard to the amount of exposure, such as an asthma attack.”

“In this chapter, we argue that formal disease process models have the potential to improve the sensitivity of epidemiology for detecting new and emerging occupational and environmental risks where there is limited mechanistic information. […] In our approach, these models are often used to create exposure or dose metrics, which are in turn used in epidemiological models to estimate exposure-disease associations. […] Our goal is a methodology to formulate strong tests of our exposure-disease hypotheses in which a hypothesis is developed in as much biological detail as it can be, expressed in a suitable dynamic (temporal) model, and tested by its fit with a rich data set, so that its flaws and misperceptions of reality are fully displayed. Rejecting such a fully developed biological hypothesis is more informative than either rejecting or failing to reject a generic or vaguely defined hypothesis.” For example, the hypothesis ‘truck drivers have more risk of lung cancer than non-drivers’13 is of limited usefulness for prevention […]. Hypothesizing that a particular chemical agent in truck exhaust is associated with lung cancer — whether the hypothesis is refuted or supported by data — is more likely to lead to successful prevention activities. […] we believe that the choice of models against which to compare the data should, so far as possible, be guided by explicit hypotheses about the underlying biological processes. In other words, you can get as much as possible from epidemiology by starting from well-thought-out hypotheses that are formalized as mathematical models into which the data will be placed. The disease process models can serve this purpose.2″

“The basic idea of empirical Bayes (EB) and semiBayes (SB) adjustments for multiple associations is that the observed variation of the estimated relative risks around their geometric mean is larger than the variation of the true (but unknown) relative risks. In SB adjustments, an a priori value for the extra variation is chosen which assigns a reasonable range of variation to the true relative risks and this value is then used to adjust the observed relative risks.7 The adjustment consists in shrinking outlying relative risks towards the overall mean (of the relative risks for all the different exposures being considered). The larger the individual variance of the relative risks, the stronger the shrinkage, so that the shrinkage is stronger for less reliable estimates based on small numbers. Typical applications in which SB adjustments are a useful alternative to traditional methods of adjustment for multiple comparisons are in large occupational surveillance studies, where many relative risks are estimated with few or no a priori beliefs about which associations might be causal.7″

“The advantage of [the SB adjustment] approach over classical Bonferroni corrections is that on the average it produces more valid estimates of the odds ratio for each occupation/exposure. If we do a study which involves assessing hundreds of occupations, the problem is not only that we get many ‘false positive’ results by chance. A second problem is that even the ‘true positives’ tend to have odds ratios that are too high. For example, if we have a group of occupations with true odds ratios around 1.5, then the ones that stand out in the analysis are those with the highest odds ratios (e.g. 2.5) which will be elevated partly because of real effects and partly by chance. The Bonferroni correction addresses the first problem (too many chance findings) but not the second, that the strongest odds ratios are probably too high. In contrast, SB adjustment addresses the second problem by correcting for the anticipated regression to the mean that would have occurred if the study had been repeated, and thereby on the average produces more valid odds ratio estimates for each occupation/exposure. […] most epidemiologists write their Methods and Results sections as frequentists and their Introduction and Discussion sections as Bayesians. In their Methods and Results sections, they ‘test’ their findings as if their data are the only data that exist. In the Introduction and Discussion, they discuss their findings with regard to their consistency with previous studies, as well as other issues such as biological plausibility. This creates tensions when a small study has findings which are not statistically significant but which are consistent with prior knowledge, or when a study finds statistically significant findings which are inconsistent with prior knowledge. […] In some (but not all) instances, things can be made clearer if we include Bayesian methods formally in the Methods and Results sections of our papers”.

“In epidemiology, risk is most often quantified in terms of relative risk — i.e. the ratio of the probability of an adverse outcome in someone with a specified exposure to that in someone who is unexposed, or exposed at a different specified level. […] Relative risks can be estimated from a wider range of study designs than individual attributable risks. They have the advantage that they are often stable across different groups of people (e.g. of different ages, smokers, and non-smokers) which makes them easier to estimate and quantify. Moreover, high relative risks are generally unlikely to be explained by unrecognized bias or confounding. […] However, individual attributable risks are a more relevant measure by which to quantify the impact of decisions in risk management on individuals. […] Individual attributable risk is the difference in the probability of an adverse outcome between someone with a specified exposure and someone who is unexposed, or exposed at a different specified level. It is the critical measure when considering the impact of decisions in risk management on individuals. […] Population attributable risk is the difference in the frequency of an adverse outcome between a population with a given distribution of exposures to a hazardous agent, and that in a population with no exposure, or some other specified distribution of exposures. It depends on the prevalence of exposure at different levels within the population, and on the individual attributable risk for each level of exposure. It is a measure of the impact of the agent at a population level, and is relevant to decisions in risk management for populations. […] Population attributable risks are highest when a high proportion of a population is exposed at levels which carry high individual attributable risks. On the other hand, an exposure which carries a high individual attributable risk may produce only a small population attributable risk if the prevalence of such exposure is low.”

“Hazard characterization entails quantification of risks in relation to routes, levels, and durations of exposure. […] The findings from individual studies are often used to determine a no observed adverse effect level (NOAEL), lowest observed effect level (LOEL), or benchmark dose lower 95% confidence limit (BMDL) for relevant effects […] [NOAEL] is the highest dose or exposure concentration at which there is no discernible adverse effect. […] [LOEL] is the lowest dose or exposure concentration at which a discernible effect is observed. If comparison with unexposed controls indicates adverse effects at all of the dose levels in an experiment, a NOAEL cannot be derived, but the lowest dose constitutes a LOEL, which might be used as a comparator for estimated exposures or to derive a toxicological reference value […] A BMDL is defined in relation to a specified adverse outcome that is observed in a study. Usually, this is the outcome which occurs at the lowest levels of exposure and which is considered critical to the assessment of risk. Statistical modelling is applied to the experimental data to estimate the dose or exposure concentration which produces a specified small level of effect […]. The BMDL is the lower 95% confidence limit for this estimate. As such, it depends both on the toxicity of the test chemical […], and also on the sample sizes used in the study (other things being equal, larger sample sizes will produce more precise estimates, and therefore higher BMDLs). In addition to accounting for sample size, BMDLs have the merit that they exploit all of the data points in a study, and do not depend so critically on the spacing of doses that is adopted in the experimental design (by definition a NOAEL or LOEL can only be at one of the limited number of dose levels used in the experiment). On the other hand, BMDLs can only be calculated where an adverse effect is observed. Even if there are no clear adverse effects at any dose level, a NOAEL can be derived (it will be the highest dose administered).”


December 8, 2017 Posted by | Books, Cancer/oncology, Epidemiology, Medicine, Statistics | Leave a comment

Occupational Epidemiology (II)

Some more observations from the book below.

“RD [Retinal detachment] is the separation of the neurosensory retina from the underlying retinal pigment epithelium.1 RD is often preceded by posterior vitreous detachment — the separation of the posterior vitreous from the retina as a result of vitreous degeneration and shrinkage2 — which gives rise to the sudden appearance of floaters and flashes. Late symptoms of RD may include visual field defects (shadows, curtains) or even blindness. The success rate of RD surgery has been reported to be over 90%;3 however, a loss of visual acuity is frequently reported by patients, particularly if the macula is involved.4 Since the natural history of RD can be influenced by early diagnosis, patients experiencing symptoms of posterior vitreous detachment are advised to undergo an ophthalmic examination.5 […] Studies of the incidence of RD give estimates ranging from 6.3 to 17.9 cases per 100 000 person-years.6 […] Age is a well-known risk factor for RD. In most studies the peak incidence was recorded among subjects in their seventh decade of life. A secondary peak at a younger age (20–30 years) has been identified […] attributed to RD among highly myopic patients.6 Indeed, depending on the severity,
myopia is associated with a four- to ten-fold increase in risk of RD.7 [Diabetics with retinopathy are also at increased risk of RD, US] […] While secondary prevention of RD is current practice, no effective primary prevention strategy is available at present. The idea is widespread among practitioners that RD is not preventable, probably the consequence of our historically poor understanding of the aetiology of RD. For instance, on the website of the Mayo Clinic — one of the top-ranked hospitals for ophthalmology in the US — it is possible to read that ‘There’s no way to prevent retinal detachment’.9

“Intraocular pressure […] is influenced by physical activity. Dynamic exercise causes an acute reduction in intraocular pressure, whereas physical fitness is associated with a lower baseline value.29 Conversely, a sudden rise in intraocular pressure has been reported during the Valsalva manoeuvre.30-32 […] Occupational physical activity may […] cause both short- and long-term variations in intraocular pressure. On the one hand, physically demanding jobs may contribute to decreased baseline levels by increasing physical fitness but, on the other hand, lifting tasks may cause an important acute increase in pressure. Moreover, the eye of a manual worker who performs repeated lifting tasks involving the Valsalva manoeuvre may undergo several dramatic changes in intraocular pressure within a single working shift. […] A case-control study was carried out to test the hypothesis that repeated lifting tasks involving the Valsalva manoeuvre could be a risk factor for RD. […] heavy lifting was a strong risk factor for RD (OR 4.4, 95% CI 1.6–13). Intriguingly, body mass index (BMI) also showed a clear association with RD (top quartile: OR 6.8, 95% CI 1.6–29). […] Based on their findings, the authors concluded that heavy occupational lifting (involving the Valsalva manoeuvre) may be a relevant risk factor for RD in myopics.

“The proportion of the world’s population over 60 is forecast to double from 11.6% in 2012 to 21.8% in 2050.1 […] the International Labour Organization notes that, worldwide, just 40% of the working age population has legal pension coverage, and only 26% of the working population is effectively covered by old-age pension schemes. […] in less developed regions, labour force participation in those over 65 is much higher than in more developed regions.8 […] Longer working lives increase cumulative exposures, as well as increasing the time since exposure — important when there is a long latency period between exposure and resultant disease. Further, some exposures may have a greater effect when they occur to older workers, e.g. carcinogens that are promoters rather than initiators. […] Older workers tend to have more chronic health conditions. […] Older workers have fewer injuries, but take longer to recover. […] For some ‘knowledge workers’, like physicians, even a relatively minor cognitive decline […] might compromise their competence. […]  Most past studies have treated age as merely a confounding variable and rarely, if ever, have considered it an effect modifier. […]  Jex and colleagues24 argue that conceptually we should treat age as the variable of interest so that other variables are viewed as moderating the impact of age. […] The single best improvement to epidemiological research on ageing workers is to conduct longitudinal studies, including follow-up of workers into retirement. Cross-sectional designs almost certainly incur the healthy survivor effect, since unhealthy workers may retire early.25 […] Analyses should distinguish ageing per se, genetic factors, work exposures, and lifestyle in order to understand their relative and combined effects on health.”

“Musculoskeletal disorders have long been recognized as an important source of morbidity and disability in many occupational populations.1,2 Most musculoskeletal disorders, for most people, are characterized by recurrent episodes of pain that vary in severity and in their consequences for work. Most episodes subside uneventfully within days or weeks, often without any intervention, though about half of people continue to experience some pain and functional limitations after 12 months.3,4 In working populations, musculoskeletal disorders may lead to a spell of sickness absence. Sickness absence is increasingly used as a health parameter of interest when studying the consequences of functional limitations due to disease in occupational groups. Since duration of sickness absence contributes substantially to the indirect costs of illness, interventions increasingly address return to work (RTW).5 […] The Clinical Standards Advisory Group in the United Kingdom reported RTW within 2 weeks for 75% of all low back pain (LBP) absence episodes and suggested that approximately 50% of all work days lost due to back pain in the working population are from the 85% of people who are off work for less than 7 days.6″

Any RTW curve over time can be described with a mathematical Weibull function.15 This Weibull function is characterized by a scale parameter λ and a shape parameter k. The scale parameter λ is a function of different covariates that include the intervention effect, preferably expressed as hazard ratio (HR) between the intervention group and the reference group in a Cox’s proportional hazards regression model. The shape parameter k reflects the relative increase or decrease in survival time, thus expressing how much the RTW rate will decrease with prolonged sick leave. […] a HR as measure of effect can be introduced as a covariate in the scale parameter λ in the Weibull model and the difference in areas under the curve between the intervention model and the basic model will give the improvement in sickness absence days due to the intervention. By introducing different times of starting the intervention among those workers still on sick leave, the impact of timing of enrolment can be evaluated. Subsequently, the estimated changes in total sickness absence days can be expressed in a benefit/cost ratio (BC ratio), where benefits are the costs saved due to a reduction in sickness absence and costs are the expenditures relating to the intervention.15″

“A crucial factor in understanding why interventions are effective or not is the timing of the enrolment of workers on sick leave into the intervention. The RTW pattern over time […] has important consequences for appropriate timing of the best window for effective clinical and occupational interventions. The evidence presented by Palmer and colleagues clearly suggests that [in the context of LBP] a stepped care approach is required. In the first step of rapid RTW, most workers will return to work even without specific interventions. Simple, short interventions involving effective coordination and cooperation between primary health care and the workplace will be sufficient to help the majority of workers to achieve an early RTW. In the second step, more expensive, structured interventions are reserved for those who are having difficulties returning, typically between 4 weeks and 3 months. However, to date there is little evidence on the optimal timing of such interventions for workers on sick leave due to LBP.14,15 […] the cost-benefits of a structured RTW intervention among workers on sick leave will be determined by the effectiveness of the intervention, the natural speed of RTW in the target population, the timing of the enrolment of workers into the intervention, and the costs of both the intervention and of a day of sickness absence. […] The cost-effectiveness of a RTW intervention will be determined by the effectiveness of the intervention, the costs of the intervention and of a day of sickness absence, the natural course of RTW in the target population, the timing of the enrolment of workers into the RTW intervention, and the time lag before the intervention takes effect. The latter three factors are seldom taken into consideration in systematic reviews and guidelines for management of RTW, although their impact may easily be as important  as classical measures of effectiveness, such as effect size or HR.”

“In order to obtain information of the highest quality and utility, surveillance schemes have to be designed, set up, and managed with the same methodological rigour as high-calibre prospective cohort studies. Whether surveillance schemes are voluntary or not, considerable effort has to be invested to ensure a satisfactory and sufficient denominator, the best numerator quality, and the most complete ascertainment. Although the force of statute is relied upon in some surveillance schemes, even in these the initial and continuing motivation of the reporters (usually physicians) is paramount. […] There is a surveillance ‘pyramid’ within which the patient’s own perception is at the base, the GP is at a higher level, and the clinical specialist is close to the apex. The source of the surveillance reports affects the numerator because case severity and case mix differ according to the level in the pyramid.19 Although incidence rate estimates may be expected to be lower at the higher levels in the surveillance pyramid this is not necessarily always the case. […] Although surveillance undertaken by physicians who specialize in the organ system concerned or in occupational disease (or in both aspects) may be considered to be the medical ‘gold standard’ it can suffer from a more limited patient catchment because of various referral filters. Surveillance by GPs will capture numerator cases as close to the base of the pyramid as possible, but may suffer from greater diagnostic variation than surveillance by specialists. Limiting recruitment to GPs with a special interest, and some training, in occupational medicine is a compromise between the two levels.20

“When surveillance is part of a statutory or other compulsory scheme then incident case identification is a continuous and ongoing process. However, when surveillance is voluntary, for a research objective, it may be preferable to sample over shorter, randomly selected intervals, so as to reduce the demands associated with the data collection and ‘reporting fatigue’. Evidence so far suggests that sampling over shorter time intervals results in higher incidence estimates than continuous sampling.21 […] Although reporting fatigue is an important consideration in tempering conclusions drawn from […] multilevel models, it is possible to take account of this potential bias in various ways. For example, when evaluating interventions, temporal trends in outcomes resulting from other exposures can be used to control for fatigue.23,24 The phenomenon of reporting fatigue may be characterized by an ‘excess of zeroes’ beyond what is expected of a Poisson distribution and this effect can be quantified.27 […] There are several considerations in determining incidence from surveillance data. It is possible to calculate an incidence rate based on the general population, on the population of working age, or on the total working population,19 since these denominator bases are generally readily available, but such rates are not the most useful in determining risk. Therefore, incidence rates are usually calculated in respect of specific occupations or industries.22 […] Ideally, incidence rates should be expressed in relation to quantitative estimates of exposure but most surveillance schemes would require additional data collection as special exercises to achieve this aim.” [for much more on these topics, see also M’ikanatha & Iskander’s book.]

“Estimates of lung cancer risk attributable to occupational exposures vary considerably by geographical area and depend on study design, especially on the exposure assessment method, but may account for around 5–20% of cancers among men, but less (<5%) among women;2 among workers exposed to (suspected) lung carcinogens, the percentage will be higher. […] most exposure to known lung carcinogens originates from occupational settings and will affect millions of workers worldwide.  Although it has been established that these agents are carcinogenic, only limited evidence is available about the risks encountered at much lower levels in the general population. […] One of the major challenges in community-based occupational epidemiological studies has been valid assessment of the occupational exposures experienced by the population at large. Contrary to the detailed information usually available for an industrial population (e.g. in a retrospective cohort study in a large chemical company) that often allows for quantitative exposure estimation, community-based studies […] have to rely on less precise and less valid estimates. The choice of method of exposure assessment to be applied in an epidemiological study depends on the study design, but it boils down to choosing between acquiring self-reported exposure, expert-based individual exposure assessment, or linking self-reported job histories with job-exposure matrices (JEMs) developed by experts. […] JEMs have been around for more than three decades.14 Their main distinction from either self-reported or expert-based exposure assessment methods is that exposures are no longer assigned at the individual subject level but at job or task level. As a result, JEMs make no distinction in assigned exposure between individuals performing the same job, or even between individuals performing a similar job in different companies. […] With the great majority of occupational exposures having a rather low prevalence (<10%) in the general population it is […] extremely important that JEMs are developed aiming at a highly specific exposure assessment so that only jobs with a high likelihood (prevalence) and intensity of exposure are considered to be exposed. Aiming at a high sensitivity would be disastrous because a high sensitivity would lead to an enormous number of individuals being assigned an exposure while actually being unexposed […] Combinations of the methods just described exist as well”.

“Community-based studies, by definition, address a wider range of types of exposure and a much wider range of encountered exposure levels (e.g. relatively high exposures in primary production but often lower in downstream use, or among indirectly exposed individuals). A limitation of single community-based studies is often the relatively low number of exposed individuals. Pooling across studies might therefore be beneficial. […] Pooling projects need careful planning and coordination, because the original studies were conducted for different purposes, at different time periods, using different questionnaires. This heterogeneity is sometimes perceived as a disadvantage but also implies variations that can be studied and thereby provide important insights. Every pooling project has its own dynamics but there are several general challenges that most pooling projects confront. Creating common variables for all studies can stretch from simple re-naming of variables […] or recoding of units […] to the re-categorization of national educational systems […] into years of formal education. Another challenge is to harmonize the different classification systems of, for example, diseases (e.g. International Classification of Disease (ICD)-9 versus ICD-10), occupations […], and industries […]. This requires experts in these respective fields as well as considerable time and money. Harmonization of data may mean losing some information; for example, ISCO-68 contains more detail than ISCO-88, which makes it possible to recode ISCO-68 to ISCO-88 with only a little loss of detail, but it is not possible to recode ISCO-88 to ISCO-68 without losing one or two digits in the job code. […] Making the most of the data may imply that not all studies will qualify for all analyses. For example, if a study did not collect data regarding lung cancer cell type, it can contribute to the overall analyses but not to the cell type-specific analyses. It is important to remember that the quality of the original data is critical; poor data do not become better by pooling.”


December 6, 2017 Posted by | Books, Cancer/oncology, Demographics, Epidemiology, Health Economics, Medicine, Ophthalmology, Statistics | Leave a comment


A few quotes from the book and some related links below. Here’s my very short goodreads review of the book.


“The main naturally occurring radionuclides of primordial origin are uranium-235, uranium-238, thorium-232, their decay products, and potassium-40. The average abundance of uranium, thorium, and potassium in the terrestrial crust is 2.6 parts per million, 10 parts per million, and 1% respectively. Uranium and thorium produce other radionuclides via neutron- and alpha-induced reactions, particularly deeply underground, where uranium and thorium have a high concentration. […] A weak source of natural radioactivity derives from nuclear reactions of primary and secondary cosmic rays with the atmosphere and the lithosphere, respectively. […] Accretion of extraterrestrial material, intensively exposed to cosmic rays in space, represents a minute contribution to the total inventory of radionuclides in the terrestrial environment. […] Natural radioactivity is [thus] mainly produced by uranium, thorium, and potassium. The total heat content of the Earth, which derives from this radioactivity, is 12.6 × 1024 MJ (one megajoule = 1 million joules), with the crust’s heat content standing at 5.4 × 1021 MJ. For comparison, this is significantly more than the 6.4 × 1013 MJ globally consumed for electricity generation during 2011. This energy is dissipated, either gradually or abruptly, towards the external layers of the planet, but only a small fraction can be utilized. The amount of energy available depends on the Earth’s geological dynamics, which regulates the transfer of heat to the surface of our planet. The total power dissipated by the Earth is 42 TW (one TW = 1 trillion watts): 8 TW from the crust, 32.3 TW from the mantle, 1.7 TW from the core. This amount of power is small compared to the 174,000 TW arriving to the Earth from the Sun.”

“Charged particles such as protons, beta and alpha particles, or heavier ions that bombard human tissue dissipate their energy locally, interacting with the atoms via the electromagnetic force. This interaction ejects electrons from the atoms, creating a track of electron–ion pairs, or ionization track. The energy that ions lose per unit path, as they move through matter, increases with the square of their charge and decreases linearly with their energy […] The energy deposited in the tissues and organs of your body by ionizing radiation is defined absorbed dose and is measured in gray. The dose of one gray corresponds to the energy of one joule deposited in one kilogram of tissue. The biological damage wrought by a given amount of energy deposited depends on the kind of ionizing radiation involved. The equivalent dose, measured in sievert, is the product of the dose and a factor w related to the effective damage induced into the living matter by the deposit of energy by specific rays or particles. For X-rays, gamma rays, and beta particles, a gray corresponds to a sievert; for neutrons, a dose of one gray corresponds to an equivalent dose of 5 to 20 sievert, and the factor w is equal to 5–20 (depending on the neutron energy). For protons and alpha particles, w is equal to 5 and 20, respectively. There is also another weighting factor taking into account the radiosensitivity of different organs and tissues of the body, to evaluate the so-called effective dose. Sometimes the dose is still quoted in rem, the old unit, with 100 rem corresponding to one sievert.”

“Neutrons emitted during fission reactions have a relatively high velocity. When still in Rome, Fermi had discovered that fast neutrons needed to be slowed down to increase the probability of their reaction with uranium. The fission reaction occurs with uranium-235. Uranium-238, the most common isotope of the element, merely absorbs the slow neutrons. Neutrons slow down when they are scattered by nuclei with a similar mass. The process is analogous to the interaction between two billiard balls in a head-on collision, in which the incoming ball stops and transfers all its kinetic energy to the second one. ‘Moderators’, such as graphite and water, can be used to slow neutrons down. […] When Fermi calculated whether a chain reaction could be sustained in a homogeneous mixture of uranium and graphite, he got a negative answer. That was because most neutrons produced by the fission of uranium-235 were absorbed by uranium-238 before inducing further fissions. The right approach, as suggested by Szilárd, was to use separated blocks of uranium and graphite. Fast neutrons produced by the splitting of uranium-235 in the uranium block would slow down, in the graphite block, and then produce fission again in the next uranium block. […] A minimum mass – the critical mass – is required to sustain the chain reaction; furthermore, the material must have a certain geometry. The fissile nuclides, capable of sustaining a chain reaction of nuclear fission with low-energy neutrons, are uranium-235 […], uranium-233, and plutonium-239. The last two don’t occur in nature but can be produced artificially by irradiating with neutrons thorium-232 and uranium-238, respectively – via a reaction called neutron capture. Uranium-238 (99.27%) is fissionable, but not fissile. In a nuclear weapon, the chain reaction occurs very rapidly, releasing the energy in a burst.”

“The basic components of nuclear power reactors, fuel, moderator, and control rods, are the same as in the first system built by Fermi, but the design of today’s reactors includes additional components such as a pressure vessel, containing the reactor core and the moderator, a containment vessel, and redundant and diverse safety systems. Recent technological advances in material developments, electronics, and information technology have further improved their reliability and performance. […] The moderator to slow down fast neutrons is sometimes still the graphite used by Fermi, but water, including ‘heavy water’ – in which the water molecule has a deuterium atom instead of a hydrogen atom – is more widely used. Control rods contain a neutron-absorbing material, such as boron or a combination of indium, silver, and cadmium. To remove the heat generated in the reactor core, a coolant – either a liquid or a gas – is circulating through the reactor core, transferring the heat to a heat exchanger or directly to a turbine. Water can be used as both coolant and moderator. In the case of boiling water reactors (BWRs), the steam is produced in the pressure vessel. In the case of pressurized water reactors (PWRs), the steam generator, which is the secondary side of the heat exchanger, uses the heat produced by the nuclear reactor to make steam for the turbines. The containment vessel is a one-metre-thick concrete and steel structure that shields the reactor.”

“Nuclear energy contributed 2,518 TWh of the world’s electricity in 2011, about 14% of the global supply. As of February 2012, there are 435 nuclear power plants operating in 31 countries worldwide, corresponding to a total installed capacity of 368,267 MW (electrical). There are 63 power plants under construction in 13 countries, with a capacity of 61,032 MW (electrical).”

“Since the first nuclear fusion, more than 60 years ago, many have argued that we need at least 30 years to develop a working fusion reactor, and this figure has stayed the same throughout those years.”

“[I]onizing radiation is […] used to improve many properties of food and other agricultural products. For example, gamma rays and electron beams are used to sterilize seeds, flour, and spices. They can also inhibit sprouting and destroy pathogenic bacteria in meat and fish, increasing the shelf life of food. […] More than 60 countries allow the irradiation of more than 50 kinds of foodstuffs, with 500,000 tons of food irradiated every year. About 200 cobalt-60 sources and more than 10 electron accelerators are dedicated to food irradiation worldwide. […] With the help of radiation, breeders can increase genetic diversity to make the selection process faster. The spontaneous mutation rate (number of mutations per gene, for each generation) is in the range 10-8–10-5. Radiation can increase this mutation rate to 10-5–10-2. […] Long-lived cosmogenic radionuclides provide unique methods to evaluate the ‘age’ of groundwaters, defined as the mean subsurface residence time after the isolation of the water from the atmosphere. […] Scientists can date groundwater more than a million years old, through chlorine-36, produced in the atmosphere by cosmic-ray reactions with argon.”

“Radionuclide imaging was developed in the 1950s using special systems to detect the emitted gamma rays. The gamma-ray detectors, called gamma cameras, use flat crystal planes, coupled to photomultiplier tubes, which send the digitized signals to a computer for image reconstruction. Images show the distribution of the radioactive tracer in the organs and tissues of interest. This method is based on the introduction of low-level radioactive chemicals into the body. […] More than 100 diagnostic tests based on radiopharmaceuticals are used to examine bones and organs such as lungs, intestines, thyroids, kidneys, the liver, and gallbladder. They exploit the fact that our organs preferentially absorb different chemical compounds. […] Many radiopharmaceuticals are based on technetium-99m (an excited state of technetium-99 – the ‘m’ stands for ‘metastable’ […]). This radionuclide is used for the imaging and functional examination of the heart, brain, thyroid, liver, and other organs. Technetium-99m is extracted from molybdenum-99, which has a much longer half-life and is therefore more transportable. It is used in 80% of the procedures, amounting to about 40,000 per day, carried out in nuclear medicine. Other radiopharmaceuticals include short-lived gamma-emitters such as cobalt-57, cobalt-58, gallium-67, indium-111, iodine-123, and thallium-201. […] Methods routinely used in medicine, such as X-ray radiography and CAT, are increasingly used in industrial applications, particularly in non-destructive testing of containers, pipes, and walls, to locate defects in welds and other critical parts of the structure.”

“Today, cancer treatment with radiation is generally based on the use of external radiation beams that can target the tumour in the body. Cancer cells are particularly sensitive to damage by ionizing radiation and their growth can be controlled or, in some cases, stopped. High-energy X-rays produced by a linear accelerator […] are used in most cancer therapy centres, replacing the gamma rays produced from cobalt-60. The LINAC produces photons of variable energy bombarding a target with a beam of electrons accelerated by microwaves. The beam of photons can be modified to conform to the shape of the tumour, which is irradiated from different angles. The main problem with X-rays and gamma rays is that the dose they deposit in the human tissue decreases exponentially with depth. A considerable fraction of the dose is delivered to the surrounding tissues before the radiation hits the tumour, increasing the risk of secondary tumours. Hence, deep-seated tumours must be bombarded from many directions to receive the right dose, while minimizing the unwanted dose to the healthy tissues. […] The problem of delivering the needed dose to a deep tumour with high precision can be solved using collimated beams of high-energy ions, such as protons and carbon. […] Contrary to X-rays and gamma rays, all ions of a given energy have a certain range, delivering most of the dose after they have slowed down, just before stopping. The ion energy can be tuned to deliver most of the dose to the tumour, minimizing the impact on healthy tissues. The ion beam, which does not broaden during the penetration, can follow the shape of the tumour with millimetre precision. Ions with higher atomic number, such as carbon, have a stronger biological effect on the tumour cells, so the dose can be reduced. Ion therapy facilities are [however] still very expensive – in the range of hundreds of millions of pounds – and difficult to operate.”

“About 50 million years ago, a global cooling trend took our planet from the tropical conditions at the beginning of the Tertiary to the ice ages of the Quaternary, when the Arctic ice cap developed. The temperature decrease was accompanied by a decrease in atmospheric CO2 from 2,000 to 300 parts per million. The cooling was probably caused by a reduced greenhouse effect and also by changes in ocean circulation due to plate tectonics. The drop in temperature was not constant as there were some brief periods of sudden warming. Ocean deep-water temperatures dropped from 12°C, 50 million years ago, to 6°C, 30 million years ago, according to archives in deep-sea sediments (today, deep-sea waters are about 2°C). […] During the last 2 million years, the mean duration of the glacial periods was about 26,000 years, while that of the warm periods – interglacials – was about 27,000 years. Between 2.6 and 1.1 million years ago, a full cycle of glacial advance and retreat lasted about 41,000 years. During the past 1.2 million years, this cycle has lasted 100,000 years. Stable and radioactive isotopes play a crucial role in the reconstruction of the climatic history of our planet”.


CUORE (Cryogenic Underground Observatory for Rare Events).
Lawrence Livermore National Laboratory.
Marie Curie. Pierre Curie. Henri Becquerel. Wilhelm Röntgen. Joseph Thomson. Ernest Rutherford. Hans Geiger. Ernest Marsden. Niels Bohr.
Ruhmkorff coil.
Pitchblende (uraninite).
Polonium. Becquerel.
Alpha decay. Beta decay. Gamma radiation.
Plum pudding model.
Robert Boyle. John Dalton. Dmitri Mendeleev. Frederick Soddy. James Chadwick. Enrico Fermi. Lise Meitner. Otto Frisch.
Periodic Table.
Exponential decay. Decay chain.
Particle accelerator. Cockcroft-Walton generator. Van de Graaff generator.
Barn (unit).
Nuclear fission.
Manhattan Project.
Chernobyl disaster. Fukushima Daiichi nuclear disaster.
Electron volt.
Thermoluminescent dosimeter.
Silicon diode detector.
Enhanced geothermal system.
Chicago Pile Number 1. Experimental Breeder Reactor 1. Obninsk Nuclear Power Plant.
Natural nuclear fission reactor.
Gas-cooled reactor.
Generation I reactors. Generation II reactor. Generation III reactor. Generation IV reactor.
Nuclear fuel cycle.
Accelerator-driven subcritical reactor.
Thorium-based nuclear power.
Small, sealed, transportable, autonomous reactor.
Fusion power. P-p (proton-proton) chain reaction. CNO cycle. Tokamak. ITER (International Thermonuclear Experimental Reactor).
Sterile insect technique.
Phase-contrast X-ray imaging. Computed tomography (CT). SPECT (Single-photon emission computed tomography). PET (positron emission tomography).
Boron neutron capture therapy.
Radiocarbon dating. Bomb pulse.
Radioactive tracer.
Radithor. The Radiendocrinator.
Radioisotope heater unit. Radioisotope thermoelectric generator. Seebeck effect.
Accelerator mass spectrometry.
Atomic bombings of Hiroshima and Nagasaki. Treaty on the Non-Proliferation of Nuclear Weapons. IAEA.
Nuclear terrorism.
Swiss light source. Synchrotron.
Chronology of the universe. Stellar evolution. S-process. R-process. Red giant. Supernova. White dwarf.
Victor Hess. Domenico Pacini. Cosmic ray.
Allende meteorite.
Age of the Earth. History of Earth. Geomagnetic reversal. Uranium-lead dating. Clair Cameron Patterson.
Glacials and interglacials.
Taung child. Lucy. Ardi. Ardipithecus kadabba. Acheulean tools. Java Man. Ötzi.
Argon-argon dating. Fission track dating.


November 28, 2017 Posted by | Archaeology, Astronomy, Biology, Books, Cancer/oncology, Chemistry, Engineering, Geology, History, Medicine, Physics | Leave a comment

A few diabetes papers of interest

i. Glycated Hemoglobin and All-Cause and Cause-Specific Mortality in Singaporean Chinese Without Diagnosed Diabetes: The Singapore Chinese Health Study.

“Previous studies have reported that elevated levels of HbA1c below the diabetes threshold (<6.5%) are associated with an increased risk for cardiovascular morbidity and mortality (312). Yet, this research base is not comprehensive, and data from Chinese populations are scant, especially in those without diabetes. This gap in the literature is important since Southeast Asian populations are experiencing epidemic rates of type 2 diabetes and related comorbidities with a substantial global health impact (1316).

Overall, there are few cohort studies that have examined the etiologic association between HbA1c levels and all-cause and cause-specific mortality. There is even lesser insight on the nature of the relationship between HbA1c and significant clinical outcomes in Southeast Asian populations. Therefore, we examined the association between HbA1c and all-cause and cause-specific mortality in the Singapore Chinese Health Study (SCHS).”

“The design of the SCHS has been previously summarized (17). Briefly, the cohort was drawn from men and women, aged 45–74 years, who belonged to one of the major dialect groups (Hokkien or Cantonese) of Chinese in Singapore. […] Between April 1993 and December 1998, 63,257 individuals completed an in-person interview that included questions on usual diet, demographics, height and weight, use of tobacco, usual physical activity, menstrual and reproductive history (women only), medical history including history of diabetes diagnosis by a physician, and family history of cancer. […] At the follow-up interview (F1), which occurred in 1999–2004, subjects were asked to update their baseline interview information. […] The study population derived from 28,346 participants of the total 54,243 who were alive and participated at F1, who provided consent at F1 to collect subsequent blood samples (a consent rate of ∼65%). The participants for this study were a random selection of individuals from the full study population who did not report a history of diabetes or CVD at the baseline or follow-up interview and reported no history of cancer.”

“During 74,890 person-years of follow-up, there were 888 total deaths, of which 249 were due to CVD, 388 were due to cancer, and 169 were recorded as respiratory mortality. […] There was a positive association between HbA1c and age, BMI, and prevalence of self-reported hypertension, while an inverse association was observed between educational attainment and HbA1c. […] The crude mortality rate was 1,186 deaths per 100,000 person-years. The age- and sex-standardized mortality rates for all-cause, CVD, and cerebrovascular each showed a J-shaped pattern according to HbA1c level. The CHD and cancer mortality rates were higher for HbA1c ≥6.5% (≥48 mmol/mol) and otherwise displayed no apparent pattern. […] There was no association between any level of HbA1c and respiratory causes of death.”

“Chinese men and women with no history of cancer, reported diabetes, or CVD with an HbA1c level ≥6.5% (≥48 mmol/mol) were at a significant increased risk of mortality during follow-up relative to their peers with an HbA1c of 5.4–5.6% (36–38 mmol/mol). No other range of HbA1c was significantly associated with risk of mortality during follow-up, and in secondary analyses, when the HbA1c level ≥6.5% (≥48 mmol/mol) was divided into four categories, this increased risk was observed in all four categories; thus, these data represent a clear threshold association between HbA1c and mortality in this population. These results are consistent with previous prospective cohort studies identifying chronically high HbA1c, outside of diabetes, to be associated with increased risk for all-cause and CVD-related mortality (312,22).”

“Hyperglycemia is a known risk factor for CVD, not limited to individuals with diabetes. This may be in part due to the vascular damage caused by oxidative stress in periods of hypo- and hyperglycemia (23,24). For individuals with impaired fasting glucose and impaired glucose tolerance, increased oxidative stress and endothelial dysfunction are present before the onset of diabetes (25). The association between chronically high levels of HbA1c and development of and death from cancer is not as well defined (9,2630). Abnormal metabolism may play a role in cancer development and death. This is important, considering cancer is the leading cause of death in Singapore for adults 15–59 years of age (31). Increased risk for cancer mortality was found in individuals with impaired glucose tolerance (30). […] Hyperinsulinemia and IGF-I are associated with increased cancer risk, possibly through mitogenic effects and tumor formation (27,28,37). This is the basis for the insulin-cancer hypothesis. Simply put, chronic levels of hyperinsulinemia reduce the production of IGF binding proteins 1 and 2. The absence of these proteins results in excess bioactive IGF-I, supporting tumor development (38). Chronic hyperglycemia, indicating high levels of insulin and IGF-I, may explain inhibition of cell apoptosis, increased cell proliferation, and increased cancer risk (39).”

ii. The Cross-sectional and Longitudinal Associations of Diabetic Retinopathy With Cognitive Function and Brain MRI Findings: The Action to Control Cardiovascular Risk in Diabetes (ACCORD) Trial.

“Brain imaging studies suggest that type 2 diabetes–related microvascular disease may affect the central nervous system in addition to its effects on other organs, such as the eye and kidney. Histopathological evidence indicates that microvascular disease in the brain can lead to white matter lesions (WMLs) visible with MRI of the brain (1), and risk for them is often increased by type 2 diabetes (26). Type 2 diabetes also has recently been associated with lower brain volume, particularly gray matter volume (79).

The association between diabetic retinopathy and changes in brain tissue is of particular interest because retinal and cerebral small vessels have similar anatomy, physiology, and embryology (10). […] the preponderance of evidence suggests diabetic retinopathy is associated with increased WML burden (3,1214), although variation exists. While cross-sectional studies support a correlation between diabetic retinopathy and WMLs (2,3,6,15), diabetic retinopathy and brain atrophy (16), diabetic retinopathy and psychomotor speed (17,18), and psychomotor speed and WMLs (5,19,20), longitudinal evidence demonstrating the assumed sequence of disease development, for example, vascular damage of eye and brain followed by cognitive decline, is lacking.

Using Action to Control Cardiovascular Risk in Diabetes (ACCORD) data, in which a subset of participants received longitudinal measurements of diabetic retinopathy, cognition, and MRI variables, we analyzed the 1) cross-sectional associations between diabetic retinopathy and evidence of brain microvascular disease and 2) determined whether baseline presence or severity of diabetic retinopathy predicts 20- or 40-month changes in cognitive performance or brain microvascular disease.”

“The ACCORD trial (21) was a multicenter randomized trial examining the effects of intensive glycemic control, blood pressure, and lipids on cardiovascular disease events. The 10,251 ACCORD participants were aged 40–79 years, had poorly controlled type 2 diabetes (HbA1c > 7.5% [58.5 mmol/mol]), and had or were at high risk for cardiovascular disease. […] The ACCORD-Eye sample comprised 3,472 participants who did not report previous vitrectomy or photocoagulation surgery for proliferative diabetic retinopathy at baseline […] ACCORD-MIND included a subset of 2,977 ACCORD participants who completed a 30-min cognitive testing battery, 614 of whom also had useable scans from the MRI substudy (23,24). […] ACCORD-MIND had visits at three time points: baseline, 20 months, and 40 months. MRI of the brain was completed at baseline and the 40-month time point.”

“Baseline diabetic retinopathy was associated with more rapid 40-month declines in DSST and MMSE [Mini-Mental State Examination] when adjusting for demographics and lifestyle factors in model 1 […]. Moreover, increasing severity of diabetic retinopathy was associated with increased amounts of decline in DSST [Digit Symbol Substitution Test] performance (−1.30, −1.76, and −2.81 for no, mild, and moderate/severe NPDR, respectively; P = 0.003) […Be careful about how to interpret that p-value – see below, US] . The associations remained virtually unchanged after further adjusting for vascular and diabetes risk factors, depression, and visual acuity using model 2.”

“This longitudinal study provides new evidence that diabetic retinopathy is associated with future cognitive decline in persons with type 2 diabetes and confirms the finding from the Edinburgh Type 2 Diabetes Study derived from cross-sectional data that lifetime cognitive decline is associated with diabetic retinopathy (32). We found that the presence of diabetic retinopathy, independent of visual acuity, predicts greater declines in global cognitive function measured with the MMSE and that the magnitude of decline in processing speed measured with the DSST increased with increasing severity of baseline diabetic retinopathy. The association with psychomotor speed is consistent with prior cross-sectional findings in community-based samples of middle-aged (18) and older adults (17), as well as prospective studies of a community-based sample of middle-aged adults (33) and patients with type 1 diabetes (34) showing that retinopathy with different etiologies predicted a subsequent decline in psychomotor speed. This study extends these findings to patients with type 2 diabetes.”

“we tested a number of different associations but did not correct P values for multiple testing” [Aargh!, US.]

iii. Incidence of Remission in Adults With Type 2 Diabetes: The Diabetes & Aging Study.

(Note to self before moving on to the paper: these people identified type 1 diabetes by self-report or diabetes onset at <30 years of age, treated with insulin only and never treated with oral agents).

“It is widely believed that type 2 diabetes is a chronic progressive condition, which at best can be controlled, but never cured (1), and that once treatment with glucose-lowering medication is initiated, it is required indefinitely and is intensified over time (2,3). However, a growing body of evidence from clinical trials and case-control studies (46) has reported the remission of type 2 diabetes in certain populations, most notably individuals who received bariatric surgery. […] Despite the clinical relevance and importance of remission, little is known about the incidence of remission in community settings (11,12). Studies to date have focused largely on remission after gastric bypass or relied on data from clinical trials, which have limited generalizability. Therefore, we conducted a retrospective cohort study to describe the incidence rates and variables associated with remission among adults with type 2 diabetes who received usual care, excluding bariatric surgery, in a large, ethnically diverse population. […] 122,781 individuals met our study criteria, yielding 709,005 person-years of total follow-up time.”

“Our definitions of remission were based on the 2009 ADA consensus statement (10). “Partial remission” of diabetes was defined as having two or more consecutive subdiabetic HbA1c measurements, all of which were in the range of 5.7–6.4% [39–46 mmol/mol] over a period of at least 12 months. “Complete remission” was defined as having two or more consecutive normoglycemic HbA1c measurements, all of which were <5.7% [<39 mmol/mol] over a period of at least 12 months. “Prolonged remission” was defined as having two or more consecutive normoglycemic HbA1c measurements, all of which were <5.7% [<39 mmol/mol] over a period of at least 60 months. Each definition of remission requires the absence of pharmacologic treatment during the defined observation period.”

“The average age of participants was 62 years, 47.1% were female, and 51.6% were nonwhite […]. The mean (SD) interval between HbA1c tests in the remission group was 256 days (139 days). The mean interval (SD) between HbA1c tests among patients not in the remission group was 212 days (118 days). The median time since the diagnosis of diabetes in our cohort was 5.9 years, and the average baseline HbA1c level was 7.4% [57 mmol/mol]. The 18,684 individuals (15.2%) in the subset with new-onset diabetes, defined as ≤2 years since diagnosis, were younger, were more likely to have their diabetes controlled by diet, and had fewer comorbidities […] The incidence densities of partial, complete, and prolonged remission in the full cohort were 2.8 (95% CI 2.6–2.9), 0.24 (95% CI 0.20–0.28), and 0.04 (95% CI 0.01–0.06) cases per 1,000 person-years, respectively […] The 7-year cumulative incidences of partial, complete, and prolonged remission were 1.5% (95% CI 1.4–1.5%), 0.14% (95% CI 0.12–0.16%), and 0.01% (95% CI 0.003–0.02%), respectively. The 7-year cumulative incidence of any remission decreased with longer time since diagnosis from a high of 4.6% (95% CI 4.3–4.9%) for individuals diagnosed with diabetes in the past 2 years to a low of 0.4% (95% CI 0.3–0.5%) in those diagnosed >10 years ago. The 7-year cumulative incidence of any remission was much lower for individuals using insulin (0.05%; 95% CI 0.03–0.1%) or oral agents (0.3%; 95% CI 0.2–0.3%) at baseline compared with diabetes patients not using medication at baseline (12%; 95% CI 12–13%).”

“In this large cohort of insured adults with type 2 diabetes not treated with bariatric surgery, we found that 1.5% of individuals with recent evidence of clinical diabetes achieved at least partial remission over a 7-year period. If these results were generalized to the 25.6 million U.S. adults living with type 2 diabetes in 2010 (25), they would suggest that 384,000 adults could experience remission over the next 7 years. However, the rate of prolonged remission was extremely rare (0.007%), translating into only 1,800 adults in the U.S. experiencing remission lasting at least 5 years. To provide context, 1.7% of the cohort died, while only 0.8% experienced any level of remission, during the calendar year 2006. Thus, the chances of dying were higher than the chances of any remission. […] Although remission of type 2 diabetes is uncommon, it does occur in patients who have not undergone surgical interventions. […] Our analysis shows that remission is rare and variable. The likelihood of remission is more common among individuals with early-onset diabetes and those not treated with glucose-lowering medications at the point of diabetes diagnosis. Although rare, remission can also occur in individuals with more severe diabetes and those previously treated with insulin.”

iv. Blood pressure control for diabetic retinopathy (Cochrane review).

“Diabetic retinopathy is a common complication of diabetes and a leading cause of visual impairment and blindness. Research has established the importance of blood glucose control to prevent development and progression of the ocular complications of diabetes. Simultaneous blood pressure control has been advocated for the same purpose, but findings reported from individual studies have supported varying conclusions regarding the ocular benefit of interventions on blood pressure. […] The primary aim of this review was to summarize the existing evidence regarding the effect of interventions to control or reduce blood pressure levels among diabetics on incidence and progression of diabetic retinopathy, preservation of visual acuity, adverse events, quality of life, and costs. A secondary aim was to compare classes of anti-hypertensive medications with respect to the same outcomes.”

“We included 15 RCTs, conducted primarily in North America and Europe, that had enrolled 4157 type 1 and 9512 type 2 diabetic participants, ranging from 16 to 2130 participants in individual trials. […] Study designs, populations, interventions, and lengths of follow-up (range one to nine years) varied among the included trials. Overall, the quality of the evidence for individual outcomes was low to moderate.”

“The evidence from these trials supported a benefit of more intensive blood pressure control intervention with respect to 4- to 5-year incidence of diabetic retinopathy (estimated risk ratio (RR) 0.80; 95% confidence interval (CI) 0.71 to 0.92) and the combined outcome of incidence and progression (estimated RR 0.78; 95% CI 0.63 to 0.97). The available evidence provided less support for a benefit with respect to 4- to 5-year progression of diabetic retinopathy (point estimate was closer to 1 than point estimates for incidence and combined incidence and progression, and the CI overlapped 1; estimated RR 0.88; 95% CI 0.73 to 1.05). The available evidence regarding progression to proliferative diabetic retinopathy or clinically significant macular edema or moderate to severe loss of best-corrected visual acuity did not support a benefit of intervention on blood pressure: estimated RRs and 95% CIs 0.95 (0.83 to 1.09) and 1.06 (0.85 to 1.33), respectively, after 4 to 5 years of follow-up. Findings within subgroups of trial participants (type 1 and type 2 diabetics; participants with normal blood pressure levels at baseline and those with elevated levels) were similar to overall findings.”

“The available evidence supports a beneficial effect of intervention to reduce blood pressure with respect to preventing diabetic retinopathy for up to 4 to 5 years. However, the lack of evidence to support such intervention to slow progression of diabetic retinopathy or to prevent other outcomes considered in this review, along with the relatively modest support for the beneficial effect on incidence, weakens the conclusion regarding an overall benefit of intervening on blood pressure solely to prevent diabetic retinopathy.”

v. Early Atherosclerosis Relates to Urinary Albumin Excretion and Cardiovascular Risk Factors in Adolescents With Type 1 Diabetes: Adolescent Type 1 Diabetes cardio-renal Intervention Trial (AdDIT).

“Children with type 1 diabetes are at greatly increased risk for the development of both renal and cardiovascular disease in later life (1,2). Evidence is accumulating that these two complications may have a common pathophysiology, with endothelial dysfunction a key early event.

Microalbuminuria is a recognized marker of endothelial damage (3) and predicts progression to proteinuria and diabetic nephropathy, as well as to atherosclerosis (4) and increased cardiovascular risk (5). It is, however, rare in adolescents with type 1 diabetes who more often have higher urinary albumin excretion rates within the normal range, which are associated with later progression to microalbuminuria and proteinuria (6).”

“The Adolescent Type 1 Diabetes cardio-renal Intervention Trial (AdDIT) (10) is designed to examine the impact of minor differences in albumin excretion in adolescents on the initiation and progression of cardiovascular and renal disease. The primary cardiovascular end point in AdDIT is carotid intima-media thickness (cIMT). Subclinical atherosclerosis can be detected noninvasively using high-resolution ultrasound to measure the intima-media thickness (IMT) of the carotid arteries, which predicts cardiovascular morbidity and mortality (11,12). […] The primary aim of this study was to examine the relationship of increased urinary albumin excretion and cardiovascular risk factors in adolescents with type 1 diabetes with structural arterial wall changes. We hypothesized that even minor increases in albumin excretion would be associated with early atherosclerosis but that this would be detectable only in the abdominal aorta. […] A total of 406 adolescents, aged 10–16 years, with type 1 diabetes for more than 1 year, recruited in five centers across Australia, were enrolled in this cross-sectional study”.

“Structural changes in the aorta and carotid arteries could be detected in >50% of adolescents with type 1 diabetes […] The difference in aIMT [aortic intima-media thickness] between type 1 diabetic patients and age- and sex-matched control subjects was equivalent to that seen with a 5- to 6-year age increase in the type 1 diabetic patients. […] Aortic IMT was […] able to better differentiate adolescents with type 1 diabetes from control subjects than was carotid wall changes. Aortic IMT enabled detection of the very early wall changes that are present with even small differences in urinary albumin excretion. This not only supports the concept of early intervention but provides a link between renal and cardiovascular disease.

The independent relationship between aIMT and urinary albumin excretion extends our knowledge of the pathogenesis of cardiovascular and renal disease in type 1 diabetes by showing that the first signs of the development of cardiovascular disease and diabetic nephropathy are related. The concept that microalbuminuria is a marker of a generalized endothelial damage, as well as a marker of renal disease, has been recognized for >20 years (3,20,21). Endothelial dysfunction is the first critical step in the development of atherosclerosis (22). Early rises in urinary albumin excretion precede the development of microalbuminuria and proteinuria (23). It follows that the first structural changes of atherosclerosis could relate to the first biochemical changes of diabetic nephropathy. To our knowledge, this is the first study to provide evidence of this.”

“In conclusion, atherosclerosis is detectable from early adolescence in type 1 diabetes. Its early independent associations are male sex, age, systolic blood pressure, LDL cholesterol, and, importantly, urinary albumin excretion. […] Early rises in urinary albumin excretion during adolescence not only are important for determining risk of progression to microalbuminuria and diabetic nephropathy but also may alert the clinician to increased risk of cardiovascular disease.”

vi. Impact of Islet Autoimmunity on the Progressive β-Cell Functional Decline in Type 2 Diabetes.

“Historically, type 2 diabetes (T2D) has not been considered to be immune mediated. However, many notable discoveries in recent years have provided evidence to support the concept of immune system involvement in T2D pathophysiology (15). Immune cells have been identified in the pancreases of phenotypic T2D patients (35). Moreover, treatment with interleukin-1 receptor agonist improves β-cell function in T2D patients (68). These studies suggest that β-cell damage/destruction mediated by the immune system may be a component of T2D pathophysiology.

Although the β-cell damage and destruction in autoimmune diabetes is most likely T-cell mediated (T), immune markers of autoimmune diabetes have primarily centered on the presence of circulating autoantibodies (Abs) to various islet antigens (915). Abs commonly positive in type 1 diabetes (T1D), especially GAD antibody (GADA) and islet cell Abs (ICA), have been shown to be more common in patients with T2D than in nondiabetic control populations, and the presence of multiple islet Abs, such as GADA, ICA, and tyrosine phosphatase-2 (insulinoma-associated protein 2 [IA-2]), have been demonstrated to be associated with an earlier need for insulin treatment in adult T2D patients (14,1620).”

“In this study, we observed development of islet autoimmunity, measured by islet Abs and islet-specific T-cell responses, in 61% of the phenotypic T2D patients. We also observed a significant association between positive islet-reactive T-cell responses and a more rapid decline in β-cell function as assessed by FCP and glucagon-SCP responses. […] The results of this pilot study led us to hypothesize that islet autoimmunity is present or will develop in a large portion of phenotypic T2D patients and that the development of islet autoimmunity is associated with a more rapid decline in β-cell function. Moreover, the prevalence of islet autoimmunity in most previous studies is grossly underestimated because these studies have not tested for islet-reactive T cells in T2D patients but have based the presence of autoimmunity on antibody testing alone […] The results of this pilot study suggest important changes to our understanding of T2D pathogenesis by demonstrating that the prevalence of islet autoimmune development is not only more prevalent in T2D patients than previously estimated but may also play an important role in β-cell dysfunction in the T2D disease process.”


September 18, 2017 Posted by | Cancer/oncology, Cardiology, Diabetes, Epidemiology, Immunology, Medicine, Nephrology, Neurology, Ophthalmology, Studies | Leave a comment

Utility of Research Autopsies for Understanding the Dynamics of Cancer

A few links:
Pancreatic cancer.
Jaccard index.
Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer.
Tissue-specific mutation accumulation in human adult stem cells during life.
Epigenomic reprogramming during pancreatic cancer progression links anabolic glucose metabolism to distant metastasis.


August 25, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Medicine, Statistics | Leave a comment

Quantifying tumor evolution through spatial computational modeling

Two general remarks: 1. She talks very fast, in my opinion unpleasantly fast – the lecture would have been at least slightly easier to follow if she’d slowed down a little. 2. A few of the lectures uploaded in this lecture series (from the IAS Mathematical Methods in Cancer Evolution and Heterogeneity Workshop) seem to have some sound issues; in this lecture there are multiple 1-2 seconds long ‘chunks’ where the sound drops out and some words are lost. This is really annoying, and a similar problem (which was likely ‘the same problem’) previously lead me to quit another lecture in the series; however in this case I decided to give it a shot anyway, and I actually think it’s not a big deal; the sound-losses are very short in duration, and usually no more than one or two words are lost so you can usually figure out what was said. During this lecture there was incidentally also some issues with the monitor roughly 27 minutes in, but this isn’t a big deal as no information was lost and unlike the people who originally attended the lecture you can just skip ahead approximately one minute (that was how long it took to solve that problem).

A few relevant links to stuff she talks about in the lecture:

A Big Bang model of human colorectal tumor growth.
Approximate Bayesian computation.
Site frequency spectrum.
Identification of neutral tumor evolution across cancer types.
Using tumour phylogenetics to identify the roots of metastasis in humans.


August 22, 2017 Posted by | Cancer/oncology, Evolutionary biology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

A few diabetes papers of interest

i. Rates of Diabetic Ketoacidosis: International Comparison With 49,859 Pediatric Patients With Type 1 Diabetes From England, Wales, the U.S., Austria, and Germany.

“Rates of DKA in youth with type 1 diabetes vary widely nationally and internationally, from 15% to 70% at diagnosis (4) to 1% to 15% per established patient per year (911). However, data from systematic comparisons between countries are limited. To address this gap in the literature, we analyzed registry and audit data from three organizations: the Prospective Diabetes Follow-up Registry (DPV) in Germany and Austria, the National Paediatric Diabetes Audit (NPDA) in England and Wales, and the T1D Exchange (T1DX) in the U.S. These countries have similarly advanced, yet differing, health care systems in which data on DKA and associated factors are collected. Our goal was to identify indicators of risk for DKA admissions in pediatric patients with >1-year duration of disease with an aim to better understand where targeted preventive programs might lead to a reduction in the frequency of this complication of management of type 1 diabetes.”

RESULTS The frequency of DKA was 5.0% in DPV, 6.4% in NPDA, and 7.1% in T1DX […] Mean HbA1c was lowest in DPV (63 mmol/mol [7.9%]), intermediate in T1DX (69 mmol/mol [8.5%]), and highest in NPDA (75 mmol/mol [9.0%]). […] In multivariable analyses, higher odds of DKA were found in females (odds ratio [OR] 1.23, 99% CI 1.10–1.37), ethnic minorities (OR 1.27, 99% CI 1.11–1.44), and HbA1c ≥7.5% (≥58 mmol/mol) (OR 2.54, 99% CI 2.09–3.09 for HbA1c from 7.5 to <9% [58 to <75 mmol/mol] and OR 8.74, 99% CI 7.18–10.63 for HbA1c ≥9.0% [≥75 mmol/mol]).”

Poor metabolic control is obviously very important, but it’s important to remember that poor metabolic control is in itself an outcome that needs to be explained. I would note that the mean HbA1c values here, especially that 75 mmol/mol one, seem really high; this is not a very satisfactory level of glycemic control and corresponds to an average glucose level of 12 mmol/l. And that’s a population average, meaning that many individuals have values much higher than this. Actually the most surprising thing to me about these data is that the DKA event rates are not much higher than they are, considering the level of metabolic control achieved. Another slightly surprising finding is that teenagers (13-17 yrs) were not actually all that much more likely to have experienced DKA than small children (0-6 yrs); the OR is only ~1.5. Of course this can not be taken as an indication that DKA in teenagers do not make up a substantial proportion of the total amount of DKA events in pediatric samples, as the type 1 prevalence is much higher in teenagers than in small children (incidence peaks in adolescence).

“In 2004–2009 in the U.S., the mean hospital cost per pediatric DKA admission was $7,142 (range $4,125–11,916) (6), and insurance claims data from 2007 reported an excess of $5,837 in annual medical expenditures for youth with insulin-treated diabetes with DKA compared with those without DKA (7). In Germany, pediatric patients with diabetes with DKA had diabetes-related costs that were up to 3.6-fold higher compared with those without DKA (8).”

“DKA frequency was lower in pump users than in injection users (OR 0.84, 99% CI 0.76–0.93). Heterogeneity in the association with DKA between registries was seen for pump use and age category, and the overall rate should be interpreted accordingly. A lower rate of DKA in pump users was only found in T1DX, in contrast to no association of pump use with DKA in DPV or NPDA. […] In multivariable analyses […], age, type 1 diabetes duration, and pump use were not significantly associated with DKA in the fully adjusted model. […] pump use was associated with elevated odds of DKA in the <6-year-olds and in the 6- to <13-year-olds but with reduced odds of DKA in the 13- to <18-year-olds.”

Pump use should probably all else equal increase the risk of DKA, but all else is never equal and in these data pump users actually had a lower DKA event rate than did diabetics treated with injections. One should not conclude from this finding that pump use decreases the risk of DKA, selection bias and unobserved heterogeneities are problems which it is almost impossible to correct for in an adequate way – I find it highly unlikely that selection bias is only a potential problem in the US (see below). There are many different ways selection bias can be a relevant problem, financial- and insurance-related reasons (relevant particularly in the US and likely the main factors the authors are considering) are far from the only potential problems; I could thus easily imagine selection dynamics playing a major role even in a hypothetical setting where all new-diagnosed children were started on pump therapy as a matter of course. In such a setting you might have a situation where very poorly controlled individuals would have 10 DKA events in a short amount of time because they didn’t take the necessary amount of blood glucose tests/disregarded alarms/forgot or postponed filling up the pump when it’s near-empty/failed to switch the battery in time/etc. etc., and then what might happen would be that the diabetologist/endocrinologist would then proceed to recommend these patients doing very poorly on pump treatment to switch to injection therapy, and what you would end up with would be a compliant/motivated group of patients on pump therapy and a noncompliant/poorly motivated group on injection therapy. This would happen even if everybody started on pump therapy and so pump therapy exposure was completely unrelated to outcomes. Pump therapy requires more of the patient than does injection therapy, and if the patient is unwilling/unable to put in the work required that treatment option will fail. In my opinion the default here should be that these treatment groups are (‘significantly’) different, not that they are similar.

A few more quotes from the paper:

“The major finding of these analyses is high rates of pediatric DKA across the three registries, even though DKA events at the time of diagnosis were not included. In the prior 12 months, ∼1 in 20 (DPV), 1 in 16 (NPDA), and 1 in 14 (T1DX) pediatric patients with a duration of diabetes ≥1 year were diagnosed with DKA and required treatment in a health care facility. Female sex, ethnic minority status, and elevated HbA1c were consistent indicators of risk for DKA across all three registries. These indicators of increased risk for DKA are similar to previous reports (10,11,18,19), and our rates of DKA are within the range in the pediatric diabetes literature of 1–15% per established patient per year (10,11).

Compared with patients receiving injection therapy, insulin pump use was associated with a lower risk of DKA only in the U.S. in the T1DX, but no difference was seen in the DPV or NPDA. Country-specific factors on the associations of risk factors with DKA require further investigation. For pump use, selection bias may play a role in the U.S. The odds of DKA in pump users was not increased in any registry, which is a marked difference from some (10) but not all historic data (20).”

ii. Effect of Long-Acting Insulin Analogs on the Risk of Cancer: A Systematic Review of Observational Studies.

NPH insulin has been the mainstay treatment for type 1 diabetes and advanced type 2 diabetes since the 1950s. However, this insulin is associated with an increased risk of nocturnal hypoglycemia, and its relatively short half-life requires frequent administration (1,2). Consequently, structurally modified insulins, known as long-acting insulin analogs (glargine and detemir), were developed in the 1990s to circumvent these limitations. However, there are concerns that long-acting insulin analogs may be associated with an increased risk of cancer. Indeed, some laboratory studies showed long-acting insulin analogs were associated with cancer cell proliferation and protected against apoptosis via their higher binding affinity to IGF-I receptors (3,4).

In 2009, four observational studies associated the use of insulin glargine with an increased risk of cancer (58). These studies raised important concerns but were also criticized for important methodological shortcomings (913). Since then, several observational studies assessing the association between long-acting insulin analogs and cancer have been published but yielded inconsistent findings (1428). […] Several meta-analyses of observational studies have investigated the association between insulin glargine and cancer risk (3437). These meta-analyses assessed the quality of included studies, but the methodological issues particular to pharmacoepidemiologic research were not fully considered. In addition, given the presence of important heterogeneity in this literature, the appropriateness of pooling the results of these studies remains unclear. We therefore conducted a systematic review of observational studies examining the association between long-acting insulin analogs and cancer incidence, with a particular focus on methodological strengths and weaknesses of these studies.”

“[W]e assessed the quality of studies for key components, including time-related biases (immortal time, time-lag, and time-window), inclusion of prevalent users, inclusion of lag periods, and length of follow-up between insulin initiation and cancer incidence.

Immortal time bias is defined by a period of unexposed person-time that is misclassified as exposed person-time or excluded, resulting in the exposure of interest appearing more favorable (40,41). Time-lag bias occurs when treatments used later in the disease management process are compared with those used earlier for less advanced stages of the disease. Such comparisons can result in confounding by disease duration or severity of disease if duration and severity of disease are not adequately considered in the design or analysis of the study (29). This is particularly true for chronic disease with dynamic treatment processes such as type 2 diabetes. Currently, American and European clinical guidelines suggest using basal insulin (e.g., NPH, glargine, and detemir) as a last line of treatment if HbA1c targets are not achieved with other antidiabetic medications (42). Therefore, studies that compare long-acting insulin analogs to nonbasal insulin may introduce confounding by disease duration. Time-window bias occurs when the opportunity for exposure differs between case subjects and control subjects (29,43).

The importance of considering a lag period is necessary for latency considerations (i.e., a minimum time between treatment initiation and the development of cancer) and to minimize protopathic and detection bias. Protopathic bias, or reverse causation, is present when a medication (exposure) is prescribed for early symptoms related to the outcome of interest, which can lead to an overestimation of the association. Lagging the exposure by a predefined time window in cohort studies or excluding exposures in a predefined time window before the event in case-control studies is a means of minimizing this bias (44). Detection bias is present when the exposure leads to higher detection of the outcome of interest due to the increased frequency of clinic visits (e.g., newly diagnosed patients with type 2 diabetes or new users of another antidiabetic medication), which also results in an overestimation of risk (45). Thus, including a lag period, such as starting follow-up after 1 year of the initiation of a drug, simultaneously considers a latency period while also minimizing protopathic and detection bias.”

“We systematically searched MEDLINE and EMBASE from 2000 to 2014 to identify all observational studies evaluating the relationship between the long-acting insulin analogs and the risk of any and site-specific cancers (breast, colorectal, prostate). […] 16 cohort and 3 case-control studies were included in this systematic review (58,1428). All studies evaluated insulin glargine, with four studies also investigating insulin detemir (15,17,25,28). […] The study populations ranged from 1,340 to 275,164 patients […]. The mean or median durations of follow-up and age ranged from 0.9 to 7.0 years and from 52.3 to 77.4 years, respectively. […] Thirteen of 15 studies reported no association between insulin glargine and detemir and any cancer. Four of 13 studies reported an increased risk of breast cancer with insulin glargine. In the quality assessment, 7 studies included prevalent users, 11 did not consider a lag period, 6 had time-related biases, and 16 had short (<5 years) follow-up.”

“Of the 19 studies in this review, immortal time bias may have been introduced in one study based on the time-independent exposure and cohort entry definitions that were used in this cohort study […] Time-lag bias may have occurred in four studies […] A variation of time-lag bias was observed in a cohort study of new insulin users (28). For the exposure definition, highest duration since the start of insulin use was compared with the lowest. It is expected that the risk of cancer would increase with longer duration of insulin use; however, the opposite was reported (with RRs ranging from 0.50 to 0.90). The protective association observed could be due to competing risks (e.g., death from cardiovascular-related events) (47,48). Patients with diabetes have a higher risk of cardiovascular-related deaths compared with patients with no diabetes (49,50). Therefore, patients with diabetes who die of cardiovascular-related events do not have the opportunity to develop cancer, resulting in an underestimation of the risk of cancer. […] Time-window bias was observed in two studies (18,22). […] HbA1c and diabetes duration were not accounted for in 15 of the 19 studies, resulting in likely residual confounding (7,8,1418,2026,28). […] Seven studies included prevalent users of insulin (8,15,18,20,21,23,25), which is problematic because of the corresponding depletion of susceptible subjects in other insulin groups compared with long-acting insulin analogs. Protopathic or detection bias could have resulted in 11 of the 19 studies because a lag period was not incorporated in the study design (6,7,1416,1821,23,28).”

CONCLUSIONS The observational studies examining the risk of cancer associated with long-acting insulin analogs have important methodological shortcomings that limit the conclusions that can be drawn. Thus, uncertainty remains, particularly for breast cancer risk.”

iii. Impact of Socioeconomic Status on Cardiovascular Disease and Mortality in 24,947 Individuals With Type 1 Diabetes.

“Socioeconomic status (SES) is a powerful predictor of cardiovascular disease (CVD) and death. We examined the association in a large cohort of patients with type 1 diabetes. […] Clinical data from the Swedish National Diabetes Register were linked to national registers, whereby information on income, education, marital status, country of birth, comorbidities, and events was obtained. […] Type 1 diabetes was defined on the basis of epidemiologic data: treatment with insulin and a diagnosis at the age of 30 years or younger. This definition has been validated as accurate in 97% of the cases listed in the register (14).”

“We included 24,947 patients. Mean (SD) age and follow-up was 39.1 (13.9) and 6.0 (1.0) years. Death and fatal/nonfatal CVD occurred in 926 and 1378 individuals. Compared with being single, being married was associated with 50% lower risk of death, cardiovascular (CV) death, and diabetes-related death. Individuals in the two lowest quintiles had twice as great a risk of fatal/nonfatal CVD, coronary heart disease, and stroke and roughly three times as great a risk of death, diabetes-related death, and CV death as individuals in the highest income quintile. Compared with having ≤9 years of education, individuals with a college/university degree had 33% lower risk of fatal/nonfatal stroke.”

“Individuals with 10–12 years of education were comparable at baseline (considering distribution of age and sex) with those with a college/university degree […]. Individuals with a college/university degree had higher income, had 5 mmol/mol lower HbA1c, were more likely to be married/cohabiting, used insulin pump more frequently (17.5% vs. 14.5%), smoked less (5.8% vs. 13.1%), and had less albuminuria (10.8% vs. 14.2%). […] Women had substantially lower income and higher education, were more often married, used insulin pump more frequently, had less albuminuria, and smoked more frequently than men […] Individuals with high income were more likely to be married/cohabiting, had lower HbA1c, and had lower rates of smoking as well as albuminuria”.

CONCLUSIONS Low SES increases the risk of CVD and death by a factor of 2–3 in type 1 diabetes.”

“The effect of SES was striking despite rigorous adjustments for risk factors and confounders. Individuals in the two lowest income quintiles had two to three times higher risk of CV events and death than those in the highest income quintile. Compared with low educational level, having high education was associated with ∼30% lower risk of stroke. Compared with being single, individuals who were married/cohabiting had >50% lower risk of death, CV death, and diabetes-related death. Immigrants had 20–40% lower risk of fatal/nonfatal CVD, all-cause death, and diabetes-related death. Additionally, we show that males had 44%, 63%, and 29% higher risk of all-cause death, CV death, and diabetes-related death, respectively.

Despite rigorous adjustments for covariates and equitable access to health care at a negligible cost (20,21), SES and sex were robust predictors of CVD disease and mortality in type 1 diabetes; their effect was comparable with that of smoking, which represented an HR of 1.56 (95% CI 1.29–1.91) for all-cause death. […] Our study shows that men with type 1 diabetes are at greater risk of CV events and death compared with women. This should be viewed in the light of a recent meta-analysis of 26 studies, which showed higher excess risk in women compared with men. Overall, women had 40% greater excess risk of all-cause mortality, and twice the excess risk of fatal/nonfatal vascular events, compared with men (29). Thus, whereas the excess risk (i.e., the risk of patients with diabetes compared with the nondiabetic population) of vascular disease is higher in women with diabetes, we show that men with diabetes are still at substantially greater risk of all-cause death, CV death, and diabetes death compared with women with diabetes. Other studies are in line with our findings (10,11,13,3032).”

iv. Interventions That Restore Awareness of Hypoglycemia in Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis.

“Hypoglycemia remains the major limiting factor toward achieving good glycemic control (1). Recurrent hypoglycemia reduces symptomatic and hormone responses to subsequent hypoglycemia (2), associated with impaired awareness of hypoglycemia (IAH). IAH occurs in up to one-third of adults with type 1 diabetes (T1D) (3,4), increasing their risk of severe hypoglycemia (SH) sixfold (3) and contributing to substantial morbidity, with implications for employment (5), driving (6), and mortality. Distribution of risk of SH is skewed: one study showed that 5% of subjects accounted for 54% of all SH episodes, with IAH one of the main risk factors (7). “Dead-in-bed,” related to nocturnal hypoglycemia, is a leading cause of death in people with T1D <40 years of age (8).”

“This systematic review assessed the clinical effectiveness of treatment strategies for restoring hypoglycemia awareness (HA) and reducing SH risk in those with IAH and performed a meta-analysis, where possible, for different approaches in restoring awareness in T1D adults. Interventions to restore HA were broadly divided into three categories: educational (inclusive of behavioral), technological, and pharmacotherapeutic. […] Forty-three studies (18 randomized controlled trials, 25 before-and-after studies) met the inclusion criteria, comprising 27 educational, 11 technological, and 5 pharmacological interventions. […] A meta-analysis for educational interventions on change in mean SH rates per person per year was performed. Combining before-and-after and RCT studies, six studies (n = 1,010 people) were included in the meta-analysis […] A random-effects meta-analysis revealed an effect size of a reduction in SH rates of 0.44 per patient per year with 95% CI 0.253–0.628. [here’s the forest plot, US] […] Most of the educational interventions were observational and mostly retrospective, with few RCTs. The overall risk of bias is considered medium to high and the study quality moderate. Most, if not all, of the RCTs did not use double blinding and lacked information on concealment. The strength of association of the effect of educational interventions is moderate. The ability of educational interventions to restore IAH and reduce SH is consistent and direct with educational interventions showing a largely positive outcome. There is substantial heterogeneity between studies, and the estimate is imprecise, as reflected by the large CIs. The strength of evidence is moderate to high.”

v. Trends of Diagnosis-Specific Work Disability After Newly Diagnosed Diabetes: A 4-Year Nationwide Prospective Cohort Study.

“There is little evidence to show which specific diseases contribute to excess work disability among those with diabetes. […] In this study, we used a large nationwide register-based data set, which includes information on work disability for all working-age inhabitants of Sweden, in order to investigate trends of diagnosis-specific work disability (sickness absence and disability pension) among people with diabetes for 4 years directly after the recorded onset of diabetes. We compared work disability trends among people with diabetes with trends among those without diabetes. […] The register data of diabetes medication and in- and outpatient hospital visits were used to identify all recorded new diabetes cases among the population aged 25–59 years in Sweden in 2006 (n = 14,098). Data for a 4-year follow-up of ICD-10 physician-certified sickness absence and disability pension days (2007‒2010) were obtained […] Comparisons were made using a random sample of the population without recorded diabetes (n = 39,056).”

RESULTS The most common causes of work disability were mental and musculoskeletal disorders; diabetes as a reason for disability was rare. Most of the excess work disability among people with diabetes compared with those without diabetes was owing to mental disorders (mean difference adjusted for confounding factors 18.8‒19.8 compensated days/year), musculoskeletal diseases (12.1‒12.8 days/year), circulatory diseases (5.9‒6.5 days/year), diseases of the nervous system (1.8‒2.0 days/year), and injuries (1.0‒1.2 days/year).”

CONCLUSIONS The increased risk of work disability among those with diabetes is largely attributed to comorbid mental, musculoskeletal, and circulatory diseases. […] Diagnosis of diabetes as the cause of work disability was rare.”


August 19, 2017 Posted by | Cancer/oncology, Cardiology, Diabetes, Health Economics, Medicine, Statistics | Leave a comment

A few diabetes papers of interest

i. Long-term Glycemic Variability and Risk of Adverse Outcomes: A Systematic Review and Meta-analysis.

“This systematic review and meta-analysis evaluates the association between HbA1c variability and micro- and macrovascular complications and mortality in type 1 and type 2 diabetes. […] Seven studies evaluated HbA1c variability among patients with type 1 diabetes and showed an association of HbA1c variability with renal disease (risk ratio 1.56 [95% CI 1.08–2.25], two studies), cardiovascular events (1.98 [1.39–2.82]), and retinopathy (2.11 [1.54–2.89]). Thirteen studies evaluated HbA1c variability among patients with type 2 diabetes. Higher HbA1c variability was associated with higher risk of renal disease (1.34 [1.15–1.57], two studies), macrovascular events (1.21 [1.06–1.38]), ulceration/gangrene (1.50 [1.06–2.12]), cardiovascular disease (1.27 [1.15–1.40]), and mortality (1.34 [1.18–1.53]). Most studies were retrospective with lack of adjustment for potential confounders, and inconsistency existed in the definition of HbA1c variability.

CONCLUSIONS HbA1c variability was positively associated with micro- and macrovascular complications and mortality independently of the HbA1c level and might play a future role in clinical risk assessment.”

Two observations related to the paper: One, although only a relatively small number of studies were included in the review, the number of patients included in some of those included studies was rather large – the 7 type 1 studies thus included 44,021 participants, and the 13 type 2 studies included in total 43,620 participants. Two, it’s noteworthy that some of the associations already look at least reasonably strong, despite interest in HbA1c variability being a relatively recent phenomenon. Confounding might be an issue, but then again it almost always might be, and to give an example, out of 11 studies analyzing the association between renal disease and HbA1c variability included in the review, ten of them support a link and the only one which does not was a small study on pediatric patients which was almost certainly underpowered to investigate such a link in the first place (the base rate of renal complications is, as mentioned before here on this blog quite recently (link 3), quite low in pediatric samples).

ii. Risk of Severe Hypoglycemia in Type 1 Diabetes Over 30 Years of Follow-up in the DCCT/EDIC Study.

(I should perhaps note here that I’m already quite familiar with the context of the DCCT/EDIC study/studies, and although readers may not be, and although background details are included in the paper, I decided not to cover such details here although they would make my coverage of the paper easier to understand. I instead decided to limit my coverage of the paper to a few observations which I myself found to be of interest.)

“During the DCCT, the rates of SH [Severe Hypoglycemia, US], including episodes with seizure or coma, were approximately threefold greater in the intensive treatment group than in the conventional treatment group […] During EDIC, the frequency of SH increased in the former conventional group and decreased in the former intensive group so that the difference in SH event rates between the two groups was no longer significant (36.6 vs. 40.8 episodes per 100 patient-years, respectively […] By the end of DCCT, with an average of 6.5 years of follow-up, 65% of the intensive group versus 35% of the conventional group experienced at least one episode of SH. In contrast, ∼50% of participants within each group reported an episode of SH during the 20 years of EDIC.”

“Of [the] participants reporting episodes of SH, during the DCCT, 54% of the intensive group and 30% of the conventional group experienced four or more episodes, whereas in EDIC, 37% of the intensive group and 33% of the conventional group experienced four or more events […]. Moreover, a subset of participants (14% [99 of 714]) experienced nearly one-half of all SH episodes (1,765 of 3,788) in DCCT, and a subset of 7% (52 of 709) in EDIC experienced almost one-third of all SH episodes (888 of 2,813) […] Fifty-one major accidents occurred during the 6.5 years of DCCT and 143 during the 20 years of EDIC […] The most frequent type of major accident was that involving a motor vehicle […] Hypoglycemia played a role as a possible, probable, or principal cause in 18 of 28 operator-caused motor vehicle accidents (MVAs) during DCCT […] and in 23 of 54 operator-caused MVAs during EDIC”.

“The T1D Exchange Clinic Registry recently reported that 8% of 4,831 adults with T1D living in the U.S. had a seizure or coma event during the 3 months before their most recent annual visit (11). During EDIC, we observed that 27% of the cohort experienced a coma or seizure event over the 20 years of 3-month reporting intervals (∼1.4% per year), a much lower annual risk than in the T1D Exchange Clinic Registry. In part, the open enrollment of patients into the T1D Exchange may be reflected without the exclusion of participants with a history of SH as in the DCCT and other clinical trials. The current data support the clinical perception that a small subset of individuals is more susceptible to SH (7% of patients with 11 or more SH episodes during EDIC, which represents 32% of all SH episodes in EDIC) […] a history of SH during DCCT and lower current HbA1c levels were the two major factors associated with an increased risk of SH during EDIC. Safety concerns were the reason why a history of frequent SH events was an exclusion criterion for enrollment in DCCT. […] Of note, we found that participants who entered the DCCT as adolescents were more likely to experience SH during EDIC.”

“In summary, although event rates in the DCCT/EDIC cohort seem to have fallen and stabilized over time, SH remains an ever-present threat for patients with T1D who use current technology, occurring at a rate of ∼36–41 episodes per 100 patient-years, even among those with longer diabetes duration. Having experienced one or more such prior events is the strongest predictor of a future SH episode.”

I didn’t actually like that summary. If a history of severe hypoglycemia was an exclusion criterion in the DCCT trial, which it was, then the event rate you’d get from this data set is highly likely to provide a biased estimator of the true event rate, as the Exchange Clinic Registry data illustrate. The true population event rate in unselected samples is higher.

Another note which may also be important to add is that many diabetics who do not have a ‘severe event’ during a specific time period might still experience a substantial number of hypoglycemic episodes; ‘severe events’ (which require the assistance of another individual) is a somewhat blunt instrument in particular for assessing quality-of-life aspects of hypoglycemia.

iii. The Presence and Consequence of Nonalbuminuric Chronic Kidney Disease in Patients With Type 1 Diabetes.

“This study investigated the prevalence of nonalbuminuric chronic kidney disease in type 1 diabetes to assess whether it increases the risk of cardiovascular and renal outcomes as well as all-cause mortality. […] This was an observational follow-up of 3,809 patients with type 1 diabetes from the Finnish Diabetic Nephropathy Study. […] mean age was 37.6 ± 11.8 years and duration of diabetes 21.2 ± 12.1 years. […] During 13 years of median follow-up, 378 developed end-stage renal disease, 415 suffered an incident cardiovascular event, and 406 died. […] At baseline, 78 (2.0%) had nonalbuminuric chronic kidney disease. […] Nonalbuminuric chronic kidney disease did not increase the risk of albuminuria (hazard ratio [HR] 2.0 [95% CI 0.9–4.4]) or end-stage renal disease (HR 6.4 [0.8–53.0]) but did increase the risk of cardiovascular events (HR 2.0 [1.4–3.5]) and all-cause mortality (HR 2.4 [1.4–3.9]). […] ESRD [End-Stage Renal Disease] developed during follow-up in 0.3% of patients with nonalbuminuric non-CKD [CKD: Chronic Kidney Disease], in 1.3% of patients with nonalbuminuric CKD, in 13.9% of patients with albuminuric non-CKD, and in 63.0% of patients with albuminuric CKD (P < 0.001).”

CONCLUSIONS Nonalbuminuric chronic kidney disease is not a frequent finding in patients with type 1 diabetes, but when present, it is associated with an increased risk of cardiovascular morbidity and all-cause mortality but not with renal outcomes.”

iv. Use of an α-Glucosidase Inhibitor and the Risk of Colorectal Cancer in Patients With Diabetes: A Nationwide, Population-Based Cohort Study.

This one relates closely to stuff covered in Horowitz & Samsom’s book about Gastrointestinal Function in Diabetes Mellitus which I just finished (and which I liked very much). Here’s a relevant quote from chapter 7 of that book (which is about ‘Hepato-biliary and Pancreatic Function’):

“Several studies have provided evidence that the risk of pancreatic cancer is increased in patients with type 1 and type 2 diabetes mellitus [136,137]. In fact, diabetes has been associated with an increased risk of several cancers, including those of the pancreas, liver, endometrium and kidney [136]. The pooled relative risk of pancreatic cancer for diabetics vs. non-diabetics in a meta-analysis was 2.1 (95% confidence interval 1.6–2.8). Patients presenting with diabetes mellitus within a period of 12 months of the diagnosis of pancreatic cancer were excluded because in these cases diabetes may be an early presenting sign of pancreatic cancer rather than a risk factor [137]”.

They don’t mention colon cancer there, but it’s obvious from the research which has been done – and which is covered extensively in that book – that diabetes has the potential to cause functional changes in a large number of components of the digestive system (and I hope to cover this kind of stuff in a lot more detail later on) so the fact that some of these changes may lead to neoplastic changes should hardly be surprising. However evaluating causal pathways is more complicated here than it might have been, because e.g. pancreatic diseases may also themselves cause secondary diabetes in some patients. Liver pathologies like hepatitis B and C also display positive associations with diabetes, although again causal pathways here are not completely clear; treatments used may be a contributing factor (interferon-treatment may induce diabetes), but there are also suggestions that diabetes should be considered one of the extrahepatic manifestations of hepatitis. This stuff is complicated.

The drug mentioned in the paper, acarbose, is incidentally a drug also discussed in some detail in the book. It belongs to a group of drugs called alpha glucosidase inhibitors, and it is ‘the first antidiabetic medication designed to act through an influence on intestinal functions.’ Anyway, some quotes from the paper:

“We conducted a nationwide, population-based study using a large cohort with diabetes in the Taiwan National Health Insurance Research Database. Patients with newly diagnosed diabetes (n = 1,343,484) were enrolled between 1998 and 2010. One control subject not using acarbose was randomly selected for each subject using acarbose after matching for age, sex, diabetes onset, and comorbidities. […] There were 1,332 incident cases of colorectal cancer in the cohort with diabetes during the follow-up period of 1,487,136 person-years. The overall incidence rate was 89.6 cases per 100,000 person-years. Patients treated with acarbose had a 27% reduction in the risk of colorectal cancer compared with control subjects. The adjusted HRs were 0.73 (95% CI 0.63–0.83), 0.69 (0.59–0.82), and 0.46 (0.37–0.58) for patients using >0 to <90, 90 to 364, and ≥365 cumulative defined daily doses of acarbose, respectively, compared with subjects who did not use acarbose (P for trend < 0.001).

CONCLUSIONS Acarbose use reduced the risk of incident colorectal cancer in patients with diabetes in a dose-dependent manner.”

It’s perhaps worth mentioning that the prevalence of type 1 is relatively low in East Asian populations and that most of the patients included were type 2 (this is also clearly indicated by this observation from the paper: “The median age at the time of the initial diabetes diagnosis was 54.1 years, and the median diabetes duration was 8.9 years.”). Another thing worth mentioning is that colon cancer is a very common type of cancer, and so even moderate risk reductions here at the individual level may translate into a substantial risk reduction at the population level. A third thing, noted in Horowitz & Samsom’s coverage, is that the side effects of acarbose are quite mild, so widespread use of the drug is not out of the question, at least poor tolerance is not likely to be an obstacle; the drug may cause e.g. excessive flatulence and something like 10% of patients may have to stop treatment because of gastrointestinal side effects, but although the side effects are annoying and may be unacceptable to some patients, they are not dangerous; it’s a safe drug which can be used even in patients with renal failure (a context where some of the other oral antidiabetic treatments available are contraindicated).

v. Diabetes, Lower-Extremity Amputation, and Death.

“Worldwide, every 30 s, a limb is lost to diabetes (1,2). Nearly 2 million people living in the U.S. are living with limb loss (1). According to the World Health Organization, lower-extremity amputations (LEAs) are 10 times more common in people with diabetes than in persons who do not have diabetes. In the U.S. Medicare population, the incidence of diabetic foot ulcers is ∼6 per 100 individuals with diabetes per year and the incidence of LEA is 4 per 1,000 persons with diabetes per year (3). LEA in those with diabetes generally carries yearly costs between $30,000 and $60,000 and lifetime costs of half a million dollars (4). In 2012, it was estimated that those with diabetes and lower-extremity wounds in the U.S. Medicare program accounted for $41 billion in cost, which is ∼1.6% of all Medicare health care spending (47). In 2012, in the U.K., it was estimated that the National Health Service spent between £639 and 662 million on foot ulcers and LEA, which was approximately £1 in every £150 spent by the National Health Service (8).”

“LEA does not represent a traditional medical complication of diabetes like myocardial infarction (MI), renal failure, or retinopathy in which organ failure is directly associated with diabetes (2). An LEA occurs because of a disease complication, usually a foot ulcer that is not healing (e.g., organ failure of the skin, failure of the biomechanics of the foot as a unit, nerve sensory loss, and/or impaired arterial vascular supply), but it also occurs at least in part as a consequence of a medical plan to amputate based on a decision between health care providers and patients (9,10). […] 30-day postoperative mortality can approach 10% […]. Previous reports have estimated that the 1-year post-LEA mortality rate in people with diabetes is between 10 and 50%, and the 5-year mortality rate post-LEA is between 30 and 80% (4,1315). More specifically, in the U.S. Medicare population mortality within a year after an incident LEA was 23.1% in 2006, 21.8% in 2007, and 20.6% in 2008 (4). In the U.K., up to 80% will die within 5 years of an LEA (8). In general, those with diabetes with an LEA are two to three times more likely to die at any given time point than those with diabetes who have not had an LEA (5). For perspective, the 5-year death rate after diagnosis of malignancy in the U.S. was 32% in 2010 (16).”

“Evidence on why individuals with diabetes and an LEA die is based on a few mainly small (e.g., <300 subjects) and often single center–based (13,1720) studies or <1 year duration of evaluation (11). In these studies, death is primarily associated with a previous history of cardiovascular disease and renal insufficiency, which are also major complications of diabetes; these complications are also associated with an increased risk of LEA. The goal of our study was to determine whether complications of diabetes well-known to be associated with death in those with diabetes such as cardiovascular disease and renal failure fully explain the higher rate of death in those who have undergone an LEA.”

“This is the largest and longest evaluation of the risk of death among those with diabetes and LEA […] Between 2003 and 2012, 416,434 individuals met the entrance criteria for the study. This cohort accrued an average of 9.0 years of follow-up and a total of 3.7 million diabetes person-years of follow-up. During this period of time, 6,566 (1.6%) patients had an LEA and 77,215 patients died (18.5%). […] The percentage of individuals who died within 30 days, 1 year, and by year 5 of their initial code for an LEA was 1.0%, 9.9%, and 27.2%, respectively. For those >65 years of age, the rates were 12.2% and 31.7%, respectively. For the full cohort of those with diabetes, the rate of death was 2.0% after 1 year of follow up and 7.3% after 5 years of follow up. In general, those with an LEA were more than three times more likely to die during a year of follow-up than an individual with diabetes who had not had an LEA. […] In any given year, >5% of those with diabetes and an LEA will die.”

“From 2003 to 2012, the HR [hazard rate, US] for death after an LEA was 3.02 (95% CI 2.90, 3.14). […] our a priori assumption was that the HR associating LEA with death would be fully diminished (i.e., it would become 1) when adjusted for the other risk factor variables. However, the fully adjusted LEA HR was diminished only ∼22% to 2.37 (95% CI 2.27, 2.48). With the exception of age >65 years, individual risk factors, in general, had minimal effect (<10%) on the HR of the association between LEA and death […] We conducted sensitivity analyses to determine the general statistical parameters of an unmeasured risk factor that could remove the association of LEA with death. We found that even if there existed a very strong risk factor with an HR of death of three, a prevalence of 10% in the general diabetes population, and a prevalence of 60% in those who had an LEA, LEA would still be associated with a statistically significant and clinically important risk of 1.30. These findings are describing a variable that would seem to be so common and so highly associated with death that it should already be clinically apparent. […] In summary, individuals with diabetes and an LEA are more likely to die at any given point in time than those who have diabetes but no LEA. While some of this variation can be explained by other known complications of diabetes, the amount that can be explained is small. Based on the results of this study, including a sensitivity analysis, it is highly unlikely that a “new” major risk factor for death exists. […] LEA is often performed because of an end-stage disease process like chronic nonhealing foot ulcer. By the time a patient has a foot ulcer and an LEA is offered, they are likely suffering from the end-stage consequence of diabetes. […] We would […] suggest that patients who have had an LEA require […] vigilant follow-up and evaluation to assure that their medical care is optimized. It is also important that GPs communicate to their patients about the risk of death to assure that patients have proper expectations about the severity of their disease.”

vi. Trends in Health Care Expenditure in U.S. Adults With Diabetes: 2002–2011.

Before quoting from the paper, I’ll remind people reading along here that ‘total medical expenditures’ != ‘total medical costs’. Lots of relevant medical costs are not included when you focus only on direct medical expenditures (sick days, early retirement, premature mortality and productivity losses associated therewith, etc., etc.). With that out of the way…

“This study examines trends in health care expenditures by expenditure category in U.S. adults with diabetes between 2002 and 2011. […] We analyzed 10 years of data representing a weighted population of 189,013,514 U.S. adults aged ≥18 years from the Medical Expenditure Panel Survey. […] Relative to individuals without diabetes ($5,058 [95% CI 4,949–5,166]), individuals with diabetes ($12,180 [11,775–12,586]) had more than double the unadjusted mean direct expenditures over the 10-year period. After adjustment for confounders, individuals with diabetes had $2,558 (2,266–2,849) significantly higher direct incremental expenditures compared with those without diabetes. For individuals with diabetes, inpatient expenditures rose initially from $4,014 in 2002/2003 to $4,183 in 2004/2005 and then decreased continuously to $3,443 in 2010/2011, while rising steadily for individuals without diabetes. The estimated unadjusted total direct expenditures for individuals with diabetes were $218.6 billion/year and adjusted total incremental expenditures were approximately $46 billion/year. […] in the U.S., direct medical costs associated with diabetes were $176 billion in 2012 (1,3). This is almost double to eight times the direct medical cost of other chronic diseases: $32 billion for COPD in 2010 (10), $93 billion for all cancers in 2008 (11), $21 billion for heart failure in 2012 (12), and $43 billion for hypertension in 2010 (13). In the U.S., total economic cost of diabetes rose by 41% from 2007 to 2012 (2). […] Our findings show that compared with individuals without diabetes, individuals with diabetes had significantly higher health expenditures from 2002 to 2011 and the bulk of the expenditures came from hospital inpatient and prescription expenditures.”



August 10, 2017 Posted by | Books, Cancer/oncology, Cardiology, Diabetes, Economics, Epidemiology, Gastroenterology, Health Economics, Medicine, Nephrology, Pharmacology | Leave a comment

Melanoma therapeutic strategies that select against resistance

A short lecture, but interesting:

If you’re not an oncologist, these two links in particular might be helpful to have a look at before you start out: BRAF (gene) & Myc. A very substantial proportion of the talk is devoted to math and stats methodology (which some people will find interesting and others …will not).


July 3, 2017 Posted by | Biology, Cancer/oncology, Genetics, Lectures, Mathematics, Medicine, Statistics | Leave a comment

Harnessing phenotypic heterogeneity to design better therapies

Unlike many of the IAS lectures I’ve recently blogged this one is a new lecture – it was uploaded earlier this week. I have to say that I was very surprised – and disappointed – that the treatment strategy discussed in the lecture had not already been analyzed in a lot of detail and been implemented in clinical practice for some time. Why would you not expect the composition of cancer cell subtypes in the tumour microenvironment to change when you start treatment – in any setting where a subgroup of cancer cells has a different level of responsiveness to treatment than ‘the average’, that would to me seem to be the expected outcome. And concepts such as drug holidays and dose adjustments as treatment responses to evolving drug resistance/treatment failure seem like such obvious approaches to try out here (…the immunologists dealing with HIV infection have been studying such things for decades). I guess ‘better late than never’.

A few papers mentioned/discussed in the lecture:

Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes.
Adaptive vs continuous cancer therapy: Exploiting space and trade-offs in drug scheduling.
Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer.


June 11, 2017 Posted by | Cancer/oncology, Genetics, Immunology, Lectures, Mathematics, Medicine, Studies | Leave a comment

Today’s Landscape of Pharmaceutical Research in Cancer

It’s been a while since I watched this lecture so I don’t remember the details very well, but I usually add notes in my bookmarks when I watch lectures so that I know what details to include in my comments here on the blog, and I have added the details from the bookmark notes below.

It is a short lecture, the lecture itself lasts only roughly 30 minutes; it doesn’t really start until roughly the 9 minutes and 30 seconds mark, and it finishes around the 44 min mark (the rest is Q&A – I skipped some of the introduction, but watched the Q&A). The lecture is not very technical, I think the content is perfectly understandable also to people without a medical background. One data point from the lecture which I thought worth including in these comments is this: According to Sigal, “roughly 30 per cent of the biopharmaceutical industry’s portfolio … is focused on research in oncology.”


May 17, 2017 Posted by | Cancer/oncology, Health Economics, Immunology, Lectures, Medicine, Pharmacology | Leave a comment

Standing on the Shoulders of Mice: Aging T-cells

Most of the lecture is not about mice, but rather about stuff like this and this (both papers are covered in the lecture). I’ve read about related topics before (see e.g this), but if you haven’t some parts of the lecture will probably be too technical for you to follow.


May 3, 2017 Posted by | Cancer/oncology, Cardiology, Genetics, Immunology, Lectures, Medicine, Papers | Leave a comment

Biodemography of aging (III)

Latent class representation of the Grade of Membership model.
Singular value decomposition.
Affine space.
Lebesgue measure.
General linear position.

The links above are links to topics I looked up while reading the second half of the book. The first link is quite relevant to the book’s coverage as a comprehensive longitudinal Grade of Membership (-GoM) model is covered in chapter 17. Relatedly, chapter 18 covers linear latent structure (-LLS) models, and as observed in the book LLS is a generalization of GoM. As should be obvious from the nature of the links some of the stuff included in the second half of the text is highly technical, and I’ll readily admit I was not fully able to understand all the details included in the coverage of chapters 17 and 18 in particular. On account of the technical nature of the coverage in Part 2 I’m not sure I’ll cover the second half of the book in much detail, though I probably shall devote at least one more post to some of those topics, as they were quite interesting even if some of the details were difficult to follow.

I have almost finished the book at this point, and I have already decided to both give the book five stars and include it on my list of favorite books on goodreads; it’s really well written, and it provides consistently highly detailed coverage of very high quality. As I also noted in the first post about the book the authors have given readability aspects some thought, and I am sure most readers would learn quite a bit from this text even if they were to skip some of the more technical chapters. The main body of Part 2 of the book, the subtitle of which is ‘Statistical Modeling of Aging, Health, and Longevity’, is however probably in general not worth the effort of reading unless you have a solid background in statistics.

This post includes some observations and quotes from the last chapters of the book’s Part 1.

“The proportion of older adults in the U.S. population is growing. This raises important questions about the increasing prevalence of aging-related diseases, multimorbidity issues, and disability among the elderly population. […] In 2009, 46.3 million people were covered by Medicare: 38.7 million of them were aged 65 years and older, and 7.6 million were disabled […]. By 2031, when the baby-boomer generation will be completely enrolled, Medicare is expected to reach 77 million individuals […]. Because the Medicare program covers 95 % of the nation’s aged population […], the prediction of future Medicare costs based on these data can be an important source of health care planning.”

“Three essential components (which could be also referred as sub-models) need to be developed to construct a modern model of forecasting of population health and associated medical costs: (i) a model of medical cost projections conditional on each health state in the model, (ii) health state projections, and (iii) a description of the distribution of initial health states of a cohort to be projected […] In making medical cost projections, two major effects should be taken into account: the dynamics of the medical costs during the time periods comprising the date of onset of chronic diseases and the increase of medical costs during the last years of life. In this chapter, we investigate and model the first of these two effects. […] the approach developed in this chapter generalizes the approach known as “life tables with covariates” […], resulting in a new family of forecasting models with covariates such as comorbidity indexes or medical costs. In sum, this chapter develops a model of the relationships between individual cost trajectories following the onset of aging-related chronic diseases. […] The underlying methodological idea is to aggregate the health state information into a single (or several) covariate(s) that can be determinative in predicting the risk of a health event (e.g., disease incidence) and whose dynamics could be represented by the model assumptions. An advantage of such an approach is its substantial reduction of the degrees of freedom compared with existing forecasting models  (e.g., the FEM model, Goldman and RAND Corporation 2004). […] We found that the time patterns of medical cost trajectories were similar for all diseases considered and can be described in terms of four components having the meanings of (i) the pre-diagnosis cost associated with initial comorbidity represented by medical expenditures, (ii) the cost peak associated with the onset of each disease, (iii) the decline/reduction in medical expenditures after the disease onset, and (iv) the difference between post- and pre-diagnosis cost levels associated with an acquired comorbidity. The description of the trajectories was formalized by a model which explicitly involves four parameters reflecting these four components.”

As I noted earlier in my coverage of the book, I don’t think the model above fully captures all relevant cost contributions of the diseases included, as the follow-up period was too short to capture all relevant costs to be included in the part iv model component. This is definitely a problem in the context of diabetes. But then again nothing in theory stops people from combining the model above with other models which are better at dealing with the excess costs associated with long-term complications of chronic diseases, and the model results were intriguing even if the model likely underperforms in a few specific disease contexts.

Moving on…

“Models of medical cost projections usually are based on regression models estimated with the majority of independent predictors describing demographic status of the individual, patient’s health state, and level of functional limitations, as well as their interactions […]. If the health states needs to be described by a number of simultaneously manifested diseases, then detailed stratification over the categorized variables or use of multivariate regression models allows for a better description of the health states. However, it can result in an abundance of model parameters to be estimated. One way to overcome these difficulties is to use an approach in which the model components are demographically-based aggregated characteristics that mimic the effects of specific states. The model developed in this chapter is an example of such an approach: the use of a comorbidity index rather than of a set of correlated categorical regressor variables to represent the health state allows for an essential reduction in the degrees of freedom of the problem.”

“Unlike mortality, the onset time of chronic disease is difficult to define with high precision due to the large variety of disease-specific criteria for onset/incident case identification […] there is always some arbitrariness in defining the date of chronic disease onset, and a unified definition of date of onset is necessary for population studies with a long-term follow-up.”

“Individual age trajectories of physiological indices are the product of a complicated interplay among genetic and non-genetic (environmental, behavioral, stochastic) factors that influence the human body during the course of aging. Accordingly, they may differ substantially among individuals in a cohort. Despite this fact, the average age trajectories for the same index follow remarkable regularities. […] some indices tend to change monotonically with age: the level of blood glucose (BG) increases almost monotonically; pulse pressure (PP) increases from age 40 until age 85, then levels off and shows a tendency to decline only at later ages. The age trajectories of other indices are non-monotonic: they tend to increase first and then decline. Body mass index (BMI) increases up to about age 70 and then declines, diastolic blood pressure (DBP) increases until age 55–60 and then declines, systolic blood pressure (SBP) increases until age 75 and then declines, serum cholesterol (SCH) increases until age 50 in males and age 70 in females and then declines, ventricular rate (VR) increases until age 55 in males and age 45 in females and then declines. With small variations, these general patterns are similar in males and females. The shapes of the age-trajectories of the physiological variables also appear to be similar for different genotypes. […] The effects of these physiological indices on mortality risk were studied in Yashin et al. (2006), who found that the effects are gender and age specific. They also found that the dynamic properties of the individual age trajectories of physiological indices may differ dramatically from one individual to the next.”

“An increase in the mortality rate with age is traditionally associated with the process of aging. This influence is mediated by aging-associated changes in thousands of biological and physiological variables, some of which have been measured in aging studies. The fact that the age trajectories of some of these variables differ among individuals with short and long life spans and healthy life spans indicates that dynamic properties of the indices affect life history traits. Our analyses of the FHS data clearly demonstrate that the values of physiological indices at age 40 are significant contributors both to life span and healthy life span […] suggesting that normalizing these variables around age 40 is important for preventing age-associated morbidity and mortality later in life. […] results [also] suggest that keeping physiological indices stable over the years of life could be as important as their normalizing around age 40.”

“The results […] indicate that, in the quest of identifying longevity genes, it may be important to look for candidate genes with pleiotropic effects on more than one dynamic characteristic of the age-trajectory of a physiological variable, such as genes that may influence both the initial value of a trait (intercept) and the rates of its changes over age (slopes). […] Our results indicate that the dynamic characteristics of age-related changes in physiological variables are important predictors of morbidity and mortality risks in aging individuals. […] We showed that the initial value (intercept), the rate of changes (slope), and the variability of a physiological index, in the age interval 40–60 years, significantly influenced both mortality risk and onset of unhealthy life at ages 60+ in our analyses of the Framingham Heart Study data. That is, these dynamic characteristics may serve as good predictors of late life morbidity and mortality risks. The results also suggest that physiological changes taking place in the organism in middle life may affect longevity through promoting or preventing diseases of old age. For non-monotonically changing indices, we found that having a later age at the peak value of the index […], a lower peak value […], a slower rate of decline in the index at older ages […], and less variability in the index over time, can be beneficial for longevity. Also, the dynamic characteristics of the physiological indices were, overall, associated with mortality risk more significantly than with onset of unhealthy life.”

“Decades of studies of candidate genes show that they are not linked to aging-related traits in a straightforward manner […]. Recent genome-wide association studies (GWAS) have reached fundamentally the same conclusion by showing that the traits in late life likely are controlled by a relatively large number of common genetic variants […]. Further, GWAS often show that the detected associations are of tiny effect […] the weak effect of genes on traits in late life can be not only because they confer small risks having small penetrance but because they confer large risks but in a complex fashion […] In this chapter, we consider several examples of complex modes of gene actions, including genetic tradeoffs, antagonistic genetic effects on the same traits at different ages, and variable genetic effects on lifespan. The analyses focus on the APOE common polymorphism. […] The analyses reported in this chapter suggest that the e4 allele can be protective against cancer with a more pronounced role in men. This protective effect is more characteristic of cancers at older ages and it holds in both the parental and offspring generations of the FHS participants. Unlike cancer, the effect of the e4 allele on risks of CVD is more pronounced in women. […] [The] results […] explicitly show that the same allele can change its role on risks of CVD in an antagonistic fashion from detrimental in women with onsets at younger ages to protective in women with onsets at older ages. […] e4 allele carriers have worse survival compared to non-e4 carriers in each cohort. […] Sex stratification shows sexual dimorphism in the effect of the e4 allele on survival […] with the e4 female carriers, particularly, being more exposed to worse survival. […] The results of these analyses provide two important insights into the role of genes in lifespan. First, they provide evidence on the key role of aging-related processes in genetic susceptibility to lifespan. For example, taking into account the specifics of aging-related processes gains 18 % in estimates of the RRs and five orders of magnitude in significance in the same sample of women […] without additional investments in increasing sample sizes and new genotyping. The second is that a detailed study of the role of aging-related processes in estimates of the effects of genes on lifespan (and healthspan) helps in detecting more homogeneous [high risk] sub-samples”.

“The aging of populations in developed countries requires effective strategies to extend healthspan. A promising solution could be to yield insights into the genetic predispositions for endophenotypes, diseases, well-being, and survival. It was thought that genome-wide association studies (GWAS) would be a major breakthrough in this endeavor. Various genetic association studies including GWAS assume that there should be a deterministic (unconditional) genetic component in such complex phenotypes. However, the idea of unconditional contributions of genes to these phenotypes faces serious difficulties which stem from the lack of direct evolutionary selection against or in favor of such phenotypes. In fact, evolutionary constraints imply that genes should be linked to age-related phenotypes in a complex manner through different mechanisms specific for given periods of life. Accordingly, the linkage between genes and these traits should be strongly modulated by age-related processes in a changing environment, i.e., by the individuals’ life course. The inherent sensitivity of genetic mechanisms of complex health traits to the life course will be a key concern as long as genetic discoveries continue to be aimed at improving human health.”

“Despite the common understanding that age is a risk factor of not just one but a large portion of human diseases in late life, each specific disease is typically considered as a stand-alone trait. Independence of diseases was a plausible hypothesis in the era of infectious diseases caused by different strains of microbes. Unlike those diseases, the exact etiology and precursors of diseases in late life are still elusive. It is clear, however, that the origin of these diseases differs from that of infectious diseases and that age-related diseases reflect a complicated interplay among ontogenetic changes, senescence processes, and damages from exposures to environmental hazards. Studies of the determinants of diseases in late life provide insights into a number of risk factors, apart from age, that are common for the development of many health pathologies. The presence of such common risk factors makes chronic diseases and hence risks of their occurrence interdependent. This means that the results of many calculations using the assumption of disease independence should be used with care. Chapter 4 argued that disregarding potential dependence among diseases may seriously bias estimates of potential gains in life expectancy attributable to the control or elimination of a specific disease and that the results of the process of coping with a specific disease will depend on the disease elimination strategy, which may affect mortality risks from other diseases.”


April 17, 2017 Posted by | Biology, Books, Cancer/oncology, Demographics, Economics, Epidemiology, Genetics, Health Economics, Medicine, Statistics | Leave a comment

Health econ stuff

In a post I published a few weeks ago I mentioned that I had decided against including some comments and observations I had written about health economics in that post because the post was growing unwieldy, but that I might post that stuff later on in a separate post. This post will include those observations, as well as some additional details I added to the post later. This sort of post is the sort of post that usually does not get past the ‘draft’ stage (in wordpress you can save posts you intend to publish later on as drafts), and as is usually the case for posts like these I already regret having written it, for multiple reasons. I should warn you from the start that this post is very long and will probably take you some time to read.

Anyway, the starting point for this post was some comments related to health insurance and health economics which I left on SSC in the past. A lot more people read those comments on SSC than will read this post so the motivation for posting it here was not to ‘increase awareness’ of the ideas and observations included in some kind of general sense; my primary motivation for adding this stuff here is rather that it’s a lot easier for me personally to find stuff I’ve written when it’s located here on this blog rather than elsewhere on the internet, and I figure that some of the things I wrote back then are topics which might well come up again later, and it would be convenient for me in that case to have a link at hand. Relatedly I have added many additional comments and observations in this post not included in the primary exchange, which it is no longer possible for me to do on SSC as my comments are no longer editable on that site.

Although the starting point for the post was as mentioned a comment exchange, I decided early on against just ‘quoting myself’ in this post, and I have thus made some changes in wording and structure in order to increase the precision of the statements included and in order to add a bit of context making the observations below easier to read and understand (and harder to misread). Major topics to which the observations included in this post relate are preventable diseases, the level of complexity that is present in the health care sector, and various topics which relate to health care cost growth. Included in the post are some perhaps not sufficiently well known complications which may arise in the context of the discussion of how different financing schemes may relate to various outcomes, and to cost growth. Much of the stuff included will probably be review to people who’ve read my previous posts on health economics, but that’s to be expected considering the nature of this post.

Although ‘normative stuff’ is not what interests me most – I generally tend to prefer discussions where the aim is to identify what happens if you do X, and I’ll often be happy to leave the discussion of whether outcome X or Y is ‘best’ to others – I do want to start out with stating a policy preference, as this preference was the starting point for the aforementioned debate that lead to the origination of this post. At the outset I should thus make clear that I would in general be in favour of changes to the financial structure of health care systems where people who take avoidable risks which systematically and demonstrably increase their expected health care expenditures at the population level pay a larger proportion of the cost than do people who did not take such avoidable risks.

Most developed societies have health care systems which are designed in a way that implicitly to some extent subsidize unhealthy behaviours. An important note in this context is incidentally that one way of looking at these things is that if you are not explicitly demanding people who behave in risky ways which tend to increase their expected costs to pay more for their health care (/insurance), then you are in fact by virtue of not doing this implicitly subsidizing those unhealthy individuals/behaviours. I mention this because some people might not like the idea of ‘subsidizing healthy behaviours’ (‘health fascism’) – which from a certain point of view is what you do if you charge people who behave in unhealthy ways more. Maybe some people would take issue with words like ‘subsidy’ and ‘implicit’, but regardless of what you call these things the major point that is important to have in mind here is that if one group of people (e.g. ‘unhealthy people’) cost more to treat (/are ill more often, get illnesses related to their behaviours, etc., etc.) than another group of people (‘healthy people’), then if you need to finance this shortfall – which you do, as you face a budget constraint – there are only two basic ways to do this; you can either charge the high-cost group (‘unhealthy people’) more, or you can require the other group (‘healthy people’) to make up the difference. Any scheme which deals with such a case of unequal net contribution rates are equivalent either to one of those schemes or a mix of the two, regardless of what you call things and how it’s done, and regardless of which groups we are talking about (old people also have higher health care expenditures than do young people, and most health care systems implicitly redistribute income from the young to the old). If you’re worried about ‘health fascism’ and the implications of subsidizing healthy behaviours (/’punishing’ unhealthy behaviours) you should at least keep in mind that if the health care costs of people who live healthy lives and people who do not are dissimilar then any system that deals with this issue – which all systems must – can either choose to ‘subsidize’ healthy behaviours or unhealthy behaviours; there’s no feasible way to design a ‘neutral system’ if the costs of the groups are dissimilar.

Having said all this, the very important next point is then that it is much more difficult to make simple schemes that would accomplish an outcome in which people who engage in unhealthy behaviours are required to pay more without at the same time introducing a significant number of new problems than people who are not familiar with this field would probably think it is. And it’s almost certainly much harder to evaluate if the proposed change actually accomplished what you wanted to accomplish than you think it is. Even if we are clear about what we want to accomplish and can all agree that that is what we are aiming for – i.e. we are disregarding the political preferences of large groups of voters and whether the setup in question is at all feasible to accomplish – this stuff is really much harder than it looks, for many reasons.

Let’s start out by assuming that smoking increases the risk of disease X by 50%. Say you can’t say which of the cases of X are caused by smoking, all you know is that smoking increases the risk at the population level. Say you don’t cover disease X at all if someone smokes, that is, smokers are required to pay the full treatment cost out of pocket if they contract disease X. It’s probably not too controversial to state that this approach might by some people be perceived of as not completely ‘fair’ to the many smokers who would have got disease X even if they had not smoked (a majority in this particular case, though of course the proportion will vary with the conditions and the risk factors in question). Now, a lot of the excess health care costs related to smoking are of this kind, and it is actually a pretty standard pattern in general with risk factors – smoking, alcohol, physical inactivity, etc. You know that these behaviours increase risk, but you usually can’t say for certain which of the specific cases you observe in clinical practice are actually (‘perfectly’/’completely’/’partially’?) attributable to the behaviour. And quite often the risk increase associated with a specific behaviour is actually really somewhat modest, compared to the relevant base rates, meaning that many of the people who engage in behaviours which increase risk and who get sick might well have got sick even if they hadn’t engaged in those risky behaviours.

On top of this problem usually it’s also the case that risk factors interact with each other. Smoking increases the risk of cancer of the esophagus, but so does alcohol and obesity, and if a person both smokes and drinks the potential interaction effect may not be linear – so you most likely often can’t just identify individual risk factors in specific studies and then pool them later and add them all together to get a proper risk assessment. A further complication is that behaviours may both increase as well as decrease risk – to stick with the example, diets high in fruits and vegetables both lower the risk of cancer of the esophagus. Exercise probably does as well – we know that exercise has important and highly complex effects on immune system function (see e.g. this post). Usually a large number of potential risk factors is at play at the same time, there may be multiple variables which lower risk and are also important to include if you want a proper risk assessment, and even if you knew in theory which interaction terms were likely to be relevant, you might even so find yourself in a situation unable to estimate the interaction terms of interest – this might take high-powered studies with large numbers of patients, which may not be available or the results of such high-powered studies may not apply to your specific subgroup of patients. Cost-effectiveness is also an issue – it’s expensive to assess risk properly. One take-away is that you’ll still have a lot of unfairness in a modified contribution rate model, and even evaluating fairness aspects of the change may be difficult to impossible because to some extent this question is unknowable. You might find yourself in a situation where you charge the obese guy more because obesity means he’s high risk, but in reality he is actually lower risk than is the non-fat guy who is charged a lower rate, because he also exercises and eats a lot of fruits and vegetables, which the other guy doesn’t.

Of course the above paragraph took it for granted that it was even possible to quantify the excess costs attributable to a specific condition. That may not be easy at all to do, and there may be large uncertainties involved. The estimated excess cost will depend upon a variety of factors which may or may not be of interest to the party performing the analysis, for example it may be very important which time frame you’re looking at and which discounting methodology is applied (see e.g. the last paragraph in this post). The usual average vs marginal cost problem (see the third-last paragraph in the post to which I link in the previous sentence – this post also has more on this topic) also applies here and is related to ‘the fat guy who exercises and is low-risk’-problem; ideally you’d want to charge people with higher health care utilization levels more (again, in a setting where we assume the excess cost is associated with life-style variables which are modifiable – this was our starting point), but if there’s a large amount of variation in costs across individuals in the specific subgroups of interest and you only have access to average costs rather than individual-level costs, then a scheme only taking into account the differences in the averages may be very sub-optimal when you look at it from the viewpoint of the individual. Care needs to be taken to avoid problems like e.g. Simpson’s paradox.

Risk factors are not the only things that cluster; so do diseases. An example:

“An analysis of the Robert Koch-Institute (RKI) from 2012 shows that more than 50 % of German people over 65 years suffer from at least one chronic disease, approximately 50 % suffer from two to four chronic diseases, and over a quarter suffer from five or more diseases [3].” (link)

78.3 % of the type 2 diabetics also suffered from hypertension in that study. Does this fact make it easier or harder to figure out what is ‘the true cost contribution’ of ‘type 2 diabetes’ and ‘hypertension’ (and, what we’re ultimately interested in in this setting – the ‘true cost contribution’ of the unhealthy behaviours which lead some individuals to develop type 2 diabetics and hypertension who would not otherwise have developed diabetes and/or hypertension (…/as early as they did)? It should be noted that diabetes was estimated to account for 11 % of total global healthcare expenditure on adults in 2013 (link). That already large proportion is expected to rise substantially in the decades to come – if you’re interested in cost growth trajectories, this is a major variable to account for. Attributability is really tricky here, and perhaps even more tricky in the case of hypertension – but for what it’s worth, according to a CDC estimate hypertension cost the US $46 billion per year, or ~$150/per person per year.

Anyway, you look at the data and you make guesses, but the point is that doctor Smith won’t know for certain if Mr. Hanson would have had a stroke even if he hadn’t smoked or not. A proposal of not providing payment for a health care service or medical product in the case of an ‘obviously risky-behaviour-related-health-condition’ may sometimes appear to be an appealing proposition and you sometimes see people make this sort of proposal in discussions of this nature, but it tends to be very difficult when you look at the details to figure out just what those ‘obviously risky-behaviour-related-health-conditions’ are, and even harder to make even remotely actuarially fair adjustments to the premiums and coverage patterns to reflect the risk. Smoking and lung cancer is a common example of a relatively ‘clean’ case, but most cases are ‘less clean’ and even here there are complications; a substantial proportion of lung cancer cases are not caused by tobacco – occupational exposures also cause a substantial proportion of cases, and: “If considered in its own disease category […] lung cancer in never smokers would represent the seventh leading cause of cancer mortality globally, surpassing cancers of the cervix, pancreas, and prostate,5 and among the top 10 causes of death in the United States.” (link) Occupational exposures (e.g. asbestos) are not likely to fully account for all cases, and for example it has also been found that other variables, including previous pneumonia infections and tuberculosis, affect risk (here are a couple of relevant links to some previous coverage I wrote on these topics).

I think many people who have preferences of this nature (‘if it’s their own fault they’re sick, they should pay for it themselves’) underestimate how difficult it may be to make changes which could be known with a reasonable level of certainty to actually have the intended consequences, even assuming everybody agreed on the goal to be achieved. This is in part because there are many other aspects and complications which need to be addressed as well. Withholding payment in the case of costly preventative illness may for example in some contexts increase cost, rather than decrease them. The risk of complications of some diseases – an important cost driver in the context of diabetes – tends to be dependent on post-diagnosis behavioural patterns. The risk of developing diabetes complications will depend upon the level of glycemic control. If you say you won’t cover complications at all in the case of ‘self-inflicted disease X’, then you also to some extent tend to remove the option of designing insurance schemes which might lower cost and complication rates post-diagnosis by rewarding ‘good’ (risk-minimizing) behaviours post-diagnosis and punishing ‘bad’ (risk-increasing) behaviours. This is not desirable in the context of diseases where post-diagnosis behaviour is an important component of the cost function, as it certainly is in the diabetes context. There are multiple potential mechanisms here, some of which are disease specific (e.g. suboptimal diet in a diagnosed type 2 diabetic) and some of which may not be (a more general mechanism could e.g. be lowered compliance/adherence to treatment in the uncovered populations because they can’t afford the drugs which are required to treat their illness; though the cost-compliance link is admittedly not completely clear in the general case, there are certainly multiple diseases where lowered compliance to treatment would be expected to increase cost long-term).

And again, also in the context of complications fairness issues are not as simple to evaluate as people might like them to be; some people may have a much harder time controlling their disease than others, or they may be more susceptible to complications given the same behaviour. Some may already have developed complications by the time of diagnosis. Such issues make it difficult to design simple rules which would achieve what you want them to achieve without having unfortunate side-effects; for example a rule that a microvascular diabetes-related complication is automatically ‘your own fault’ (so we won’t pay for it), which might be motivated by the substantial amount of research linking glycemic control with complication risk, would punish some diabetics who have had the disease for a longer amount of time (many complications are not only strongly linked to Hba1c but also display a substantial degree of duration-dependence; for example in type 1 diabetics one study found that diabetic retinopathy was present in 13% of patients with a duration of disease less than 5 years, whereas the corresponding figure was 90% for individuals with a disease duration of 10–15 years (Sperling et al., p. 393). I also recall reading a study finding that Hba1c itself is increasing with diabetes duration, which may be partly accounted for by the higher risk of hypoglycemia related to hypoglycemia-unawareness-syndromes in individuals with long-standing disease), individuals with diseases which are relatively hard to control (perhaps due to genetics, or maybe again due to the fact that they have had the disease for a longer amount of time; the presence of hypoglycemia unawareness is as alluded to above to a substantial degree duration-dependent, and this problem increases the risk of hospitalizations, which are expensive), diabetics who developed complications before they knew they were sick (a substantial proportion of type 2 diabetics develop some degree of microvascular damage pre-diagnosis), and diabetics with genetic variants which confer an elevated risk of complications (“observations suggest that involvement of genetic factors is increasing the risk of complications” (Sperling et al., p. 226), and for example in the DCCT trial familial clustering of both neuropathy and retinopathy was found; clustering which persisted after controlling for Hba1c – for more on these topics, see e.g. Sperling et al.’s chapter 11).

Other decision rules would similarly lead to potentially problematic incentives and fairness issues; for example requiring individuals to meet a specific Hba1c goal might be more desirable than to just not cover complications, but that one also leads to potential problems; ideally such an Hba1c goal should be individualized, because of the above-mentioned complexities and others I have not mentioned here; to require a newly-diagnosed individual to meet the same goals as someone who has had diabetes for decades does not make sense, and neither does it make sense to require these two groups to meet exactly the same Hba1c goal as the middle-aged female diabetic who desires to become pregnant (diabetes greatly increases the risk of pregnancy complications, and strict glycemic control is extremely important in this patient group). It’s important to note that these issues don’t just relate to whether or not the setup is perceived of as fair, but it also relates to whether or not you would expect the intended goals to actually be met or not when you implement the rule. If you were to require that a long-standing diabetic with severe hypoglycemia unawareness had to meet the same Hba1c goal as the newly diagnosed individual, this might well lead to higher overall cost, because said individual might suffer a large number of hypoglycemia-related hospitalizations which would have been avoidable if a more lax requirement was imposed; when you decrease Hba1c you decrease the risk of long-term complications, but you increase the risk of hypoglycemia. A few numbers might make it easier to make sense of how expensive hospitalizations really are, and why I emphasize them here. In this diabetes-care publication they assign a cost for an inpatient day for a diabetes-related hospitalization at $2,359 and an emergency visit at ~$800. The same publication estimates the total average annual excess expenditures of diabetics below the age of 45 at $4,394. Going to the hospital is really expensive (43% of the total medical costs of diabetes are accounted for by hospital inpatient care in that publication).

A topic which was brought up in the SSC discussion was the question of the extent to which private providers have a greater incentive to ‘get things right’ in terms of assessing risk. I don’t take issue with this notion in general, but there are a lot of complicating factors in the health care context. One factor of interest is that it is costly to get things right. If you’re looking at this from an insurance perspective, larger insurance providers may be better at getting things right because they can afford to hire specialists who provide good cost estimates – getting good cost estimates is really hard, as I’ve noted above. Larger providers translate into fewer firms, which increases firm concentration and may thus increase collusion risk, which may again increase the prices of health care services. Interestingly if your aim is to minimize health care cost growth increased market power of private firms may actually be a desirable state of affairs/goal, because cost growth is a function of both unit prices and utilization levels, and higher premiums are likely to translate into lower utilization rates, which may lower overall costs and -cost growth. I decided to include this observation here also in order to illustrate that what is an optimal outcome depends on what your goal is, and in the setting of the health care sector you sometimes need to be very careful about thinking about what your actual goal is, and which other goals might be relevant.

When private insurance providers become active in a market that also includes a government entity providing a level of guaranteed coverage, total medical outlays may easily increase rather than decrease. The firms may meed an unmet need, but some of that unmet need may be induced demand (here’s a related link). Additionally, the bargaining power of various groups of medical personnel may change in such a setting, leading to changes in compensation schedules which may not be considered desirable/fair. An increase in total outlays may or may not be considered a desirable outcome, but this does illustrate once again the point that you need to be careful about what you are trying to achieve.

There’s a significant literature on how the level of health care integration, both at the vertical and horizontal level, both in terms of financial structure and e.g. in terms of service provision structure, may impact health care costs, and this is an active area of research where we in some contexts do not yet know the answers.

Even when cost minimization mechanisms are employed in the context of private firms and the firm in question is efficient, the firm may not internalize all relevant costs. This may paradoxically lead to higher overall cost, due to coverage decisions taken ‘upstream’ influencing costs ‘downstream’ in an adverse manner; I have talked about this topic on this blog before. A diabetic might be denied coverage of glucose testing materials by his private insurer, and that might mean that the diabetic instead gets hospitalized for a foreseeable and avoidable complication (hypoglycemic coma due to misdosing), but because it might not be the same people paying for the testing material and the subsequent hospitalization it might not matter to the people denying coverage of the testing materials, and/so they won’t take it into account when they’re making their coverage decisions. That sort of thing is quite common in the health care sector – different entities pay for and receive payments for different things, and this is once again a problem to keep in mind if you’re interested in health care evaluation; interventions which seem to lower cost may not do so in reality, because the intervention lead to higher health care utilization elsewhere in the system. If incentives are not well-aligned things may go badly wrong, and they are often not well-aligned in the health care sector. When both the private and public sectors are involved in either the financial arrangements and/or actual health service provision – which is the default health care system setup for developed societies – this usually leads to highly complex systems, where the scope for such problems to appear seems magnified, rather than the opposite. I would assume that in many cases it matters a lot more that incentives are well-aligned than which specific entity is providing insurance or health care in the specific context, in part a conclusion drawn from the coverage included in Simmons, Wenzel & Zgibor‘s book.

In terms of the incentive structures of the people involved in the health care sector, this stuff is of course also adding another layer of complexity. In all sectors of the economy you have people with different interests who interact with each other, and when incentives change outcomes change. Outcomes may be car batteries, or baseball bats, or lectures. Evaluating outcomes is easier in some settings than in others, and I have already touched upon some of the problems that might be present when you’re trying to evaluate outcomes in the health care context. How easy it is to evaluate outcomes will naturally vary across sub-sectors of the health care sector but a general problem which tends to surface here is the existence of various forms of asymmetrical information. There are multiple layers, but a few examples are worth mentioning. To put it bluntly, the patient tends to know his symptoms and behavioural patterns – which may be disease-relevant, and this aspect is certainly important to include when discussing preventative illnesses caused at least in part by behaviours which increase the risk of said illnesses – better than his doctor, and the doctor will in general tend to know much more about the health condition and potential treatment options than will the patient. The patient wants to get better, but he also wants to look good in the eyes of the doctor, which means he might not be completely truthful when interacting with the doctor; he might downplay how much alcohol he drinks, misrepresent how often he exercises, or he may lie about smoking habits or about how much he weighs. These things make risk-assessments more difficult than they otherwise might have been. As for the GPs, usually we here have some level of regulation which restricts their behaviour to some extent, and part of the motivation for such regulation is to reduce the level of induced demand which might otherwise be the result of information asymmetry in the context of stuff like relevant treatment effects. If a patient is not sufficiently competent to evaluate the treatments he receives (‘did the drug the doctor ordered really work, or would I have gotten better without it?’), there’s a risk he might be talked into undergoing needless procedures or take medications for which he has no need, especially if the doctor who advises him has a financial interest in the treatment modality on offer.

General physicians have different incentives from nurses and specialists working in hospitals, and all of these groups may experience conflicts of interests when they’re dealing with insurance providers and with each other. Patients as mentioned have their own set of incentives, which may not align perfectly with those of the health care providers. Different approaches to how to deal with such problems lead to different organizational setups, all of which influence both the quantity and quality of care, subject to various constraints. It’s an active area of research whether decreasing competition between stakeholders/service providers may decrease costs; one thing that is relatively clear from diabetes research with which I have familiarized myself is that when different types of care providers coordinate activities, this tends to lead to better outcomes (and sometimes, but not always, lower costs), because some of the externalized costs become internalized by virtue of the coordination. It seems very likely to me that conclusions to such questions will be different for different subsectors of the health care sector. A general point might be that more complex diseases should be expected to be more likely to generate cost savings from increased coordination than should relatively simple diseases (if you’re fuzzy about what the concept of disease complexity refers to, this post includes some relevant observations). This may be important, because complex diseases also should probably tend to be more expensive to treat in general, because the level of need in patients is higher.

It’s perhaps hardly surprising, considering the problems I’ve already discussed related to how difficult it may be to properly assess costs, that there’s a big discussion to be had about how to even estimate costs (and benefits) in specific contexts, and that people write books about these kinds of things. A lot of things have already been said on this topic and a lot more could be said, but one general point perhaps worth repeating is that it may in the health care sector be very difficult to figure out what things (‘truly’) cost (/’is worth’). If you only have a public sector entity dealing with a specific health problem and patients are not charged for receiving treatment, it may be very difficult to figure out what things ‘should’ cost because relevant prices are simply missing from the picture. You know what the government entity paid the doctors in wages and what it paid for the drugs, but the link between payment and value is sometimes a bit iffy here. There are ways to at least try to address some of these issues, but as already noted people write books about these kinds of things so I’m not going to provide all the highlights here – I refer to the previous posts I’ve written on these topics instead.

Another important related point is that medical expenditures and medical costs are not synonyms. There are many costs associated with illness which are not directly related to e.g. a payment to a doctor. People who are ill may be less productive while they are at work, they may have more sick-days, they may retire earlier, their spouse may cut down on work hours to take care of them instead of going to work, a family caretaker may become ill as a result of the demands imposed by the caretaker role (for example Alzheimer’s disease significantly increases the risk of depression in the spouse). Those costs are relevant, there are literatures on these things, and in some contexts such ‘indirect costs’ (e.g. lower productivity at work and early retirement) may make up a very substantial proportion of the total costs of a health condition. I have seen diabetes cost estimates which indicated that the indirect costs may account for as much as 50 % of the total costs.

If there’s a significant disconnect between total costs and medical expenditures then minimizing expenditures may not be desirable from an economic viewpoint. A reasonable assessment model will/should in the context of models of outlays include both a monetary cost parameter and a quality/quantity (ideally both) parameter; if you neglect to take account of the latter, in some sense you’re only dealing with what you pay out, not what you get for that payment (which is relevant). If you don’t take into account indirect costs you implicitly allow cost switching practices to potentially muddle the picture and make assessments more difficult; for example if you provide fewer long-term care facilities then the number of people involved in ‘informal care’ (e.g. family members having to take care of granny) will go up, and that is going to have secondary effects downstream which should also be assessed (you improve the budget in the context of the long-term care facilities, but you may at the same time increase demands on e.g. psychiatric institutions and marginally lower especially the female labour market participation rate. The net effect may still be positive, but the point is that an evaluation will/should include costs like these in the analysis, at least if you want anything remotely close to the full picture).

Let’s return to those smokers we talked about earlier. A general point not mentioned yet is that if you don’t cover smokers in the public sector because of cost considerations, many of them may also not be covered by private insurance either. This is because a group of individuals that is high risk and expensive to treat will be demanded high premiums (or the insurance providers would go out of business), and for the sake of this discussion we’re now assuming smokers are expensive. If that is so, many of them probably would not be able to afford the premiums demanded. Now, one of the health problems which are very common in smokers is chronic obstructive pulmonary disease (COPD). Admission rates for COPD patients differ as much as 10-fold between European countries, and one of the most important parameters regarding pharmacoeconomics is the hospitalization rate (both observations are from this text). What does this mean? It means that we know that admission rate from COPD is highly responsive to the treatment regime; populations well-treated have much fewer hospitalizations. 4% of all Polish hospitalizations are due to COPD. If you remove the public sector subsidies, the most likely scenario you get seems to me to be a poor-outcomes scenario with lots of hospitalizations. Paying for those is likely to be a lot more expensive than it is to treat the COPD pharmacologically in the community. And if smokers aren’t going to be paying for it, someone else will have to do that. If you both deny them health insurance and refuse them treatment if they cannot pay for it they may just die of course, but in most cost-assessment models that’s a high-cost outcome, not a low-cost outcome (e.g. due to lost work-life productivity etc. Half of people with COPD are of working age, see the text referred to above.). This is one example where the ‘more fair’ option might lead to higher costs, rather than lower costs. Some people might still consider such an outcome desirable, it depends on the maximand of interest, but such outcomes are worth considering when assessing the desirability of different systems.

A broadly similar dynamic, in the context of post-diagnosis behaviour and links to complications and costs, may be present in the context of type 2 diabetes. I know much more about diabetes than I do about respirology, but certainly in the case of diabetes this is a potentially really big problem. Diabetics who are poorly regulated tend to die a lot sooner than other people, they develop horrible complications, they stop being able to work, etc. etc. Some of those costs you can ignore if you’re willing to ‘let them die in the streets’ (as the expression goes), but a lot of those costs are indirect costs due to lower productivity, and those costs aren’t going anywhere, regardless of who may or may not be paying the medical bills of these people. Even if they have become sick due to a high-risk behaviour of their own choosing, their health care costs post-diagnosis will still be highly dependent upon their future medical care and future health insurance coverage. Denying them coverage for all diabetes-related costs post-diagnosis may, paradoxical though it may seem to some, not be the cost-minimizing option.

I already talked about information asymmetries. Another problematic aspect linked to information management also presents itself here in a model of this nature (‘deny all diabetes-related coverage to known diabetics’); people who suspect they might be having type 2 diabetes may choose not to disclose this fact to a health care provider because of the insurance aspect (denial of coverage problems). Insurance providers can of course (and will try to) counter this by things like mandatory screening protocols, but this is expensive, and even assuming they are successful you again not only potentially neglect to try to minimize the costs of the high-cost individuals in the population (the known diabetics, who might be cheaper long-term if they had some coverage), you also price a lot of non-diabetics out of the market (because premiums went up to pay for the screening). And some of those non-diabetics are diabetics to-be, who may get a delayed diagnosis as a result, with an associated higher risk of (expensive) complications. Again, as in the smoking context if the private insurer does not cover the high-cost outcomes someone else will have to do that, and the blind diabetic in a wheel-chair is not likely to be able to pay for his dialysis himself.

More information may in some situations lead to a breakdown in insurance markets. This is particularly relevant in the context of genetics and genetic tests. If you have full information, or close to it, the problem you have to some extent stops being an insurance problem and instead becomes a problem of whether or not to, and to which extent you want to-, explicitly compensate people for having been dealt a bad hand by nature. To put it in very general terms, insurance is a better framework for diseases which can in principle be cured than it is for chronic conditions where future outlays are known with a great level of certainty; the latter type of disease tends to be difficult to handle in an insurance context.

People who have one disease may develop other diseases as time progresses, and having disease X may increase or decrease the risk of disease Y. People study such disease variability patterns, and have done so for years, but there’s still a lot of stuff we don’t know – here’s a recent post on these topics. Such patterns are interesting for multiple reasons. One major motivation for studying these things is that ‘different’ diseases may have common mechanisms, and the identification of these mechanisms may lead to new treatment options. A completely different motivation for studying these things relate rather to the kind of stuff covered in this post, where you instead wonder about economic aspects; for example, if the smoker stops smoking he may gain weight and eventually develop type 2 diabetes instead of developing some smoking-related condition. Is this outcome better or worse than the other? It’s important to keep in mind when evaluating changes in compensation schedules/insurance structures that diseases are not independent, and this is a problem regardless of whether you’re interested in total costs or ‘just’ direct outlays. Say you’re ‘only’ worried about outlays and you are trying to figure out if it is a good idea to deny coverage to smokers, and you know that ex-smokers are likely to gain weight and have an increased risk of type 2 diabetes. Then the relevant change in cost is not the money you save on smoking-related illness, it’s the cost change you arrive at when after you account for those savings also account for the increased cost of treating type 2 diabetes. Disease interdependencies are probably as complex as risk factor interdependencies – the two phenomena are to some extent representing the same basic phenomenon – so this makes true cost evaluation even harder than it already was. Not all relevant costs at the societal level are of course medical costs; if people live longer, and they rely partly on a pension scheme to which they are no longer contributing, that cost is also relevant.

If a group of people who live longer cost more than a group of people who do not live as long, and you need to cover the associated shortfall, then – as we concluded in the beginning – there are really only two ways to handle this: Make them pay more than the people who do not live as long, or make the people who do not live as long pay more to cover the shortfall. Another way to look at this is that in this situation you can either tax people ‘for not living long enough’, or you can tax people for ‘not dying at the appropriate time’. On the other hand (?), if a group of people who die early turns out to be the higher-cost group in the relevant comparison (perhaps because they have shorter working lives and so pay into the system for a shorter amount of time), then you can deal with this problem by… either taxing them for ‘not living long enough’ or by punishing the people who live long lives for ‘not dying at the appropriate time’. No, of course it doesn’t matter which group is high cost, the solution mechanism is the same in both cases – make one of the groups pay more. And every time you tweak things you change the incentives of various people, and implicit effects like these hide somewhere in the background.


March 31, 2017 Posted by | Cancer/oncology, Diabetes, Economics, Health Economics, rambling nonsense | Leave a comment

Biodemography of aging (II)

In my first post about the book I included a few general remarks about the book and what it’s about. In this post I’ll continue my coverage of the book, starting with a few quotes from and observations related to the content in chapter 4 (‘Evidence for Dependence Among Diseases‘).

“To compare the effects of public health policies on a population’s characteristics, researchers commonly estimate potential gains in life expectancy that would result from eradication or reduction of selected causes of death. For example, Keyfitz (1977) estimated that eradication of cancer would result in 2.265 years of increase in male life expectancy at birth (or by 3 % compared to its 1964 level). Lemaire (2005) found that the potential gain in the U.S. life expectancy from cancer eradication would not exceed 3 years for both genders. Conti et al. (1999) calculated that the potential gain in life expectancy from cancer eradication in Italy would be 3.84 years for males and 2.77 years for females. […] All these calculations assumed independence between cancer and other causes of death. […] for today’s populations in developed countries, where deaths from chronic non-communicable diseases are in the lead, this assumption might no longer be valid. An important feature of such chronic diseases is that they often develop in clusters manifesting positive correlations with each other. The conventional view is that, in a case of such dependence, the effect of cancer eradication on life expectancy would be even smaller.”

I think the great majority of people you asked would have assumed that the beneficial effect of hypothetical cancer eradication in humans on human life expectancy would be much larger than this, but that’s just an impression. I’ve seen estimates like these before, so I was not surprised – but I think many people would be if they knew this. A very large number of people die as a result of developing cancer today, but the truth of the matter is that if they hadn’t died from cancer they’d have died anyway, and on average probably not really all that much later. I linked to Richard Alexander’s comments on this topic in my last post about the book, and again his observations apply so I thought I might as well add the relevant quote from the book here:

“In the course of working against senescence, selection will tend to remove, one by one, the most frequent sources of mortality as a result of senescence. Whenever a single cause of mortality, such as a particular malfunction of any vital organ, becomes the predominant cause of mortality, then selection will more effectively reduce the significance of that particular defect (meaning those who lack it will outreproduce) until some other achieves greater relative significance. […] the result will be that all organs and systems will tend to deteriorate together. […] The point is that as we age, and as senescence proceeds, large numbers of potential sources of mortality tend to lurk ever more malevolently just “below the surface,”so that, unfortunately, the odds are very high against any dramatic lengthening of the maximum human lifetime through technology.”

Remove one cause of death and there are plenty of others standing in line behind it. We already knew that; two hundred years ago one out of every four deaths in England was the result of tuberculosis, but developing treatments for tuberculosis and other infectious diseases did not mean that English people stopped dying; these days they just die from cardiovascular disease and cancer instead. Do note in the context of that quote that Alexander is talking about the maximum human lifetime, not average life expectancy; again, we know and have known for a long time that human technology can have a dramatic effect on the latter variable. Of course a shift in one distribution will be likely to have spill-over effects on the other (if more people are alive at the age of 70, the potential group of people also living on to reach e.g. 100 years is higher, even if the mortality rate for the 70-100 year old group did not change) the point is just that these effects are secondary effects and are likely to be marginal at best.

Anyway, some more stuff from the chapter. Just like the previous chapter in the book did, this one also includes analyses of very large data sets:

The Multiple Cause of Death (MCD) data files contain information about underlying and secondary causes of death in the U.S. during 1968–2010. In total, they include more than 65 million individual death certificate records. […] we used data for the period 1979–2004.”

There’s some formal modelling stuff in the chapter which I won’t go into in detail here, this is the chapter in which I encountered the comment about ‘the multivariate lognormal frailty model’ I included in my first post about the book. One of the things the chapter looks at are the joint frequencies of deaths from cancer and other fatal diseases; it turns out that there are multiple diseases that are negatively related with cancer as a cause of death when you look at the population-level data mentioned above. The chapter goes into some of the biological mechanisms which may help explain why these associations look the way they do, and I’ll quote a little from that part of the coverage. A key idea here is (as always..?) that there are tradeoffs at play; some genetic variants may help protect you against e.g. cancer, but at the same time increase the risk of other diseases for the same reason that they protect you against cancer. In the context of the relationship between cancer deaths and deaths from other diseases they note in the conclusion that: “One potential biological mechanism underlying the negative correlation among cancer and other diseases could be related to the differential role of apoptosis in the development of these diseases.” The chapter covers that stuff in significantly more detail, and I decided to add some observations from the chapter on these topics below:

“Studying the role of the p53 gene in the connection between cancer and cellular aging, Campisi (2002, 2003) suggested that longevity may depend on a balance between tumor suppression and tissue renewal mechanisms. […] Although the mechanism by which p53 regulates lifespan remains to be determined, […] findings highlight the possibility that careful manipulation of p53 activity during adult life may result in beneficial effects on healthy lifespan. Other tumor suppressor genes are also involved in regulation of longevity. […] In humans, Dumont et al. (2003) demonstrated that a replacement of arginine (Arg) by proline (Pro) at position 72 of human p53 decreases its ability to initiate apoptosis, suggesting that these variants may differently affect longevity and vulnerability to cancer. Van Heemst et al. (2005) showed that individuals with the Pro/Pro genotype of p53 corresponding to reduced apoptosis in cells had significantly increased overall survival (by 41%) despite a more than twofold increased proportion of cancer deaths at ages 85+, together with a decreased proportion of deaths from senescence related causes such as COPD, fractures, renal failure, dementia, and senility. It was suggested that human p53 may protect against cancer but at a cost of longevity. […] Other biological factors may also play opposing roles in cancer and aging and thus contribute to respective trade-offs […]. E.g., higher levels of IGF-1 [have been] linked to both cancer and attenuation of phenotypes of physical senescence, such as frailty, sarcopenia, muscle atrophy, and heart failure, as well as to better muscle regeneration”.

“The connection between cancer and longevity may potentially be mediated by trade-offs between cancer and other diseases which do not necessarily involve any basic mechanism of aging per se. In humans, it could result, for example, from trade-offs between vulnerabilities to cancer and AD, or to cancer and CVD […] There may be several biological mechanisms underlying the negative correlation among cancer and these diseases. One can be related to the differential role of apoptosis in their development. For instance, in stroke, the number of dying neurons following brain ischemia (and thus probability of paralysis or death) may be less in the case of a downregulated apoptosis. As for cancer, the downregulated apoptosis may, conversely, mean a higher risk of the disease because more cells may survive damage associated with malignant transformation. […] Also, the role of the apoptosis may be different or even opposite in the development of cancer and Alzheimer’s disease (AD). Indeed, suppressed apoptosis is a hallmark of cancer, while increased apoptosis is a typical feature of AD […]. If so, then chronically upregulated apoptosis (e.g., due to a genetic polymorphism) may potentially be protective against cancer, but be deleterious in relation to AD. […] Increased longevity can be associated not only with increased but also with decreased chances of cancer. […] The most popular to-date “anti-aging” intervention, caloric restriction, often results in increased maximal life span along with reduced tumor incidence in laboratory rodents […] Because the rate of apoptosis was significantly and consistently higher in food restricted mice regardless of age, James et al. (1998) suggested that caloric restriction may have a cancer-protective effect primarily due to the upregulated apoptosis in these mice.”

Below I’ll discuss content covered in chapter 5, which deals with ‘Factors That May Increase Vulnerability to Cancer and Longevity in Modern Human Populations’. I’ll start out with a few quotes:

“Currently, the overall cancer incidence rate (age-adjusted) in the less developed world is roughly half that seen in the more developed world […] For countries with similar levels of economic development but different climate and ethnic characteristics […], the cancer rate patterns look much more similar than for the countries that share the same geographic location, climate, and ethnic distribution, but differ in the level of economic development […]. This suggests that different countries may share common factors linked to economic prosperity that could be primarily responsible for the modern increases in overall cancer risk. […] Population aging (increases in the proportion of older people) may […] partly explain the rise in the global cancer burden […]; however, it cannot explain increases in age-specific cancer incidence rates over time […]. Improved diagnostics and elevated exposures to carcinogens may explain increases in rates for selected cancer sites, but they cannot fully explain the increase in the overall cancer risk, nor incidence rate trends for most individual cancers (Jemal et al. 2008, 2013).”

“[W]e propose that the association between the overall cancer risk and the economic progress and spread of the Western lifestyle could in part be explained by the higher proportion of individuals more susceptible to cancer in the populations of developed countries, and discuss several mechanisms of such an increase in the proportion of the vulnerable. […] mechanisms include but are not limited to: (i) Improved survival of frail individuals. […] (ii) Avoiding or reducing traditional exposures. Excessive disinfection and hygiene typical of the developed world can diminish exposure to some factors that were abundant in the past […] Insufficiently or improperly trained immune systems may be less capable of resisting cancer. (iii) Burden of novel exposures. Some new medicines, cleaning agents, foods, etc., that are not carcinogenic themselves may still affect the natural ways of processing carcinogens in the body, and through this increase a person’s susceptibility to established carcinogens. [If this one sounds implausible to you, I’ll remind you that drug metabolism is complicatedUS] […] (iv) Some of the factors linked to economic prosperity and the Western lifestyle (e.g., delayed childbirth and food enriched with growth factors) may antagonistically influence aging and cancer risk.”

They provide detailed coverage of all of these mechanisms in the chapter, below I have included a few select observations from that part of the coverage.

“There was a dramatic decline in infant and childhood mortality in developed countries during the last century. For example, the infant mortality rate in the United States was about 6 % of live births in 1935, 3 % in 1950, 1.3 % in 1980, and 0.6 % in 2010. That is, it declined tenfold over the course of 75 years […] Because almost all children (including those with immunity deficiencies) survive, the proportion of the children who are inherently more vulnerable could be higher in the more developed countries. This is consistent with a typically higher proportion of children with chronic inflammatory immune disorders such as asthma and allergy in the populations of developed countries compared to less developed ones […] Over-reduction of such traditional exposures may result in an insufficiently/improperly trained immune system early in life, which could make it less able to resist diseases, including cancer later in life […] There is accumulating evidence of the important role of these effects in cancer risk. […] A number of studies have connected excessive disinfection and lack of antigenic stimulation (especially in childhood) of the immune system in Westernized communities with increased risks of both chronic inflammatory diseases and cancer […] The IARC data on migrants to Israel […] allow for comparison of the age trajectories of cancer incidence rates between adult Jews who live in Israel but were born in other countries […] [These data] show that Jews born in less developed regions (Africa and Asia) have overall lower cancer risk than those born in the more developed regions (Europe and America).  The discrepancy is unlikely to be due to differences in cancer diagnostics because at the moment of diagnosis all these people were citizens of the same country with the same standard of medical care. These results suggest that surviving childhood and growing up in a less developed country with diverse environmental exposures might help form resistance to cancer that lasts even after moving to a high risk country.”

I won’t go much into the ‘burden of novel exposures’ part, but I should note that exposures that may be relevant include factors like paracetamol use and antibiotics for treatment of H. pylori. Paracetamol is not considered carcinogenic by the IARC, but we know from animal studies that if you give rats paratamol and then expose them to an established carcinogen (with the straightforward name N-nitrosoethyl-N-hydroxyethylamine), the number of rats developing kidney cancer goes up. In the context of H. pylori, we know that these things may cause stomach cancer, but when you treat rats with metronidazol (which is used to treat H. pylori) and expose them to an established carcinogen, they’re more likely to develop colon cancer. The link between colon cancer and antibiotics use has been noted in other contexts as well; decreased microbial diversity after antibiotics use may lead to suppression of the bifidobacteria and promotion of E. coli in the colon, the metabolic products of which may lead to increased cancer risk. Over time an increase in colon cancer risk and a decrease in stomach cancer risk has been observed in developed societies, but aside from changes in diet another factor which may play a role is population-wide exposure to antibiotics. Colon and stomach cancers are incidentally not the only ones of interest in this particular context; it has also been found that exposure to chloramphenicol, a broad-spectrum antibiotic used since the 40es, increases the risk of lymphoma in mice when the mice are exposed to a known carcinogen, despite the drug itself again not being clearly carcinogenic on its own.

Many new exposures aside from antibiotics are of course relevant. Two other drug-related ones that might be worth mentioning are hormone replacement therapy and contraceptives. HRT is not as commonly used today as it was in the past, but to give some idea of the scope here, half of all women in the US aged 50-65 are estimated to have been on HRT at the peak of its use, around the turn of the millennium, and HRT is assumed to be partly responsible for the higher incidence of hormone-related cancers observed in female populations living in developed countries. It’s of some note that the use of HRT dropped dramatically shortly after this peak (from 61 million prescriptions in 2001 to 21 million in 2004), and that the incidence of estrogen-receptor positive cancers subsequently dropped. As for oral contraceptives, these have been in use since the 1960s, and combined hormonal contraceptives are known to increase the risk of liver- and breast cancer, while seemingly also having a protective effect against endometrial cancer and ovarian cancer. The authors speculate that some of the cancer incidence changes observed in the US during the latter half of the last century, with a decline in female endometrial and ovarian cancer combined with an increase in breast- and liver cancer, could in part be related to widespread use of these drugs. An estimated 10% of all women of reproductive age alive in the world, and 16% of those living in the US, are estimated to be using combined hormonal contraceptives. In the context of the protective effect of the drugs, it should perhaps be noted that endometrial cancer in particular is strongly linked to obesity so if you are not overweight you are relatively low-risk.

Many ‘exposures’ in a cancer context are not drug-related. For example women in Western societies tend to go into menopause at a higher age, and higher age of menopause has been associated with hormone-related cancers; but again the picture is not clear in terms of how the variable affects longevity, considering that later menopause has also been linked to increased longevity in several large studies. In the studies the women did have higher mortality from the hormone-related cancers, but on the other hand they were less likely to die from some of the other causes, such as pneumonia, influenza, and falls. Age of childbirth is also a variable where there are significant differences between developed countries and developing countries, and this variable may also be relevant to cancer incidence as it has been linked to breast cancer and melanoma; in one study women who first gave birth after the age of 35 had a 40% increased risk of breast cancer compared to mothers who gave birth before the age of 20 (good luck ‘controlling for everything’ in a context like that, but…), and in a meta-analysis the relative risk for melanoma was 1.47 for women in the oldest age group having given birth, compared to the youngest (again, good luck controlling for everything, but at least it’s not just one study). Lest you think this literature only deals with women, it’s also been found that parental age seems to be linked to cancers in the offspring (higher parental age -> higher cancer risk in the offspring), though the effect sizes are not mentioned in the coverage.

Here’s what they conclude at the end of the chapter:

“Some of the factors associated with economic prosperity and a Western lifestyle may influence both aging and vulnerability to cancer, sometimes oppositely. Current evidence supports a possibility of trade-offs between cancer and aging-related phenotypes […], which could be influenced by delayed reproduction and exposures to growth factors […]. The latter may be particularly beneficial at very old age. This is because the higher levels of growth factors may attenuate some phenotypes of physical senescence, such as decline in regenerative and healing ability, sarcopenia, frailty, elderly fractures and heart failure due to muscles athrophy. They may also increase the body’s vulnerability to cancer, e.g., through growth promoting and anti-apoptotic effects […]. The increase in vulnerability to cancer due to growth factors can be compatible with extreme longevity because cancer is a major contributor to mortality mainly before age 85, while senescence-related causes (such as physical frailty) become major contributors to mortality at oldest old ages (85+). In this situation, the impact of growth factors on vulnerability to death could be more deleterious in middle-to-old life (~before 85) and more beneficial at older ages (85+).

The complex relationships between aging, cancer, and longevity are challenging. This complexity warns against simplified approaches to extending longevity without taking into account the possible trade-offs between phenotypes of physical aging and various health disorders, as well as the differential impacts of such tradeoffs on mortality risks at different ages (e.g., Ukraintseva and Yashin 2003a; Yashin et al. 2009; Ukraintseva et al. 2010, 2016).”


March 7, 2017 Posted by | Books, Cancer/oncology, Epidemiology, Genetics, Immunology, Medicine, Pharmacology | Leave a comment