Econstudentlog

Epilepsy Diagnosis & Treatment – 5 New Things Every Physician Should Know

Links to related stuff:
i. Sudden unexpected death in epilepsy (SUDEP).
ii. Status epilepticus.
iii. Epilepsy surgery.
iv. Temporal lobe epilepsy.
v. Lesional epilepsy surgery.
vi. Nonlesional neocortical epilepsy.
vii. A Randomized, Controlled Trial of Surgery for Temporal-Lobe Epilepsy.
viii. Stereoelectroencephalography.
ix. Accuracy of intracranial electrode placement for stereoencephalography: A systematic review and meta-analysis. (The results of the review is not discussed in the lecture, for obvious reasons – lecture is a few years old, this review is brand new – but seemed relevant to me.)
x. MRI-guided laser ablation in epilepsy treatment.
xi. Laser thermal therapy: real-time MRI-guided and computer-controlled procedures for metastatic brain tumors.
xii. Critical review of the responsive neurostimulator system for epilepsy (Again, not covered but relevant).
xiii. A Multicenter, Prospective Pilot Study of Gamma Knife Radiosurgery for Mesial Temporal Lobe Epilepsy: Seizure Response, Adverse Events, and Verbal Memory.
xiv. Gamma Knife radiosurgery for recurrent or residual seizures after anterior temporal lobectomy in mesial temporal lobe epilepsy patients with hippocampal sclerosis: long-term follow-up results of more than 4 years (Not covered but relevant).

July 19, 2017 Posted by | Lectures, Medicine, Neurology, Studies | Leave a comment

Detecting Cosmic Neutrinos with IceCube at the Earth’s South Pole

I thought there were a bit too many questions/interruptions for my taste, mainly because you can’t really hear the questions posed by the members of the audience, but aside from that it’s a decent lecture. I’ve added a few links below which covers some of the topics discussed in the lecture.

Neutrino astronomy.
Antarctic Impulse Transient Antenna (ANITA).
Hydrophone.
Neutral pion decays.
IceCube Neutrino Observatory.
Evidence for High-Energy Extraterrestrial Neutrinos at the IceCube Detector (Science).
Atmospheric and astrophysical neutrinos above 1 TeV interacting in IceCube.
Notes on isotropy.
Measuring the flavor ratio of astrophysical neutrinos.
Blazar.
Supernova 1987A neutrino emissions.

July 18, 2017 Posted by | Astronomy, Lectures, Physics, Studies | Leave a comment

A few diabetes papers of interest

i. Long-Acting C-Peptide and Neuropathy in Type 1 Diabetes: A 12-Month Clinical Trial.

“Lack of C-peptide in type 1 diabetes may be an important contributing factor in the development of microvascular complications. Replacement of native C-peptide has been shown to exert a beneficial influence on peripheral nerve function in type 1 diabetes. The aim of this study was to evaluate the efficacy and safety of a long-acting C-peptide in subjects with type 1 diabetes and mild to moderate peripheral neuropathy. […] C-peptide, an integral component of the insulin biosynthesis, is the 31-amino acid peptide that makes up the connecting segment between the parts of the proinsulin molecule that become the A and B chains of insulin. It is split off from proinsulin and secreted together with insulin in equimolar amounts. Much new information on C-peptide physiology has appeared during the past 20 years […] Studies in animal models of diabetes and early clinical trials in patients with type 1 diabetes (T1DM) demonstrate that C-peptide in physiological replacement doses elicits beneficial effects on early stages of diabetes-induced functional and structural abnormalities of the peripheral nerves, the autonomic nervous system, and the kidneys (9). Even though much is still to be learned about C-peptide and its mechanism of action, the available evidence presents the picture of a bioactive peptide with therapeutic potential.”

“This was a multicenter, phase 2b, randomized, double-blind, placebo-controlled, parallel-group study. The study screened 756 subjects and enrolled 250 at 32 clinical sites in the U.S. (n = 23), Canada (n = 2), and Sweden (n = 7). […] A total of 250 patients with type 1 diabetes and peripheral neuropathy received long-acting (pegylated) C-peptide in weekly dosages […] for 52 weeks. […] Once-weekly subcutaneous administration of long-acting C-peptide for 52 weeks did not improve SNCV [sural nerve conduction velocity], other electrophysiological variables, or mTCNS [modified Toronto Clinical Neuropathy Score] but resulted in marked improvement of VPT [vibration perception threshold] compared with placebo. […] During the course of the 12-month study period, there were no significant changes in fasting blood glucose. Levels of HbA1c remained stable and varied within the treatment groups on average less than 0.1% (0.9 mmol/mol) between baseline and 52 weeks. […] There was a gradual lowering of VPT, indicating improvement in subjects receiving PEG–C-peptide […] after 52 weeks, subjects in the low-dose group had lowered their VPT by an average of 31% compared with baseline; the corresponding value for the high-dose group was 19%. […] The difference in VPT response between the dose groups did not attain statistical significance. In contrast to the SNCV results, VPT in the placebo group changed very little from baseline during the study […] The mTCNS, pain, and sexual function scores did not change significantly during the study nor did subgroup analysis involving the subjects most affected at baseline reveal significant differences between subjects treated with PEG–C-peptide or placebo subjects.”

“Evaluation of the safety population showed that PEG–C-peptide was well tolerated and that there was a low and similar incidence of treatment-related adverse events (11.3–16.4%) in all three treatment groups […] A striking finding in the current study is the observation of a progressive improvement in VPT during the 12-month treatment with PEG–C-peptide […], despite nonsignificant changes in SNCV. This finding may reflect differences in the mechanisms of conduction versus transduction of neural impulses. Changes in transduction reflect membrane receptor characteristics limited to the distal extreme of specific subtypes of sensory axons. In the case of vibration, the principal receptor is Pacinian corpuscles in the skin that are innervated by Aβ fibers. Transduction takes place uniquely at the distal extreme of the axon and is largely influenced by the integrity of this limited segment. Studies have documented that the initial effect of toxic neuropathy is a loss of the surface area of the pseudopod extensions of the distal axon within the Pacinian corpuscle and a consequent diminution of transduction (30). In contrast, changes in the speed of conduction are largely a function of factors that influence the elongated tract of the nerve, including the cross-sectional diameter of axons, the degree of myelination, and the integrity of ion clusters at the nodes of Ranvier (31). Thus, it is reasonable that some aspects of distal sensory function may be influenced by a treatment option that has little or no direct effect on nerve conduction velocity. The alternative is the unsupported belief that any intervention in the onset and progression of a sensory neuropathy must alter conduction velocity.

The marked VPT improvement observed in the current study, although associated with nonsignificant changes in SNCV, other electrophysiological variables, or mTCNS, can be interpreted as targeted improvement in a key aspect of sensory function (e.g., the conversion of mechanical energy to neural signals — transduction). […] Because progressive deficits in sensation are often considered the hallmark of diabetic polyneuropathy, the observed effects of C-peptide in the current study are an important finding.”

ii. Hyperbaric Oxygen Therapy Does Not Reduce Indications for Amputation in Patients With Diabetes With Nonhealing Ulcers of the Lower Limb: A Prospective, Double-Blind, Randomized Controlled Clinical Trial.

“Hyperbaric oxygen therapy (HBOT) is used for the treatment of chronic diabetic foot ulcers (DFUs). The controlled evidence for the efficacy of this treatment is limited. The goal of this study was to assess the efficacy of HBOT in reducing the need for major amputation and improving wound healing in patients with diabetes and chronic DFUs.”

“Patients with diabetes and foot lesions (Wagner grade 2–4) of at least 4 weeks’ duration participated in this study. In addition to comprehensive wound care, participants were randomly assigned to receive 30 daily sessions of 90 min of HBOT (breathing oxygen at 244 kPa) or sham (breathing air at 125 kPa). Patients, physicians, and researchers were blinded to group assignment. At 12 weeks postrandomization, the primary outcome was freedom from meeting the criteria for amputation as assessed by a vascular surgeon. Secondary outcomes were measures of wound healing. […] One hundred fifty-seven patients were assessed for eligibility, with 107 randomly assigned and 103 available for end point adjudication. Criteria for major amputation were met in 13 of 54 patients in the sham group and 11 of 49 in the HBOT group (odds ratio 0.91 [95% CI 0.37, 2.28], P = 0.846). Twelve (22%) patients in the sham group and 10 (20%) in the HBOT group were healed (0.90 [0.35, 2.31], P = 0.823).”

CONCLUSIONS HBOT does not offer an additional advantage to comprehensive wound care in reducing the indication for amputation or facilitating wound healing in patients with chronic DFUs.”

iii. Risk Factors Associated With Severe Hypoglycemia in Older Adults With Type 1 Diabetes.

“Older adults with type 1 diabetes (T1D) are a growing but underevaluated population (14). Of particular concern in this age group is severe hypoglycemia, which, in addition to producing altered mental status and sometimes seizures or loss of consciousness, can be associated with cardiac arrhythmias, falls leading to fractures, and in some cases, death (57). In Medicare beneficiaries with diabetes, hospitalizations related to hypoglycemia are now more frequent than those for hyperglycemia and are associated with high 1-year mortality (6). Emergency department visits due to hypoglycemia also are common (5). […] The T1D Exchange clinic registry reported a remarkably high frequency of severe hypoglycemia resulting in seizure or loss of consciousness in older adults with long-standing T1D (9). One or more such events during the prior year was reported by 1 in 5 of 211 participants ≥65 years of age with ≥40 years’ duration of diabetes (9).”

“Despite the high frequency of severe hypoglycemia in older adults with long-standing T1D, little information is available about the factors associated with its occurrence. We conducted a case-control study in adults ≥60 years of age with T1D of ≥20 years’ duration to assess potential contributory factors for the occurrence of severe hypoglycemia, including cognitive and functional measurements, social support, depression, hypoglycemia unawareness, various aspects of diabetes management, residual insulin secretion (as measured by C-peptide levels), frequency of biochemical hypoglycemia, and glycemic control and variability. […] A case-control study was conducted at 18 diabetes centers in the T1D Exchange Clinic Network. […] Case subjects (n = 101) had at least one severe hypoglycemic event in the prior 12 months. Control subjects (n = 100), frequency-matched to case subjects by age, had no severe hypoglycemia in the prior 3 years.”

RESULTS Glycated hemoglobin (mean 7.8% vs. 7.7%) and CGM-measured mean glucose (175 vs. 175 mg/dL) were similar between case and control subjects. More case than control subjects had hypoglycemia unawareness: only 11% of case subjects compared with 43% of control subjects reported always having symptoms associated with low blood glucose levels (P < 0.001). Case subjects had greater glucose variability than control subjects (P = 0.008) and experienced CGM glucose levels <60 mg/dL for ≥20 min on 46% of days compared with 33% of days in control subjects (P = 0.10). […] When defining high glucose variability as a coefficient of variation greater than the study cohort’s 75th percentile (0.481), 38% of case and 12% of control subjects had high glucose variability (P < 0.001).”

CONCLUSIONS In older adults with long-standing type 1 diabetes, greater hypoglycemia unawareness and glucose variability are associated with an increased risk of severe hypoglycemia.”

iv. Type 1 Diabetes and Polycystic Ovary Syndrome: Systematic Review and Meta-analysis.

“Even though PCOS is mainly an androgen excess disorder, insulin resistance and compensatory endogenous hyperinsulinemia, in close association with obesity and abdominal adiposity, are implicated in the pathogenesis of PCOS in many patients (3,4). In agreement, women with PCOS are at high risk for developing type 2 diabetes and gestational diabetes mellitus (3). […] Type 1 diabetes is a disease produced by an autoimmune injury to the endocrine pancreas that results in the abolition of endogenous insulin secretion. We hypothesized 15 years ago that PCOS could be associated with type 1 diabetes (8). The rationale was that women with type 1 diabetes needed supraphysiological doses of subcutaneous insulin to reach insulin concentrations at the portal level capable of suppressing hepatic glucose secretion, thus leading to exogenous systemic hyperinsulinism. Exogenous hyperinsulinism could then contribute to androgen excess in predisposed women, leading to PCOS as happens in insulin-resistance syndromes.

We subsequently published the first report of the association of PCOS with type 1 diabetes consisting of the finding of a threefold increase in the prevalence of this syndrome compared with that of women from the general population […]. Of note, even though this association was confirmed by all of the studies that addressed the issue thereafter (1016), with prevalences of PCOS as high as 40% in some series (10,16), this syndrome is seldom diagnosed and treated in women with type 1 diabetes.

With the aim of increasing awareness of the frequent association of PCOS with type 1 diabetes, we have conducted a systematic review and meta-analysis of the prevalence of PCOS and associated hyperandrogenic traits in adolescent and adult women with type 1 diabetes. […] Nine primary studies involving 475 adolescent or adult women with type 1 diabetes were included. The prevalences of PCOS and associated traits in women with type 1 diabetes were 24% (95% CI 15–34) for PCOS, 25% (95% CI 17–33) for hyperandrogenemia, 25% (95% CI 16–36) for hirsutism, 24% (95% CI 17–32) for menstrual dysfunction, and 33% (95% CI 24–44) for PCOM. These figures are considerably higher than those reported earlier in the general population without diabetes.”

CONCLUSIONS PCOS and its related traits are frequent findings in women with type 1 diabetes. PCOS may contribute to the subfertility of these women by a mechanism that does not directly depend on glycemic/metabolic control among other negative consequences for their health. Hence, screening for PCOS and androgen excess should be included in current guidelines for the management of type 1 diabetes in women.”

v. Impaired Awareness of Hypoglycemia in Adults With Type 1 Diabetes Is Not Associated With Autonomic Dysfunction or Peripheral Neuropathy.

“Impaired awareness of hypoglycemia (IAH), defined as a diminished ability to perceive the onset of hypoglycemia, is associated with an increased risk of severe hypoglycemia in people with insulin-treated diabetes (13). Elucidation of the pathogenesis of IAH may help to minimize the risk of severe hypoglycemia.

The glycemic thresholds for counterregulatory responses, generation of symptoms, and cognitive impairment are reset at lower levels of blood glucose in people who have developed IAH (4). This cerebral adaptation appears to be induced by recurrent exposure to hypoglycemia, and failure of cerebral autonomic mechanisms may be implicated in the pathogenesis (4). Awareness may be improved by avoidance of hypoglycemia (57), but this is very difficult to achieve and does not restore normal awareness of hypoglycemia (NAH) in all people with IAH. Because the prevalence of IAH in adults with type 1 diabetes increases with progressive disease duration (2,8,9), mechanisms that involve diabetic complications have been suggested to underlie the development of IAH.

Because activation of the autonomic nervous system is a fundamental physiological response to hypoglycemia and provokes many of the symptoms of hypoglycemia, autonomic neuropathy was considered to be a cause of IAH for many years (10). […] Studies of people with type 1 diabetes that have examined the glycemic thresholds for symptom generation in those with and without autonomic neuropathy (13,14,16) have [however] found no differences, and autonomic symptom generation was not delayed. […] The aim of the current study was […] to evaluate a putative association between IAH and the presence of autonomic neuropathy using composite Z (cZ) scores based on a battery of contemporary methods, including heart rate variability during paced breathing, the cardiovascular response to tilting and the Valsalva maneuver, and quantitative light reflex measurements by pupillometry.”

“Sixty-six adults with type 1 diabetes were studied, 33 with IAH and 33 with normal awareness of hypoglycemia (NAH), confirmed by formal testing. Participants were matched for age, sex, and diabetes duration. […] The [study showed] no difference in measures of autonomic function between adults with long-standing type 1 diabetes who had IAH, and carefully matched adults with type 1 diabetes with NAH. In addition, no differences between IAH and NAH participants were found with respect to the NCS [nerve conduction studies], thermal thresholds, and clinical pain or neuropathy scores. Neither autonomic dysfunction nor somatic neuropathy was associated with IAH. We consider that this study provides considerable value and novelty in view of the rigorous methodology that has been used. Potential confounding variables have been controlled for by the use of well-matched groups of participants, validated methods for classification of awareness, a large battery of neurophysiological tests, and a novel statistical approach to provide very high sensitivity for the detection of between-group differences.”

vi. Glucose Variability: Timing, Risk Analysis, and Relationship to Hypoglycemia in Diabetes.

“Glucose control, glucose variability (GV), and risk for hypoglycemia are intimately related, and it is now evident that GV is important in both the physiology and pathophysiology of diabetes. However, its quantitative assessment is complex because blood glucose (BG) fluctuations are characterized by both amplitude and timing. Additional numerical complications arise from the asymmetry of the BG scale. […] Our primary message is that diabetes control is all about optimization and balance between two key markers — frequency of hypoglycemia and HbA1c reflecting average BG and primarily driven by the extent of hyperglycemia. GV is a primary barrier to this optimization […] Thus, it is time to standardize GV measurement and thereby streamline the assessment of its two most important components — amplitude and timing.”

“Although reducing hyperglycemia and targeting HbA1c values of 7% or less result in decreased risk of micro- and macrovascular complications (14), the risk for hypoglycemia increases with tightening glycemic control (5,6). […] Thus, patients with diabetes face a lifelong optimization problem: reducing average glycemic levels and postprandial hyperglycemia while simultaneously avoiding hypoglycemia. A strategy for achieving such an optimization can only be successful if it reduces glucose variability (GV). This is because bringing average glycemia down is only possible if GV is constrained — otherwise blood glucose (BG) fluctuations would inevitably enter the range of hypoglycemia (9).”

“In health, glucose metabolism is tightly controlled by a hormonal network including the gut, liver, pancreas, and brain to ensure stable fasting BG levels and transient postprandial glucose fluctuations. In other words, BG fluctuations in type 1 diabetes result from the activity of a complex metabolic system perturbed by behavioral challenges. The frequency and extent of these challenges and the ability of the person’s system to absorb them determine the stability of glycemic control. The degree of system destabilization depends on each individual’s physiological parameters of glucose–insulin kinetics, including glucose appearance from food, insulin secretion, insulin sensitivity, and counterregulatory response.”

“There is strong evidence that feeding behavior is abnormal in both uncontrolled diabetes and hypoglycemia and that feeding signals within the brain and hormones affecting feeding, such as leptin and ghrelin, are implicated in diabetes (1214). Insulin secretion and action vary with the type and duration of diabetes. In type 1 diabetes, insulin secretion is virtually absent, which destroys the natural insulin–glucagon feedback loop and thereby diminishes the dampening effect of glucagon on hypoglycemia. In addition, insulin is typically administered subcutaneously, which adds delays to insulin action and thereby amplifies the amplitude of glucose fluctuations. […] impaired hypoglycemia counterregulation and increased GV in the hypoglycemic range are particularly relevant to type 1 diabetes: It has been shown that glucagon response is impaired (15), and epinephrine response is typically attenuated as well (16). Antecedent hypoglycemia shifts down BG thresholds for autonomic and cognitive responses, thereby further impairing both the hormonal defenses and the detection of hypoglycemia (17). Studies have established relationships between intensive therapy, hypoglycemia unawareness, and impaired counterregulation (16,1820) and concluded that recurrent hypoglycemia spirals into a “vicious cycle” known as hyperglycemia-associated autonomic failure (HAAF) (21). Our studies showed that increased GV and the extent and frequency of low BG are major contributors to hypoglycemia and that such changes are detectable by frequent BG measurement (2225).”

“The traditional statistical calculation of BG includes standard deviation (SD) (27), coefficient of variation (CV), or other metrics, such as the M-value introduced in 1965 (28), the mean amplitude of glucose excursions (MAGE) introduced in 1970 (29), the glycemic lability index (30), or the mean absolute glucose (MAG) change (31,32). […] the low BG index (LBGI), high BG index (HBGI), and average daily risk range (ADRR) […] are [all] based on a transformation of the BG measurement scale […], which aims to correct the substantial asymmetry of the BG measurement scale. Numerically, the hypoglycemic range (BG <70 mg/dL) is much narrower than that in the hyperglycemic range (BG >180 mg/dL) (34). As a result, whereas SD, CV, MAGE, and MAG are inherently biased toward hyperglycemia and have a relatively weak association with hypoglycemia, the LBGI and ADRR account well for the risk of hypoglycemic excursions. […] The analytical form of the scale transformation […] was based on accepted clinical assumptions, not on a particular data set, and was fixed 17 years ago, which made the approach extendable to any data set (34). On the basis of this transformation, we have developed our theory of risk analysis of BG data (35), defining a computational risk space that proved to be very suitable for quantifying the extent and frequency of glucose excursions. The utility of the risk analysis has been repeatedly confirmed (9,25,3638). We first introduced the LBGI and HBGI, which were specifically designed to be sensitive only to the low and high end of the BG scale, respectively, accounting for hypo- and hyperglycemia without overlap (24). Then in 2006, we introduced the ADRR, a measure of GV that is equally sensitive to hypo- and hyperglycemic excursions and is predictive of extreme BG fluctuations (38). Most recently, corrections were introduced that allowed the LBGI and HBGI to be computed from CGM data with results directly comparable to SMBG [self-monitoring of BG] (39).”

“[A]lthough GV has richer information content than just average glucose (HbA1c), its quantitative assessment is not straightforward because glucose fluctuations carry two components: amplitude and timing.

The standard assessment of GV is measuring amplitude. However, when measuring amplitude we should be mindful that deviations toward hypoglycemia are not equal to deviations toward hyperglycemia—a 20 mg/dL decline in BG levels from 70 to 50 mg/dL is clinically more important than a 20 mg/dL raise of BG from 160 to 180 mg/dL. We explained how to fix that with a well-established rescaling of the BG axis introduced more than 15 years ago (34). […] In addition, we should be mindful of the timing of BG fluctuations. There are a number of measures assessing GV amplitude from routine SMBG, but the timing of readings is frequently ignored even if the information is available (42). Yet, contrary to widespread belief, BG fluctuations are a process in time and the speed of transition from one BG state to another is of clinical importance. With the availability of CGM, the assessment of GV timing became not only possible but also required (32). Responding to this necessity, we should keep in mind that the assessment of temporal characteristics of GV benefits from mathematical computations that go beyond basic arithmetic. Thus, some assistance from the theory and practice of time series and dynamical systems analysis would be helpful. Fortunately, these fields are highly developed, theoretically and computationally, and have been used for decades in other areas of science […] The computational methods are standardized and available in a number of software products and should be used for the assessment of GV. […] There is no doubt that the timing of glucose fluctuations is clinically important, but there is a price to pay for its accurate assessment—a bit higher level of mathematical complexity. This, however, should not be a deterrent.”

vii. Predictors of Increased Carotid Intima-Media Thickness in Youth With Type 1 Diabetes: The SEARCH CVD Study.

“Adults with childhood-onset type 1 diabetes are at increased risk for premature cardiovascular disease (CVD) morbidity and mortality compared with the general population (1). The antecedents of CVD begin in childhood (2), and early or preclinical atherosclerosis can be detected as intima-media thickening in the artery wall (3). Carotid intima-media thickness (IMT) is an established marker of atherosclerosis because of its associations with CVD risk factors (4,5) and CVD outcomes, such as myocardial infarction and stroke in adults (6,7).

Prior work […] has shown that youth with type 1 diabetes have higher carotid IMT than control subjects (813). In cross-sectional studies, risk factors associated with higher carotid IMT include younger age at diabetes onset, male sex, adiposity, higher blood pressure (BP) and hemoglobin A1c (HbA1c), and lower vitamin C levels (8,9,11). Only one study has evaluated CVD risk factors longitudinally and the association with carotid IMT progression in youth with type 1 diabetes (14). In a German cohort of 70 youth with type 1 diabetes, Dalla Pozza et al. (14) demonstrated that CVD risk factors, including BMI z score (BMIz), systolic BP, and HbA1c, worsened over time. They also found that baseline HbA1c and baseline and follow-up systolic BP were significant predictors of change in carotid IMT over 4 years.”

“Before the current study, no published reports had assessed the impact of changes in CVD risk factors and carotid IMT in U.S. adolescents with type 1 diabetes. […] Participants in this study were enrolled in SEARCH CVD, an ancillary study to the SEARCH for Diabetes in Youth that was conducted in two of the five SEARCH centers (Colorado and Ohio). […] This report includes 298 youth who completed both baseline and follow-up SEARCH CVD visits […] At the initial visit, youth with type 1 diabetes were a mean age of 13.3 ± 2.9 years (range 7.6–21.3 years) and had an average disease duration of 3.6 ± 3.3 years. […] Follow-up data were obtained at a mean age of 19.2 ± 2.7 years, when the average duration of type 1 diabetes was 10.1 ± 3.9 years. […] In the current study, we show that older age (at baseline) and male sex were significantly associated with follow-up IMT. By using AUC measurements, we also show that a higher BMIz exposure over ∼5 years was significantly associated with IMT at follow-up. From baseline to follow-up, the mean BMI increased from within normal limits (21.1 ± 4.3 kg/m2) to overweight (25.1 ± 4.8 kg/m2), defined as a BMI ≥25 kg/m2 in adults (26,27). This large change in BMI may explain why BMIz was the only modifiable risk factor to be associated with follow-up IMT in the final models. Whether the observed increase in BMIz over time is part of the natural evolution of diabetes, aging in an obesogenic society, or a consequence of intensive insulin therapy is not known.”

“Data from the DCCT/EDIC cohorts have suggested nontraditional risk factors, including acute phase reactants, thrombolytic factors, cytokines/adipokines (34), oxidized LDL, and advanced glycation end products (30) may be important biomarkers of increased CVD risk in adults with type 1 diabetes. However, many of these nontraditional risk factors […] were not found to associate with IMT until 8–12 years after the DCCT ended, at the time when traditional CVD risk factors were also found to predict IMT. Collectively, these findings suggest that many traditional and nontraditional risk factors are not identified as relevant until later in the atherosclerotic process and highlight the critical need to better identify risk factors that may influence carotid IMT early in the course of type 1 diabetes because these may be important modifiable CVD risk factors of focus in the adolescent population. […] Although BMIz was the only identified risk factor to predict follow-up IMT at this age [in our study], it is possible that increases in dyslipidemia, BP, smoking, and HbA1c are related to carotid IMT but only after longer duration of exposure.”

July 13, 2017 Posted by | Studies, Medicine, Diabetes, Neurology, Cardiology | Leave a comment

A few diabetes papers of interest

i. An Inverse Relationship Between Age of Type 2 Diabetes Onset and Complication Risk and Mortality: The Impact of Youth-Onset Type 2 Diabetes.

“This study compared the prevalence of complications in 354 patients with T2DM diagnosed between 15 and 30 years of age (T2DM15–30) with that in a duration-matched cohort of 1,062 patients diagnosed between 40 and 50 years (T2DM40–50). It also examined standardized mortality ratios (SMRs) according to diabetes age of onset in 15,238 patients covering a wider age-of-onset range.”

“After matching for duration, despite their younger age, T2DM15–30 had more severe albuminuria (P = 0.004) and neuropathy scores (P = 0.003). T2DM15–30 were as commonly affected by metabolic syndrome factors as T2DM40–50 but less frequently treated for hypertension and dyslipidemia (P < 0.0001). An inverse relationship between age of diabetes onset and SMR was seen, which was the highest for T2DM15–30 (3.4 [95% CI 2.7–4.2]). SMR plots adjusting for duration show that for those with T2DM15–30, SMR is the highest at any chronological age, with a peak SMR of more than 6 in early midlife. In contrast, mortality for older-onset groups approximates that of the background population.”

“Young people with type 2 diabetes are likely to be obese, with a clustering of unfavorable cardiometabolic risk factors all present at a very early age (3,4). In adolescents with type 2 diabetes, a 10–30% prevalence of hypertension and an 18–54% prevalence of dyslipidemia have been found, much greater than would be expected in a population of comparable age (4).”

CONCLUSIONS The negative effect of diabetes on morbidity and mortality is greatest for those diagnosed at a young age compared with T2DM of usual onset.”

It’s important to keep base rates in mind when interpreting the reported SMRs, but either way this is interesting.

ii. Effects of Sleep Deprivation on Hypoglycemia-Induced Cognitive Impairment and Recovery in Adults With Type 1 Diabetes.

OBJECTIVE To ascertain whether hypoglycemia in association with sleep deprivation causes greater cognitive dysfunction than hypoglycemia alone and protracts cognitive recovery after normoglycemia is restored.”

CONCLUSIONS Hypoglycemia per se produced a significant decrement in cognitive function; coexisting sleep deprivation did not have an additive effect. However, after restoration of normoglycemia, preceding sleep deprivation was associated with persistence of hypoglycemic symptoms and greater and more prolonged cognitive dysfunction during the recovery period. […] In the current study of young adults with type 1 diabetes, the impairment of cognitive function that was associated with hypoglycemia was not exacerbated by sleep deprivation. […] One possible explanation is that hypoglycemia per se exerts a ceiling effect on the degree of cognitive dysfunction as is possible to demonstrate with conventional tests.”

iii. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up.

“The DCCT randomly assigned 1,441 patients with type 1 diabetes to intensive versus conventional therapy for a mean of 6.5 years, after which 93% were subsequently monitored during the observational Epidemiology of Diabetes Interventions and Complications (EDIC) study. Cardiovascular disease (nonfatal myocardial infarction and stroke, cardiovascular death, confirmed angina, congestive heart failure, and coronary artery revascularization) was adjudicated using standardized measures.”

“During 30 years of follow-up in DCCT and EDIC, 149 cardiovascular disease events occurred in 82 former intensive treatment group subjects versus 217 events in 102 former conventional treatment group subjects. Intensive therapy reduced the incidence of any cardiovascular disease by 30% (95% CI 7, 48; P = 0.016), and the incidence of major cardiovascular events (nonfatal myocardial infarction, stroke, or cardiovascular death) by 32% (95% CI −3, 56; P = 0.07). The lower HbA1c levels during the DCCT/EDIC statistically account for all of the observed treatment effect on cardiovascular disease risk.”

CONCLUSIONS Intensive diabetes therapy during the DCCT (6.5 years) has long-term beneficial effects on the incidence of cardiovascular disease in type 1 diabetes that persist for up to 30 years.”

I was of course immediately thinking that perhaps they had not considered if this might just be the result of the Hba1c differences achieved during the trial being maintained long-term (during follow-up), and so what they were doing was not as much measuring the effect of the ‘metabolic memory’ component as they were just measuring standard population outcome differences resulting from long-term Hba1c differences. But they (of course) had thought about that, and that’s not what’s going on here, which is what makes it particularly interesting:

“Mean HbA1c during the average 6.5 years of DCCT intensive therapy was ∼2% (20 mmol/mol) lower than that during conventional therapy (7.2 vs. 9.1% [55.6 vs. 75.9 mmol/mol], P < 0.001). Subsequently during EDIC, HbA1c differences between the treatment groups dissipated. At year 11 of EDIC follow-up and most recently at 19–20 years of EDIC follow-up, there was only a trivial difference between the original intensive and conventional treatment groups in the mean level of HbA1c

They do admittedly find a statistically significant difference between the Hba1cs of the two groups when you look at (weighted) Hba1cs long-term, but that difference is certainly nowhere near large enough to explain the clinical differences in outcomes you observe. Another argument in favour of the view that what’s driving these differences is metabolic memory is the observation that the difference in outcomes between the treatment and control groups are smaller now than they were ten years ago (my default would probably be to if anything expect the outcomes of the two groups to converge long-term if the samples were properly randomized to start with, but this is not the only plausible model and it sort of depends on how you model the risk function, as they also talk about in the paper):

“[T]he risk reduction of any CVD with intensive therapy through 2013 is now less than that reported previously through 2004 (30% [P = 0.016] vs. 47% [P = 0.005]), and likewise, the risk reduction per 10% lower mean HbA1c through 2013 was also somewhat lower than previously reported but still highly statistically significant (17% [P = 0.0001] vs. 20% [P = 0.001]).”

iv. Commonly Measured Clinical Variables Are Not Associated With Burden of Complications in Long-standing Type 1 Diabetes: Results From the Canadian Study of Longevity in Diabetes.

“The Canadian Study of Longevity in Diabetes actively recruited 325 individuals who had T1D for 50 or more years (5). Subjects completed a questionnaire, and recent laboratory tests and eye reports were provided by primary care physicians and eye specialists, respectively. […] The 325 participants were 65.5 ± 8.5 years old with diagnosis at age 10 years (interquartile range [IQR] 6.0, 16) and duration of 54.9 ± 6.4 years.”

“In univariable analyses, the following were significantly associated with a greater burden of complications: presence of hypertension, statin, aspirin and ACE inhibitor or ARB use, higher Problem Areas in Diabetes (PAID) and Geriatric Depression Scale (GDS) scores, and higher levels of triglycerides and HbA1c. The following were significantly associated with a lower burden of complications: current physical activity, higher quality of life, and higher HDL cholesterol.”

“In the multivariable analysis, a higher PAID score was associated with a greater burden of complications (risk ratio [RR] 1.15 [95% CI 1.06–1.25] for each 10-point-higher score). Aspirin and statin use were also associated with a greater burden of complications (RR 1.24 [95% CI 1.01–1.52] and RR 1.34 [95% CI 1.05–1.70], respectively) (Table 1), whereas HbA1c was not.”

“Our findings indicate that in individuals with long-standing T1D, burden of complications is largely not associated with historical characteristics or simple objective measurements, as associations with statistical significance likely reflect reverse causality. Notably, HbA1c was not associated with burden of complications […]. This further confirms that other unmeasured variables such as genetic, metabolic, or physiologic characteristics may best identify mechanisms and biomarkers of complications in long-standing T1D.”

v. Cardiovascular Risk Factor Targets and Cardiovascular Disease Event Risk in Diabetes: A Pooling Project of the Atherosclerosis Risk in Communities Study, Multi-Ethnic Study of Atherosclerosis, and Jackson Heart Study.

“Controlling cardiovascular disease (CVD) risk factors in diabetes mellitus (DM) reduces the number of CVD events, but the effects of multifactorial risk factor control are not well quantified. We examined whether being at targets for blood pressure (BP), LDL cholesterol (LDL-C), and glycated hemoglobin (HbA1c) together are associated with lower risks for CVD events in U.S. adults with DM. […] We studied 2,018 adults, 28–86 years of age with DM but without known CVD, from the Atherosclerosis Risk in Communities (ARIC) study, Multi-Ethnic Study of Atherosclerosis (MESA), and Jackson Heart Study (JHS). Cox regression examined coronary heart disease (CHD) and CVD events over a mean 11-year follow-up in those individuals at BP, LDL-C, and HbA1c target levels, and by the number of controlled risk factors.”

“Of 2,018 DM subjects (43% male, 55% African American), 41.8%, 32.1%, and 41.9% were at target levels for BP, LDL-C, and HbA1c, respectively; 41.1%, 26.5%, and 7.2% were at target levels for any one, two, or all three factors, respectively. Being at BP, LDL-C, or HbA1c target levels related to 17%, 33%, and 37% lower CVD risks and 17%, 41%, and 36% lower CHD risks, respectively (P < 0.05 to P < 0.0001, except for BP in CHD risk); those subjects with one, two, or all three risk factors at target levels (vs. none) had incrementally lower adjusted risks of CVD events of 36%, 52%, and 62%, respectively, and incrementally lower adjusted risks of CHD events of 41%, 56%, and 60%, respectively (P < 0.001 to P < 0.0001). Propensity score adjustment showed similar findings.”

“In our pooled analysis of subjects with DM in three large-scale U.S. prospective studies, the more factors among HbA1c, BP, and LDL-C that were at goal levels, the lower are the observed CHD and CVD risks (∼60% lower when all three factors were at goal levels compared with none). However, fewer than one-tenth of our subjects were at goal levels for all three factors. These findings underscore the value of achieving target or lower levels of these modifiable risk factors, especially in combination, among persons with DM for the future prevention of CHD and CVD events.”

In some studies you see very low proportions of patients reaching target variables because the targets are stupid (to be perfectly frank about it). The HbA1c target applied in this study was a level <53.0 mmol/mol (7%), which is definitely not crazy if the majority of the individuals included were type 2, which they almost certainly were. You can argue about the BP goal, but it’s obvious here that the authors are perfectly aware of the contentiousness of this variable.

It’s incidentally noteworthy – and the authors do take note of it, of course – that one of the primary results of this study (~60% lower risk when all risk factors reach the target goal), which includes a large proportion of African Americans in the study sample, is almost identical to the results of the Danish Steno-2 clinical trial, which included only Danish white patients (and the results of which I have discussed here on the blog before). In the Steno study, the result was “a 57% reduction in CVD death and a 59% reduction in CVD events.”

vi. Illness Identity in Adolescents and Emerging Adults With Type 1 Diabetes: Introducing the Illness Identity Questionnaire.

“The current study examined the utility of a new self-report questionnaire, the Illness Identity Questionnaire (IIQ), which assesses the concept of illness identity, or the degree to which type 1 diabetes is integrated into one’s identity. Four illness identity dimensions (engulfment, rejection, acceptance, and enrichment) were validated in adolescents and emerging adults with type 1 diabetes. Associations with psychological and diabetes-specific functioning were assessed.”

“A sample of 575 adolescents and emerging adults (14–25 years of age) with type 1 diabetes completed questionnaires on illness identity, psychological functioning, diabetes-related problems, and treatment adherence. Physicians were contacted to collect HbA1c values from patients’ medical records. Confirmatory factor analysis (CFA) was conducted to validate the IIQ. Path analysis with structural equation modeling was used to examine associations between illness identity and psychological and diabetes-specific functioning.”

“The first two identity dimensions, engulfment and rejection, capture a lack of illness integration, or the degree to which having diabetes is not well integrated as part of one’s sense of self. Engulfment refers to the degree to which diabetes dominates a person’s identity. Individuals completely define themselves in terms of their diabetes, which invades all domains of life (9). Rejection refers to the degree to which diabetes is rejected as part of one’s identity and is viewed as a threat or as unacceptable to the self. […] Acceptance refers to the degree to which individuals accept diabetes as a part of their identity, besides other social roles and identity assets. […] Enrichment refers to the degree to which having diabetes results in positive life changes, benefits one’s identity, and enables one to grow as a person (12). […] These changes can manifest themselves in different ways, including an increased appreciation for life, a change of life priorities, and a more positive view of the self (14).”

“Previous quantitative research assessing similar constructs has suggested that the degree to which individuals integrate their illness into their identity may affect psychological and diabetes-specific functioning in patients. Diabetes intruding upon all domains of life (similar to engulfment) [has been] related to more depressive symptoms and more diabetes-related problems […] In contrast, acceptance has been related to fewer depressive symptoms and diabetes-related problems and to better glycemic control (6,15). Similarly, benefit finding has been related to fewer depressive symptoms and better treatment adherence (16). […] The current study introduces the IIQ in individuals with type 1 diabetes as a way to assess all four illness identity dimensions.”

“The Cronbach α was 0.90 for engulfment, 0.84 for rejection, 0.85 for acceptance, and 0.90 for enrichment. […] CFA indicated that the IIQ has a clear factor structure, meaningfully differentiating four illness identity dimensions. Rejection was related to worse treatment adherence and higher HbA1c values. Engulfment was related to less adaptive psychological functioning and more diabetes-related problems. Acceptance was related to more adaptive psychological functioning, fewer diabetes-related problems, and better treatment adherence. Enrichment was related to more adaptive psychological functioning. […] the concept of illness identity may help to clarify why certain adolescents and emerging adults with diabetes show difficulties in daily functioning, whereas others succeed in managing developmental and diabetes-specific challenges.”

June 30, 2017 Posted by | Cardiology, Diabetes, Medicine, Psychology, Studies | Leave a comment

A few papers

i. To Conform or to Maintain Self-Consistency? Hikikomori Risk in Japan and the Deviation From Seeking Harmony.

A couple of data points and observations from the paper:

“There is an increasing number of youth in Japan who are dropping out of society and isolating themselves in their bedrooms from years to decades at a time. According to Japan’s Ministry of Health, Labor and Welfare’s first official 2003 guidelines on this culture-bound syndrome, hikikomori (social isolation syndrome) has the following specific diagnostic criteria: (1) no motivation to participate in school or work; (2) no signs of schizophrenia or any other known psychopathologies; and (3) persistence of social withdrawal for at least six months.”

“One obvious dilemma in studying hikikomori is that most of those suffering from hikikomori, by definition, do not seek treatment. More importantly, social isolation itself is not even a symptom of any of the DSM diagnosis often assigned to an individual afflicted with hikikomori […] The motivation for isolating oneself among a hikikomori is simply to avoid possible social interactions with others who might know or judge them (Zielenziger, 2006).”

“Saito’s (2010) and Sakai and colleagues’ (2011) data suggest that 10% to 15% of the hikikomori population suffer from an autism spectrum disorder. […] in the first epidemiological study conducted on hikikomori that was as close to a nation-wide random sample as possible, Koyama and colleagues (2010) conducted a face-to-face household survey, including a structured diagnostic interview, by randomly picking households and interviewing 4,134 individuals. They confirmed a hikikomori lifetime prevalence rate of 1.2% in their nationwide sample. Among these hikikomori individuals, the researchers found that only half suffered from a DSM-IV diagnosis. However, and more importantly, there was no particular diagnosis that was systematically associated with hikikomori. […] the researchers concluded that any DSM diagnosis was an epiphenomenon to hikikomori at best and that hikikomori is rather a “psychopathology characterized by impaired motivation” p. 72).”

ii. Does the ‘hikikomori’ syndrome of social withdrawal exist outside Japan?: A preliminary international investigation.

Purpose

To explore whether the ‘hikikomori’ syndrome (social withdrawal) described in Japan exists in other countries, and if so, how patients with the syndrome are diagnosed and treated.

Methods

Two hikikomori case vignettes were sent to psychiatrists in Australia, Bangladesh, India, Iran, Japan, Korea, Taiwan, Thailand and the USA. Participants rated the syndrome’s prevalence in their country, etiology, diagnosis, suicide risk, and treatment.

Results

Out of 247 responses to the questionnaire (123 from Japan and 124 from other countries), 239 were enrolled in the analysis. Respondents’ felt the hikikomori syndrome is seen in all countries examined and especially in urban areas. Biopsychosocial, cultural, and environmental factors were all listed as probable causes of hikikomori, and differences among countries were not significant. Japanese psychiatrists suggested treatment in outpatient wards and some did not think that psychiatric treatment is necessary. Psychiatrists in other countries opted for more active treatment such as hospitalization.

Conclusions

Patients with the hikikomori syndrome are perceived as occurring across a variety of cultures by psychiatrists in multiple countries. Our results provide a rational basis for study of the existence and epidemiology of hikikomori in clinical or community populations in international settings.”

“Our results extend rather than clarify the debate over diagnosis of hikikomori. In our survey, a variety of diagnoses, such as psychosis, depression anxiety and personality disorders, were proffered. Opinions as to whether hikikomori cases can be diagnosed using ICD-10/DSV-IV criteria differed depending on the participants’ countries and the cases’ age of onset. […] a recent epidemiological survey in Japan reported approximately a fifty-fifty split between hikikomori who had experienced a psychiatric disorder and had not [14]. These data and other studies that have not been able to diagnose all cases of hikikomori may suggest the existence of ‘primary hikikomori’ that is not an expression of any other psychiatric disorder [28,8,9,5,29]. In order to clarify differences between ‘primary hikikomori’ (social withdrawal not associated with any underlying psychiatric disorder) and ‘secondary hikikomori’ (social withdrawal caused by an established psychiatric disorder), further epidemiological and psychopathological studies are needed. […] Even if all hikikomori cases prove to be within some kind of psychiatric disorders, it is valuable to continue to focus on the hikikomori phenomenon because of its associated morbidity, similar to how suicidality is examined in various fields of psychiatry [30]. Reducing the burden of hikikomori symptoms, regardless of what psychiatric disorders patients may have, may provide a worthwhile improvement in their quality of life, and this suggests another direction of future hikikomori research.”

“Our case vignette survey indicates that the hikikomori syndrome, previously thought to exist only in Japan, is perceived by psychiatrists to exist in many other countries. It is particularly perceived as occurring in urban areas and might be associated with rapid global sociocultural changes. There is no consensus among psychiatrists within or across countries about the causes, diagnosis and therapeutic interventions for hikikomori yet.”

iii. Hikikomori: clinical and psychopathological issues (review). A poor paper, but it did have a little bit of data of interest:

“The prevalence of hikikomori is difficult to assess […]. In Japan, more than one million cases have been estimated by experts, but there is no population-based study to confirm these data (9). […] In 2008, Kiyota et al. summarized 3 population-based studies involving 12 cities and 3951 subjects, highlighting that a percentage comprised between 0.9% and 3.8% of the sample had an hikikomori history in anamnesis (11). The typical hikikomori patient is male (4:1 male-to-female ratio) […] females constitute a minor fraction of the reported cases, and usually their period of social isolation is limited.”

iv. Interpreting results of ethanol analysis in postmortem specimens: A review of the literature.

A few observations from the paper:

“A person’s blood-alcohol concentration (BAC) and state of inebriation at the time of death is not always easy to establish owing to various postmortem artifacts. The possibility of alcohol being produced in the body after death, e.g. via microbial contamination and fermentation is a recurring issue in routine casework. If ethanol remains unabsorbed in the stomach at the time of death, this raises the possibility of continued local diffusion into surrounding tissues and central blood after death. Skull trauma often renders a person unconscious for several hours before death, during which time the BAC continues to decrease owing to metabolism in the liver. Under these circumstances blood from an intracerebral or subdural clot is a useful specimen for determination of ethanol. Bodies recovered from water are particular problematic to deal with owing to possible dilution of body fluids, decomposition, and enhanced risk of microbial synthesis of ethanol. […] Alcoholics often die at home with zero or low BAC and nothing more remarkable at autopsy than a fatty liver. Increasing evidence suggests that such deaths might be caused by a pronounced ketoacidosis.”

“The concentrations of ethanol measured in blood drawn from different sampling sites tend to vary much more than expected from inherent variations in the analytical methods used [49]. Studies have shown that concentrations of ethanol and other drugs determined in heart blood are generally higher than in blood from a peripheral vein although in any individual case there are likely to be considerable variations [50–53].”

“The BAC necessary to cause death is often an open question and much depends on the person’s age, drinking experience and degree of tolerance development [78]. The speed of drinking plays a role in alcohol toxicity as does the kind of beverage consumed […] Drunkenness and hypothermia represent a dangerous combination and deaths tend to occur at a lower BAC when people are exposed to cold, such as, when an alcoholic sleeps outdoors in the winter months [78]. Drinking large amounts of alcohol to produce stupor and unconsciousness combined with positional asphyxia or inhalation of vomit are common causes of death in intoxicated individuals who die of suffocation [81–83]. The toxicity of ethanol is often considerably enhanced by the concomitant use of other drugs with their site of action in the brain, especially opiates, propoxyphene, antidepressants and some sedative hypnotics [84]. […] It seems reasonable to assume that the BAC at autopsy will almost always be lower than the maximum BAC reached during a drinking binge, owing to metabolism of ethanol taking place up until the moment of death [85–87]. During the time after discontinuation of drinking until death, the BAC might decrease appreciably depending on the speed of alcohol elimination from blood, which in heavy drinkers could exceed 20 or 30 mg/100 mL per h (0.02 or 0.03 g% per h) [88].”

“When the supply of oxygen to the body ends, the integrity of cell membranes and tissue compartments gradually disintegrate through the action of various digestive enzymes. This reflects the process of autolysis (self digestion) resulting in a softening and liquefaction of the tissue (freezing the body prevents autolysis). During this process, bacteria from the bowel invade the surrounding tissue and vascular system and the rate of infiltration depends on many factors including the ambient temperature, position of the body and whether death was caused by bacterial infection. Glucose concentrations increase in blood after death and this sugar is probably the simplest substrate for microbial synthesis of ethanol [20,68]. […] Extensive trauma to a body […] increases the potential for spread of bacteria and heightens the risk of ethanol production after death [217]. Blood-ethanol concentrations as high as 190 mg/100 mL have been reported in postmortem blood after particularly traumatic events such as explosions and when no evidence existed to support ingestion of ethanol before the disaster [218].”

v. Interventions based on the Theory of Mind cognitive model for autism spectrum disorder (ASD) (Cochrane review).

“The ‘Theory of Mind’ (ToM) model suggests that people with autism spectrum disorder (ASD) have a profound difficulty understanding the minds of other people – their emotions, feelings, beliefs, and thoughts. As an explanation for some of the characteristic social and communication behaviours of people with ASD, this model has had a significant influence on research and practice. It implies that successful interventions to teach ToM could, in turn, have far-reaching effects on behaviours and outcome.”

“Twenty-two randomised trials were included in the review (N = 695). Studies were highly variable in their country of origin, sample size, participant age, intervention delivery type, and outcome measures. Risk of bias was variable across categories. There were very few studies for which there was adequate blinding of participants and personnel, and some were also judged at high risk of bias in blinding of outcome assessors. There was also evidence of some bias in sequence generation and allocation concealment.”

“Studies were grouped into four main categories according to intervention target/primary outcome measure. These were: emotion recognition studies, joint attention and social communication studies, imitation studies, and studies teaching ToM itself. […] There was very low quality evidence of a positive effect on measures of communication based on individual results from three studies. There was low quality evidence from 11 studies reporting mixed results of interventions on measures of social interaction, very low quality evidence from four studies reporting mixed results on measures of general communication, and very low quality evidence from four studies reporting mixed results on measures of ToM ability. […] While there is some evidence that ToM, or a precursor skill, can be taught to people with ASD, there is little evidence of maintenance of that skill, generalisation to other settings, or developmental effects on related skills. Furthermore, inconsistency in findings and measurement means that evidence has been graded of ‘very low’ or ‘low’ quality and we cannot be confident that suggestions of positive effects will be sustained as high-quality evidence accumulates. Further longitudinal designs and larger samples are needed to help elucidate both the efficacy of ToM-linked interventions and the explanatory value of the ToM model itself.”

vi. Risk of Psychiatric and Neurodevelopmental Disorders Among Siblings of Probands With Autism Spectrum Disorders.

“The Finnish Prenatal Study of Autism and Autism Spectrum Disorders used a population-based cohort that included children born from January 1, 1987, to December 31, 2005, who received a diagnosis of ASD by December 31, 2007. Each case was individually matched to 4 control participants by sex and date and place of birth. […] Among the 3578 cases with ASD (2841 boys [79.4%]) and 11 775 controls (9345 boys [79.4%]), 1319 cases (36.9%) and 2052 controls (17.4%) had at least 1 sibling diagnosed with any psychiatric or neurodevelopmental disorder (adjusted RR, 2.5; 95% CI, 2.3-2.6).”

Conclusions and Relevance Psychiatric and neurodevelopmental disorders cluster among siblings of probands with ASD. For etiologic research, these findings provide further evidence that several psychiatric and neurodevelopmental disorders have common risk factors.”

vii. Treatment for epilepsy in pregnancy: neurodevelopmental outcomes in the child (Cochrane review).

“Accumulating evidence suggests an association between prenatal exposure to antiepileptic drugs (AEDs) and increased risk of both physical anomalies and neurodevelopmental impairment. Neurodevelopmental impairment is characterised by either a specific deficit or a constellation of deficits across cognitive, motor and social skills and can be transient or continuous into adulthood. It is of paramount importance that these potential risks are identified, minimised and communicated clearly to women with epilepsy.”

“Twenty-two prospective cohort studies were included and six registry based studies. Study quality varied. […] the IQ of children exposed to VPA [sodium valproate] (n = 112) was significantly lower than for those exposed to CBZ [carbamazepine] (n = 191) (MD [mean difference] 8.69, 95% CI 5.51 to 11.87, P < 0.00001). […] IQ was significantly lower for children exposed to VPA (n = 74) versus LTG [lamotrigine] (n = 84) (MD -10.80, 95% CI -14.42 to -7.17, P < 0.00001). DQ [developmental quotient] was higher in children exposed to PHT (n = 80) versus VPA (n = 108) (MD 7.04, 95% CI 0.44 to 13.65, P = 0.04). Similarly IQ was higher in children exposed to PHT (n = 45) versus VPA (n = 61) (MD 9.25, 95% CI 4.78 to 13.72, P < 0.0001). A dose effect for VPA was reported in six studies, with higher doses (800 to 1000 mg daily or above) associated with a poorer cognitive outcome in the child. We identified no convincing evidence of a dose effect for CBZ, PHT or LTG. Studies not included in the meta-analysis were reported narratively, the majority of which supported the findings of the meta-analyses.”

“The most important finding is the reduction in IQ in the VPA exposed group, which are sufficient to affect education and occupational outcomes in later life. However, for some women VPA is the most effective drug at controlling seizures. Informed treatment decisions require detailed counselling about these risks at treatment initiation and at pre-conceptual counselling. We have insufficient data about newer AEDs, some of which are commonly prescribed, and further research is required. Most women with epilepsy should continue their medication during pregnancy as uncontrolled seizures also carries a maternal risk.”

Do take note of the effect sizes reported here. To take an example, the difference between being treated with valproate and lamotrigine might equal 10 IQ points in the child – these are huge effects.

June 11, 2017 Posted by | Medicine, Neurology, Pharmacology, Psychiatry, Psychology, Studies | 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

A few papers

i. Quality of life of adolescents with autism spectrum disorders: comparison to adolescents with diabetes.

“The goals of our study were to clarify the consequences of autistic disorder without mental retardation on […] adolescents’ daily lives, and to consider them in comparison with the impact of a chronic somatic disease (diabetes) […] Scores for adolescents with ASD were significantly lower than those of the control and the diabetic adolescents, especially for friendships, leisure time, and affective and sexual relationships. On the other hand, better scores were obtained for the relationships with parents and teachers and for self-image. […] For subjects with autistic spectrum disorders and without mental retardation, impairment of quality of life is significant in adolescence and young adulthood. Such adolescents are dissatisfied with their relationships, although they often have real motivation to succeed with them.”

As someone who has both conditions, that paper was quite interesting. A follow-up question of some personal interest to me would of course be this: How do the scores/outcomes of these two groups compare to the scores of the people who have both conditions simultaneously? This question is likely almost impossible to answer in any confident manner, certainly if the conditions are not strongly dependent (unlikely), considering the power issues; global prevalence of autism is around 0.6% (link), and although type 1 prevalence is highly variable across countries, the variation just means that in some countries almost nobody gets it whereas in other countries it’s just rare; prevalence varies from 0.5 per 100.000 to 60 per 100.000 children aged 0-15 years. Assuming independence, if you look at combinations of the sort of conditions which affect one in a hundred people with those affecting one in a thousand, you’ll need on average in the order of 100.000 people to pick up just one individual with both of the conditions of interest. It’s bothersome to even try to find people like that, and good luck doing any sort of sensible statistics on that kind of sample. Of course type 1 diabetes prevalence increases with age in a way that autism does not because people continue to be diagnosed with it into late adulthood, whereas most autistics are diagnosed as children, so this makes the rarity of the condition less of a problem in adult samples, but if you’re looking at outcomes it’s arguable whether it makes sense to not differentiate between someone diagnosed with type 1 diabetes as a 35 year old and someone diagnosed as a 5 year old (are these really comparable diseases, and which outcomes are you interested in?). At least that is the case for developed societies where people with type 1 diabetes have high life expectancies; in less developed societies there may be stronger linkage between incidence and prevalence because of high mortality in the patient group (because people who get type 1 diabetes in such countries may not live very long because of inadequate medical care, which means there’s a smaller disconnect between how many new people get the disease during each time period and how many people in total have the disease than is the case for places where the mortality rates are lower). You always need to be careful about distinguishing between incidence and prevalence when dealing with conditions like T1DM with potential high mortality rates in settings where people have limited access to medical care because differential cross-country mortality patterns may be important.

ii. Exercise for depression (Cochrane review).

Background

Depression is a common and important cause of morbidity and mortality worldwide. Depression is commonly treated with antidepressants and/or psychological therapy, but some people may prefer alternative approaches such as exercise. There are a number of theoretical reasons why exercise may improve depression. This is an update of an earlier review first published in 2009.

Objectives

To determine the effectiveness of exercise in the treatment of depression in adults compared with no treatment or a comparator intervention. […]

Selection criteria 

Randomised controlled trials in which exercise (defined according to American College of Sports Medicine criteria) was compared to standard treatment, no treatment or a placebo treatment, pharmacological treatment, psychological treatment or other active treatment in adults (aged 18 and over) with depression, as defined by trial authors. We included cluster trials and those that randomised individuals. We excluded trials of postnatal depression.

Thirty-nine trials (2326 participants) fulfilled our inclusion criteria, of which 37 provided data for meta-analyses. There were multiple sources of bias in many of the trials; randomisation was adequately concealed in 14 studies, 15 used intention-to-treat analyses and 12 used blinded outcome assessors.For the 35 trials (1356 participants) comparing exercise with no treatment or a control intervention, the pooled SMD for the primary outcome of depression at the end of treatment was -0.62 (95% confidence interval (CI) -0.81 to -0.42), indicating a moderate clinical effect. There was moderate heterogeneity (I² = 63%).

When we included only the six trials (464 participants) with adequate allocation concealment, intention-to-treat analysis and blinded outcome assessment, the pooled SMD for this outcome was not statistically significant (-0.18, 95% CI -0.47 to 0.11). Pooled data from the eight trials (377 participants) providing long-term follow-up data on mood found a small effect in favour of exercise (SMD -0.33, 95% CI -0.63 to -0.03). […]

Authors’ conclusions

Exercise is moderately more effective than a control intervention for reducing symptoms of depression, but analysis of methodologically robust trials only shows a smaller effect in favour of exercise. When compared to psychological or pharmacological therapies, exercise appears to be no more effective, though this conclusion is based on a few small trials.”

iii. Risk factors for suicide in individuals with depression: A systematic review.

“The search strategy identified 3374 papers for potential inclusion. Of these, 155 were retrieved for a detailed evaluation. Thirty-two articles fulfilled the detailed eligibility criteria. […] Nineteen studies (28 publications) were included. Factors significantly associated with suicide were: male gender (OR = 1.76, 95% CI = 1.08–2.86), family history of psychiatric disorder (OR = 1.41, 95% CI= 1.00–1.97), previous attempted suicide (OR = 4.84, 95% CI = 3.26–7.20), more severe depression (OR = 2.20, 95% CI = 1.05–4.60), hopelessness (OR = 2.20, 95% CI = 1.49–3.23) and comorbid disorders, including anxiety (OR = 1.59, 95% CI = 1.03–2.45) and misuse of alcohol and drugs (OR = 2.17, 95% CI = 1.77–2.66).
Limitations: There were fewer studies than suspected. Interdependence between risk factors could not be examined.”

iv. Cognitive behaviour therapy for social anxiety in autism spectrum disorder: a systematic review.

“Individuals who have autism spectrum disorders (ASD) commonly experience anxiety about social interaction and social situations. Cognitive behaviour therapy (CBT) is a recommended treatment for social anxiety (SA) in the non-ASD population. Therapy typically comprises cognitive interventions, imagery-based work and for some individuals, behavioural interventions. Whether these are useful for the ASD population is unclear. Therefore, we undertook a systematic review to summarise research about CBT for SA in ASD.”

I mostly include this review here to highlight how reviews aren’t everything – I like them, but you can’t do reviews when a field hasn’t been studied. This is definitely the case here. The review was sort of funny, but also depressing. So much work for so little insight. Here’s the gist of it:

“Using a priori criteria, we searched for English-language peer-reviewed empirical studies in five databases. The search yielded 1364 results. Titles, abstracts and relevant publications were independently screened by two reviewers. Findings: Four single case studies met the review inclusion criteria; data were synthesised narratively. Participants (three adults and one child) were diagnosed with ASD and social anxiety disorder.”

You search the scientific literature systematically, you find more than a thousand results, and you carefully evaluate which ones of them should be included in this kind of study …and what you end up with is 4 individual case studies…

(I won’t go into the results of the study as they’re pretty much worthless.)

v. Immigrant Labor Market Integration across Admission Classes.

“We examine patterns of labor market integration across immigrant groups. The study draws on Norwegian longitudinal administrative data covering labor earnings and social insurance claims over a 25‐year period and presents a comprehensive picture of immigrant‐native employment and social insurance differentials by admission class and by years since entry.”

Some quotes from the paper:

“A recent study using 2011 administrative data from Sweden finds an average employment gap to natives of 30 percentage points for humanitarian migrants (refugees) and 26 percentage point for family immigrants (Luik et al., 2016).”

“A considerable fraction of the immigrants leaves the country after just a few years. […] this is particularly the case for immigrants from the old EU and for students and work-related immigrants from developing countries. For these groups, fewer than 50 percent remain in the country 5 years after entry. For refugees and family migrants, the picture is very different, and around 80 percent appear to have settled permanently in the country. Immigrants from the new EU have a settlement pattern somewhere in between, with approximately 70 percent settled on a permanent basis. An implication of such differential outmigration patterns is that the long-term labor market performance of refugees and family immigrants is of particular economic and fiscal importance. […] the varying rates of immigrant inflows and outflows by admission class, along with other demographic trends, have changed the composition of the adult (25‐66) population between 1990 and 2015. In this population segment, the overall immigrant share increased from 4.9 percent in 1990 to 18.7 percent in 2015 — an increase by a factor of 3.8 over 25 years. […] Following the 2004 EU enlargement, the fraction of immigrants in Norway has increased by a steady rate of approximately one percentage point per year.”

“The trends in population and employment shares varies considerably across admission classes, with employment shares of refugees and family immigrants lagging their growth in population shares. […] In 2014, refugees and family immigrants accounted for 12.8 percent of social insurance claims, compared to 5.7 percent of employment (and 7.7 percent of the adult population). In contrast, the two EU groups made up 9.3 percent of employment (and 8.8 percent of the adult population) but only 3.6 percent of social insurance claimants. Although these patterns do illuminate the immediate (short‐term) fiscal impacts of immigration at each particular point in time, they are heavily influenced by each year’s immigrant composition – in terms of age, years since migration, and admission classes – and therefore provide little information about long‐term consequences and impacts of fiscal sustainability. To assess the latter, we need to focus on longer‐term integration in the Norwegian labor market.”

Which they then proceed to do in the paper. From the results of those analyses:

“For immigrant men, the sample average share in employment (i.e., whose main source of income is work) ranges from 58 percent for refugees to 89 percent for EU immigrants, with family migrants somewhere between (around 80 percent). The average shares with social insurance as the main source of income ranges from only four percent for EU immigrants to as much as 38 percent for refugees. The corresponding shares for native men are 87 percent in employment and 12 percent with social insurance as their main income source. For women, the average shares in employment vary from 46 percent for refugees to 85 percent for new EU immigrants, whereas the average shares in social insurance vary from five percent for new EU immigrants to 42 percent for refugees. The corresponding rates for native women are 80 percent in employment and 17 percent with social insurance as their main source of income.”

“The profiles estimated for refugees are particularly striking. For men, we find that the native‐immigrant employment gap reaches its minimum value at 20 percentage points after five to six years of residence. The gap then starts to increase quite sharply again, and reaches 30 percentage points after 15 years. This development is mirrored by a corresponding increase in social insurance dependency. For female refugees, the employment differential reaches its minimum of 30 percentage points after 5‐9 years of residence. The subsequent decline is less dramatic than what we observe for men, but the differential stands at 35 percentage points 15 years after admission. […] The employment difference between refugees from Bosnia and Somalia is fully 22.2 percentage points for men and 37.7 points for women. […] For immigrants from the old EU, the employment differential is slightly in favor of immigrants regardless of years since migration, and the social insurance differentials remain consistently negative. In other words, employment of old EU immigrants is almost indistinguishable from that of natives, and they are less likely to claim social insurance benefits.”

vi. Glucose Peaks and the Risk of Dementia and 20-Year Cognitive Decline.

“Hemoglobin A1c (HbA1c), a measure of average blood glucose level, is associated with the risk of dementia and cognitive impairment. However, the role of glycemic variability or glucose excursions in this association is unclear. We examined the association of glucose peaks in midlife, as determined by the measurement of 1,5-anhydroglucitol (1,5-AG) level, with the risk of dementia and 20-year cognitive decline.”

“Nearly 13,000 participants from the Atherosclerosis Risk in Communities (ARIC) study were examined. […] Over a median time of 21 years, dementia developed in 1,105 participants. Among persons with diabetes, each 5 μg/mL decrease in 1,5-AG increased the estimated risk of dementia by 16% (hazard ratio 1.16, P = 0.032). For cognitive decline among participants with diabetes and HbA1c <7% (53 mmol/mol), those with glucose peaks had a 0.19 greater z score decline over 20 years (P = 0.162) compared with those without peaks. Among participants with diabetes and HbA1c ≥7% (53 mmol/mol), those with glucose peaks had a 0.38 greater z score decline compared with persons without glucose peaks (P < 0.001). We found no significant associations in persons without diabetes.

CONCLUSIONS Among participants with diabetes, glucose peaks are a risk factor for cognitive decline and dementia. Targeting glucose peaks, in addition to average glycemia, may be an important avenue for prevention.”

vii. Gaze direction detection in autism spectrum disorder.

“Detecting where our partners direct their gaze is an important aspect of social interaction. An atypical gaze processing has been reported in autism. However, it remains controversial whether children and adults with autism spectrum disorder interpret indirect gaze direction with typical accuracy. This study investigated whether the detection of gaze direction toward an object is less accurate in autism spectrum disorder. Individuals with autism spectrum disorder (n = 33) and intelligence quotients–matched and age-matched controls (n = 38) were asked to watch a series of synthetic faces looking at objects, and decide which of two objects was looked at. The angle formed by the two possible targets and the face varied following an adaptive procedure, in order to determine individual thresholds. We found that gaze direction detection was less accurate in autism spectrum disorder than in control participants. Our results suggest that the precision of gaze following may be one of the altered processes underlying social interaction difficulties in autism spectrum disorder.”

“Where people look at informs us about what they know, want, or attend to. Atypical or altered detection of gaze direction might thus lead to impoverished acquisition of social information and social interaction. Alternatively, it has been suggested that abnormal monitoring of inner states […], or the lack of social motivation […], would explain the reduced tendency to follow conspecific gaze in individuals with ASD. Either way, a lower tendency to look at the eyes and to follow the gaze would provide fewer opportunities to practice GDD [gaze direction detection – US] ability. Thus, impaired GDD might either play a causal role in atypical social interaction, or conversely be a consequence of it. Exploring GDD earlier in development might help disentangle this issue.”

June 1, 2017 Posted by | Diabetes, Economics, Epidemiology, Medicine, Neurology, Psychiatry, Psychology, Studies | Leave a comment

A few diabetes papers of interest

i. Association Between Blood Pressure and Adverse Renal Events in Type 1 Diabetes.

“The Joint National Committee and American Diabetes Association guidelines currently recommend a blood pressure (BP) target of <140/90 mmHg for all adults with diabetes, regardless of type (13). However, evidence used to support this recommendation is primarily based on data from trials of type 2 diabetes (46). The relationship between BP and adverse outcomes in type 1 and type 2 diabetes may differ, given that the type 1 diabetes population is typically much younger at disease onset, hypertension is less frequently present at diagnosis (3), and the basis for the pathophysiology and disease complications may differ between the two populations.

Prior prospective cohort studies (7,8) of patients with type 1 diabetes suggested that lower BP levels (<110–120/70–80 mmHg) at baseline entry were associated with a lower risk of adverse renal outcomes, including incident microalbuminuria. In one trial of antihypertensive treatment in type 1 diabetes (9), assignment to a lower mean arterial pressure (MAP) target of <92 mmHg (corresponding to ∼125/75 mmHg) led to a significant reduction in proteinuria compared with a MAP target of 100–107 mmHg (corresponding to ∼130–140/85–90 mmHg). Thus, it is possible that lower BP (<120/80 mmHg) reduces the risk of important renal outcomes, such as proteinuria, in patients with type 1 diabetes and may provide a synergistic benefit with intensive glycemic control on renal outcomes (1012). However, fewer studies have examined the association between BP levels over time and the risk of more advanced renal outcomes, such as stage III chronic kidney disease (CKD) or end-stage renal disease (ESRD)”.

“The primary objective of this study was to determine whether there is an association between lower BP levels and the risk of more advanced diabetic nephropathy, defined as macroalbuminuria or stage III CKD, within a background of different glycemic control strategies […] We included 1,441 participants with type 1 diabetes between the ages of 13 and 39 years who had previously been randomized to receive intensive versus conventional glycemic control in the Diabetes Control and Complications Trial (DCCT). The exposures of interest were time-updated systolic BP (SBP) and diastolic BP (DBP) categories. Outcomes included macroalbuminuria (>300 mg/24 h) or stage III chronic kidney disease (CKD) […] During a median follow-up time of 24 years, there were 84 cases of stage III CKD and 169 cases of macroalbuminuria. In adjusted models, SBP in the 2 (95% CI 1.05–1.21), and a 1.04 times higher risk of ESRD (95% CI 0.77–1.41) in adjusted Cox models. Every 10 mmHg increase in DBP was associated with a 1.17 times higher risk of microalbuminuria (95% CI 1.03–1.32), a 1.15 times higher risk of eGFR decline to 2 (95% CI 1.04–1.29), and a 0.80 times higher risk of ESRD (95% CI 0.47–1.38) in adjusted models. […] Because these data are observational, they cannot prove causation. It remains possible that subtle kidney disease may lead to early elevations in BP, and we cannot rule out the potential for reverse causation in our findings. However, we note similar trends in our data even when imposing a 7-year lag between BP and CKD ascertainment.”

CONCLUSIONS A lower BP (<120/70 mmHg) was associated with a substantially lower risk of adverse renal outcomes, regardless of the prior assigned glycemic control strategy. Interventional trials may be useful to help determine whether the currently recommended BP target of 140/90 mmHg may be too high for optimal renal protection in type 1 diabetes.”

It’s important to keep in mind when interpreting these results that endpoints like ESRD and stage III CKD are not the only relevant outcomes in this setting; even mild-stage kidney disease in diabetics significantly increase the risk of death from cardiovascular disease, and a substantial proportion of patients may die from cardiovascular disease before reaching a late-stage kidney disease endpoint (here’s a relevant link).

Identifying Causes for Excess Mortality in Patients With Diabetes: Closer but Not There Yet.

“A number of epidemiological studies have quantified the risk of death among patients with diabetes and assessed the causes of death (26), with highly varying results […] Overall, the studies to date have confirmed that diabetes is associated with an increased risk of all-cause mortality, but the magnitude of this excess risk is highly variable, with the relative risk ranging from 1.15 to 3.15. Nevertheless, all studies agree that mortality is mainly attributable to cardiovascular causes (26). On the other hand, studies of cancer-related death have generally been lacking despite the diabetes–cancer association and a number of plausible biological mechanisms identified to explain this link (8,9). In fact, studies assessing the specific causes of noncardiovascular death in diabetes have been sparse. […] In this issue of Diabetes Care, Baena-Díez et al. (10) report on an observational study of the association between diabetes and cause-specific death. This study involved 55,292 individuals from 12 Spanish population cohorts with no prior history of cardiovascular disease, aged 35 to 79 years, with a 10-year follow-up. […] This study found that individuals with diabetes compared with those without diabetes had a higher risk of cardiovascular death, cancer death, and noncardiovascular noncancer death with similar estimates obtained using the two statistical approaches. […] Baena-Díez et al. (10) showed that individuals with diabetes have an approximately threefold increased risk of cardiovascular mortality, which is much higher than what has been reported by recent studies (5,6). While this may be due to the lack of adjustment for important confounders in this study, there remains uncertainty regarding the magnitude of this increase.”

“[A]ll studies of excess mortality associated with diabetes, including the current one, have produced highly variable results. The reasons may be methodological. For instance, it may be that because of the wide range of age in these studies, comparing the rates of death between the patients with diabetes and those without diabetes using a measure based on the ratio of the rates may be misleading because the ratio can vary by age [it almost certainly does vary by age, US]. Instead, a measure based on the difference in rates may be more appropriate (16). Another issue relates to the fact that the studies include patients with longstanding diabetes of variable duration, resulting in so-called prevalent cohorts that can result in muddled mortality estimates since these are necessarily based on a mix of patients at different stages of disease (17). Thus, a paradigm change may be in order for future observational studies of diabetes and mortality, in the way they are both designed and analyzed. With respect to cancer, such studies will also need to tease out the independent contribution of antidiabetes treatments on cancer incidence and mortality (1820). It is thus clear that the quantification of the excess mortality associated with diabetes per se will need more accurate tools.”

iii. Risk of Cause-Specific Death in Individuals With Diabetes: A Competing Risks Analysis. This is the paper some of the results of which were discussed above. I’ll just include the highlights here:

RESULTS We included 55,292 individuals (15.6% with diabetes and overall mortality of 9.1%). The adjusted hazard ratios showed that diabetes increased mortality risk: 1) cardiovascular death, CSH = 2.03 (95% CI 1.63–2.52) and PSH = 1.99 (1.60–2.49) in men; and CSH = 2.28 (1.75–2.97) and PSH = 2.23 (1.70–2.91) in women; 2) cancer death, CSH = 1.37 (1.13–1.67) and PSH = 1.35 (1.10–1.65) in men; and CSH = 1.68 (1.29–2.20) and PSH = 1.66 (1.25–2.19) in women; and 3) noncardiovascular noncancer death, CSH = 1.53 (1.23–1.91) and PSH = 1.50 (1.20–1.89) in men; and CSH = 1.89 (1.43–2.48) and PSH = 1.84 (1.39–2.45) in women. In all instances, the cumulative mortality function was significantly higher in individuals with diabetes.

CONCLUSIONS Diabetes is associated with premature death from cardiovascular disease, cancer, and noncardiovascular noncancer causes.”

“Summary

Diabetes is associated with premature death from cardiovascular diseases (coronary heart disease, stroke, and heart failure), several cancers (liver, colorectal, and lung), and other diseases (chronic obstructive pulmonary disease and liver and kidney disease). In addition, the cause-specific cumulative mortality for cardiovascular, cancer, and noncardiovascular noncancer causes was significantly higher in individuals with diabetes, compared with the general population. The dual analysis with CSH and PSH methods provides a comprehensive view of mortality dynamics in the population with diabetes. This approach identifies the individuals with diabetes as a vulnerable population for several causes of death aside from the traditionally reported cardiovascular death.”

iv. Disability-Free Life-Years Lost Among Adults Aged ≥50 Years With and Without Diabetes.

RESEARCH DESIGN AND METHODS Adults (n = 20,008) aged 50 years and older were followed from 1998 to 2012 in the Health and Retirement Study, a prospective biannual survey of a nationally representative sample of adults. Diabetes and disability status (defined by mobility loss, difficulty with instrumental activities of daily living [IADL], and/or difficulty with activities of daily living [ADL]) were self-reported. We estimated incidence of disability, remission to nondisability, and mortality. We developed a discrete-time Markov simulation model with a 1-year transition cycle to predict and compare lifetime disability-related outcomes between people with and without diabetes. Data represent the U.S. population in 1998.

RESULTS From age 50 years, adults with diabetes died 4.6 years earlier, developed disability 6–7 years earlier, and spent about 1–2 more years in a disabled state than adults without diabetes. With increasing baseline age, diabetes was associated with significant (P < 0.05) reductions in the number of total and disability-free life-years, but the absolute difference in years between those with and without diabetes was less than at younger baseline age. Men with diabetes spent about twice as many of their remaining years disabled (20–24% of remaining life across the three disability definitions) as men without diabetes (12–16% of remaining life across the three disability definitions). Similar associations between diabetes status and disability-free and disabled years were observed among women.

CONCLUSIONS Diabetes is associated with a substantial reduction in nondisabled years, to a greater extent than the reduction of longevity. […] Using a large, nationally representative cohort of Americans aged 50 years and older, we found that diabetes is associated with a substantial deterioration of nondisabled years and that this is a greater number of years than the loss of longevity associated with diabetes. On average, a middle-aged adult with diabetes has an onset of disability 6–7 years earlier than one without diabetes, spends 1–2 more years with disability, and loses 7 years of disability-free life to the condition. Although other nationally representative studies have reported large reductions in complications (9) and mortality among the population with diabetes in recent decades (1), these studies, akin to our results, suggest that diabetes continues to have a substantial impact on morbidity and quality of remaining years of life.”

v. Association Between Use of Lipid-Lowering Therapy and Cardiovascular Diseases and Death in Individuals With Type 1 Diabetes.

“People with type 1 diabetes have a documented shorter life expectancy than the general population without diabetes (1). Cardiovascular disease (CVD) is the main cause of the excess morbidity and mortality, and despite advances in management and therapy, individuals with type 1 diabetes have a markedly elevated risk of cardiovascular events and death compared with the general population (2).

Lipid-lowering treatment with hydroxymethylglutaryl-CoA reductase inhibitors (statins) prevents major cardiovascular events and death in a broad spectrum of patients (3,4). […] We hypothesized that primary prevention with lipid-lowering therapy (LLT) can reduce the incidence of cardiovascular morbidity and mortality in individuals with type 1 diabetes. The aim of the study was to examine this in a nationwide longitudinal cohort study of patients with no history of CVD. […] A total of 24,230 individuals included in 2006–2008 NDR with type 1 diabetes without a history of CVD were followed until 31 December 2012; 18,843 were untreated and 5,387 treated with LLT [Lipid-Lowering Therapy] (97% statins). The mean follow-up was 6.0 years. […] Hazard ratios (HRs) for treated versus untreated were as follows: cardiovascular death 0.60 (95% CI 0.50–0.72), all-cause death 0.56 (0.48–0.64), fatal/nonfatal stroke 0.56 (0.46–0.70), fatal/nonfatal acute myocardial infarction 0.78 (0.66–0.92), fatal/nonfatal coronary heart disease 0.85 (0.74–0.97), and fatal/nonfatal CVD 0.77 (0.69–0.87).

CONCLUSIONS This observational study shows that LLT is associated with 22–44% reduction in the risk of CVD and cardiovascular death among individuals with type 1 diabetes without history of CVD and underlines the importance of primary prevention with LLT to reduce cardiovascular risk in type 1 diabetes.”

vi. Prognostic Classification Factors Associated With Development of Multiple Autoantibodies, Dysglycemia, and Type 1 Diabetes—A Recursive Partitioning Analysis.

“In many prognostic factor studies, multivariate analyses using the Cox proportional hazards model are applied to identify independent prognostic factors. However, the coefficient estimates derived from the Cox proportional hazards model may be biased as a result of violating assumptions of independence. […] RPA [Recursive Partitioning Analysis] classification is a useful tool that could prioritize the prognostic factors and divide the subjects into distinctive groups. RPA has an advantage over the proportional hazards model in identifying prognostic factors because it does not require risk factor independence and, as a nonparametric technique, makes no requirement on the underlying distributions of the variables considered. Hence, it relies on fewer modeling assumptions. Also, because the method is designed to divide subjects into groups based on the length of survival, it defines groupings for risk classification, whereas Cox regression models do not. Moreover, there is no need to explicitly include covariate interactions because of the recursive splitting structure of tree model construction.”

“This is the first study that characterizes the risk factors associated with the transition from one preclinical stage to the next following a recommended staging classification system (9). The tree-structured prediction model reveals that the risk parameters are not the same across each transition. […] Based on the RPA classification, the subjects at younger age and with higher GAD65Ab [an important biomarker in the context of autoimmune forms of diabetes, US – here’s a relevant link] titer are at higher risk for progression to multiple positive autoantibodies from a single autoantibody (seroconversion). Approximately 70% of subjects with a single autoantibody were positive for GAD65Ab, much higher than for insulin autoantibody (24%) and IA-2A [here’s a relevant link – US] (5%). Our study results are consistent with those of others (2224) in that seroconversion is age related. Previous studies in infants and children at an early age have shown that progression from single to two or more autoantibodies occurs more commonly in children 25). The subjects ≤16 years of age had almost triple the 5-year risk compared with subjects >16 years of age at the same GAD65Ab titer level. Hence, not all individuals with a single islet autoantibody can be thought of as being at low risk for disease progression.”

“This is the first study that identifies the risk factors associated with the timing of transitions from one preclinical stage to the next in the development of T1D. Based on RPA risk parameters, we identify the characteristics of groups with similar 5-year risks for advancing to the next preclinical stage. It is clear that individuals with one or more autoantibodies or with dysglycemia are not homogeneous with regard to the risk of disease progression. Also, there are differences in risk factors at each stage that are associated with increased risk of progression. The potential benefit of identifying these groups allows for a more informed discussion of diabetes risk and the selective enrollment of individuals into clinical trials whose risk more appropriately matches the potential benefit of an experimental intervention. Since the risk levels in these groups are substantial, their definition makes possible the design of more efficient trials with target sample sizes that are feasible, opening up the field of prevention to additional at-risk cohorts. […] Our results support the evidence that autoantibody titers are strong predictors at each transition leading to T1D development. The risk of the development of multiple autoantibodies was significantly increased when the GAD65Ab titer level was elevated, and the risk of the development of dysglycemia was increased when the IA-2A titer level increased. These indicate that better risk prediction on the timing of transitions can be obtained by evaluating autoantibody titers. The results also suggest that an autoantibody titer should be carefully considered in planning prevention trials for T1D in addition to the number of positive autoantibodies and the type of autoantibody.”

May 17, 2017 Posted by | Diabetes, Immunology, Medicine, Nephrology, Statistics, Studies | Leave a comment

A few diabetes papers of interest

A couple of weeks ago I decided to cover some of the diabetes articles I’d looked at and bookmarked in the past, but there were a lot of articles and I did not get very far. This post will cover some more of these articles I had failed to cover here despite intending to do so at some point. Considering that I these days relatively regularly peruse e.g. the Diabetes Care archives I am thinking of making this sort of post a semi-regular feature of the blog.

i. Association Between Diabetes and Hippocampal Atrophy in Elderly Japanese: The Hisayama Study.

“A total of 1,238 community-dwelling Japanese subjects aged ≥65 years underwent brain MRI scans and a comprehensive health examination in 2012. Total brain volume (TBV), intracranial volume (ICV), and hippocampal volume (HV) were measured using MRI scans for each subject. We examined the associations between diabetes-related parameters and the ratios of TBV to ICV (an indicator of global brain atrophy), HV to ICV (an indicator of hippocampal atrophy), and HV to TBV (an indicator of hippocampal atrophy beyond global brain atrophy) after adjustment for other potential confounders.”

“The multivariable-adjusted mean values of the TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios were significantly lower in the subjects with diabetes compared with those without diabetes (77.6% vs. 78.2% for the TBV-to-ICV ratio, 0.513% vs. 0.529% for the HV-to-ICV ratio, and 0.660% vs. 0.676% for the HV-to-TBV ratio; all P < 0.01). These three ratios decreased significantly with elevated 2-h postload glucose (PG) levels […] Longer duration of diabetes was significantly associated with lower TBV-to-ICV, HV-to-ICV, and HV-to-TBV ratios. […] Our data suggest that a longer duration of diabetes and elevated 2-h PG levels, a marker of postprandial hyperglycemia, are risk factors for brain atrophy, particularly hippocampal atrophy.”

“Intriguingly, our findings showed that the subjects with diabetes had significantly lower mean HV-to-TBV ratio values, indicating […] that the hippocampus is predominantly affected by diabetes. In addition, in our subjects a longer duration and a midlife onset of diabetes were significantly associated with a lower HV, possibly suggesting that a long exposure of diabetes particularly worsens hippocampal atrophy.”

The reason why hippocampal atrophy is a variable of interest to these researchers is that hippocampal atrophy is a feature of Alzheimer’s Disease, and diabetics have an elevated risk of AD. This is incidentally far from the first study providing some evidence for the existence of potential causal linkage between impaired glucose homeostasis and AD (see e.g. also this paper, which I’ve previously covered here on the blog).

ii. A Population-Based Study of All-Cause Mortality and Cardiovascular Disease in Association With Prior History of Hypoglycemia Among Patients With Type 1 Diabetes.

“Although patients with T1DM may suffer more frequently from hypoglycemia than those with T2DM (8), very few studies have investigated whether hypoglycemia may also increase the risk of CVD (6,9,10) or death (1,6,7) in patients with T1DM; moreover, the results of these studies have been inconclusive (6,9,10) because of the dissimilarities in their methodological aspects, including their enrollment of populations with T1DM with different levels of glycemic control, application of different data collection methods, and adoption of different lengths of observational periods.”

“Only a few population-based studies have examined the potential cumulative effect of repeated severe hypoglycemia on all-cause mortality or CVD incidence in T1DM (9). The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study of T2DM found a weakly inverse association between the annualized number of hypoglycemic episodes and the risk of death (11,12). By contrast, some studies find that repeated hypoglycemia may be an aggravating factor to atherosclerosis in T1DM (13,14). Studies on the compromised sympathetic-adrenal reaction in patients with repeated hypoglycemia have been inconclusive regarding whether such a reaction may further damage intravascular coagulation and thrombosis (15) or decrease the vulnerability of these patients to adverse health outcomes (12).

Apart from the lack of information on the potential dose–gradient effect associated with severe hypoglycemic events in T1DM from population-based studies, the risks of all-cause mortality/CVD incidence associated with severe hypoglycemia occurring at different periods before all-cause mortality/CVD incidence have never been examined. In this study, we used the population-based medical claims of a cohort of patients with T1DM to examine whether the risks of all-cause mortality/CVD incidence are associated with previous episodes of severe hypoglycemia in different periods and whether severe hypoglycemia may pose a dose–gradient effect on the risks of all-cause mortality/CVD incidence.”

“Two nested case-control studies with age- and sex-matched control subjects and using the time-density sampling method were performed separately within a cohort of 10,411 patients with T1DM in Taiwan. The study enrolled 564 nonsurvivors and 1,615 control subjects as well as 743 CVD case subjects and 1,439 control subjects between 1997 and 2011. History of severe hypoglycemia was identified during 1 year, 1–3 years, and 3–5 years before the occurrence of the study outcomes.”

“Prior severe hypoglycemic events within 1 year were associated with higher risks of all-cause mortality and CVD (adjusted OR 2.74 [95% CI 1.96–3.85] and 2.02 [1.35–3.01], respectively). Events occurring within 1–3 years and 3–5 years before death were also associated with adjusted ORs of 1.94 (95% CI 1.39–2.71) and 1.68 (1.15–2.44), respectively. Significant dose–gradient effects of severe hypoglycemia frequency on mortality and CVD were observed within 5 years. […] we found that a greater frequency of severe hypoglycemia occurring 1 year before death was significantly associated with a higher OR of all-cause mortality (1 vs. 0: 2.45 [95% CI 1.65–3.63]; ≥2 vs. 0: 3.49 [2.01–6.08], P < 0.001 for trend). Although the strength of the association was attenuated, a significant dose–gradient effect still existed for severe hypoglycemia occurring in 1–3 years (P < 0.001 for trend) and 3–5 years (P < 0.015 for trend) before death. […] Exposure to repeated severe hypoglycemic events can lead to higher risks of mortality and CVD.”

“Our findings are supported by two previous studies that investigated atherosclerosis risk in T1DM (13,14). The DCCT/EDIC project reported that the prevalence of coronary artery calcification, an established atherosclerosis marker, was linearly correlated with the incidence rate of hypoglycemia on the DCCT stage (14). Giménez et al. (13) also demonstrated that repeated episodes of hypoglycemia were an aggravating factor for preclinical atherosclerosis in T1DM. […] The mechanism of hypoglycemia that predisposes to all-cause mortality/CVD incidence remains unclear.”

iii. Global Estimates on the Number of People Blind or Visually Impaired by Diabetic Retinopathy: A Meta-analysis From 1990 to 2010.

“On the basis of previous large-scale population-based studies and meta-analyses, diabetic retinopathy (DR) has been recognized as one of the most common and important causes for visual impairment and blindness (1–19). These studies in general showed that DR was the leading cause of blindness globally among working-aged adults and therefore has a significant socioeconomic impact (20–22).”

“A previous meta-analysis (21) summarizing 35 studies with more than 20,000 patients with diabetes estimated a prevalence of any DR of 34.6%, of diabetic macular edema of 6.8%, and of vision-threating DR of 10.2% within the diabetes population. […] Yau et al. (21) estimated that ∼93 million people had some DR and 28 million people had sight-threatening stages of DR. However, this meta-analysis did not address the prevalence of visual impairment and blindness due to DR and thus the impact of DR on the general population. […] We therefore conducted the present meta-analysis of all available population-based studies performed worldwide within the last two decades as part of the Global Burden of Disease Study 2010 (GBD) to estimate the number of people affected by blindness and visual impairment.”

“DR [Diabetic Retinopathy] ranks as the fifth most common cause of global blindness and of global MSVI [moderate and severe vision impairment] (25). […] this analysis estimates that, in 2010, 1 out of every 39 blind people had blindness due to DR and 1 out of every 52 people had visual impairment due to DR. […] Globally in 2010, out of overall 32.4 million blind and 191 million visually impaired people, 0.8 million were blind and 3.7 million were visually impaired because of DR, with an alarming increase of 27% and 64%, respectively, spanning the two decades from 1990 to 2010. DR accounted for 2.6% of all blindness in 2010 and 1.9% of all MSVI worldwide, increasing from 2.1% and 1.3%, respectively, in 1990. […] The number of persons with visual impairment due to DR worldwide is rising and represents an increasing proportion of all blindness/MSVI causes. Age-standardized prevalence of DR-related blindness/MSVI was higher in sub-Saharan Africa and South Asia.”

“Our data suggest that the percentage of blindness and MSVI attributable to DR was lower in low-income regions with younger populations than in high-income regions with older populations. There are several reasons that may explain this observation. First, low-income societies may have a higher percentage of unoperated cataract or undercorrected refractive error–related blindness and MSVI (25), which is probably related to access to visual and ocular health services. Therefore, the proportional increase in blindness and MSVI attributable to DR may be rising because of the decreasing proportion attributable to cataract (25) as a result of the increasing availability of cataract surgery in many parts of the world (29) during the past decade. Improved visualization of the fundus afforded by cataract surgery should also improve the detection of DR. The increase in the percentage of global blindness caused by DR within the last two decades took place in all world regions except Western Europe and high-income North America where there was a slight decrease. This decrease may reflect the effect of intensified prevention and treatment of DR possibly in part due to the introduction of intravitreal injections of steroids and anti-VEGF (vascular endothelial growth factor) drugs (30,31).

Second, in regions with poor medical infrastructure, patients with diabetes may not live long enough to experience DR (32). This reduces the number of patients with diabetes, and, furthermore, it reduces the number of patients with DR-related vision loss. Studies in the literature have reported that the prevalence of severe DR decreased from 1990 to 2010 (21) while the prevalence of diabetes simultaneously increased (27), which implies a reduction in the prevalence of severe DR per person with diabetes. […] Third, […] younger populations may have a lower prevalence of diabetes (33). […] Therefore, considering further economic development in rural regions, improvements in medical infrastructure, the general global demographic transition to elderly populations, and the association between increasing economic development and obesity, we project the increase in the proportion of DR-related blindness and MSVI to continue to rise in the future.”

iv. Do Patient Characteristics Impact Decisions by Clinicians on Hemoglobin A1c Targets?

“In setting hemoglobin A1c (HbA1c) targets, physicians must consider individualized risks and benefits of tight glycemic control (1,2) by recognizing that the risk-benefit ratio may become unfavorable in certain patients, including the elderly and/or those with multiple comorbidities (3,4). Customization of treatment goals based on patient characteristics is poorly understood, partly due to insufficient data on physicians’ decisions in setting targets. We used the National Health and Nutrition Examination Survey (NHANES) to analyze patient-reported HbA1c targets set by physicians and to test whether targets are correlated with patient characteristics.”

“we did not find any evidence that U.S. physicians systematically consider important patient-specific information when selecting the intensity of glycemic control. […] the lack of variation with patient characteristics suggests overreliance on a general approach, without consideration of individual variation in the risks and benefits (or patient preference) of tight control.”

v. Cardiovascular Autonomic Neuropathy, Sexual Dysfunction, and Urinary Incontinence in Women With Type 1 Diabetes.

“This study evaluated associations among cardiovascular autonomic neuropathy (CAN), female sexual dysfunction (FSD), and urinary incontinence (UI) in women with type I diabetes mellitus (T1DM). […] We studied 580 women with T1DM in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study (DCCT/EDIC).”

“At EDIC year 17, FSD was observed in 41% of women and UI in 30%. […] We found that CAN was significantly more prevalent among women with FSD and/or UI, because 41% of women with FSD and 44% with UI had positive measures of CAN compared with 30% without FSD and 38% without UI at EDIC year 16/17. We also observed bivariate associations between FSD and several measures of CAN […] In long-standing T1DM, CAN may predict development of FSD and may be a useful surrogate for generalized diabetic autonomic neuropathy.”

“Although autonomic dysfunction has been considered an important factor in the etiology of many diabetic complications, including constipation, exercise intolerance, bladder dysfunction, erectile dysfunction, orthostatic hypotension, and impaired neurovascular function, our study is among the first to systematically demonstrate a link between CAN and FSD in a large cohort of well-characterized patients with T1DM (14).”

vi. Correlates of Medication Adherence in the TODAY Cohort of Youth With Type 2 Diabetes.

“A total of 699 youth 10–17 years old with recent-onset type 2 diabetes and ≥80% adherence to metformin therapy for ≥8 weeks during a run-in period were randomized to receive one of three treatments. Participants took two study pills twice daily. Adherence was calculated by pill count from blister packs returned at visits. High adherence was defined as taking ≥80% of medication; low adherence was defined as taking <80% of medication.”

“In this low socioeconomic cohort, high and low adherence did not differ by sex, age, family income, parental education, or treatment group. Adherence declined over time (72% high adherence at 2 months, 56% adherence at 48 months, P < 0.0001). A greater percentage of participants with low adherence had clinically significant depressive symptoms at baseline (18% vs. 12%, P = 0.0415). No adherence threshold predicted the loss of glycemic control. […] Most pediatric type 1 diabetes studies (5–7) consistently document a correlation between adherence and race, ethnicity, and socioeconomic status, and studies of adults with type 2 diabetes (8,9) have documented that depressed patients are less adherent to their diabetes regimen. There is a dearth of information in the literature regarding adherence to medication in pediatric patients with type 2 diabetes.”

“In the cohort, the presence of baseline clinically significant depressive symptoms was associated with subsequent lower adherence. […] The TODAY cohort demonstrated deterioration in study medication adherence over time, irrespective of treatment group assignment. […] Contrary to expectation, demographic factors (sex, race-ethnicity, household income, and parental educational level) did not predict medication adherence. The lack of correlation with these factors in the TODAY trial may be explained by the limited income and educational range of the families in the TODAY trial. Nearly half of the families in the TODAY trial had an annual income of <$25,000, and, for over half of the families, the highest level of parental education was a high school degree or lower. In addition, our run-in criteria selected for more adherent subjects. All subjects had to have >80% adherence to M therapy for ≥8 weeks before they could be randomized. This may have limited variability in medication adherence postrandomization. It is also possible that selecting for more adherent subjects in the run-in period also selected for subjects with a lower frequency of depressive symptoms.”

“In the TODAY trial, baseline clinically significant depressive symptoms were more prevalent in the lower-adherence group, suggesting that regular screening for depressive symptoms should be undertaken to identify youth who were at high risk for poor medication adherence. […] Studies in adults with type 2 diabetes (2328) consistently report that depressed patients are less adherent to their diabetes regimen and experience more physical complications of diabetes. Identifying youth who are at risk for poor medication adherence early in the course of disease would make it possible to provide support and, if needed, specific treatment. Although we were not able to determine whether the treatment of depressive symptoms changed adherence over time, our findings support the current guidelines for psychosocial screening in youth with diabetes (29,30).”

vii. Increased Risk of Incident Chronic Kidney Disease, Cardiovascular Disease, and Mortality in Patients With Diabetes With Comorbid Depression.

Another depression-related paper, telling another part of the story. If depressed diabetics are less compliant/adherent, which seems – as per the above study – to be the case both in the context of the adult and pediatric patient population, then you might also expect this reduced compliance/adherence to ‘translate’ into this group having poorer metabolic control, and thus be at higher risk of developing microvascular complications such as nephropathy. This seems to be what we observe, at least according to the findings of this study:

“It is not known if patients with diabetes with depression have an increased risk of chronic kidney disease (CKD). We examined the association between depression and incident CKD, mortality, and incident cardiovascular events in U.S. veterans with diabetes.”

“Among a nationally representative prospective cohort of >3 million U.S. veterans with baseline estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2, we identified 933,211 patients with diabetes. Diabetes was ascertained by an ICD-9-CM code for diabetes, an HbA1c >6.4%, or receiving antidiabetes medication during the inclusion period. Depression was defined by an ICD-9-CM code for depression or by antidepressant use during the inclusion period. Incident CKD was defined as two eGFR levels 2 separated by ≥90 days and a >25% decline in baseline eGFR.”

“Depression was associated with 20% higher risk of incident CKD (adjusted hazard ratio [aHR] and 95% CI: 1.20 [1.19–1.21]). Similarly, depression was associated with increased all-cause mortality (aHR and 95% CI: 1.25 [1.24–1.26]). […] The presence of depression in patients with diabetes is associated with higher risk of developing CKD compared with nondepressed patients.”

It’s important to remember that the higher reported eGFRs in the depressed patient group may not be important/significant, and they should not be taken as an indication of relatively better kidney function in this patient population – especially in the type 2 context, the relationship between eGFR and kidney function is complicated. I refer to Bakris et al.‘s text on these topics for details (blog coverage here).

May 6, 2017 Posted by | Cardiology, Diabetes, Medicine, Nephrology, Neurology, Psychology, Studies | Leave a comment

Random stuff

i. Effects of Academic Acceleration on the Social-Emotional Status of Gifted Students.

I’ve never really thought about myself as ‘gifted’, but during a conversation with a friend not too long ago I was reminded that my parents discussed with my teachers at one point early on if it would be better for me to skip a grade or not. This was probably in the third grade or so. I was asked, and I seem to remember not wanting to – during my conversation with the friend I brought up some reasons I had (…may have had?) for not wanting to, but I’m not sure if I remember the context correctly and so perhaps it’s better to just say that I can’t recall precisely why I was against this idea, but that I was. Neither of my parents were all that keen on the idea anyway. Incidentally the question of grade-skipping was asked in a Mensa survey answered by a sizeable proportion of all Danish members last year; I’m not allowed to cover that data here (or I would have already), but I don’t think I’ll get in trouble by saying that grade-skipping was quite rare even in this group of people – this surprised me a bit.

Anyway, a snippet from the article:

“There are widespread myths about the psychological vulnerability of gifted students and therefore fears that acceleration will lead to an increase in disturbances such as anxiety, depression, delinquent behavior, and lowered self-esteem. In fact, a comprehensive survey of the research on this topic finds no evidence that gifted students are any more psychologically vulnerable than other students, although boredom, underachievement, perfectionism, and succumbing to the effects of peer pressure are predictable when needs for academic advancement and compatible peers are unmet (Neihart, Reis, Robinson, & Moon, 2002). Questions remain, however, as to whether acceleration may place some students more at risk than others.”

Note incidentally that relative age effects (how is the grade/other academic outcomes of individual i impacted by the age difference between individual i and his/her classmates) vary across countries, but are usually not insignificant; most places you look the older students in the classroom do better than their younger classmates, all else equal. It’s worth having both such effects as well as the cross-country heterogeneities (and the mechanisms behind them) in mind when considering the potential impact of acceleration on academic performance – given differences across countries there’s no good reason why ‘acceleration effects’ should be homogenous across countries either. Relative age effects are sizeable in most countries – see e.g. this. I read a very nice study a while back investigating the impact of relative age on tracking options of German students and later life outcomes (the effects were quite large), but I’m too lazy to go look for it now – I may add it to this post later (but I probably won’t).

ii. Publishers withdraw more than 120 gibberish papers. (…still a lot of papers to go – do remember that at this point it’s only a small minority of all published gibberish papers which are computer-generated…)

iii. Parental Binge Alcohol Abuse Alters F1 Generation Hypothalamic Gene Expression in the Absence of Direct Fetal Alcohol Exposure.

Nope, this is not another article about how drinking during pregnancy is bad for the fetus (for stuff on that, see instead e.g. this post – link i.); this one is about how alcohol exposure before conception may harm the child:

“It has been well documented that maternal alcohol exposure during fetal development can have devastating neurological consequences. However, less is known about the consequences of maternal and/or paternal alcohol exposure outside of the gestational time frame. Here, we exposed adolescent male and female rats to a repeated binge EtOH exposure paradigm and then mated them in adulthood. Hypothalamic samples were taken from the offspring of these animals at postnatal day (PND) 7 and subjected to a genome-wide microarray analysis followed by qRT-PCR for selected genes. Importantly, the parents were not intoxicated at the time of mating and were not exposed to EtOH at any time during gestation therefore the offspring were never directly exposed to EtOH. Our results showed that the offspring of alcohol-exposed parents had significant differences compared to offspring from alcohol-naïve parents. Specifically, major differences were observed in the expression of genes that mediate neurogenesis and synaptic plasticity during neurodevelopment, genes important for directing chromatin remodeling, posttranslational modifications or transcription regulation, as well as genes involved in regulation of obesity and reproductive function. These data demonstrate that repeated binge alcohol exposure during pubertal development can potentially have detrimental effects on future offspring even in the absence of direct fetal alcohol exposure.”

I haven’t read all of it but I thought I should post it anyway. It is a study on rats who partied a lot early on in their lives and then mated later on after they’d been sober for a while, so I have no idea about the external validity (…I’m sure some people will say the study design is unrealistic – on account of the rats not also being drunk while having sex…) – but good luck setting up a similar prospective study on humans. I think it’ll be hard to do much more than just gather survey data (with a whole host of potential problems) and perhaps combine this kind of stuff with studies comparing outcomes (which?) across different geographical areas using things like legal drinking age reforms or something like that as early alcohol exposure instruments. I’d say that even if such effects are there they’ll be very hard to measure/identify and they’ll probably get lost in the noise.

iv. The relationship between obesity and type 2 diabetes is complicated. I’ve seen it reported elsewhere that this study ‘proved’ that there’s no link between obesity and diabetes or something like that – apparently you need headlines like that to sell ads. Such headlines make me very, tired.

v. Scientific Freud. On a related note I have been considering reading the Handbook of Cognitive Behavioral Therapy, but I haven’t gotten around to that yet.

vi. If people from the future write an encyclopedic article about your head, does that mean you did well in life? How you answer that question may depend on what they focus on when writing about the head in question. Interestingly this guy didn’t get an article like that.

March 1, 2014 Posted by | alcohol, Diabetes, Genetics, Personal, Psychology, Studies, Wikipedia | 2 Comments

Open Thread

Share whatever you like – links, books, christmas present ideas (I’m planning on giving that whole thing a miss, but I’m not the only one reading the comments), …

My contributions to the discussion below:

i. Alcohol may not just be bad for the fetuses that make it out of the birth canal:

“Of the 186 pregnancies, 131 resulted in delivery of a child, and 55 (30 percent) were spontaneously aborted. Of the abortions, 34 were detected only by urinary hCG before or at 6 completed gestational weeks. The 21 clinically recognized abortions occurred in the interval after 6 and by 15 completed gestational weeks.

A high intake of alcohol by women or their partners was associated with a higher frequency of spontaneous abortions than was a low intake (table 1). Women who experienced a spontaneous abortion were older and had, on average, longer menstrual cycles, a higher caffeine intake, and partners with a higher caffeine intake than did women who gave birth (table 1). No association was found between spontaneous abortion and the partner’s smoking habits, partner’s age, body mass index, and partner’s reproductive illnesses; contraception last used; education for both man and woman; or hours at work for both partners.

The crude associations between female and male alcohol intakes and spontaneous abortion shown in figures 1 and 2 changed only slightly by adjustment for the confounders listed in table 2. Female alcohol intake was associated with a 2–3 times higher adjusted risk of spontaneous abortion compared with no intake, and male intake was associated with a 2–5 times increase in the adjusted risk. However, only the relative risks for male and female intakes of 10 or more drinks/week compared with no intake were statistically significant. We found a high correlation between male and female alcohol intakes. Additional adjustment for male intake revealed a lower risk of spontaneous abortion associated with female alcohol intake, whereas the higher risk associated with a high male alcohol intake changed only slightly following adjustment for female intake […] women in this study with a moderate or high alcohol intake [also] have an increased waiting time to pregnancy”

The quotes above are from Alcohol Consumption at the Time of Conception and Spontaneous Abortion, by Henriksen, Hjollund et al.

ii. Half of US clinical trials go unpublished.

I should note that I don’t know enough about this stuff to comment intelligently on the findings. I’m planning to read Principles and Practice of Clinical Trial Medicine at some point in the not-too-distant future, and so I figured I ought to wait until I have had a go at that book to comment on this stuff. I wanted to add the link anyway though, in part so that I’d remember it in case it’ll be a while until I read Chin & Lee’s book.

iii. On a more personal note, Monday evening I beat an International Master for the first time in my life. It was in a one-minute bullet game (each player gets one minute to play the entire game) so it was not a particularly well played game, but I consider this to be a somewhat significant milestone still – IMs are really good chess players (‘An International Master is usually in the top 0.25% of all tournament players at the time he or she receives the title’ – from the wiki-link above). My opponent was Migchiel De Jong – here’s his Fide profile, here’s the game. There’s incidentally no doubt this was the guy I played – his full name is on his profile and his blitz rating was above 2600 when I played him (which is high – higher than some GMs on the site). It wasn’t a case of me getting outplayed but winning on time anyway – rather I had a mate in one in the game which he spotted after he’d made his move, and he resigned as a consequence of spotting the mate even though I missed it. When he resigned he had only 0.3 seconds left on his clock, so this may have been a contributing factor; if he’d not resigned he’d have lost on time. I had 3.6 seconds left which was of course the main reason why I didn’t spot the mate – I was too busy making moves in the time scramble in order not to lose on time to look for mates.. The time-trouble was incidentally also of course the reason why I was only up a piece when he resigned and why I did not take his queen when he blundered it a few moves earlier (bullet-chess can get pretty wild…).

(Your turn…)

December 4, 2013 Posted by | Chess, Medicine, Open Thread, Personal, Studies | 12 Comments

A divorce paper

On the Variation of Divorce Risks in Europe: Findings from a Meta-Analysis of European Longitudinal Studies:

“The aim of this article is to integrate empirical research on divorce risks in Europe and to explain the variation of empirical findings between European countries by the different levels of modernization and differences in the strength of marriage norms. We focus on the effects of premarital cohabitation, the presence of children, and the experience with parental divorce on marital stability. More than 260 studies on divorce risks could be identified, and 120 were used for further meta-analytical examinations. We show that there is considerable heterogeneity of divorce risks within as well as between countries. Explaining the variation of effect sizes between European countries, it could be shown that in countries where more rigid marriage norms prevail cohabitation has a stronger effect on marital stability than in countries where marriage norms are weaker. Furthermore, the lower the divorce barriers are, the weaker is the association between the parental divorce and the divorce risk of the offspring.”

Some data and results from the paper (click tables and figures to see them in a higher resolution):

Table 1

The table shows the estimated effect sizes of premarital cohabitation on the divorce risk in various European countries; a positive effect size indicates a higher likelihood of divorce among couples who lived together before they got married, whereas a negative effect size indicates a smaller divorce risk for couples who did not cohabitate before they got married. They note in the paper that, “The European overall effect indicates a positive relationship between cohabitation and the risk of divorce, that is, cohabiting couples have a 33 per cent higher risk to divorce than couples who do not share a common household before marriage.” However the effecs are highly heterogenous across countries, and more specifically they find that: “In countries in which traditional marriage norms are strongly institutionalized, cohabitation has a stronger effect than in countries in which marriage norms are weaker.” The institutional framework is important. The Q-statistic is a heterogeneity-measure – read the paper if you want the details..

What about children? Here’s a brief summary:

Children

Effect sizes are almost universally negative (children = smaller risk of divorce) and a lot of them are highly significant (more than half of them are significant at the 1% confidence level). As they note, “The presence of children strongly decreases the risk of divorce”. Note that the effect sizes vary but tend to be large; in the Netherlands, the country with the largest effect size, married couples with children are 70% less likely to divorce than are couples without children. The average estimated effect size is 50% so this is a huge effect. However I would be cautious about making a lot of inferences based on this finding without at the very least having a closer look at the studies on which these results are based; for example it’s unclear if they have taken into account that there may be unobserved heterogeneity problems playing a role when comparing married couples with- and without children here; lots of marriages break up early on (using Danish data I have previously estimated that once the marriage has lasted 9 years, half of the total divorce risk the Danish couple confronted ex ante will basically have been accounted for; i.e. the total risk that you’ll divorce your partner during the first 9 years is as big as is the risk that you’ll do it at any point after the 9th year of marriage – see the last figure in this post), and it does not seem unlikely e.g. that sampled marriages involving children may, ceteris paribus, have lasted a longer time on average than have sampled marriages without children (most European couples get married before they have children so the likelihood that a couple will have children is positively correlated with the marriage duration), meaning that these marriages were less likely to get broken up, regardless of the children. If they conditioned on marriage duration when calculating these effects this particular problem is dealt with, but I don’t know if they did that (and I’m not going to go through all those studies in order to find out..) and there may be a lot of other ways in which marriages with and without children differ; differences that may also relate to divorce probability (education, income, labour market status, …). Note that the fact that the studies included in the meta-study are longitudinal studies does not on its own solve the potential ‘duration problem’ (/selection problem); you can easily follow two couples for the same amount of time and still have radically different (ex ante) divorce likelihoods – and comparing unadjusted (group?) hazard rates and making conclusions based on those seems problematic if you have selection issues like these. Researchers aren’t stupid, so the studies here may all have taken care of this particular potential problem. But I’m sure there are problems they haven’t handled. Caution is warranted – part of the estimated ‘children effect’ is likely not to go through the children at all.

How about the parents? How does the fact that your parents got divorced impact your own likelihood of divorce?

Parents divorce

“Nearly all the reported effect sizes indicate positive associations between the stability of the parental marriage and the stability of children’s marriage”. There are huge cross-country differences – in Italy an individual whose parents got divorced is almost three times as likely to get divorced him/herself as is an individual whose parents did not divorce, whereas the risk increase in Poland amounts to only (a statistically insignificant) 14%.

Lastly, I’ll note that:

“No empirical support was found for any of our hypotheses which link the level of modernization to the risk of divorce. A least with respect to the divorce risk, we considered the level of socioeconomic development not to be an important macro-variable. Also, we could not find any significant relationships between the strength of divorce barriers and the effect of children on marital stability.”

I would not have expected these results if you’d asked me beforehand. Then again e.g. the differences in socioeconomic development among the countries included here are not that big, so it may just be a power issue.

October 25, 2013 Posted by | Data, Demographics, marriage, Studies | 6 Comments

Stuff

i. Troubadour, gainsay, sordid, repast, calumniate, skinflint, gentile, enjoin, prestidigitation, compunction, madrigal, bacchanalian, reify, effete, seamy, betoken, codicil, peripatetic, reactionary, mendicant, osculate, expiation, propitiation, viand, panegyric, fulsome, paean, rarefied, vitiate, bibulous, delineate, wistful, hirsute, staid, bandy, mettle, saturnine, prorogue, legerdemain, caesura, dilatory, prolix, din, hoary, obsequious, spoonerism, gratuitous, diverting, contrite, grouse, preen, poignant, roil, aver, importune, lampoon, flagitious, expedient, parlous, obdurate, piebald, dolorous, parsimony, mawkish, natty, blithely, fractious, pique, bathos, cant, recreant, plumb, diaphanous, argot, ursine, frisson, insouciant, meretricious, upbraid, pugnacious, curate, plaintively, spate, cabal, slake, odium, encomium, mulct, turgid, disport, ply, cavort, cloying, sable, svelte, idempotent, teleological, inchoate, comity, bucolic.

The above is a list of the first 100 words I’ve ‘mastered’ on the vocabulary.com site. Of course I knew some of them already, but I’ve also learned quite a few new words here along the way and it’d be incorrect to say that I haven’t also gotten a better grasp of some of the words with which I was already familiar. Here’s how it works. A few of the assessment questions so far have been in my opinion really poor (allowing for multiple correct answers, only one of which is accepted as correct), but in general this seems like an extremely useful site and the site does allow you to provide feedback if you think a question is poorly worded.

Do note that average vocabulary sizes are really rather small, all things considered: “Most adult native test-takers range from 20,000-35,000 words”. I think that you can probably progress rather rapidly with a tool like this, if you use it consistently. Note that the site doesn’t completely stop asking you questions about the words you’ve ‘mastered’; brush-up questions are added occasionally to aid retention. The starting point is as far as I can remember based on educational background, so if you’re a graduate student you shouldn’t worry that the site will start out by asking you if you know the word ‘house’ or ‘cat’. I’m pretty sure even walking dictionaries will find plenty of words along the way that they are unfamiliar with.

I’ll probably stop going on about the site now, but I really like it at this point and so I figured I should post at least a few posts about it before letting it go. It’s a very neat tool.

ii. For the last two years I have been involved in a medical trial aimed at figuring out if a specific drug might be used to delay the development of retinopathy in diabetics. My participation in the trial ended this week. The trial was more or less a direct result of a smaller trial in which I also participated, which showed some promising initial results – here’s the relevant paper. The researcher conducting the trial I just participated in will publish a paper about it later on, and I’ll naturally blog that when it’s published. There has been talk about continuing the project (/…that is, starting a new project) for the participants who got the active drug – half of the people in this trial got placebo – in order to increase the follow-up period. If I got the active drug (whether or not I did is not clear at this point, but I’ll be told relatively soon) I’ll probably participate in the new trial as well. No, the person who’s going to analyze the data will not be told whether or not I got the active drug – I asked about this part, but the study design is such that the double blind aspect is not compromised; the researcher who’ll figure out whether or not I got the active drug is not involved in the data analysis at all.

Medical trials often have trouble finding participants and selection into such trials is far from random. If you live in Denmark, you should check out this site. I assume similar resources exist in other countries…

A couple more 60 symbols videos below. I’ve now watched most of the videos they’ve posted, and I really like this stuff:

“He was a very strange man. And yet he’s absolutely wonderful!” – I could easily have said something similar about him. I’d much, much rather spend time with someone like that than with a ‘normal’ (boring) person. (Here’s a related link. Also, this.)

iv. The Relationship between Anxiety and the Social Judgements of Approachability And Trustworthiness:

“The aim of the current study was to examine the relationship between individual differences in anxiety and the social judgements of trustworthiness and approachability. We assessed levels of state and trait anxiety in eighty-two participants who rated the trustworthiness and approachability of a series of unexpressive faces. Higher levels of trait anxiety (controlling for age, sex and state anxiety) were associated with the judgement of faces as less trustworthy. In contrast, there was no significant association between trait anxiety and judgements of approachability. These findings indicate that trait anxiety is a significant predictor of trustworthiness evaluations and illustrate the importance of considering the role of individual differences in the evaluation of trustworthiness. We propose that trait anxiety may be an important variable to control for in future studies assessing the cognitive and neural mechanisms underlying trustworthiness. This is likely to be particularly important for studies involving clinical populations who often experience atypical levels of anxiety.”

v. Mass extinction of lizards and snakes at the Cretaceous – Paleogene boundary:

“The Cretaceous–Paleogene (K-Pg) boundary is marked by a major mass extinction, yet this event is thought to have had little effect on the diversity of lizards and snakes (Squamata). A revision of fossil squamates from the Maastrichtian and Paleocene of North America shows that lizards and snakes suffered a devastating mass extinction coinciding with the Chicxulub asteroid impact. Species-level extinction was 83%, and the K-Pg event resulted in the elimination of many lizard groups and a dramatic decrease in morphological disparity. Survival was associated with small body size and perhaps large geographic range. The recovery was prolonged; diversity did not approach Cretaceous levels until 10 My after the extinction, and resulted in a dramatic change in faunal composition. The squamate fossil record shows that the end-Cretaceous mass extinction was far more severe than previously believed, and underscores the role played by mass extinctions in driving diversification.”

A little more:

“Survival at the K-Pg boundary is highly nonrandom. Small size has been identified as a determinant of survival (36), yet size selectivity is evident even among the squamates. The most striking pattern is the extinction of all large lizards and snakes. […] The largest known early Paleocene lizard is Provaranosaurus acutus. Comparisons with varanids suggest an SVL [snout-vent length, US] of 305 mm and a mass of 415 g (Dataset S1), compared with an estimated SVL of 850 mm and mass of 6 kg for the largest Maastrichtian lizard, Palaeosaniwa. The largest early Paleocene snake is Helagras prisciformis, with an estimated SVL >950 mm and a mass >520 g, compared with >1,700 mm and 2.9 kg for the largest Maastrichtian snake, Cerberophis. […]

Size selectivity may help explain why nonavian dinosaurs became extinct, suggesting that it was nonavian dinosaurs’ failure to evolve a diverse fauna of small-bodied species, rather than a decrease in the diversity of large-bodied forms, that ultimately sealed their fate. A number of small, nonavian dinosaurs are now known from the Late Cretaceous, including alvarezsaurids (37) and microraptorine dromaeosaurids (38), and taphonomic biases almost certainly obscure the true diversity of small dinosaurs (38, 39). However, the fact remains that during the late Maastrichtian, small dinosaurs were vastly outnumbered by other small vertebrates, including a minimum of 30 squamates, 18 birds (15), and 50 mammal species (40). Strikingly, birds—the only dinosaurs to survive— were the only dinosaurs with a high diversity of smallbodied (<5 kg) forms (15). In this context, a discussion of a decline in large dinosaur diversity in the Maastrichtian (9) is perhaps beside the point. A high diversity of large herbivores and carnivores in the latest Maastrichtian would have been unlikely to change the fate of the nonavian dinosaurs, because no animals occupying these niches survived. Instead, the rarity of small dinosaurs—resulting perhaps from being outcompeted by squamates and mammals for these niches —led to their downfall. […]

Extinction at the K-Pg boundary was followed by recovery in the Paleocene and Eocene. A number of new lizard lineages occur in the basal Paleocene, notably the stem varanoid Provaranosaurus, xantusiids, and amphisbaenians (27). These may represent opportunistic invaders that colonized the area in the aftermath to exploit niches left vacant by the extinction, as seen among mammals (10, 44). Despite this, early Paleocene diversity is considerably lower than late Maastrichtian diversity (Fig. 3). Subsequently, ecological release provided by the extinction allowed the survivors to stage an adaptive radiation, paralleling the adaptive radiations staged by mammals (6, 45, 46), birds (46, 47), and fish (48). The community that emerges in the early Eocene is dominated by groups that are either minor components of the Cretaceous fauna or unknown from the Cretaceous […] diversity does not approach Cretaceous levels until the early Eocene, 10 My later […] Unlike mammals, […] squamates appear to have simply reoccupied the niches they occupied before the extinction. This reoccupation of niches was […] delayed; by the middle Paleocene, lizards had yet to recover the range of body sizes and morphotypes found in the Maastrichtian (Fig. 5).”

October 4, 2013 Posted by | Biology, Ecology, language, Lectures, Medicine, Paleontology, Personal, Physics, Psychology, Studies, Zoology | Leave a comment

Self-pity and self-esteem…

i. “Pity has been defined as “sympathetic heartfelt sorrow for one that is suffering physically or mentally or that is otherwis e distressed or unhappy” (Webster’s Third New International Dictionary, 1961, p. 1726). Self-pity is pity directed toward the self. Consequently, self-pity may be defined as a sympathetic, heartfelt sorrow for oneself prompted by one’s own physical or mental suffering, distress, or unhappiness. Interviews with individuals suffering from chronic illness (Charmaz, 1980) have indicated that self-pity is often accompanied by feelings of sadness and loss and a heightened sense of injustice. Moreover, for a person who feels self-pity, it is characteristic to feel envy of others who have not suffered a similar loss or fate. This is expressed in questions like “Why not them?”, “Why me?”, or “What did I do to de serve this?”, which typically accompany the internal monologue associated with experiences of self-pity (Charmaz, 1980; Grunert, 1988). The experience of self-pity is not restricted to individuals suffering from chronic illness or severe losses. Rather, it is an emotional experience which, in all likelihood, all humans encounter occasionally (Kahn, 1965). Life holds many opportunities to feel sorry for oneself. […]

Self-pitying persons are characterized as likely to overindulge in their failures, hardships, and losses, and the circumstances elicited by these setbacks, thus becoming self-consciously preoccupied with their own suffering (Charmaz, 1980). Nevertheless, self-pity is not an emotional response directed exclusively towards the self. Whereas the primary focus in self-pity may be on the self , self-pity also has a strong interpersonal component. Quite often, self-pity is an emotional response directed towards others with the goal of attracting attention, empathy, or help (Kahn, 1965). In this respect, however, it is a strategy doomed to fail. Whereas initially the display of self-pity may evoke empathy from others (Milrod, 1972), pervasive self-pity will not. On the contrary, people who show pervasive self-pity are most likely to be rejected. Even for individuals who suffer from chronic illness, the period of time is quite limited during which the social environment will allow for a display of self-pity. After a while, people are expected to accept their fate, stop complaining, and carry on with their lives (Charmaz, 1980).”

“the psychiatric and psychoanalytic literature holds that self-pity is linked to feelings of both loneliness and anger. Clinical observations suggest that individuals who experience self-pity usually expect more from the environment than the environment is willing to give (Kahn, 1965). Personal relationships are perceived as unstable and characterized by high demandingness on the part of the person who experiences self-pity, and who sees his or her environment as unwilling to provide the empathy, comfort, and support he or she demands. Consequently, a person who feels self-pity is permanently frustrated. This permanent frustration with others may have two consequences. First, it may lead to social withdrawal and feelings of loneliness (Charmaz, 1980; Kahn, 1965). Second, it may lead to feelings of aggression, hostility, and anger (Kahn, 1965; Milrod, 1972; Wilson, 1985). However, open displays of aggression, hostility, and anger are in conflict with the aims of attracting empathy, support, and acknowledgment from others. […] individuals with a susceptibility for self-pity often are characterized by great self-insecurity. Thus, they may lack the self-assertiveness needed to confront others openly. As a consequence, the direct expression of aggression and hostility will be inhibited. Only mild forms of anger will be expressed, whereas strong anger will be suppressed, turned inward, or even turned against oneself (Milrod, 1991; Wilson, 1985). Under the surface, however, the anger against others will continue to exist, often accompanied by ruminations about retributions for the past (Charmaz, 1980). […] self-pity clearly falls into the class of ineffective coping strategies that are more likely to exaggerate a problem and create new difficulties than to help deal successfully with stressful situations. […] the present findings confirm observations reported in the clinical literature that self-pity is related to loneliness. However, as the two-dimensional conceptualization following Weiss’s (1973) typology of loneliness showed, self-pity was related only to emotional loneliness, but not to social loneliness. […] in line with the clinical literature and previous findings, the present findings show that self-pity is closely related to depression, even when common variance with gender and other facets of neuroticism are controlled for.”

Above quotes are from: Self-Pity: Exploring the Links to Personality, Control Beliefs, and Anger, by Joachim Stöber.

ii. Rumination mediates the prospective effect of low self-esteem on depression: a five-wave longitudinal study.

“Previous research supports the vulnerability model of low self-esteem and depression, which states that low self-esteem operates as a prospective risk factor for depression. However, it is unclear which processes mediate the effect of low self-esteem. To test for the mediating effect of rumination, the authors used longitudinal mediation models, which included exclusively prospective effects and controlled for autoregressive effects of the constructs. Data came from 663 individuals (aged 16 to 62 years), who were assessed 5 times over an 8-month period. The results indicated that low self-esteem predicted subsequent rumination, which in turn predicted subsequent depression, and that rumination partially mediated the prospective effect of low self-esteem on depression. These findings held for both men and women, and for both affective-cognitive and somatic symptoms of depression.” […]

“A growing body of research suggests that low self-esteem is a risk factor for the development of depression (e.g., Kernis et al., 1998; Orth, Robins, & Roberts, 2008; Orth, Robins, Trzesniewski, Maes, & Schmitt, 2009; Roberts & Monroe, 1992; Sowislo & Orth, 2011). In these studies, which used longitudinal designs and controlled for prior levels of the constructs, low self-esteem — which is defined as “a person’s appraisal of his or her value” (Leary & Baumeister, 2000, p. 2) — prospectively predicted changes in the level of depression. Overall, the evidence supports the vulnerability model, which states that low self-esteem is a diathesis exerting causal influence in the onset and maintenance of depression (e.g., Beck, 1967; Metalsky, Joiner, Hardin, & Abramson, 1993). […] An alternative model of the relation between low self-esteem and depression is the scar model, which states that low self-esteem is an outcome rather than a cause of depression, because episodes of depression may leave permanent scars in the self-concept of the individual (cf. Coyne, Gallo, Klinkman, & Calarco, 1998; Rohde, Lewinsohn, & Seeley, 1990; Shahar & Davidson, 2003; for an overview of the scar and vulnerability model, see Zeigler-Hill, 2011). It is important to note that the vulnerability model and the scar model are not mutually exclusive because both processes (i.e., low self-esteem contributing to depression and depression eroding self-esteem) might operate simultaneously. Yet, the extant literature speaks against the scar model (cf. Ormel, Oldehinkel, & Vollebergh, 2004; Orth et al., 2008; Orth, Robins, & Meier, 2009; Orth, Robins, Trzesniewski, et al., 2009; Sowislo & Orth, 2011; but see Shahar & Davidson, 2003).”

iii. “our review showed that high self-esteem is closely associated with self-enhancement, a bias that has both beneficial and detrimental consequences. Jean Twenge, Keith Campbell, and their colleagues have recently found that narcissism, the dark side of high self-esteem, has risen dramatically over the last 25 years (Associated Press, 2007)

The motive of self-enhancement and the dependency of self-esteem on the approval of others who are also motivated to self-enhance virtually ensure that not everyone will get the esteem they desire. Research inspired by sociometer theory has shown that self-esteem is closely attuned to social acceptance (Leary, 2004). Consider a pair of individuals, each of whom has a choice between approving of the other and withholding approval. The self-enhancement motive implies a preference ranking that constitutes a Prisoner’s Dilemma. […] Matters improve inasmuch as people find a way to coordinate their behaviors by projecting their own choices strategically onto one another or by playing the approval game repeatedly (Krueger, 2007). Still, it is unrealistic to expect perfect coordination where everyone pats everyone else on the back. Members of human groups are notorious for negotiating status, power, and prestige, often by creatively deceitful means. In a provocative urban ethnography, Anderson (1994) found self-esteem to be a scarce and contested resource, which individuals could gain at the expense of others. The goal of raising self-esteem across the board is seductive because it is not a zero-sum game. Yet because individuals are, in part, the source of the self-esteem of others, not everyone can attain the highest score.”

From Is the Allure of Self-Esteem a Mirage After All?, a brief note by Krueger, Vohs and Baumeister.

iv. Self-Esteem Development From Age 14 to 30 Years: A Longitudinal Study, by Erol and Orth.

“We examined the development of self-esteem in adolescence and young adulthood. Data came from the Young Adults section of the National Longitudinal Survey of Youth, which includes 8 assessments across a 14-year period of a national probability sample of 7,100 individuals age 14 to 30 years. Latent growth curve analyses indicated that self-esteem increases during adolescence and continues to increase more slowly in young adulthood. Women and men did not differ in their self-esteem trajectories. […] At each age, emotionally stable, extraverted, and conscientious individuals experienced higher self-esteem than emotionally unstable, introverted, and less conscientious individuals. Moreover, at each age, high sense of mastery, low risk taking, and better health predicted higher self-esteem. Finally, the results suggest that normative increase in sense of mastery accounts for a large proportion of the normative increase in self-esteem.”

“Low self-esteem in adolescence and young adulthood is a risk factor for negative outcomes in important life domains. For example, Trzesniewski et al. (2006) found that low self-esteem during adolescence predicts poorer mental and physical health, worse economic well-being, and higher levels of criminal activity in young adulthood. Similarly, other studies found that low self-esteem prospectively predicts antisocial behavior, eating disturbances, depression, and suicidal ideation (Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005; McGee & Williams, 2000; Orth, Robins, & Roberts, 2008).”

v. Identity Status and Self-Esteem: A Meta-Analysis, by Ryeng, Kroger and Martinussen. Unfortunately I’ve not been able to find an ungated version to which I can link, but here’s part of the abstract:

“This study examines the relationship between Marcia’s identity statuses and self-esteem measures through techniques of meta-analysis. Global self-esteem, as used here, refers to one’s positive or negative attitudes toward oneself, degree of self-respect, self-worth, and faith in one’s own capacities. Identity theory would predict strong linkages between the development of self-esteem and identity; however, previous research findings have been inconsistent regarding the nature of this relationship. Two conflicting explanatory models are examined here: (a) high self-esteem is linked with “high” identity status (achievement and moratorium) and low self-esteem with “low” identity status (foreclosure and diffusion); and (b) high self-esteem is linked with identity commitment and low self-esteem with lack of identity commitment. […] Results do not provide clear support for either explanatory model, although support exists from categorical measures of identity status that high self-esteem is linked with the committed identity statuses.”

vi. Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies, by Sowislo and Orth.

“Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression ( .16) was significantly stronger than the effect of depression on self-esteem ( .08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with .10, and anxiety predicted self-esteem with .08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression.”

vii. Life-Span Development of Self-Esteem and Its Effects on Important Life Outcomes, by Orth, Robins, and Widaman.

“We examined the life-span development of self-esteem and tested whether self-esteem influences the development of important life outcomes, including relationship satisfaction, job satisfaction, occupational status, salary, positive and negative affect, depression, and physical health. Data came from the Longitudinal Study of Generations. Analyses were based on 5 assessments across a 12-year period of a sample of 1,824 individuals ages 16 to 97 years. First, growth curve analyses indicated that self-esteem increases from adolescence to middle adulthood, reaches a peak at about age 50 years, and then decreases in old age. Second, cross-lagged regression analyses indicated that self-esteem is best modeled as a cause rather than a consequence of life outcomes. Third, growth curve analyses, with self-esteem as a time-varying covariate, suggested that self-esteem has medium-sized effects on life-span trajectories of affect and depression, small to medium-sized effects on trajectories of relationship and job satisfaction, a very small effect on the trajectory of health, and no effect on the trajectory of occupational status. These findings replicated across 4 generations of participants—children, parents, grandparents, and their great-grandparents. Together, the results suggest that self-esteem has a significant prospective impact on real-world life experiences and that high and low self-esteem are not mere epiphenomena of success and failure in important life domains.”

A figure from the article (click to view full size):

Self-esteem life trajectory

“Although the present study suggests an earlier peak of the self-esteem trajectory (i.e., at about age 50 years) than in previous research (at about age 60 years; Orth et al., 2010), the overall shape of the trajectory was similar. The repeated finding of a relatively strong decline of self-esteem in old age is of particular importance, given conflicting reviews of the literature […]

Surprisingly, gender did not affect the level or the trajectory of self-esteem; in contrast, previous research has suggested that men tend to report higher self-esteem than women, at least in adolescence and adulthood, although the effect size is generally small (Kling, Hyde, Showers, & Buswell, 1999; Orth et al., 2010; Robins, Trzesniewski, Tracy, Gosling, & Potter, 2002). Moreover, in the present study, no cohort differences in the trajectory of self-esteem were found, replicating findings from Erol and Orth (2011) and Orth et al. (2010). Thus, although the claim that there has been a generational increase in self-esteem levels (i.e., that more recent generations have higher self-esteem than previous generations) has intuitive appeal (Twenge & Campbell, 2001, 2008), the available evidence suggests that the average self-esteem trajectory has not changed across the generations born in the 20th century (Trzesniewski & Donnellan, 2010; Trzesniewski, Donnellan, & Robins, 2008) […]

The present research also addressed the important question of whether self-esteem is better thought of as a cause or a consequence of life outcomes. We tested for reciprocal prospective relations between self-esteem and a set of life outcomes that are central to having a successful and fulfilling life, including measures of well-being (positive affect, negative affect, and depression), enjoying and succeeding in work, having a satisfying romantic relationship, and physical health. With regard to depression, we replicated previous studies showing that low self-esteem prospectively predicts depression but that the effect of depression on low self-esteem is small or nonsignificant (Metalsky et al., 1993; Orth, Robins, & Meier, 2009; Orth, Robins, Trzesniewski, et al., 2009; Roberts & Monroe, 1992). A similar pattern emerged for measures of dispositional positive and negative affect: Self-esteem predicted increases in positive affect and decreases in negative affect, controlling for prior levels in the constructs, but positive affect did not predict subsequent self-esteem, and negative affect had a statistically significant but small negative effect on self-esteem. In addition, we found that self-esteem was prospectively related to higher levels of relationship satisfaction, job satisfaction, occupational status, salary, and physical health, con- trolling for prior levels of these variables, but none of these life outcomes had reciprocal effects on self-esteem (or, if significant, the coefficients were small). Moreover, all results held across generations. Thus, regardless of whether one was born in the early 1900s or in the 1980s, self-esteem had significant benefits for people’s experiences of love, work, and health, supporting hypotheses about the beneficial consequences of high self-esteem (Donnellan, Trzesniewski, Robins, Moffitt, & Caspi, 2005; Swann et al., 2007; Trzesniewski et al., 2006; but see Baumeister et al., 2003).”

September 27, 2013 Posted by | Psychology, Studies | Leave a comment

Stuff

i. I’ve played some good chess over the last few weeks. I’m currently participating in an unrated chess tournament –  the format is two games per evening (one with the white pieces and one with the black), with 45 minutes per person per game. The time control means that although the games aren’t rated, they’re at least long enough to be what I’d consider ‘semi-serious’.

Here’s a recent game I played, from that tournament – I was white. It wasn’t without flaws on my part but it was ‘good enough’ as he was basically lost out of the opening. I wasn’t actually sure if 7.Qd4 could be played (this should tell you all you need to know about how much I know about the Pirc…) but I was told after the game that it was playable – my opponent had seen it in a book before, but he’d forgotten how the theory went and so he made a blunder. It was the second game that evening, played shortly after I’d held my opponent, a ca. 2000 FIDE rated player, to a draw in the first game. I mention the first game also because I think it’s quite likely that the outcome of that game played a role in the mistake he made in the second game. The average rating of my opponents so far has been 1908 (I’ve also drawn a 2173 FIDE guy along the way, though the chess in that case was not that great), and I’m at +1 after six games. I’ve beaten FMs before in bullet and blitz, but as mentioned these games are a tad more serious than, say, random 3 minute games online, and this is one of the first times I’ve encountered opponents as strong as this in a ‘semi-serious’ setting. And I’m doing quite well. It probably can’t go on, but I’m enjoying it while it lasts.

ii. An interesting medical lecture about vaccines:

iii. Estimating Gender Disparities in Federal Criminal Cases.

“This paper assesses gender disparities in federal criminal cases. It finds large gender gaps favoring women throughout the sentence length distribution (averaging over 60%), conditional on arrest offense, criminal history, and other pre-charge observables. Female arrestees are also significantly likelier to avoid charges and convictions entirely, and twice as likely to avoid incarceration if convicted. Prior studies have reported much smaller sentence gaps because they have ignored the role of charging, plea-bargaining, and sentencing fact-finding in producing sentences. Most studies control for endogenous severity measures that result from these earlier discretionary processes and use samples that have been winnowed by them. I avoid these problems by using a linked dataset tracing cases from arrest through sentencing. Using decomposition methods, I show that most sentence disparity arises from decisions at the earlier stages, and use the rich data to investigate causal theories for these gender gaps.”

Here’s what she’s trying to figure out: “In short, I ask: do otherwise-similar men and women who are arrested for the same crimes end up with the same punishments, and if not, at what points do their fates diverge?”

Some stuff from the paper:

“The estimated gender disparities are strikingly large, conditional on observables. Most notably, treatment as male is associated with a 63% average increase in sentence length, with substantial unexplained gaps throughout the sentence distribution. These gaps are much larger than those estimated by previous research. This is because, as the sequential decomposition demonstrates, the gender gap in sentences is mostly driven by decisions earlier in the justice process—most importantly sentencing fact-finding, a prosecutor-driven process that other literature has ignored.

But why do these disparities exist? Despite the rich set of covariates, unobservable gender differences are still possible, so I cannot definitively answer the causal question. However, several plausible theories have testable implications, and I take advantage of the unusually rich dataset to explore them. I find substantial support for some theories (particularly accommodation of childcare responsibilities and perceived role differences in group crimes), but that these appear only to partially explain the observed disparities.” […]

“Columns 11-12 of Table 5 show that the gender gap is substantially larger among black than non-black defendants (74% versus 51%). The race-gender interaction adds to our understanding of racial disparity: racial disparities among men significantly favor whites,29 but among women, the race gap in this sample is insignificant (and reversed in sign). The interaction also offers another theory for the gender gap: it might partly reflect a “black male effect”—a special harshness toward black men, who are by far the most incarcerated group in the U.S. […] This theory only goes so far, however — the gender gap even among non-blacks is over 50%, far larger than the race gap among men.”

iv. Low glycaemic index, or low glycaemic load, diets for diabetes mellitus?

“Nutritional factors affect blood glucose levels, however there is currently no universal approach to the optimal dietary strategy for diabetes. Different carbohydrate foods have different effects on blood glucose and can be ranked by the overall effect on the blood glucose levels using the so-called glycaemic index. By contributing a gradual supply of glucose to the bloodstream and hence stimulating lower insulin release, low glycaemic index foods, such as lentils, beans and oats, may contribute to improved glycaemic control, compared to high glycaemic index foods, such as white bread. The so-called glycaemic load represents the overall glycaemic effect of the diet and is calculated by multiplying the glycaemic index by the grammes of carbohydrates.

We identified eleven relevant randomised controlled trials, lasting 1 to 12 months, involving 402 participants. Metabolic control (measured by glycated haemoglobin A1c (HbA1c), a long-term measure of blood glucose levels) decreased by 0.5% HbA1c with low glycaemic index diet, which is both statistically and clinically significant. Hypoglycaemic episodes significantly decreased with low glycaemic index diet compared to high glycaemic index diet. No study reported on mortality, morbidity or costs.”

v. I started reading Dinosaurs Past and Present a few days ago. It’s actually a quite short and neat book, but I haven’t gotten very far as other things have gotten in the way. I just noticed that a recently published PlosOne study deals with some of the same topics covered in the book – I haven’t read it yet but if you’re curious you can read the article on Forearm Posture and Mobility in Quadrupedal Dinosaurs here.

September 25, 2013 Posted by | Chess, Data, Diabetes, Immunology, Lectures, Medicine, Paleontology, Personal, Studies | Leave a comment

A few Cochrane reviews and links

I’ve spent the last few days at my parents’ place and haven’t had much time for blogging due to social obligations. I read The Murder on the Links the day before yesterday and I’ll finish Lord Edgware Dies later today – I’ll probably blog the books tomorrow. For now I’ll just post a few Cochrane reviews and a couple of links:

i. Abstinence-only programs for preventing HIV infection in high-income countries (as defined by the World Bank). (link to the full paper here)

“Abstinence-only programs are widespread and well-funded, particularly in the United States and countries supported by the US President’s Emergency Plan for AIDS Relief. On the premise that sexual abstinence is the best and only way to prevent HIV, abstinence-only interventions aim to prevent, stop, or decrease sexual activity. These programs differ from abstinence-plus designs: abstinence-plus programs promote safer-sex strategies (e.g., condom use) along with sexual abstinence, but abstinence-only programs do not, and instead often highlight the limitations of condom use. An up-to-date review suggests that abstinence-only programs do not affect HIV risk in low-income countries; this review examined the evidence in high-income countries.

This review included thirteen randomized controlled trials comparing abstinence-only programs to various control groups (e.g., “usual care,” no intervention). Although we conducted an extensive international search for trials, all included studies enrolled youth in the US (total baseline enrollment=15,940 participants). Programs were conducted in schools, community centers, and family homes; all were delivered in family units or groups of young people. We could not conduct a meta-analysis because of missing data and variation in program designs. However, findings from the individual trials were remarkably consistent.

Overall, the trials did not indicate that abstinence-only programs can reduce HIV risk as indicated by behavioral outcomes (e.g., unprotected vaginal sex) or biological outcomes (e.g., sexually transmitted infection). Instead, the programs consistently had no effect on participants’ incidence of unprotected vaginal sex, frequency of vaginal sex, number of sex partners, sexual initiation, or condom use.”

ii. Healthcare financing systems for increasing the use of tobacco dependence treatment.

The short version:

“Apart from providing counselling and drug treatment, strategies that reduce or cover the costs of accessing or providing these treatments could help smokers quit.

We found eleven trials, eight of which involve financial interventions directed at smokers and three of which involve financial interventions directed at healthcare providers.

Covering all the costs of smoking cessation treatment for smokers when compared to providing no financial benefits increased the proportion of smokers attempting to quit, using smoking cessation treatments, and succeeding in quitting. Although the absolute differences in quitting were small, the costs per person successfully quitting were low or moderate. Financial incentives directed at healthcare providers did not have an effect on smoking cessation.”

From the paper:

Summary of main results:

With very high to modest levels of consistency, we detected a statistically significant positive effect of full financial interventions targeting smokers with regard to abstinence from smoking compared to provision of no financial intervention at six months follow-up or more (all abstinence measures: RR 2.45, 95% CI 1.17 to 5.12). The effect of full financial interventions was also extended to favourable outcomes on the use of smoking cessation treatments: the pooled effect of full coverage compared with no financial intervention on the use of smoking cessation treatments was highly significant for each treatment type (NRT, bupropion, and behavioural interventions).Despite the observation of multiple favourable effects of full as compared to no financial intervention, when full coverage was compared to partial coverage, results showed no significant effect on smoking cessation or quit attempts. […]

Five studies presented data on cost effectiveness. When full benefit was compared with partial or no benefit, the costs per quitter ranged from $119 to $6,450. [the $6,450 estimate is an outlier in that group; the other estimates are all much lower, at or below $1500/quitter – US] […]

In this review, covering the full cost to smokers of using smoking cessation treatment increased the number of successful quitters, the number of participants making a quit attempt, and the use of smoking cessation treatment when compared with no financial coverage. As the majority of the studies were rated at high or unclear risk of bias in three or more domains, and there was variation between the settings, interventions and participants of the included studies, the results should be interpreted cautiously. The differences in self-reported abstinence rate, number of participants making a quit attempt and use of smoking cessation treatments were modest.”

iii. Psychosocial and pharmacological treatments for deliberate self harm.

“Deliberate self-harm is a major health problem associated with considerable risk of subsequent self-harm, including completed suicide. This systematic review evaluated the effectiveness of various treatments for deliberate self-harm patients in terms of prevention of further suicidal behaviour. […]

Main results:

A total of 23 trials were identified in which repetition of deliberate self-harm was reported as an outcome variable. The trials were classified into 11 categories. The summary odds ratio indicated a trend towards reduced repetition of deliberate self-harm for problem-solving therapy compared with standard aftercare (0.70; 0.45 to 1.11) and for provision of an emergency contact card in addition to standard care compared with standard aftercare alone (0.45; 0.19 to 1.07). The summary odds ratio for trials of intensive aftercare plus outreach compared with standard aftercare was 0.83 (0.61 to 1.14), and for antidepressant treatment compared with placebo was 0.83 (0.47 to 1.48). […]

Authors’ conclusions:

There still remains considerable uncertainty about which forms of psychosocial and physical treatments of self-harm patients are most effective, inclusion of insufficient numbers of patients in trials being the main limiting factor. There is a need for larger trials of treatments associated with trends towards reduced rates of repetition of deliberate self-harm. The results of small single trials which have been associated with statistically significant reductions in repetition must be interpreted with caution and it is desirable that such trials are also replicated.”

A few other links which are not from the Cochrane site:

iv. Plausible indeed!

v. Errors in DCP2 cost-effectiveness estimate for deworming.”Over the past few months, GiveWell has undertaken an in-depth investigation of the cost-effectiveness of deworming, a treatment for parasitic worms that are very common in some parts of the developing world. While our investigation is ongoing, we now believe that one of the key cost-effectiveness estimates for deworming is flawed, and contains several errors that overstate the cost-effectiveness of deworming by a factor of about 100. This finding has implications not just for deworming, but for cost-effectiveness analysis in general: we are now rethinking how we use published cost-effectiveness estimates for which the full calculations and methods are not public. […]we see this case as a general argument for expecting transparency, rather than taking recommendations on trust – no matter how pedigreed the people making the recommendations. Note that the DCP2 was published by the Disease Control Priorities Project, a joint enterprise of The World Bank, the National Institutes of Health, the World Health Organization, and the Population Reference Bureau, which was funded primarily by a $3.5 million grant from the Gates Foundation. The DCP2 chapter on helminth infections, which contains the $3.41/DALY estimate, has 18 authors, including many of the world’s foremost experts on soil-transmitted helminths.”

vi. Evolution, Creationism, Intelligent Design – a Gallup poll from last year. According to that poll a majority of Americans (56%) think creationism should be taught in public school science classes. One of the questions asked were: If the public schools in your community taught the theory of evolution, — that is, the idea that human beings evolved from other species of animals — would you be upset, or not?  A third of the people asked (34%) answered yes to this question. Incidentally in related news it should be noted that in a recent poll of South Korean biology teachers, 40% of them “agreed with the statement that “much of the scientific community doubts if evolution occurs”; and half disagreed that “modern humans are the product of evolutionary processes”.”

In slightly related news, according to an older poll conducted shortly before the turn of the century roughly one in five Americans asked back then didn’t know that the Earth revolves around the Sun. Other countries didn’t do any better:

“Gallup also asked the following basic science question, which has been used to indicate the level of public knowledge in two European countries in recent years: “As far as you know, does the earth revolve around the sun or does the sun revolve around the earth?” In the new poll, about four out of five Americans (79%) correctly respond that the earth revolves around the sun, while 18% say it is the other way around. These results are comparable to those found in Germany when a similar question was asked there in 1996; in response to that poll, 74% of Germans gave the correct answer, while 16% thought the sun revolved around the earth, and 10% said they didn’t know. When the question was asked in Great Britain that same year, 67% answered correctly, 19% answered incorrectly, and 14% didn’t know.”

You do have a potential ‘this is a silly question so I want to mess with the people asking it’-effect lurking in the background, but that’s probably mostly related to people giving the wrong answer deliberately. But even if many of the people asked perhaps gave the wrong answer deliberately, there’s still a substantial number of people answering that they ‘don’t know.’ I found the numbers surprising and I would love to see some updated estimates; a brief googling didn’t turn up anything.

July 28, 2013 Posted by | Data, Demographics, Economics, Evolutionary biology, Infectious disease, Medicine, Psychology, Religion, Studies | 5 Comments

Cochrane reviews

I recently added the Cochrane site to my sidebar, but I figured a post was in order as well – people almost never click the links in the sidebar. I’ve blogged reviews from the Cochrane foundation a couple of times before, but I’ve only ever read studies via links from other channels; I’ve never really sat down and had a good long look at the stuff available at the site. I have had a closer look now, and I like what I see.

If you care about evidence-based medicine and health stuff more generally this site is a goldmine. Let’s say you want to know something about “organ transplantation” – one search later and the results of 602 reviews on the topic are now available to you.. “Cancer” gives you 695. “Type 2 diabetes” – 1759.

In my opinion more people should know about a site like this, and more people should use it to obtain greater knowledge about health matters. It would be very surprising if some of the reviews did not contain troublesome flaws and inaccuracies, but compared to the type of information and -information sources most people make use of when making health-related decisions in their everyday lives this stuff is pure gold.

May 28, 2013 Posted by | Diabetes, Medicine, Studies | Leave a comment

Stuff

i. Better Colleges Failing to Lure Talented Poor, by David Leonhardt.

applicants“Only 34 percent of high-achieving high school seniors in the bottom fourth of income distribution attended any one of the country’s 238 most selective colleges […] Among top students in the highest income quartile, that figure was 78 percent. […]

Among high-achieving, low-income students, 6 percent were black, 8 percent Latino, 15 percent Asian-American and 69 percent white […]

The researchers defined high-achieving students as those very likely to gain admission to a selective college, which translated into roughly the top 4 percent nationwide. Students needed to have at least an A-minus average and a score in the top 10 percent among students who took the SAT or the ACT.

Of these high achievers, 34 percent came from families in the top fourth of earners, 27 percent from the second fourth, 22 percent from the third fourth and 17 percent from the bottom fourth. (The researchers based the income cutoffs on the population of families with a high school senior living at home, with $41,472 being the dividing line for the bottom quartile and $120,776 for the top.) […]

If they make it to top colleges, high-achieving, low-income students tend to thrive there, the paper found. Based on the most recent data, 89 percent of such students at selective colleges had graduated or were on pace to do so, compared with only 50 percent of top low-income students at nonselective colleges.”

For people with access to nber papers, here’s the direct link to the study.

ii. What effect size would you expect?

The p-value isn’t the only thing you should care about when evaluating small-N studies and larger N replication attempts. It shouldn’t be news, but lots of people get this stuff wrong. Do remember that even in the replication studies, N may be quite small.

iii. Will we ever regenerate limbs?

“Seifert doubts we will ever have an injectable cocktail of molecules that triggers regeneration. There’s too much complexity in the transition from wound to blastema to new limb, he says. It will also be a lengthy process. […] “Even if a human could grow a limb back, it might take 15-20 years,” says Seifert. A finger might be more realistic.”

iv. New insights into differences in brain organization between Neanderthals and anatomically modern humans. Razib Khan’s blog has some comments in case you’re curious.

iv. ‘The 99% percent’ weren’t really all that representative, it seems: The Geospatial Characteristics of a Social Movement Communication Network:

“Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.”

Figure 2. Divergences in geographic distribution of users.

v. Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level.

“we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. […] results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.”

N = 28, so it’s a small sample size. But at least the results “seem to support the idea that aerobic training produces selective benefits in cognitive performance.”

vi. How you behave online can tell (a lot? something? a bit? – people seem to disagree about how ‘impressive’ the findings are…) about who you are: Private traits and attributes are predictable from digital records of human behavior, by Kosinski, Stillwell & Graepel.

Figure 2 is probably the main figure from this paper – it “shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female)”:

Fig 2

vii. Farm Use of Antibiotics Defies Scrutiny.

“Eighty percent of the antibiotics sold in the United States goes to chicken, pigs, cows and other animals that people eat, yet producers of meat and poultry are not required to report how they use the drugs — which ones, on what types of animal, and in what quantities. This dearth of information makes it difficult to document the precise relationship between routine antibiotic use in animals and antibiotic-resistant infections in people”

This is insane. I had no idea the problem in the US was this big.

viii. One of my guilty pleasures:

(If you just want to watch the chess, you can skip the first 3 minutes or so.)

March 17, 2013 Posted by | Chess, Economics, Medicine, Papers, Psychology, Random stuff, Statistics, Studies | Leave a comment

Stuff

i. Sample Size in Psychological Research Over the Past 30 Years.

“Summary. —The American Psychological Association (APA) Task Force on Statistical Inference was formed in 1996 in response to a growing body of research demonstrating methodological issues that threatened the credibility of psychological research, and made recommendations to address them. One issue was the small, even dramatically inadequate, size of samples used in studies published by leading journals. The present study assessed the progress made since the Task Force’s final report in 1999. Sample sizes reported in four leading APA journals in 1955, 1977, 1995, and 2006 were compared using nonparametric statistics, while data from the last two waves were fit to a hierarchical generalized linear growth model for more in-depth analysis. Overall, results indicate that the recommendations for increasing sample sizes have not been integrated in core psychological research, although results slightly vary by field. This and other implications are discussed in the context of current methodological critique and practice.”

I unfortunately can’t find an ungated copy of this paper online, but here’s a little more stuff from the paper:

“Cohen (1962) concluded, “Increased sample size is likely to prove the most effective general prescription for improving power” (p. 153), but there is little evidence that the field has taken note. After reviewing the literature, Holmes (1979) reported finding only two studies that examined sample sizes directly. One study reported the number of articles published about single-subject samples (Dukes, 1965), and the other examined sample sizes reported in two British journals, finding that every reported study had N ≤ 25 (Cochrane & Duffy, 1974).
Holmes (1979, 1983) himself examined sample sizes in four APA journals in 1955 and 1977, and reported median sample sizes for the total study and each of the comparison groups. His general conclusions were that sample size had not changed significantly between 1955 and 1977, and that the typical sample size in psychology did not seem large […] the purpose of the present study was to examine sample sizes reported in the same four journals examined by Holmes (1979, 1983), but in more recent volumes. Two additional data collections were undertaken, one in 1995 (about the time the Task Force was formed), and the other in 2006 […]

Table 1(click to view in a higher resolution)

So yeah, the median sample size was 32 in 1995 and 40 in 2006. 25% of published studies had n=14 or less in 1995, and n=18 or less in 2006. The sample size that occured most often in the 1995 sample was n=8; in 2006 it was 16.

“Our modeling showed that sample size depends on the field. Smaller samples are needed in experimental settings, presumably because sufficient control of extraneous variation is in place, and standard errors tend to be smaller. (Higher cost per participant may also be a factor, due to sophisticated measurement equipment or laboratory controls.) However some fields, such as applied and developmental psychology, depend much more on quasi-experimental research because of their greater emphasis on comparisons of naturally occurring groups and ecological validity. Such research designs result in more variation in the data, and larger samples are necessary to gain feasible standard errors. (Lower cost per participant may also be a factor, because of the availability of institutional archival data.) […]

We found that overall, the relatively small sample sizes found by Holmes did not increase significantly over the next 29 years. However, there was significant variability in the change in sample size over time by field, with increases from 1977 to 2006 appearing in the Journal of Abnormal Psychology and Developmental Psychology, and no change in Experimental Psychology or Applied Psychology (which actually showed a slight decrease for individual sample size).
The third hypothesis was that sample sizes remained unchanged after the Task Force report in 1999. A change would have been reflected in a significant difference in sample size between 1995 and 2006, but none was found. This result is not surprising, given previous research on power (e.g., Cohen, 1962; Sedlmeier & Gigerenzer, 1989; Rossi, 1990; Maddock & Rossi, 2001; Maxwell, 2004) and Holmes’ own studies on sample size (Holmes, 1979, 1983; Holmes, et al., 1981). However, it is troubling, especially when one considers the increased use of sophisticated multivariate analyses and statistical modeling techniques during this time that would require the employment of larger sample sizes (Merenda, 2007; Rodgers, 2010).”

Here’s a link to one of the ungated power studies mentioned in the paper.

ii. Old pictures. Lots of old pictures.

iii. BookOs.

iv. “What [would happen] if I took a swim in a typical spent nuclear fuel pool? Would I need to dive to actually experience a fatal amount of radiation? How long could I stay safely at the surface?”

And now you know.

There’s a little background stuff on the subject here.

v. For some reason this picture touched me deeply (click to view full size):

Mongolian_woman_condemned_to_die_of_starvation_(retouched)Via Wikipedia.

vi. “Facebook killed TV.” – from this Paul Graham essay on Why TV Lost.

vii. The End of History Illusion.

“We measured the personalities, values, and preferences of more than 19,000 people who ranged in age from 18 to 68 and asked them to report how much they had changed in the past decade and/or to predict how much they would change in the next decade. Young people, middle-aged people, and older people all believed they had changed a lot in the past but would change relatively little in the future. People, it seems, regard the present as a watershed moment at which they have finally become the person they will be for the rest of their lives. This “end of history illusion” had practical consequences, leading people to overpay for future opportunities to indulge their current preferences.”

Unfortunately I’ve not been able to find an ungated link, but here’s a bit more from the concluding remarks of the paper:

“Across six studies of more than 19,000 participants, we found consistent evidence to indicate that people underestimate how much they will change in the future, and that doing so can lead to suboptimal decisions. Although these data cannot tell us what causes the end of history illusion, two possibilities seem likely. First, most people believe that their personalities are attractive, their values admirable, and their preferences wise (10); and having reached that exalted state, they may be reluctant to entertain the possibility of change. People also like to believe that they know themselves well (11), and the possibility of future change may threaten that belief. In short, people are motivated to think well of themselves and to feel secure in that understanding, and the end of history illusion may help them accomplish these goals.

Second, there is at least one important difference between the cognitive processes that allow people to look forward and backward in time (12). Prospection is a constructive process, retrospection is a reconstructive process, and constructing new things is typically more difficult than reconstructing old ones (13, 14). The reason this matters is that people often draw inferences from the ease with which they can remember or imagine (15, 16). If people find it difficult to imagine the ways in which their traits, values, or preferences will change in the future, they may assume that such changes are unlikely. In short, people may confuse the difficulty of imagining personal change with the unlikelihood of change itself.

Although the magnitude of this end of history illusion in some of our studies was greater for younger people than for older people, it was nonetheless evident at every stage of adult life that we could analyze. Both teenagers and grandparents seem to believe that the pace of personal change has slowed to a crawl and that they have recently become the people they will remain. History, it seems, is always ending today.”

February 8, 2013 Posted by | Psychology, Random stuff, Statistics, Studies | 4 Comments

Absolute Risk of Suicide After First Hospital Contact in Mental Disorder

This new article is rather awesome, if for no other reason then because it involves so many people and follow them over such a long time-frame:

Objective  To estimate, in a national cohort, the absolute risk of suicide within 36 years after the first psychiatric contact.

Design  Prospective study of incident cases followed up for as long as 36 years. Median follow-up was 18 years.

Setting  Individual data drawn from Danish longitudinal registers.

Participants  A total of 176 347 persons born from January 1, 1955, through December 31, 1991, were followed up from their first contact with secondary mental health services after 15 years of age until death, emigration, disappearance, or the end of 2006. For each participant, 5 matched control individuals were included.”

176.347 people followed for roughly two decades on average. That’s a lot of data. What did they find? Some of the main results:

Results  Among men, the absolute risk of suicide (95% confidence interval [CI]) was highest for bipolar disorder, (7.77%; 6.01%-10.05%), followed by unipolar affective disorder (6.67%; 5.72%-7.78%) and schizophrenia (6.55%; 5.85%-7.34%). Among women, the highest risk was found among women with schizophrenia (4.91%; 95% CI, 4.03%-5.98%), followed by bipolar disorder (4.78%; 3.48%-6.56%). In the nonpsychiatric population, the risk was 0.72% (95% CI, 0.61%-0.86%) for men and 0.26% (0.20%-0.35%) for women. Comorbid substance abuse and comorbid unipolar affective disorder significantly increased the risk. The co-occurrence of deliberate self-harm increased the risk approximately 2-fold. Men with bipolar disorder and deliberate self-harm had the highest risk (17.08%; 95% CI, 11.19%-26.07%).”

As mentioned they of course they didn’t just limit themselves to following ‘the sick people’ – they also needed people to compare them with… So:

“To estimate the cumulative incidence of suicide among people with no history of mental illness, we adopted a slightly alternative strategy. For each person with a history of any mental illness (as defined in the“Assessment of Suicide and Mental Illness” subsection), we randomly selected 5 people of the same sex and same birth date who had no history of mental illness (time matched). Using the described strategy, we followed up this healthy population (881 735 persons) to provide absolute suicide risks. Because this healthy population was selected at random among all 2.46 million people included in the study population, the estimates obtained represent the absolute risk of suicide among all 2.46 million people without a mental disorder.”

Again, that’s a lot of data – representativeness really is unlikely to be an issue here (at least when dealing with the situation in Denmark). As they put it in the paper: “This is the first analysis of the absolute risk of suicide in a total national cohort of individuals followed up from the first psychiatric contact, and it represents, to our knowledge, the hitherto largest sample with the longest and most complete follow-up.”

Results in a bit more detail:


(click to view full size). I’ve previously seen it argued in papers on anorexia that it’s the phychiatric disorder with the highest mortality rate, so I was a bit surprised by the relatively low numbers here. On the other hand that may be related to the fact that they tend to starve themselves to death rather than take their own lives in the traditional sense, which means that a lot of those excess deaths are not considered suicides. Note that a big majority of all suicides committed are committed by people with a mental illness and that the risk increase from a diagnosis is really quite significant; given the estimates, females with a mental illness are more than 8 times as likely to kill themselves than females without a mental illness, and males are 6 times more likely. Schizophrenic females are almost 20 times as likely to commit suicide than are females without a mental illness. Add substance abuse as well and these females are more than 30 times as likely to commit suicide (the absolute risk is around 7% in that case). The risk is substantially increased for almost all groups when you add substance abuse.

Do also note that not all people in the ‘mental illness’ group are actually people with a mental illness; personality disorders are not usually considered mental illnesses by health professionals, but the study includes in the group of people with mental illnesses people with: “any mental illness (any ICD-8 or ICD-10 code) if they had been admitted to a psychiatric hospital or had been in outpatient care with one of these diagnoses.” (The “any ICD-8 or ICD-10 code” means that people with personality disorders are included in the group as well). This is probably ‘fair enough’ given that at least some of these groups clearly have elevated suicide levels, but it’s worth having in mind that it should change the interpretation slightly. How about people who’ve attempted suicide?

The deliberate self-harm/attempted suicide group is obviously a high-risk group. The follow-up period is shorter than for the other estimates (30 years, rather than 36) so these estimates are perhaps best thought of as lower bounds. There’s some uncertainty regarding the estimates because the sample sizes aren’t that big (which is a good thing I think…), but roughly 1 in 6 Danish males with bipolar affective disorder killed themselves during the period. The absolute risks here are substantial; for the ‘any mental illness’ group, one in 12 committed suicide during the period. Although the female numbers are substantially lower for the group as a whole, for some illnesses the absolute risk is comparable to that of the males (and the excess risk much, much higher). More than one in ten females with schizophrenia and a suicide attempt in the past committed suicide during the follow-up period.

I should perhaps mention here that there may be some significant tail risk unaccounted for in the data, despite the long follow-up period which might lead you to think these are good estimates of the ‘lifetime probability of suicide’. The suicide-rate of Danish males above the age of 85 is the highest of all age groups, and it’s five times as high as the suicide risk of males at the age of 25-29 (Danish link). This is not just a Danish thing – similar dynamics have been observed elsewhere. Age matters a lot here, but people tend to care less when old people kill themselves than when young people do.

November 19, 2012 Posted by | Data, Epidemiology, Medicine, Psychology, Studies | Leave a comment