I’ve finished the book.
It’s a Springer book, so those of you who’ve encountered these books before will know that this is not an easy-to-read popular-science book. The general level is high and occasionally I felt almost completely lost; chapter 8 for example was very technical. As I’ve pointed out before, I don’t like to fault authors for not taking into account the possibility that their books may also be picked up by ignorant fools who don’t know anything, but if you have a hard time understanding what the author is getting at it will affect your reading experience in a negative manner. It should be noted, though, that although it’s not an easy book to read you’ll learn a lot of stuff if you put in some effort (…and/but if you don’t put in some effort you’ll never finish it, and you’ll get nothing out of it at all).
Some of the chapters deal with similar stuff, and I got the impression a couple of times that the authors of a specific chapter had not read the other chapters. On the other hand it’s very clear in other chapters/contexts that they most certainly did, but even so there are a few things which are repeated a few times along the way which perhaps did not need to be repeated. On the third hand the book is structured in such a way that each chapter is pretty much self-contained (which is presumably part of the explanation for the occasional repetitions), and the fact that you probably don’t necessarily need to read it cover to cover from chapter one to chapter 9 the way I did to get a lot out of the book would presumably be appealing to some people.
I gave it four stars on goodreads, because of the high quality of the material included.
Some stuff from the last chapters, with some hopefully helpful links added to make the passages easier to understand (perhaps needless to say no such links are included in the book, so if you find the links helpful you’ll probably need to look up some stuff along the way if you decide to read it yourself…) as well as some comments here and there:
“Emerging studies clearly indicate that a bidirectional crosstalk is established between all cellular components of AT [adipose tissue, US] and cancer cells and that the tumor-surrounding AT contributes to inflammation, extracellular matrix remodeling as well as energy supply within the tumors. In this chapter, we present evidences showing how AT locally affects tumor progression in given types of tumors and how these results might be attractive to explain the link between obesity and the poor prognosis of some cancers. This will be preceded by the overall description of AT composition and function with special emphasis on the specificity of adipose depots, key aspects that need to be taken in account when paracrine effects of AT on tumor progression is considered. […]
The past two decades have provided substantial evidence for the major role of the tissue local environment for tumor progression. Cancer is now considered as a tissue-based disease in which malignant cells interact dynamically with the surrounding supportive tissue, the tumor stroma, composed by multiple normal cell types such as fibroblasts, infiltrating immune cells, and endothelial cells within the context of extracellular matrix . This stroma/tumor cell interaction involves constant bidirectional crosstalk between normal and malignant cells. Cancer cells usually generate a supportive microenvironment by activating the wound-healing response of the host . Conversely, the stromal cells, such as for example, cancerassociated fibroblasts (CAFs) or tumor-associated macrophages (TAMs), promote tumor progression through different mechanisms including enhancement of tumor survival, growth, and spread, by secreting growth factors, chemokines, extracellular matrix (ECM) components, and ECM-modifying enzymes [3,4]. Constituents of the tumor microenvironment can arise from two major sources: recruitment from nearby local tissue or systemic recruitment from distant tissues via circulation. Among the different cell types frequently found at close proximity of evolving tumors, little attention has been given to cells that compose the adipose tissue (AT) although a growing interest can be noted in recent years. Throughout the body, AT is mainly described as subcutaneous (i.e., superficial and deep hypodermic location) and visceral depots. Visceral adipose tissue (VAT) surrounds the inner organs and can be divided into omental, mesenteric, retroperitoneal (surrounding the kidney), gonadal, perivascular, and pericardial depots . Of note, AT is also present in the breast (mammary adipose tissue or MAT) and in the bone marrow (BM). All these specific regional depots exhibit differences in structure, function, composition, and secretion profiles . […] The cellular heterogeneity of AT adds an additional degree of complexity when AT/cancer cells crosstalk is considered. […] All the cells from adipose tissue (including mature adipocytes) produces a large number of secretory bioactive substances, such as hormones, growth factors, chemokines, proangiogenic or proinflammatory molecules , which could directly affect adjacent tumors. AT is therefore an excellent candidate to influence tumor behavior through heterotypic paracrine signaling processes and might prove to be critical for tumor survival, growth, local, and distant invasion. […] Fat depots from different region of the body have different incidence in pathology because they display distinct functional and structural properties in terms of energy metabolism and bioactive molecule (adipokines) release as well. Regional heterogeneity plays a central role in mammalian AT homeostasis.” (I talked about these aspects in the last post, but I figured I should give at least part of the ‘medical textbook version’ here..) […]
“Ovarian cancer is a highly fatal disease, with only about 40 % of women with ovarian cancer still alive more than 5 years postdiagnosis. This poor survival is largely attributable to the fact that a majority of ovarian cancer in developed countries is diagnosed with metastatic spread. The omentum, a peritoneal organ rich in AT and immune cells, has been shown to be a preferred site of metastatic dissemination in ovarian cancer patients. Omental dissemination, which is often accompanied by ascites, facilitates the further spread of the tumors .” […]
“Prostate cancer is the most common malignancy in males in Western countries, representing the second leading cause of cancer death. Prostate is surrounded by AT and tumor admixed with periprostatic fat is the most easily recognized manifestation of extraprostatic extension, a well-established adverse prognostic factor for prostate cancer [79,80]. Periprostatic AT (PPAT) is considered as VAT, but the specificities of this depot in terms of metabolism and adipokines secretion remain largely unknown. At laboratory levels, the contribution of this tissue to cancer progression has been first suggested by the report of Finley et al. that analyzed the PPAT features in patients undergoing prostatectomy for cancer . In this study, the authors found that the level of IL-6 secreted by PPAT-conditioned medium (CM) was almost 375 times greater than the circulating levels of the cytokine in the same patient. Both IL-6 levels in PPAT and activation of IL-6 related signaling pathways were correlated to tumor aggressiveness . Therefore, this study strongly suggests that PPAT represents an important source of IL-6 that favors tumor progression. Interestingly, several studies already reported that increased serum IL-6 and soluble interleukin-6 receptor levels are associated with aggressiveness of the disease and with a poor prognosis in prostate cancer patients, underlying the importance of this pathway in PC progression (for review see ). […] Recent studies suggest that, like in breast cancer, a bidirectional crosstalk exists between PC cells and surrounding AT.” […]
“During the last decade, pancreatic cancer has become the fourth leading cause of cancer-related death in the USA and the sixth leading cause in Europe. Despite major advances in surgical techniques and adjuvant therapies, overall 5-year survival remains under 5 %. While very few, if any, laboratory studies have been performed to date on the crosstalk between pancreatic cancers and AT, several clinical data have suggested that an adipose-rich environment leads to a deleterious outcome on this disease. […] it has been demonstrated that peripancreatic fat invasion is correlated to a poorer survival for pancreatic cancers . Recent epidemiologic studies also suggest that obesity doubles the relative risk of pancreatic cancer . In addition, central adiposity has been shown to be an independent risk factor in development of pancreatic cancer as well as to contribute to a poorer survival . Interestingly, it has been demonstrated that increased pancreatic fat (pancreatic steatosis) promotes dissemination and lethality of pancreatic cancer .” […]
“The relationship between AT and cancer is complex and involves both paracrine and endocrine effects whose relative contribution to tumor progression remains to be determined. Regarding paracrine effects, we have underlined in this chapter the need to consider the appropriate neighboring AT for each cancer subtypes in experimental studies. […] there is clear variations between the different AT in terms of secretion and sensitivity to lipolysis […] Nevertheless, regarding AT/cancer crosstalk, there are common features found in several cancer subtypes. […] it is very important to underline that adipose cells are not inert to their surrounding and that their phenotype are profoundly modified by cancer cell secretions.” […]
“Present data suggest caution about the clinical use of lipotransfer-derived WAT cells for breast reconstruction in patients with breast cancer [15,16].”
I thought I should make a brief stop here to cover the observation above in a little more detail, because I think it’s a good illustration of why the finer details of how these things work actually matter. Now, one might well be tempted to say that if we know that fat people get cancer more often and have worse prognoses (this is, incidentally, a gross oversimplification – as should be clear from the posts), well – do we really need to know all that much more about how it all works out at the microscopical level and so on? Why not just tell people to lose weight and just leave it at that? Findings like the ones in [15,16] above indicate that it matters what goes on in these tissues. What did the studies tell us? Well, it has been observed that female breast cancer survivors who have undergone a specific type of reconstructive surgery (‘lipotransfer procedure for esthetical purposes’) had higher cancer recurrence risk than did females who had not undergone such a procedure; this is important information with clinical relevance. One basic idea behind what may be happening is that the adipose tissue that is transplanted into the reconstructed breast(/s) may work as a fuel source for any remaining cancerous cells still hiding in the tissues (/and it may spark new tumor development through the crosstalk and paracrine signalling mechanisms already mentioned). Note that this information may not yet be well known – see e.g. this webpage about reconstructive breast surgery from the website of Johns Hopkins University, which is hardly an institution to be found at the bottom of the barrel: “we try to give women the look and feel of an actual breast, using creative techniques such as fat grafting, also known as lipofilling or fat transfer. Fat can be taken from another part of your body, possibly the abdomen or somewhere on your buttocks, through liposuction. The fat will be purified and carefully layered within the new breast to create the desired shape. Our surgeons are experienced at these techniques.” They may want to reconsider at the very least the extent to which they are using these techniques. Anyway, back to the book:
“surgical options for treatment of the severely obese population have increased in popularity over the last few decades, with an estimated 344,000 cases performed globally in 2008 [40,41]. As previously noted, lifestyle therapy for weight loss intervention is generally insufficient for extremely obese patients and effective long-term weight loss using pharmacological therapy has been limited, leaving bariatric surgery as the only medical intervention providing substantial, long-term weight loss for most severely obese patients. […] Because post-bariatric surgical patients generally experience significant and sustained weight loss [2,47], they represent a unique population to study the relationship between voluntary weight loss and cancer risk. […] Generally, 80 % of patients who seek bariatric surgery are female.” […]
“Since 2009, there have been five reviews exploring the potential relationship between bariatric surgery and subsequent cancer risk [22,40,59–61], and two additional reviews of cancer risk associated with either weight loss from bariatric surgery or nonsurgical weight loss therapies [11,20]. […] [the following are some results from these studies:] Reported cancers subsequent to bariatric surgery were 117 cancers in the surgical group compared to 169 cancers among the control groups, representing an HR of 0.67 (95 % CI 0.53–0.85; p=0.0009). For female participants only, the surgical group had a reported 79 cancers compared to 130 cancers in the control females, giving an HR value of 0.58 (95 % CI 0.44–0.77; p = 0.0001). […] After a maximum of 5-year followup, the reported number of visits to the physician/hospital that led to a cancer-related diagnosis for the weight loss surgical group was 21 visits (2.0 %) compared with 487 visits (8.5 %) among the control group. This difference was reported to have a relative risk of 0.22 (95% CI 0.14–0.35; p=0.001) . […] the relative risk for breast cancer was 0.17 (95% CI 0.01–0.31; p=0.001). […] For cancer deaths, the bariatric surgical group was 60 % lower when compared the control group ( p = 0.001; 31 deaths among surgical group compared to 73 deaths in control groups). […] For all cancers combined, there was a 24 % reduction in cancer incidence among the surgical group compared to controls (HR 0.76, 95% CI, 0.65–0.89; p=0.0006). […] Based upon these analyses, it was estimated that about 71 gastric bypass surgeries would be necessary to prevent one incident cancer . […] [And so they conclude:] there are now studies that demonstrate a reduction in cancer mortality among postbariatric patients compared to severely obese, nonoperated controls. In addition, one prospective study (SOS study) and a few observational studies have also demonstrated a reduction in cancer incidence following metabolic surgery. To date, the reduced cancer risk benefits have been limited to females and there appears to be a stronger correlation of benefit associated with cancers that are “likely” to be obesity related. Given these limitations, the general consensus is that intentional weight loss does lead to a reduction in cancer incidence .” […]
“Multiple reviews have been published on the effect of metabolic surgery on diabetes, including a meta-analysis by Buchwald et al., which reported a 78.1 % remission of diabetes and an 86.6 % improvement or remission in diabetes following bariatric surgery . The intriguing element related to diabetes remission is that a significant number of bariatric surgical patients (i.e., gastric bypass patients) have discarded their antidiabetic medication and returned to a normal blood glucose by the time they are discharged from the hospital following their metabolic surgery (i.e., 2–3 days after surgery) and long before significant weight loss has occurred . Again, mechanisms accounting for this remarkable remittance or improvement of diabetes following surgery are multiple. In an analogous way, the reduced risk of cancer following metabolic surgery is also likely to be linked with several biological mechanisms, which may or may not be directly associated with weight loss.”
I’ve read the first third of this book, and it’s been a quite interesting read so far. Some parts have been easier to read than others and occasionally it gets a bit technical, but overall it’s a quite readable book for someone with my background and I’m certainly learning some new stuff by reading this.
Some observations from the book:
“obesity and metabolic syndrome are linked to various chronic diseases [6,7] including cardiovascular disease, type II diabetes, and the focus of this chapter, cancer. Importantly, not all obese individuals develop the metabolic dysregulation usually associated with obesity and metabolic syndrome, and these “metabolically healthy obese” individuals do not have elevated cancer risk. An estimated 30 % of obese individuals in the USA are metabolically healthy . Conversely, some nonobese individuals can develop the metabolic perturbations usually associated with obesity, and these individuals appear to be more prone to chronic diseases including cancer . Thus, an emerging hypothesis is that the obesity-related metabolic perturbations, and not specific dietary components or increased adiposity, are at the crux of the obesity–cancer connection.” […]
“Evidence-based guidelines for cancer prevention urge maintenance of a lean phenotype . Overall, an estimated 15–20 % of all cancer deaths in the USA are attributable to overweight and obese body types . Obesity is associated with increased mortality from cancer of the prostate and stomach in men; breast (postmenopausal), endometrium, cervix, uterus, and ovaries in women; and kidney (renal cell), colon, esophagus (adenocarcinoma), pancreas, gallbladder, and liver in both genders . While the relationships between metabolic syndrome and specific cancers are less well established, first reports from the Metabolic Syndrome and Cancer Project, a European cohort study of ~580,000 adults, confirm associations between obesity (or BMI) in metabolic syndrome and risks of colorectal, thyroid, and cervical cancer .”
“During obesity, adipose tissue responds to the excess energy by increasing adipocyte size (hypertrophy) and enhancing adipocyte proliferation (hyperplasia) . Adipocyte size strongly correlates with insulin resistance and secretion of proinflammatory cytokines . Moreover, location of the adipose tissue also determines risk for metabolic diseases. […] Healthy adipose tissue must be able to rapidly respond to excess energy intake by inducing adipocyte hypertrophy and hyperplasia, remodeling of the extracellular matrix, and enhanced neovascularization to nourish the adipose tissue. In pathological states such as insulin resistance associated with obesity, rapid adipocyte hypertrophy occurs with restricted angiogenesis resulting in cellular hypoxia, and thereby resulting in local inflammation . Macrophages surrounding necrotic adipocytes phagocytize fatty acids, which are released from the adipocyte. This produces bloated, lipid overburdened macrophages, which is characteristic of chronic inflammation and often observed in obese individuals . […] inflammation is a recognized hallmark of cancer, and growing evidence continues to indicate that chronic inflammation is associated with increased cancer risk [75–77]. Several tissue-specific inflammatory lesions are established neoplastic precursors for invasive cancer, including gastritis for gastric cancer, inflammatory bowel disease for colon cancer, and pancreatitis for pancreatic cancer [78,79].”
“When lipid storage capacity in adipose tissue is exceeded, surplus lipids often accumulate within muscle, liver, and pancreatic tissue . As a consequence, hepatic and pancreatic steatosis can develop; both have been positively associated with insulin resistance and ultimately lead to impairment of lipid processing and clearance within these tissues . […] The term nonalcoholic fatty liver disease (NAFLD) refers to a disease spectrum that includes variable degrees of simple steatosis, nonalcoholic steatohepatitis (NASH), and cirrhosis [19,20]. Simple steatosis is benign, whereas NASH is defined by the presence of hepatocyte injury, inflammation, and/or fibrosis, which can lead to cirrhosis, liver failure, and hepatocellular carcinoma. […] NASH occurs in 20 % of cases of NAFLD and ~5–20 % of NASH cases progress to cirrhosis; 80 % of cryptogenic cirrhosis cases present with NASH . Of this group, ~0.5 % will eventually progress to hepatocellular carcinoma […] In Western populations, overnutrition/obesity is the most common cause of NAFLD” […] NAFLD has evolved in parallel to the obesity pandemic as the most prevalent liver disease worldwide. Whereas the fact that chronic liver inflammation as observed in nonalcoholic steatohepatitis (NASH) finally leads to the development of hepatocellular carcinoma is well accepted , its association with increased formation of adenomatous polyps and CRC has just recently been established [124,125].”
“Hyperglycemia, a hallmark of metabolic syndrome, is associated with insulin resistance, aberrant glucose metabolism, chronic inflammation, and the production of other metabolic hormones such as IGF-1, leptin, and adiponectin . […] In metabolic syndrome, the amount of bioavailable IGF-1 increases […] Elevated circulating IGF-1 is an established risk factor for many cancer types [38,39].”
VEGF [Vascular Endothelial Growth Factor], a heparin-binding glycoprotein produced by adipocytes and tumor cells, has angiogenic, mitogenic, and vascular permeability-enhancing activities specific for endothelial cells . Circulating levels of VEGF are increased in obese, relative to lean, humans and animals, and increased tumoral expression of VEGF is associated with poor prognosis in several obesity-related cancers . The need for nutrients and oxygen triggers tumor cells to produce VEGF, which leads to the formation of new blood vessels to nourish the rapidly growing tumor and may facilitate the metastatic spread of tumors cells .”
“Epidemiological studies indicate that obesity represents a significant risk factor for the development of various cancers such as prostate and breast cancer, leading cancers in the Western world. An impressive body of evidence, however, also indicates that the risk of colorectal adenoma, and cancer (CRC) is increased in subjects with obesity and related metabolic syndrome [2,3]. […] Colorectal cancer is the second leading cancer death in the Western world and its death rate correlates with body mass index . […] Recent CRC screening studies suggest that obesity and an increased body mass index are a significant additional risk factor for the development of colonic polyps with evidence that advanced adenomas arise in men almost a decade earlier than in women . […] menopausal status appears to modify the relationship between BMI and colon cancer with a strong association between BMI and colon cancer risk seen in premenopausal but not postmenopausal women . […] being obese prior to being diagnosed with colon cancer increases your risk of dying from the disease [29–32]. […] more and more studies are now demonstrating the location of body fat tissue is the best predictor of all-cause and colorectal cancer mortality […] colon cancer survival may be less likely for patients who are […] too thin at diagnosis .”
“In a meta-analysis of 52 studies (24 case–control and 28 cohort studies) examining the link between physical activity and colon cancer, a significant 24 % reduced risk of colon cancer in people who were most active compared with the least was found . This supports other reviews of the association between physical activity and colon cancer in the Asian and European populations [49,50]. […] Physical activity also appears to affect disease outcome and recurrence after diagnosis and treatment with the greatest effect on colon cancer incidence . […] new well-controlled clinical trials on obesity prevention and obesity treatment are necessary before therapeutic implications of WAT [White Adipose Tissue] reduction on cancer predisposition are completely understood. One of the possibly important considerations is the number of adipocytes and the accompanying stromal/vascular cells in WAT increasing in obesity and remaining increased even upon subsequent weight loss, which occurs via adipocyte size reduction. The pool of ASC [Adipose Stem Cells] is likely to remain intact and could contribute to cancer onset or progression despite calorie restriction and reduced adiposity.”
“There is general agreement that obesity is associated with an increased incidence of breast cancer in postmenopausal women (reviewed in [14–17]). […] The European Prospective Investigation into Cancer and Nutrition (EPIC) study , which had 57,923 postmenopausal participants, is of particular interest because of its large size, its prospective design, and the observations made concerning exogenous estrogens as a confounder. The results showed that a long-term weight gain was related to an increase in risk, but only in those who were not taking hormone replacement medication: compared with women with a stable body weight the relative risk for women who gained 15–20 kg was 1.5 with a confidence interval of 1.60–2.13. As reported by others, adiposity ceased to be a risk factor in current replacement therapy users, who were already at a high risk for breast cancer compared with nonusers. […] Preexisting obesity and postoperative weight gain are associated with poor prognosis in both premenopausal and postmenopausal breast cancer patients. […] A pivotal review of the literature by Chlebowski et al.  found that in 26 out of 34 studies individual studies, totaling 29,460 women, obesity was related to an increased risk of recurrence or reduced survival.”
“Daling et al.  have provided a major contribution to our understanding in the relationships between body fat mass and tumor biomarkers of progression in young breast cancer patients. In their study, not only was a combination of obesity and an absence of ER expression in premenopausal breast cancer patients aged younger than 45 years associated with an increased risk of dying from the disease, but those with BMI values in the highest quartile were more likely to have larger tumors of high histologic grade. This observation is particularly significant because it implies that large tumors in overweight/obese women grow at a faster rate than tumors of similar size from leaner women, rather than simply arising from delayed diagnosis due to palpation difficulty in obese women.”
“Wolf et al.  and Schott et al.  suggested that up to 16 % of breast cancer patients have diabetes, and that T2D may be associated with a 10–20 % excessive risk of breast cancer. […] There is ample epidemiological evidence that diabetes contributes to breast cancer risk [17,36–40]. […] Overall survival in cancer patients, with or without preexisting diabetes, has shown diabetes to be associated with an increased all-cause mortality risk. […] The Danish Breast Cancer Cooperative Group, with 18,762 newly diagnosed T2D cases, found that the recurrence with metastases was 46 % higher in obese women with a BMI of 30 kg/m^2 or greater beyond the first 5 years.”
The relationship between obesity and prostate cancer is a complicated one. […] The explanation for this confusion may rest, at least in part, in the reports that obesity as a positive risk factor for prostate cancer relates specifically with the aggressive phenotype [56–60] […] a meta-analysis by Discacciati et al.  of the results from 25 studies that examined disease stage and BMI showed not only a positive relationship between obesity and advanced prostate cancer but also a decrease in the risk for localized disease. The association between obesity and an aggressive prostate cancer phenotype is reflected in the relationship between the BMI and prostate cancer mortality rate. For example, in one large retrospective cohort study by Andersson et al.  […] there was a significantly larger prostate cancer mortality rate in the higher BMI categories”
Two studies have been reported in which meta-analysis was used to examine previously published investigations into the relationship between diabetes mellitus and prostate cancer risk [66,67]. […] [The first] meta-analysis showed that there was an inverse relationship between diabetes and prostate cancer risk, which translated to a 9 % reduction in risk. […] The overall conclusion […in the second meta-analysis] was the same: diabetic men have a significantly decreased risk of developing prostate cancer (RR = 0.84; 95% CI, 0.76–0.93). […] Gong et al.  reported a large prospective study of diabetes and prostate cancer from the USA after the two meta-analyses described above had been published that also took account of potential confounding by obesity. Men with diabetes had a 34 % lower risk of prostate cancer compared with men without diabetes that was not affected by adjustment for the BMI […] In contrast to these results, recently published studies have found that the presence of diabetes is positively associated with prostate cancers of high-grade [71–73] and late-stage tumors  ], a reversal in the observed relationship that needs to be considered in the context of the duration of the presence of T2D and the detection of prostate cancer by prostatic-specific antigen screening.”
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:
“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.”
“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.
i. Yesterday I had a very bad and prolonged hypoglycemic episode which lasted hours. I was in a semi-conscious state for a long time before realizing there was a problem, and the situation did not improve much even after intake of significant amounts of dextrose. This is by far the closest I’ve been to a hospital admission for more than a year – I had both severe neurological symptoms and GI-tract involvement. I don’t think I’ve ever been admitted without GI-tract involvement, and this tends to worsen outcomes significantly – it’s hard to reverse a disease process the main treatment of which is putting stuff into your stomach and keeping it there if you have severe nausea and vomit up the stuff you eat.
I really hope that if something like this happens again I’ll be smart enough to actually call an ambulance, or at the very least involve other people so that they can help me if things go really bad. I like to tell myself that I am a very self-reliant and independent person in general – the sort of person who don’t like to ask other people for help and so rarely do. And nobody likes to be seen and judged by others when they’re at their weakest. Combine these facts with the inherent difficulty of assessing when a situation such as this one is sufficiently severe to merit involving other people while you’re having neurological symptoms impacting your thought processes and impairing judgment, and you have the perfect recipe for a situation where you end up making bad decisions and running a major risk of things going very wrong by not getting help. I should really become better at reminding myself (to the extent that it’s possible; as mentioned impaired judgment is a symptom here, so this stuff is not completely under my control) that when I’m in a state like this I’m just a very sick person who very well may need other people’s help simply to survive. Type 1 diabetics die from such hypoglycemic episodes all the time.
Here’s a related post from the past.
ii. (Yet) A(nother) medical lecture:
iii. An event that changed the world:
Objectives To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.
Design Systematic review of randomised controlled trials.
Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.
Study selection: Studies showing the effects of using a parachute during free fall.
Main outcome measure Death or major trauma, defined as an injury severity score > 15.
Results We were unable to identify any randomised controlled trials of parachute intervention.
Conclusions As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.”
v. On Being Sane in Insane Places, by David L. Rosenhan.
“At its heart, the question of whether the sane can be distinguished from the insane (and whether degrees of insanity can be distinguished from each other) is a simple matter: Do the salient characteristics that lead to diagnoses reside in the patients themselves or in the environments and contexts in which observers find them? From Bleuler, through Kretchmer, through the formulators of the recently revised Diagnostic and Statistical Manual of the American Psychiatric Association, the belief has been strong that patients present symptoms, that those symptoms can be categorized, and, implicitly, that the sane are distinguishable from the insane. More recently, however, this belief has been questioned. Based in part on theoretical and anthropological considerations, but also on philosophical, legal, and therapeutic ones, the view has grown that psychological categorization of mental illness is useless at best and downright harmful, misleading, and pejorative at worst. Psychiatric diagnoses, in this view, are in the minds of observers and are not valid summaries of characteristics displayed by the observed. [3-5]
Gains can be made in deciding which of these is more nearly accurate by getting normal people (that is, people who do not have, and have never suffered, symptoms of serious psychiatric disorders) admitted to psychiatric hospitals and then determining whether they were discovered to be sane and, if so, how. If the sanity of such pseudopatients were always detected, there would be prima facie evidence that a sane individual can be distinguished from the insane context in which he is found. Normality (and presumably abnormality) is distinct enough that it can be recognized wherever it occurs, for it is carried within the person. If, on the other hand, the sanity of the pseudopatients were never discovered, serious difficulties would arise for those who support traditional modes of psychiatric diagnosis. Given that the hospital staff was not incompetent, that the pseudopatient had been behaving as sanely as he had been out of the hospital, and that it had never been previously suggested that he belonged in a psychiatric hospital, such an unlikely outcome would support the view that psychiatric diagnosis betrays little about the patient but much about the environment in which an observer finds him.
This article describes such an experiment.”
Here’s the wikipedia article about the experiment. Below some more stuff from the paper:
“Eight sane people gained secret admission to 12 different hospitals . […] the pseudopatients were never detected. Admitted, except in one case, with a diagnosis of schizophrenia , each was discharged with a diagnosis of schizophrenia “in remission.” The label “in remission” should in no way be dismissed as a formality, for at no time during any hospitalization had any question been raised about any pseudopatient’s simulation. Nor are there any indications in the hospital records that the pseudopatient’s status was suspect. Rather, the evidence is strong that, once labeled schizophrenic, the pseudopatient was stuck with that label. If the pseudopatient was to be discharged, he must naturally be “in remission”; but he was not sane, nor, in the institution’s view, had he ever been sane. […] Length of hospitalization ranged from 7 to 52 days, with an average of 19 days.” […]
“Failure to detect sanity during the course of hospitalization may be due to the fact that physicians operate with a strong bias toward what statisticians call the Type 2 error . This is to say that physicians are more inclined to call a healthy person sick (a false positive, Type 2) than a sick person healthy (a false negative, Type 1). The reasons for this are not hard to find: it is clearly more dangerous to misdiagnose illness than health. Better to err on the side of caution, to suspect illness even among the healthy.” […]
“The following experiment was arranged at a research and teaching hospital whose staff had heard these findings but doubted that such an error could occur in their hospital. The staff was informed that at some time during the following three months, one or more pseudopatients would attempt to be admitted into the psychiatric hospital. Each staff member was asked to rate each patient who presented himself at admissions or on the ward according to the likelihood that the patient was a pseudopatient. A 10-point scale was used, with a 1 and 2 reflecting high confidence that the patient was a pseudopatient.
Judgments were obtained on 193 patients who were admitted for psychiatric treatment. All staff who had had sustained contact with or primary responsibility for the patient — attendants, nurses, psychiatrists, physicians, and psychologists — were asked to make judgments. Forty-one patients were alleged, with high confidence, to be pseudopatients by at least one member of the staff. Twenty-three were considered suspect by at least one psychiatrist. Nineteen were suspected by one psychiatrist and one other staff member. Actually, no genuine pseudopatient (at least from my group) presented himself during this period.
The experiment is instructive. It indicates that the tendency to designate sane people as insane can be reversed when the stakes (in this case, prestige and diagnostic acumen) are high. But what can be said of the 19 people who were suspected of being “sane” by one psychiatrist and another staff member? Were these people truly “sane” or was it rather the case that in the course of avoiding the Type 2 error the staff tended to make more errors of the first sort — calling the crazy “sane”? There is no way of knowing. But one thing is certain: any diagnostic process that lends itself too readily to massive errors of this sort cannot be a very reliable one. […]
It is clear that we cannot distinguish the sane from the insane in psychiatric hospitals. The hospital itself imposes a special environment in which the meaning of behavior can easily be misunderstood. The consequences to patients hospitalized in such an environment — the powerlessness, depersonalization, segregation, mortification, and self-labeling — seem undoubtedly counter-therapeutic.”
“The share of one-person households in the U.S. maintained by men ages 15 to 64 rose to 34% in 2012, up from 23% in 1970, according to a Census report on the status of families released Tuesday. For women of the same age, this figure actually dropped slightly, to 30% in 2012 from 31% in 1970.
The findings may reflect, in part, the sharp increase in divorce rates in the U.S. throughout the 1970s, Census said. The dominant living arrangement for children following their parents’ divorce is custody by mothers.”
I would have preferred to read the actual Census report and I did go have a look for it; but when I click the pdf link to the report in question at the census site all I get is an error message (link) – they seem to have put up a corrupt link. Annoying. Here are some related Danish numbers which I blogged a while ago. Although the 2012 report doesn’t seem to be available, there’s a lot of 2009-2011 data on related matters here. I messed around a little with that data – below some stuff from that source:
Naturally there’s a big gender disparity; at the age range of 24-29, 89.1% of males have never married whereas only 80,7% of the females have never married. For people in the 25-29 year age range 64% of males and 50,1% of females have never married. You’d expect the numbers to converge somewhat ‘over time’ (/as people get older) and they do, but not until we reach the age group of 55-64 year olds do the proportion of females who have never married surpass the proportion of males who have not (and these numbers are quite small – less than 9% have never married at that age, both when looking at males and females).
Higher earnings seem to confer an advantage when it comes to minimizing the risk of never getting married, which is of course a big surprise. For example, of the 45-49 year old people with a reported income of $25,000 to $39,999 17,6% of them have never married, whereas the corresponding number for people with an income of $40,000-75,000 is 11,5%. For people with incomes in the $75,000-$100,000 range the number is 5.5%, and incidentally the number of 45-49 year olds with incomes above $100k who’ve never married is also 5,5%. The relationship is not perfectly linear, but it’s clear that people with higher earnings have a higher likelihood of getting married. Incidentally almost a third of people in that age range who reported annual earnings less than $5000 have never married (29.2%).
The numbers above are from the first third of the first document. There’s a lot of data available here if you’re curious.
ii. Global Reality of Type 1 Diabetes Care in 2013. Not much to see here – here’s why I bookmarked it:
“from a global perspective, the most common cause of death for a child with type 1 diabetes is lack of access to insulin (2). Yet, this is not just a problem for low-income countries, with one recent study in the U.S. noting that discontinuation of insulin therapy represents the leading precipitating cause of diabetic ketoacidosis (3). Indeed, lack of insulin explained 68% of such episodes in people living in an inner-city setting, with approximately one-third of people reporting a lack of financial resources to buy insulin and eking out their insulin supplies.”
We’re talking about the United States of America, a very rich country – and in fact the country in the world with the highest health care expenditures. And still you have type 1 diabetics who go into ketoacidosis because they can’t afford their drugs. That’s messed up. Note that low medical subsidies to type 1s may not necessarily be cost saving at a systemic level as hospital admissions are very expensive; based on the average estimates at the link and these length of stay estimates, a back of the envelope estimate of the average cost of a DKA-related hospital admission would be $5.500. This estimate is probably too low as this study (which I may blog in more detail later) estimated non-compliance-related DKA-admissions to cost on average roughly $7.500 (and the non-compliance admissions were actually significantly cheaper than the other admissions on a per-case basis). To put this estimate into perspective, the mean annual cost of intensive diabetes care per diabetic patient in the U.S. is $4,000 (same link).
iii. Related to i., but I figured it deserved to be linked to separately: A theory of marriage, by Gary Becker.
iv. Some maps illustrating racial segregation patterns in the US. Don’t miss the sixth map of Detroit. The one of Saint Louis is also…
v. Vocabulary.com. I haven’t used it much yet, so I don’t really know if it’s any good – but it looks interesting and I’ve missed such a resource. I sometimes feel a bit guilty about not working harder on improving my vocabulary, especially on account of the fact that I’ve basically ended up only speaking two languages – I used to speak French reasonably well, but that’s many years ago and at this point I’d rather spend time improving my English than spend a lot of effort on a third language which most likely will only be of very limited use to me.
“Robots offer new possibilities for investigating animal social behaviour. This method enhances controllability and reproducibility of experimental techniques, and it allows also the experimental separation of the effects of bodily appearance (embodiment) and behaviour. In the present study we examined dogs’ interactive behaviour in a problem solving task (in which the dog has no access to the food) with three different social partners, two of which were robots and the third a human behaving in a robot-like manner. The Mechanical UMO (Unidentified Moving Object) and the Mechanical Human differed only in their embodiment, but showed similar behaviour toward the dog. In contrast, the Social UMO was interactive, showed contingent responsiveness and goal-directed behaviour and moved along varied routes. The dogs showed shorter looking and touching duration, but increased gaze alternation toward the Mechanical Human than to the Mechanical UMO. This suggests that dogs’ interactive behaviour may have been affected by previous experience with typical humans. We found that dogs also looked longer and showed more gaze alternations between the food and the Social UMO compared to the Mechanical UMO. These results suggest that dogs form expectations about an unfamiliar moving object within a short period of time and they recognise some social aspects of UMOs’ behaviour. This is the first evidence that interactive behaviour of a robot is important for evoking dogs’ social responsiveness.”
From the discussion:
“The aim of this study was to investigate whether dogs are able to differentiate agents on the basis of their behaviour and show social behaviours toward an UMO (Unidentified Moving Object) if the agent behaves appropriately in an interactive situation. In order to observe such interaction we modelled an experimental situation in which the dog is faced with inaccessible food. Miklósi et al  showed that in this case dogs increase their looking time at a human helper and show gaze alternation between the inaccessible food and the human. These observations have been replicated by Gaunet  and Horn et al , and the authors implicated that the dogs’ behaviour reflects communicative intentions. The present experiment showed that these behaviour features also emerge in the dogs while they are interacting with an UMO, moreover the onset of these behaviours is facilitated by the social features of the UMO: Dogs look longer and show more gaze alternation if the UMO carries eyes, shows variations in its path of movement, displays goal-directed behaviour and contingent reactivity (reacts to the looking action of the dog by retrieving the inaccessible food item).”
If you’re curious about how they actually did this stuff, don’t miss the neat video towards the end.
First, a link. I hadn’t heard about Gresham College until yesterday, so I’m assuming that some readers are at this point unaware of the existence of this resource. With that out of the way – some lectures!
I don’t want to talk a lot about the stuff covered here, but I probably should mention that I’m pretty sure I read an article not long ago showing that biennial eye screenings are more cost-effective than annual screenings, and that expected outcomes in the two cases are pretty similar. I’m too lazy to look up the article though, this is just to say that if you’re a diabetic getting your eyes screened regularly, you probably shouldn’t lose sleep about the fact that they only look at your eyes every second year.
I think I may have mentioned this before, but in my childhood I caught myself wondering if our cat could actually tell the difference between me and some other person – that question related to the bigger question of how the cat perceived the world; if I looked different enough for it to tell that I wasn’t somebody else. Well, if I’d had a pet sheep instead there’d have been no doubt:
“So this is sheep, showing that they can discriminate between faces by pressing panels with their nose […] they’re extremely good at doing this, they can remember or discriminate up to about 50 different sheep faces … it’s probably more than that, this is as far as we went – and at least 10 different human faces, and they can remember them for several years.” (from the video, roughly 40 minutes in)
i. I’ve read The Murder of Roger Ackroyd. I’ll say very little about the book here because I don’t want to spoil it in any way – but I do want to say that the book is awesome. I read it in one sitting, and I gave it 5 stars on goodreads (av.: 4,09); I think it’s safe to say it’s one of the best crime novels I’ve ever read (and I’ll remind you again that even though I haven’t read that much crime fiction, I have read some – e.g. every Sherlock Holmes story ever published and every inspector Morse novel written by Colin Dexter). The cleverness of the plot reminded me of a few Asimov novels I read a long time ago. A short while after I’d finished the book I was in the laundry room about to start the washing machine and a big smile spread on my face, I was actually close to laughing – because damn, the book is just so clever, so brilliant!
I highly recommend the book.
ii. I have been watching a few of the videos in the Introduction to Higher Mathematics youtube-series by Bill Shillito, here are a couple of examples:
I’m not super impressed by these videos at this point, but I figured I might as well link to them anyway. There are 19 videos in the playlist.
iii. Mind the Gap: Disparity Between Research Funding and Costs of Care for Diabetic Foot Ulcers. A brief comment from this month’s issue of Diabetes Care. The main point:
“Diabetic foot ulceration (DFU) is a serious and prevalent complication of diabetes, ultimately affecting some 25% of those living with the disease (1). DFUs have a consistently negative impact on quality of life and productivity […] Patients with DFUs also have morbidity and mortality rates equivalent to aggressive forms of cancer (2). These ulcers remain an important risk factor for lower-extremity amputation as up to 85% of amputations are preceded by foot ulcers (6). It should therefore come as no surprise that some 33% of the $116 billion in direct costs generated by the treatment of diabetes and its complications was linked to the treatment of foot ulcers (7). Another study has suggested that 25–50% of the costs related to inpatient diabetes care may be directly related to DFUs (2). […] The cost of care of people with diabetic foot ulcers is 5.4 times higher in the year after the first ulcer episode than the cost of care of people with diabetes without foot ulcers (10). […]
We identified 22,531 NIH-funded projects in diabetes between 2002–2011. Remarkably, of these, only 33 (0.15%) were specific to DFUs. Likewise, these 22,531 NIH-funded projects yielded $7,161,363,871 in overall diabetes funding, and of this, only $11,851,468 (0.17%) was specific to DFUs. Thus, a 604-fold difference exists between overall diabetes funding and that allocated to DFUs. […] As DFUs are prevalent and have a negative impact on the quality of life of patients with diabetes, it would stand to reason that U.S. federal funding specifically for DFUs would be proportionate with this burden. Unfortunately, this yawning gap in funding (and commensurate development of a culture of sub-specialty research) stands in stark contrast to the outsized influence of DFUs on resource utilization within diabetes care. This disparity does not appear to be isolated to [the US].”
I’ve read about diabetic foot care before, but I had no idea about this stuff. Of the roughly 175.000 peer-reviewed publications about diabetes published in the period of 2000-2009, only 1200 of them – 0.69% – were about the diabetic foot. You can quibble over the cost estimates and argue that perhaps they’ve overstated because these guys want more money, but I think that it’s highly unlikely that the uncertainties related to the cost estimates are so big as to somehow make the current (research) ressource allocation scheme appear cost efficient in a CBA with reasonable assumptions – there simply has to be some low-hanging fruit here.
A slightly related (if you stretch the definition of ‘related’ a little) article which I also found interesting here.
iv. “How quickly would the ocean’s drain if a circular portal 10 meters in radius leading into space was created at the bottom of Challenger Deep, the deepest spot in the ocean? How would the Earth change as the water is being drained?”
And, “Supposing you did Drain the Oceans, and dumped the water on top of the Curiosity rover, how would Mars change as the water accumulated?”
v. Take news of cancer ‘breakthrough’ with a big grain of salt. I’d have added the word ‘any’ and probably an ‘s’ to the word breakthrough as well if I’d authored the headline, in order to make a more general point – but be that as it may… The main thrust:
“scientific breakthroughs should not be announced at press conferences using the vocabulary of public relations professionals.
The language of science and medicine should be cautious and humble because diseases like cancer are relentless and humbling. […]
The reality is that biomedical research is a slow process that yields small incremental results. If there is a lesson to retain from the tale of CFI-400945, it’s that finding new treatments takes a lot of time and a lot of money. It is a venture worthy of support, but unworthy of exaggerated expectations and casual overstatement.
Hype only serves to create false hope.”
People who’re not familiar with how science actually works (and how related processes such as drug development work) often have weird ideas about how fast things tend to proceed and how (/un?)likely a ‘promising’ result in the lab might be to be translated into, say, a new treatment option available to the general patient population. And yeah, that set of ‘people who’re not familiar with how science works’ would include almost everybody.
It should be noted, as I’m sure Picard knows, that it’s a lot easier to get funding for your project if you’re exaggerating benefits and downplaying costs; if you’re too optimistic; if you’re saying nice things about the guy writing the checks even though you think he’s an asshole; etc. Some types of dishonesty are probably best perceived of as nothing more than ‘good salesmanship’ whereas other types might have different interpretations; but either way it’d be silly to pretend that stuff like false hope does not sell a lot of tickets (and newspapers, and diluted soap water, and…). Given that, it’s hardly likely that things will change much anytime soon – the demand for information here is much higher than is the demand for accurate information. But it’s nice to read an article like this one every now and then anyway.
“The finding of abnormal lung function in some diabetic subjects suggests that the lung should be considered a “target organ” in diabetes mellitus; however, the clinical implications of these findings in terms of respiratory disease are at present unknown.”
Malcolm Sandler wrote this almost 25 years ago. What’s happened since then? Well, I should perhaps point out that you still today have a situation where highly educated individuals who’ve had diabetes for decades may not even be aware that their disease may affect the lung tissue – I should know, because until a few years ago I didn’t know this. You care about the kidneys, you care about the feet, the eyes, the heart, sometimes the autonomous nervous system – but your lungs aren’t very likely to be brought up in a discussion with the endocrinonologist unless you happen to be a smoker, and in that case the concern is cancer risk and cardiovascular risk.
One main explanation is likely that the effects of the disease are minor, and so do not have much influence on the quality of life of the patient:
“Clear decrements in lung function have been reported in patients with diabetes over the past 2 decades, and many reports have suggested plausible pathophysiological mechanisms. However, at the present time, there are no reports of functional limitations of activities of daily living ascribable to pulmonary disease in patients with diabetes. Accordingly, this review is directed toward a description of the nature of reported lung dysfunction in diabetes, with an emphasis on the emerging potential clinical implications of such dysfunction.” (my emphasis, quote from this review)
I am interested in this matter because, well, at least partly because I’m just the kind of person who takes an interest in such matters. But recently I’ve also started to become a bit curious about whether the disease may have already have had an impact on my own lung function, ‘compared to baseline’. It’s far from certain – most studies find that microvascular complications are correlated (say if your eyes start to display signs of damage, it’s more likely that one may also observe damage to the kidneys) and that the link between those complications and metabolic control is strong; and my metabolic control is close to optimal, and my eyes and kidneys look fine.
I’m a long-distance runner. I run ~35 km/week now (and increasing with ~3 km/week), so of course I should not have breathing difficulties walking up and down stairs, and I don’t. And as the quote above makes clear even for patients who may be impacted, the damage is not likely to be all that major. So the fact that I don’t have any overt lung problems isn’t relevant – we wouldn’t expect such to present anyway. But it is worth asking whether I perform as well as I would do without my disease when I run. The obvious answer would be ‘of course not’ – for reasons unrelated to my lungs (taking blood samples take time, loading up on carbohydrates during a run after the blood sample is taken takes time – and I can’t do these things while running). But is there an impact from the lungs as well? I don’t know. Maybe. You can’t observe the counterfactual.
Which is why I thought this recent-ish meta-analysis was interesting:
“Background: Research into the association between diabetes and pulmonary function has resulted in inconsistent outcomes among studies. We performed a metaanalysis to clarify this association.
Methods: From a systematic search of the literature, we included 40 studies describing pulmonary function data of 3,182 patients with diabetes and 27,080 control subjects. Associations were summarized pooling the mean difference (MD) (standard error) between patients with diabetes and control subjects of all studies for key lung function parameters.
Results: For all studies, the pooled MD for FEV 1 , FVC, and diffusion of the lungs for carbon monoxide were -5.1 (95% CI, -6.4 to -3.7; P<.001), -6.3 (95% CI, -8.0 to -4.7; P<.001), and -7.2 (95% CI, -10.0 to -4.4; P<.001) % predicted, respectively, and for FEV 1 /FVC 0.1% (95% CI, -0.8 to 1.0; P = .78). Metaregression analyses showed that between-study heterogeneity was not explained by BMI, smoking, diabetes duration, or glycated hemoglobin (all P<.05).
Conclusions: Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. […]
Our metaanalysis shows that diabetes, in the absence of overt pulmonary disease, is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. The results were irrespective of BMI, smoking, diabetes duration, and HbA1c levels. In subanalyses, the association seemed to be more pronounced in type 2 diabetes than in type 1 diabetes. Our study adds evidence for yet another organ system to be involved in bothtype 1 and type 2 diabetes. As a consequence of exclusion criteria, the levels of functional impairment fell within values that are generally considered to be normal. However, to place this in perspective, the magnitude of impairment found in our study closely resembles that of smoking per se.57 Similarly, given the relatively high prevalence of diabetes in COPD,58 it is tempting to speculate that (uncontrolled) diabetes may accelerate progressive lung function decline. However, from our metaanalysis summarizing crosssectional studies, it is difficult to draw conclusions on causality and progression into overt pulmonary diseases.” (my emphasis)
Whether you smoke or not is certainly not a trivial effect when you’re considering the fitness level of a long-distance runner! I know the effects are smaller for T1’s, but this is most certainly an effect to have in mind. Back when I ran my marathon three years ago both me and my brother were surprised that he did so much better than I did (he came in more than half an hour before I did, despite the fact that we both assumed beforehand that I was the one who was in better shape).
I consider some of the findings quite weird, and it’s hard to make heads or tails of some of this stuff:
“One would expect that a longer exposure to diabetes would proportionally increase the chance of connective tissue being nonenzymatically glycated. However, our study suggests that a longer duration is not necessarily associated with additional loss of pulmonary reserves. This is in line with previous longitudinal studies on this topic.59,60 […]
It is intriguing to observe that the pulmonary system remains relatively spared in diabetes when compared with other organs with wide microvascular beds. It is speculated that the large pulmonary reserves protect against severe pulmonary dysfunction.
Because neither the duration of diabetes nor glycemic state appeared to influence the association in our study, one might question whether there is a causal relationship between diabetes and impaired pulmonary function.”
I’ll try to keep my eyes open for updates on this stuff – although the estimated effects may not be big enough for people to seek out medical advice, they’re huge if you’re a long-distance runner considering whether it’s even worth it to participate in future official runs solely for the sake of improving your performance in such competitions.
On a sidenote I should point out that I don’t (/no longer) run in order to obtain a faster time in an official run – I run because I like to run, and I no longer have much desire to participate in official runs – but I’d be lying if I said I didn’t care at all about that stuff some years back when I started out participating in such runs. Imagine what happens with your desire to participate in such official runs if you don’t seem to be able to improve your time much even with strict adherence to running schedules, especially considering the fact that other people who in other respects are similar to you can out-perform you without doing a lot of work. I was above 70 km/week and had several 30+ kilometer runs behind me before my marathon; my brother never even crossed the 40 km/week threshold. And he beat me by more than half an hour. Go figure. I had a bad run for diabetes-related reasons so during the day this was not a surprising outcome, but it was a profoundly annoying outcome. And no, I was not ‘overtraining'; I was rather at the point where a 25+ km run was the ‘standard running distance’ – you know, that distance you managed without thinking much about it every Tuesday, and Saturday, with a short 20 km run in between – and I decreased the kilometer count up to the run as advised by the plan I was following (more or less stringently, but compared to the people whom I entered the goal line with the word ‘more’ is by far the more accurate one). And no, it’s not like I hadn’t heard about interval training, and it’s not like this stuff is hard to implement in a hilly place like Aarhus.
I did make progress from I started running to the point where I decided not to really consider ‘official runs’ to be be worth it anymore – the first half-marathon took me more than 2 hours, the best one I did in an hour and 47 minutes (this performance was achieved at a point in time where I ran 65 km/week and at least cared somewhat about speed and time taken – so, yeah… Compare this again with my brother, whose next goal is 1.35, without ever having been near 50 km/week). Right now my ‘standard running distance’ is 12-15 km – I like to run, but I have a very limited desire to participate in official runs in the future. It’s not worth it – if I go back to very-high intensity training I may improve my official performances, but that could just as easily be due to factors completely unrelated to my actual shape, like whether I was lucky about the starting blood glucose (fewer tests during the run, less time wasted on that), or whether I’d slept well. Who cares? And it’s not like I need to participate in these runs to motivate myself to get out there – I find running enjoyable as it is, especially in the summer when the weather is nice.
But in case you’d forgotten because of all the personal stuff in the end – to just reiterate the main points that made me start out writing this post:
“Diabetes is associated with a modest, albeit statistically significant, impaired pulmonary function in a restrictive pattern. […] the magnitude of impairment found in our study closely resembles that of smoking”.
This is perhaps also a good illustration of how dangerous diabetes is; the fact that the disease may impact the performance of the lungs in a manner not too dissimilar from smoking is not even considered clinically relevant; the patients have much bigger problems to worry about as it is.
i. I had a doctor’s appointment today and got the results of my bloodwork back. My Hba1c was 48, or 6.5%. This is the lowest it’s been for as long as I can remember. I have had some trouble with hypoglycemic episodes now and then, but not significantly more than usual and I’ve had no major episodes. I believe the lowered Hba1c is probably mostly a result of lowered nocturnal blood glucose values. These have however at some points been uncomfortably low, so I’m not sure 6,5 is a realistic long-term goal and because of those uncomfortably low values I have made adjustments along the way which probably means that the Hba1c may be a bit higher next time if other things stay pretty much the same (which I know they won’t; for instance I’m planning on significantly increasing my running over the next four months). But even so I was very happy about this result, as I choose to believe that it means I’ll actually be able to obtain <7.0% results in the future without major adverse events if I’m careful and vigilant.
This recent post goes into more detail about the hypoglycemia risk and what it’s about. This Danish post has some data on the distribution of Hba1c results among Danish diabetics – the relevant figure is this one (with 6.5%, I’m in the 10% fractile).
ii. I’m now ‘officially’ a researcher. I have just become a member of Statistics Denmark’s research programme (-forskerordning), which means that I’ve obtained access to a specific data set which I’ll do work on during the next year. Danish registers contain a lot of good information compared to the registers of most other countries, so I may actually be able to look at stuff that a lot of researchers elsewhere are simply not able to analyze due to data issues – which is exciting. Unfortunately I’ll not be comfortable blogging anything about this stuff, as there are a huge number of restrictions on data access/sharing etc. – but I believe it’ll be interesting to work with this stuff and I’m looking forward to it.
iii. A couple of Khan Academy videos:
Abstract: “We analyzed one decade of data collected by the Programme for International Student Assessment (PISA), including the mathematics and reading performance of nearly 1.5 million 15 year olds in 75 countries. Across nations, boys scored higher than girls in mathematics, but lower than girls in reading. The sex difference in reading was three times as large as in mathematics. There was considerable variation in the extent of the sex differences between nations. There are countries without a sex difference in mathematics performance, and in some countries girls scored higher than boys. Boys scored lower in reading in all nations in all four PISA assessments (2000, 2003, 2006, 2009). Contrary to several previous studies, we found no evidence that the sex differences were related to nations’ gender equality indicators. Further, paradoxically, sex differences in mathematics were consistently and strongly inversely correlated with sex differences in reading: Countries with a smaller sex difference in mathematics had a larger sex difference in reading and vice versa. We demonstrate that this was not merely a between-nation, but also a within-nation effect. This effect is related to relative changes in these sex differences across the performance continuum: We did not find a sex difference in mathematics among the lowest performing students, but this is where the sex difference in reading was largest. In contrast, the sex difference in mathematics was largest among the higher performing students, and this is where the sex difference in reading was smallest. The implication is that if policy makers decide that changes in these sex differences are desired, different approaches will be needed to achieve this for reading and mathematics. Interventions that focus on high-achieving girls in mathematics and on low achieving boys in reading are likely to yield the strongest educational benefits.”
Abstract: “A cornerstone of modern biomedical research is the use of mouse models to explore basic pathophysiological mechanisms, evaluate new therapeutic approaches, and make go or no-go decisions to carry new drug candidates forward into clinical trials. Systematic studies evaluating how well murine models mimic human inflammatory diseases are non-existent. Here, we show that, although acute inflammatory stresses from different etiologies result in highly similar genomic responses in humans, the responses in corresponding mouse models correlate poorly with the human conditions and also, one another. Among genes changed significantly in humans, the murine orthologs are close to random in matching their human counterparts (e.g.,R^2 between 0.0 and 0.1). In addition to improvements in the current animal model systems, our study supports higher priority for translational medical research to focus on the more complex human conditions rather than relying on mouse models to study human inflammatory diseases.”
vi. Married men at the age of 40 can expect to live on average 7.1 years longer than unmarried men at the age of 40, and 6.6 years longer than divorced men at the age of 40. For women the life expectancy difference between the married and unmarried group is 4.8 years, and the difference between married women and divorced women is 4.3 years. The excess mortality for unmarried men in their forties (compared with married males) is around 250%, and for men in their fifties it’s still above 200%.
The data reported above is from a new publication by Statistics Denmark which you can read here. Here’s a related publication. Here is a recent publication on the education levels of Danish emigrants. All three publications are unfortunately in Danish.
vii. Nasa – The Tyranny of the Rocket Equation. This part was surprising to me, because I’d never really thought about this:
“If the radius of our planet were larger, there could be a point at which an Earth escaping rocket could not be built. Let us assume that building a rocket at 96% propellant (4% rocket), currently the limit for just the Shuttle External Tank, is the practical limit for launch vehicle engineering. Let us also choose hydrogen-oxygen, the most energetic chemical propellant known and currently capable of use in a human rated rocket engine. By plugging these numbers into the rocket equation, we can transform the calculated escape velocity into its equivalent planetary radius. That radius would be about 9680 kilometers (Earth is 6670 km). If our planet was 50% larger in diameter, we would not be able to venture into space, at least using rockets for transport.”
Here’s the link to the article. I’d missed this one, even if it’s a few years old (from December 2008). It is a great article and it covers a lot of stuff – I’ve decided to quote extensively from it below:
“Glycemic control, a worthwhile goal for people with diabetes, is limited by the barrier of iatrogenic hypoglycemia (1). Iatrogenic hypoglycemia 1) causes recurrent morbidity in most people with type 1 diabetes and many with advanced type 2 diabetes and is sometimes fatal, 2) compromises physiological and behavioral defenses against subsequent falling plasma glucose concentrations and thus causes a vicious cycle of recurrent hypoglycemia, and 3) precludes maintenance of euglycemia over a lifetime of diabetes and therefore full realization of the vascular benefits of glycemic control. […] Unfortunately, maintenance of euglycemia over a lifetime of diabetes cannot be accomplished safely with currently available treatment methods because of the barrier of hypoglycemia (1). […]
Hypoglycemia is a fact of life for most people with type 1 diabetes (1). The average patient has untold numbers of episodes of asymptomatic hypoglycemia and suffers two episodes of symptomatic hypoglycemia per week (thousands of such episodes over a lifetime of diabetes). He or she suffers one or more episodes of severe, temporarily disabling hypoglycemia, often with seizure or coma, per year. There is no evidence that this problem has abated over the decade and a half since it was highlighted by the report of the DCCT (2) in 1993. For example, in 2007 the U.K. Hypoglycemia Study Group (9) reported an incidence of severe hypoglycemia of 110 episodes per 100 patient-years (nearly twice that in the DCCT) in patients with type 1 diabetes, who were necessarily treated with insulin, for <5 years and an incidence of 320 episodes per 100 patient-years in those with type 1 diabetes for >15 years. […]
Although they represent only a small fraction of the total hypoglycemia experience, estimates of the frequency of severe hypoglycemia, particularly if determined in prospective, population-based studies, are the most reliable because they are dramatic events that are more likely to be reported (by the patient or an associate) (1). The prospective, population-based data of Donnelly et al. (10) indicate that the overall incidence of hypoglycemia in insulin-treated type 2 diabetes is approximately one-third of that in type 1 diabetes. The incidence of any and of severe hypoglycemia was ∼4,300 and 115 episodes per 100 patient-years, respectively, in type 1 diabetes and ∼1,600 and 35 episodes per 100 patient-years, respectively, in insulin-treated type 2 diabetes. In addition, in population-based studies the incidence of severe hypoglycemia requiring emergency treatment in insulin-treated type 2 diabetes was ∼40% (11) and ∼100% (12) of that in type 1 diabetes. Since the prevalence of type 2 diabetes is ∼20-fold greater than that of type 1 diabetes, and most people with type 2 diabetes ultimately require treatment with insulin, these data suggest that most episodes of iatrogenic hypoglycemia, including severe hypoglycemia, occur in people with type 2 diabetes. […]
Iatrogenic hypoglycemia causes recurrent physical and psychological morbidity and some mortality, impairs defenses against subsequent hypoglycemia, and precludes maintenance of euglycemia over a lifetime of diabetes (1). Hypoglycemia causes brain fuel deprivation that, if unchecked, results in functional brain failure that is typically corrected after the plasma glucose concentration is raised (13). Rarely, it causes sudden, presumably cardiac arrhythmic death or, if it is profound and prolonged, brain death (13). To the extent that there is a macrovascular benefit of glycemic control (6), the barrier of hypoglycemia also contributes to cardiovascular morbidity and mortality.
The physical morbidity of an episode of hypoglycemia ranges from unpleasant symptoms to seizure and coma (1). Hypoglycemia can impair judgment, behavior, and performance of physical tasks. Permanent neurological damage is rare. While there is concern that recurrent hypoglycemia might cause chronic cognitive impairment, long-term follow-up of the DCCT patients is largely reassuring in that regard (14). […]
Three early reports indicated that 2–4% of people with diabetes die from hypoglycemia (1). More recent reports indicated that 6% (14), 7% (15), and 10% (16) of deaths of people with type 1 diabetes were the result of hypoglycemia. Up to 10% of episodes of severe sulfonylurea-induced hypoglycemia in type 2 diabetes may be fatal (17). [my emphasis, US]
In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study, 10,251 patients with type 2 diabetes at high cardiovascular risk (but with no history of frequent or recent serious hypoglycemic events) were randomized to either intensive glycemic therapy with an A1C goal of <6.0% or to standard glycemic therapy (7). After a median follow-up of 3.4 years, with stable median A1C levels of 6.4 and 7.5%, respectively, intensive glycemic therapy was discontinued because 5.0% of the patients in the intensive therapy group, compared with 4.0% of those in the standard therapy group, had died. […] the most plausible cause of excess mortality during intensive therapy in the ACCORD study is iatrogenic hypoglycemia […]
Glucose is an obligate oxidative fuel for the brain under physiological conditions (1). The brain accounts for >50% of whole-body glucose utilization. The brain can oxidize alternative fuels, such as ketones, if their circulating levels rise high enough to enter the brain in quantity, but that is seldom the case. Because it cannot synthesize glucose, utilize physiological levels of circulating nonglucose fuels effectively, or store more than a few minutes supply of glucose as glycogen, the brain requires a virtually continuous supply of glucose from circulation. Since facilitated blood-to-brain glucose transport is a direct function of the arterial plasma glucose concentration, that supply requires maintenance of plasma glucose concentration. At some level of hypoglycemia (perhaps ∼50–55 mg/dl [2.8–3.1 mmol/l] since symptoms normally occur at that level [19–21]), blood-to-brain glucose transport becomes limiting to brain glucose metabolism and, therefore, function. […]
Early in the course of type 2 diabetes, by far the most common type of diabetes, hyperglycemia may respond to lifestyle changes, specifically weight loss, or to plasma glucose–lowering drugs that should not, and probably do not, cause hypoglycemia. In theory, when such drugs are effective in the absence of side effects, there is no reason not to accelerate their dosing until euglycemia is achieved. Over time, however, as people with type 2 diabetes become progressively more insulin deficient, these drugs, even in combination, fail to maintain glycemic control. Insulin secretagogues are also effective early in type 2 diabetes, but they can cause hyperinsulinemia and therefore introduce the risk of hypoglycemia. Euglycemia is not an appropriate goal during therapy with an insulin secretagogue or with insulin in people with type 2 diabetes. Nonetheless, as discussed earlier, the frequency of hypoglycemia is relatively low (at least with current glycemic goals that are above the euglycemic range) during treatment with an insulin secretagogue, or even with insulin, early in type 2 diabetes (9) when defenses against hypoglycemia are intact. Thus, over much of the course of the most common type of diabetes it is possible to achieve a meaningful degree of glycemic control with no risk or relatively low risk of hypoglycemia. The challenge is greater in people with advanced type 2 diabetes or type 1 diabetes because of compromised defenses against hypoglycemia. In such patients, therapy with insulin is demonstrably effective, but it is not demonstrably safe. Nonetheless, concerns about hypoglycemia should not be used as an excuse for poor glycemic control by patients or their caregivers. Both should strive to achieve and maintain the greatest degree of glycemic control that can be accomplished safely in a given person with diabetes at a given stage of the progression of his or her diabetes.”
For diabetics, especially type 1 diabetics, there’s a very real risk that the (life-saving and non-optional) treatment may kill the patient. All this stuff above may sound very theoretical, but it’s not. It’s quite simple, really: For me personally, pretty much every time I eat a meal I have a decision to make. I have to take some insulin to enable my body to process the carbohydrates in the meal, and I need to estimate how big of a dosis is optimal. ‘The barrier of hypoglycemia’ is the reason why even though ‘my problem’ is that I don’t produce insulin I can’t just take a ‘big enough’ dosis of insulin when that stuff is needed and solve the problem that way without having to worry – the point is that if the dosis I take is ‘too big’, I’ll get hypoglycemia. And if it’s not ‘big enough’ I may not necessarily get symptoms, but I’ll still harm my body and increase the risk of complications later on (and if I consistently take too little on a day to day basis, the long-term risk will go up a lot). It’s simply impossible to ‘get it just right every time’, it’s very easy to get it wrong among other things because the insulin’s therapeutic index is quite low, and the consequences of getting it wrong may be very severe – and the tradeoff is always there, every day, every meal. I know I’ve written about it before but a lot of people, even relatively well-informed people, I’ve talked to about my disease don’t know this, and this tradeoff really is at the very heart of what living with diabetes is all about.
By Edwards, Foster & Sanders. I’m currently finishing this book.
I’m pretty sure nobody who’s reading this blog semi-regularly will want to read this book; unless you’re a diabetic or a health care provider working with diabetics I guess it’s not easy to come up with a lot of good reasons why you should read it. But I decided to blog it anyway. If you do decide to read it, unless you’re used to medical textbooks I’d advise you to schedule your reading so that you only open this book when it’s been a while since you’ve last eaten; unless you’re at least somewhat desensitized to this kind of stuff (like I am at this point..) you’ll probably want to vomit after looking at some of the pictures in this book. This book covers everything from ‘Stage 1: The normal foot’ to ‘Stage 6 – The unsalvageable foot’. Good luck guessing what the latter looks like – if you don’t want to guess you can go here for images of a similar nature.
I know a lot more about the subject than I used to do at this point, but not all of this was new stuff to me – I’ve covered related matters before here on the blog, for instance here. However most of the stuff I’ve read in the past related to ‘epidemiology’, whereas I have not spent much time dealing with the actual disease process – what’s going on when the foot ‘goes bad’, how it may happen, what can be done about it, etc. – and here the book most certainly delivers.
I figure some curious diabetic googlers may stumble upon this article later on, and so it makes sense here to emphasize one thing before going any further – a point the authors of the book emphasize as well: There is no such thing as a trivial lesion of the diabetic foot; all foot problems need early diagnosis and appropriate intervention. If you’re a diabetic even a small wound/ulcer on your foot indicate that you’re (at least…) in stage 3 of their 6 stage categorization system. Next step after that is the infected foot, and the step after that one involves necrosis. You do not want to go there. What I found really surprising was the asymmetry between the time it takes for damage to be caused and the time it takes to heal that damage afterwards; necrosis can develop really fast, but healing may on the other hand take a very long time, especially if rapid and effective intervention is not undertaken at the early stage of the disease process – for example: “The average amount of time spent in a cast by diabetic patients with Charcot’s osteoarthropathy is 6 months but some patients may need a cast for over a year.” And here’s another related fact: “In our experience, fractures in stage 2 and higher stage diabetic feet take two or three times as long to heal as they do in low-risk or normal feet. Many health-care professionals appear to be unaware of this.”
I found the patient case studies particularly interesting, because they somehow make it all much more concrete and tangible. Of course it’s nice to know that: “Throughout their lifetime 15% of patients will develop ulceration; 85% of amputations result from non-healing ulcers” – but the case studies somehow makes all of this a bit more ‘real’. I have posted some of them below. I would like to remind you that I am 27 years old and that I have had diabetes for over 25 years at this point.
“A 17-year-old girl with type 1 diabetes of 4 years’ duration was referred to the diabetic foot clinic for education. A paternal uncle had type 1 diabetes and neuropathic ulceration. Her background was a chaotic one of great poverty and social deprivation with a history of truanting and running away from home. Her HbAlc was 14%. She had frequent admissions to hospital for ketotic episodes and traumatic lesions to her heels and her navel which became infected. She was educated in foot care and footwear but continued to wear unsuitable shoes; she also frequently missed appointments at the diabetic clinic and diabetic foot clinic. However, she agreed to attend the clinic in emergency and to take antibiotics if her foot lesions became infected. She had no more admissions for foot problems, but subsequently developed severe neuropathy, proliferative retinopathy and end-stage renal failure and is currently on dialysis.” [Usually we think of chronic complications to diabetes mellitus as disease processes that only rear their ugly heads after many years, if not decades, with the illness; this case study is a good reminder that even relatively short time periods with poor metabolic control/compliance can have catastrophic consequences for the individual in question.]
“A 62-year-old lady with type 1 diabetes of 40 years’ duration, retinopathy and neuropathy went on holiday to Blackpool, removed her shoes and socks and sat in a deckchair on the beach for 3 h. Her head and torso were shaded by an umbrella but her feet and legs were exposed to the sun. She suffered a full-thickness burn on the dorsum of her right foot (Fig. 3.10). She was admitted to hospital for debridement and skin grafting and the foot healed in 6 weeks.”
Yes, you got that right – going to the beach for 3 hours lead to a burn that took 6 weeks to heal. Here’s a similar example:
“A 25-year-old male patient with type 1 diabetes mellitus of 14 years’ duration, profound neuropathy and endstage renal failure treated with dialysis, slept in a bed next to a central heating radiator. During the night, in his sleep, his leg slipped against the radiator. He sustained full-thickness burns to his leg, but only attended the diabetic foot clinic when these became malodorous. There was a moist leathery eschar with purulent discharge. He was admitted to hospital for intravenous antibiotics. The burns were surgically debrided and split-skin grafts applied from a donor site on his thigh. He healed in 5 months.”
Note how well the last three guys are doing – they’re all in their 40es:
“A 46-year-old man with type 1 diabetes of 33 years’ duration, end-stage renal failure treated by renal transplantation and severe neuropathy, received regular foot checks under a renal foot study protocol. Three days before he went on holiday to the Channel Islands his feet were routinely checked and nothing abnormal was discerned. Two weeks later he came to the clinic on his return from holiday to report that his foot was ‘a little swollen’. He reported no trauma to the foot, but had been walking more than usual on cobbled pavements. The foot was red, 5°C hotter than the contralateral foot and very swollen. X-ray revealed a Lisfranc’s fracture-dislocation and he developed a rockerbottom foot. He was treated in a total-contact plaster cast for 6 months following which he wore bespoke boots to accommodate his deformity.”
“A 40-year-old male with type 1 diabetes of 30 years’ duration, proliferative retinopathy treated with laser photocoagulation, sensory neuropathy and autonomic neuropathy including postural hypotension, developed an acute right mid-foot Charcot’s osteoarthropathy. Because of a previous episode of severe sepsis…”
“A 44-year-old woman with type 1 diabetes of 26 years’ duration, proliferative retinopathy, profound neuropathy and end-stage renal failure treated by renal transplant had her feet checked at monthly intervals at the renal unit as part of a research protocol. Her foot pulses were palpable. She was educated in foot care, foot inspections and early reporting of any problems. However, during a 3-year period she suffered nine separate episodes of foot trauma, none of which she reported early: they were detected at her renal unit appointment. Causes of trauma included blisters from ill-fitting shoes, picking at dry skin, pulling off pieces of nail and being ‘trodden on by a baby’. In the last episode she stubbed her toe while walking barefoot, did not report the injury and presented late to the renal unit with spreading cellulitis, wet necrosis and septicaemia. She was resuscitated and treated with intravenous antibiotics and underwent 1st ray amputation to remove the source of her sepsis. Despite this, her septicaemia progressed and became overwhelming and she suffered a cardiac arrest and could not be resuscitated.”
i. Remember ‘the good old days’ of film-making? Here’s a reminder: The Hays Code.
“1. No picture shall be produced that will lower the moral standards of those who see it. Hence the sympathy of the audience should never be thrown to the side of crime, wrongdoing, evil or sin.
2. Correct standards of life, subject only to the requirements of drama and entertainment, shall be presented.
3. Law, natural or human, shall not be ridiculed, nor shall sympathy be created for its violation. […]
The sanctity of the institution of marriage and the home shall be upheld. Pictures shall not infer that low forms of sex relationship are the accepted or common thing.
1. Adultery, sometimes necessary plot material, must not be explicitly treated, or justified, or presented attractively.
2. Scenes of Passion
a. They should not be introduced when not essential to the plot.
b. Excessive and lustful kissing, lustful embraces, suggestive postures and gestures, are not to be shown.
c. In general passion should so be treated that these scenes do not stimulate the lower and baser element. […]
1. No film or episode may throw ridicule on any religious faith.
2. Ministers of religion in their character as ministers of religion should not be used as comic characters or as villains. […]
The reason why ministers of religion may not be comic characters or villains is simply because the attitude taken toward them may easily become the attitude taken toward religion in general. Religion is lowered in the minds of the audience because of the lowering of the audience’s respect for a minister.”
ii. I’d love to see some corresponding Danish numbers:
“Italians born in 1970, who are about 43 now, will pay 50% more in taxes as a percentage of their lifetime income than those born in 1952, according to research from the Bank of Italy and the University of Verona. The research also found they will receive half the pension benefits that Italy’s 60-somethings are getting or are poised to get.” (link, via MR)
iii. Longevity AmongHunter-Gatherers: A Cross-Cultural Examination. Some main findings and conclusions from the paper:
“Post-reproductive longevity is a robust feature of hunter-gatherers and of the life cycle of Homo sapiens. Survivorship to grandparental age is achieved by over two-thirds of people who reach sexual maturity and can last an average of 20 years.
Adult mortality appears to be characterized by two stages. Mortality rates remain stable and fairly low at around 1 percent per year from the age of maturity until around age 40. After age 40, the rate of mortality increase is exponential (Gompertz) with a mortality rate doubling time of about 6–9 years. The two decades without detectable senescence in early and mid-adulthood appear to be an important component of human life span extension.
The average modal age of adult death for hunter-gatherers is 72 with a range of 68–78 years. This range appears to be the closest functional equivalent of an “adaptive” human life span.
Departures from this general pattern in published estimates of life expectancy in past populations (e.g., low child and high adult mortality) are most likely due to a combination of high levels of contact-related infectiousdisease, excessive violence or homicide, and methodological problems that lead to poor age estimates of older individuals and inappropriate use of model life tables for deriving demographic estimates.
Illnesses account for 70 percent, violence and accidents for 20 percent, and degenerative diseases for 9 percent of all deaths in our sample. Illnesses largely include infectious and gastrointestinal disease, although less than half of all deaths in our sample are from contact-related disease.
Comparisons among hunter-gatherers, acculturated hunter-gatherers, wild chimpanzees, and captive chimpanzees illustrate the interaction of improved conditions and species differences. Within species, improved conditions tend to decrease mortality rates at all ages, with a diminishing effect at older ages. Human and chimpanzee mortality diverge dramatically at older ages, revealing selection for a longer adult period in humans. […]
Our results contradict Vallois’s (1961: 222) claim that among early humans, “few individuals passed forty years, and it is only quite exceptionally that any passed fifty,” and the more traditional Hobbesian view of a nasty, brutish, and short human life (see also King and Jukes 1969; Weiss 1981). The data show that modal adult lifespan is 68–78 years, and that it was not uncommon for individuals to reach these ages”
iv. What is it like when one of your parents gets Alzheimer’s? It’s not fun.
In people with impaired glucose tolerance interventions are clinically and cost effective
Screening for type 2 diabetes to allow early detection might be cost effective in certain groups
What this study adds
Modelling the whole screening and intervention pathway from screening to death shows that screening for type 2 diabetes and impaired glucose tolerance, followed by interventions, seems to be cost effective compared with no screening
Uncertainty still exists concerning the cost effectiveness of screening for type 2 diabetes alone
Screening populations with a higher prevalence of glucose intolerance might result in better clinical outcomes, although cost effectiveness seems unaffected”
vi. PLOS-ONE: Minimal Intensity Physical Activity (Standing and Walking) of Longer Duration Improves Insulin Action and Plasma Lipids More than Shorter Periods of Moderate to Vigorous Exercise (Cycling) in Sedentary Subjects When Energy Expenditure Is Comparable.
N is small but even so this is an interesting finding.
vii. “Commercial fishing operations in the past 40 years have precipitated a dramatic change in ocean fish stocks, with tuna and other big predators declining and small fish like anchovies and sardines surging. That’s the conclusion of the most ambitious study ever completed of fish populations in the Earth’s oceans, conducted by Villy Christensen of the University of British Columbia’s Fisheries Centre.In the past 100 years, 80% of the biomass of fish in the world’s oceans has been lost, Christensen says in a AAAS video that coincided with a symposium at the Annual Meeting. “Just in the last 40 years, we have lost 60% of the biomass,” he explained. “So we’ve seen some very serious declines, and there’s no doubt about what the cause is: We’re talking about overfishing—overfishing at the global scale.” […] Christensen’s team of scientists based their conclusions on more than 200 marine ecosystem models and more than 68,000 estimates of fish biomass from 1880 to 2007, the Vancouver Sun reported, citing a University of British Columbia news release.”
At this point, right now while writing this, I’m too scared to go to sleep. It is not the first time, it’ll not be the last.
What’s there to be afraid of? Well, I should start out by pointing out that I always measure blood glucose before going to sleep. And I mean always. There are no exceptions to this rule, zero. I mean none – going to sleep without knowing my blood glucose just doesn’t happen. If I’m around others I may be discreet about the matter, but it’s non-negotiable. More than 90% of my lifetime hypoglycemia-related hospitalizations have been sleep-related. In the past I’ve fallen asleep in my own bed and woken up many hours after I should have in a hospital bed more than a few times, with no memory of how I got there. The fear that I’d one day just not wake up at all became much more real after I moved away from home.
I measured a blood glucose half an hour ago, just before going to sleep. The measured value was in the same neighbourhood as the highest of the ones measured here (2.3 mmol/l). I had zero awareness anything was wrong and if I’d not had a decision rule never to go to sleep without testing, I’d probably just have gone to bed without thinking anything might be wrong. The blood glucose level is now back in the normal range, but I’m hesitant to go to sleep until it has increased a bit more than it has – especially as it’s not completely clear to me what caused this in the first place (double dosis of the slow-acting insulin two days ago?).
If I had just gone to sleep, things could have been ‘interesting’. This is a part of diabetes normal people usually don’t get to see. The fact of the matter is that I’ll sometimes be afraid simply to go to sleep because the disease may kill me in my sleep. It almost did last year.
I should perhaps point out that this episode wasn’t really anything super special. These things happen, not ‘on a regular basis'; but they do happen.
I have friends (they may be reading along) who’ve pointed out that they consider my level of risk aversion to be excessive and who’ve advised me that I’d perhaps be happier if I was more willing to take on risk in general. I think growing up with diabetes changes how you think about risk. Nights like these are probably part of the reason why I have a hard time following that advice. The people giving advice should know this side of the equation too. Most people without the disease don’t.
The R-squared and the estimated effect size in a simple linear model both look almost identical at this point in time as they did 55 observations ago – I’ve posted both the old scatterplot (first) and an updated version (second) below – click to view the full size versions:
I have however been a little suspicious about a few data-points which were collected around the time of the London Chess Classics tournament last year – I spent a significant amount of time on chess during that week and my playing strength when playing blitz games went up a lot those days too (I gained ~150 elo points over 4-5 days, which is a lot – I’ve lost that rating again at this point). Here’s what the image looks like without those observations:
I am not convinced that ‘blood glucose has no effect on tactics trainer performance’ is the conclusion to draw from this data-set, so I’m still collecting data at this point. The true data generating process of course includes many variables not included above – you may want to reread the first article if you want to know more about the ‘true’ DTG.
I wrote in my first post that: “I know myself well enough to know that I don’t want to bother with non-linear models when I look at this stuff later; it’s a poor and underspecified model to begin with.” If I actually have to work with methods which prove useful when analysing this type of dataset during my statistics course this semester (do remember that I have not included all the data I’ve gathered in the above plots), I may change my mind about how much work I’ll do on this dataset. Maybe I’ll be reminded of useful ways to handle stuff like this during the course; stuff that I’ve forgotten about at this point. We’ll see how it goes.
If anyone else would like to have a look at the data, just leave a comment below – I’d be happy to send you a copy of the data.
“Coelacanth (pron.: /ˈsiːləkænθ/) is a rare order of fish that includes two extant species: West Indian Ocean coelacanth (Latimeria chalumnae) and the Indonesian coelacanth (Latimeria menadoensis). They follow the oldest known living lineage of Sarcopterygii (lobe-finned fish and tetrapods), which means they are more closely related to lungfish, reptiles and mammals than to the common ray-finned fishes. They are found along the coastlines of the Indian Ocean and Indonesia. Since there are only two species of coelacanth and both are threatened, it is the most endangered order of animals in the world. The West Indian Ocean coelacanth is a critically endangered species.
Coelacanths belong to the subclass Actinistia, a group of lobed-finned fish that are related to lungfish and certain extinct Devonian fish such as osteolepiforms, porolepiforms, rhizodonts, and Panderichthys. Coelacanths were thought to have gone extinct in the Late Cretaceous, but were rediscovered in 1938 off the coast of South Africa. The coelacanth is considered a “living fossil” due to its apparent lack of significant evolution over the past millions of years. The coelacanth is thought to have evolved into roughly its current form approximately 400 million years ago.”
ii. Continued fraction.
“Progeria (also known as “Hutchinson–Gilford (Progeria) Syndrome“, and “Progeria syndrome“) is an extremely rare genetic disease wherein symptoms resembling aspects of aging are manifested at an early age. The word progeria comes from the Greek words “pro” (πρό), meaning “before”, and “géras” (γῆρας), meaning “old age”. The disorder has very low incidences and occurs in an estimated 1 per 8 million live births. Those born with progeria typically live to their mid teens and early twenties. It is a genetic condition that occurs as a new mutation, and is rarely inherited. Although the term progeria applies strictly speaking to all diseases characterized by premature aging symptoms, and is often used as such, it is often applied specifically in reference to Hutchinson-Gilford Progeria Syndrome (HGPS).
Scientists are particularly interested in progeria because it might reveal clues about the normal process of aging. Progeria was first described in 1886 by Jonathan Hutchinson. It was also described independently in 1897 by Hastings Gilford. The condition was later named Hutchinson-Gilford Progeria Syndrome (HGPS).”
iv. Dieppe Raid.
“The Dieppe Raid, also known as the Battle of Dieppe, Operation Rutter and, later, Operation Jubilee, was a Second World War Allied attack on the German-occupied port of Dieppe. The raid took place on the northern coast of France on 19 August 1942. The assault began at 5:00 a.m. and by 10:50 a.m. the Allied commanders were forced to call a retreat. Over 6,000 infantrymen, predominantly Canadian, were supported by limited Royal Navy and large Royal Air Force contingents. […]
Objectives included seizing and holding a major port for a short period, both to prove it was possible and to gather intelligence from prisoners and captured materials, including naval intelligence in a hotel in town and a radar installation on the cliffs above it. Although neither were completely successful, some knowledge was gained while assessing the German responses. The Allies also wanted to destroy coastal defences, port structures and all strategic buildings. The raid could have given a morale boost to the troops, Resistance, and general public, while assuring the Soviet Union of the commitment of the United Kingdom and the United States.
No major objectives of the raid were accomplished. A total of 3,623 of the 6,086 men (almost 60%) who made it ashore were either killed, wounded, or captured. The Royal Air Force failed to lure the Luftwaffe into open battle, and lost 96 aircraft (at least 32 to flak or accidents), compared to 48 lost by the Luftwaffe. The Royal Navy lost 33 landing craft and one destroyer.”
So yeah, it didn’t go that well.
v. Obesity in the Pacific. The main figure from the article, click to enlarge:
In Nauru you’re pretty much a statistical outlier if you’re not overweight. “In the Marshall Islands in 2008 there were 8,000 cases of diabetes in a population of only 53,000.” That’s close to 1 in 6. There’s more data in the related article on Epidemiology of obesity.
vi. Fixed action pattern. Part of the fun of reading this article is derived from the fact that it makes use of an abbreviation which is quite often used, but usually means something else… (An example from the article: “Replicating the releasing mechanism required to trigger a FAP is known as code-breaking.”)
“Polygynous animals are often highly dimorphic, and show large sex-differences in the degree of intra-sexual competition and aggression, which is associated with biased operational sex ratios (OSR). For socially monogamous, sexually monomorphic species, this relationship is less clear. Among mammals, pair-living has sometimes been assumed to imply equal OSR and low frequency, low intensity intra-sexual competition; even when high rates of intra-sexual competition and selection, in both sexes, have been theoretically predicted and described for various taxa. Owl monkeys are one of a few socially monogamous primates. Using long-term demographic and morphological data from 18 groups, we show that male and female owl monkeys experience intense intra-sexual competition and aggression from solitary floaters. Pair-mates are regularly replaced by intruding floaters (27 female and 23 male replacements in 149 group-years), with negative effects on the reproductive success of both partners. Individuals with only one partner during their life produced 25% more offspring per decade of tenure than those with two or more partners. The termination of the pair-bond is initiated by the floater, and sometimes has fatal consequences for the expelled adult. The existence of floaters and the sporadic, but intense aggression between them and residents suggest that it can be misleading to assume an equal OSR in socially monogamous species based solely on group composition. Instead, we suggest that sexual selection models must assume not equal, but flexible, context-specific, OSR in monogamous species.”
You sort of want to extrapolate out of sample (/…out of species?) here, but be careful:
“Our findings differ from those reported for some monogamous birds, where remaining life-time reproductive success (i.e., the expected future gains) of the individual that initiates or tolerates a ‘divorce’ was higher than if it remained with its initial partner. For example, in kittiwakes (Rissa tridactyla) and many other pair-living birds, but also in some human societies, it is sometimes advantageous to ‘divorce’, if partners prove incompatible , , . In contrast, our data strongly indicate that break-ups were associated with factors extrinsic to the pair, and that partners did not voluntarily leave or “divorce” as it has been reported for birds, gibbons, and (in at least one case) brown titi monkeys (Callicebus brunneus) –, , . On the other hand, in some species (oystercatchers, Haematopus ostralegus), the reproductive success of stable pairs is not only higher, but there are also accrued benefits with increased duration of the pair-bond, independent of effects of age or experience . This was not the case for owl monkeys, since the number of offspring produced did not change with increased duration of the pair-bond (Fig. 2).”
ii. Smbc (click to watch in a higher resolution):
“The ability to control fire was a crucial turning point in human evolution, but the question when hominins first developed this ability still remains. Here we show that micromorphological and Fourier transform infrared microspectroscopy (mFTIR) analyses of intact sediments at the site of Wonderwerk Cave, Northern Cape province, South Africa, provide unambiguous evidence—in the form of burned bone and ashed plant remains—that burning took place in the cave during the early Acheulean occupation, approximately 1.0 Ma. To the best of our knowledge, this is the earliest secure evidence for burning in an archaeological context.”
[Another reminder that SMBC is awesome: Here’s a recent comic which is very handy here – it explains what a Fourier transform is, in case you don’t know… (If you actually want to know there’s always wikipedia…)]
iv. I never covered this here and though some of you may already have read it I thought I might as well link to Ed Yong’s write-up on replication studies in Nature published last year. A few quotes from the article:
“Positive results in psychology can behave like rumours: easy to release but hard to dispel. They dominate most journals, which strive to present new, exciting research. Meanwhile, attempts to replicate those studies, especially when the findings are negative, go unpublished, languishing in personal file drawers or circulating in conversations around the water cooler. “There are some experiments that everyone knows don’t replicate, but this knowledge doesn’t get into the literature,” says Wagenmakers. The publication barrier can be chilling, he adds. “I’ve seen students spending their entire PhD period trying to replicate a phenomenon, failing, and quitting academia because they had nothing to show for their time.
These problems occur throughout the sciences, but psychology has a number of deeply entrenched cultural norms that exacerbate them. It has become common practice, for example, to tweak experimental designs in ways that practically guarantee positive results. And once positive results are published, few researchers replicate the experiment exactly, instead carrying out ‘conceptual replications’ that test similar hypotheses using different methods. This practice, say critics, builds a house of cards on potentially shaky foundations.
These problems have been brought into sharp focus by some high-profile fraud cases, which many believe were able to flourish undetected because of the challenges of replication. Now psychologists are trying to fix their field.”
Good luck with that. I don’t see a fix happening anytime soon. A few numbers:
“In a survey of 4,600 studies from across the sciences, Daniele Fanelli, a social scientist at the University of Edinburgh, UK, found that the proportion of positive results rose by more than 22% between 1990 and 2007 (ref. 3). Psychology and psychiatry, according to other work by Fanelli4, are the worst offenders: they are five times more likely to report a positive result than are the space sciences, which are at the other end of the spectrum […]. The situation is not improving. In 1959, statistician Theodore Sterling found that 97% of the studies in four major psychology journals had reported statistically significant positive results5. When he repeated the analysis in 1995, nothing had changed6.”
But maybe other fields are just as bad? Well, as already mentioned the space sciences do better – and that goes for other fields too (though I’d say there seems to be major problems in many areas besides psychology and psychiatry):
A major problem here is that unless you’re actually a researcher in the field or know whom to ask, the file drawer effect can be completely invisible to you.
v. Globalization of Diabetes – The role of diet, lifestyle, and genes. A new publication in Diabetes Care. As usual when they say ‘diabetes’ they mean ‘type 2 diabetes’. Some numbers from the article:
“According to the International Diabetes Federation (1), diabetes affects at least 285 million people worldwide, and that number is expected to reach 438 million by the year 2030, with two-thirds of all diabetes cases occurring in low- to middle-income countries. The number of adults with impaired glucose tolerance will rise from 344 million in 2010 to an estimated 472 million by 2030.
Globally, it was estimated that diabetes accounted for 12% of health expenditures in 2010, or at least $376 billion—a figure expected to hit $490 billion in 2030 (2). […] Asia accounts for 60% of the world’s diabetic population. [Do note that this does not mean that Asian countries are on average overrepresented in the diabetes statistics. Asia also has roughly 60% of the World’s population. – US] […] In 1980, less than 1% of Chinese adults had the disease. By 2008, the prevalence had reached nearly 10% […] in urban areas of south India, the prevalence of diabetes has reached nearly 20% […] Compared with Western populations, Asians develop diabetes at younger ages, at lower degrees of obesity, and at much higher rates given the same amount of weight gain […]
If current worldwide trends continue, the number of overweight people (BMI >25 kg/m^2) is projected to increase from 1.3 billion in 2005 to nearly 2.0 billion by 2030 (6). […] the prevalence of overweight and obesity in Chinese adults increased from 20% in 1992 to 29.9% in 2002 (8) […]
In the NHS (26), each 2-h/day increment of time spent watching television (TV) was associated with a 14% increase in diabetes risk. […] Each 1-h/day increment of brisk walking was associated with a 34% reduction in risk […] Cigarette smoking is an independent risk factor for type 2 diabetes. A meta-analysis found that current smokers had a 45% increased risk of developing diabetes compared with nonsmokers (29). Moreover, there was a dose-response relationship between the number of cigarettes smoked and diabetes risk. [That one I did not know about!] […] Light-to-moderate alcohol consumption is associated with reduced risk of diabetes. A meta-analysis of 370,000 individuals with 12 years of follow-up showed a U-shaped relationship, with a 30–40% reduced risk of the disease among those consuming 1–2 drinks/day compared with heavy drinkers or abstainers (37). […]
common variants of the TCF7L2 gene that are significantly associated with diabetes risk are present in 20–30% of Caucasian populations but only 3–5% of Asians […] Conversely, a variant in the KCNQ1 gene associated with a 20–30% increased risk of diabetes in several Asian populations (43,44) is common in East Asians, but rare in Caucasians […]
Several randomized clinical trials have demonstrated that diabetes is preventable. One of the first diabetes prevention trials was conducted in Daqing, China (58). After 6 years of active intervention, risk was reduced by 31, 46, and 42% in the diet-only, exercise-only, and diet-plus-exercise groups, respectively, compared with the control group. In a subsequent 14-year follow-up study, the intervention groups were combined and compared with control subjects to assess how long the benefits of lifestyle change can extend beyond the period of active intervention (59). Compared with control subjects, individuals in the combined lifestyle intervention group had a 51% lower risk of diabetes during the active intervention period, and a 43% lower risk over a 20-year follow-up.”
vi. Why chess sucks.
I thought I should update the blog even though these days I don’t do a lot of blogging-worthy stuff.
i. A blog I recently discovered: Empirical Zeal. There’s some interesting posts there, for example I liked this one on the state of Indian rural education (though the findings reported are not exactly worthy of celebration).
ii. The acquisition of language by children. From the introduction:
“Imagine that you are faced with the following challenge. You must discover the internal structure of a system that contains tens of thousands of units, all generated from a small set of materials. These units, in turn, can be assembled into an infinite number of combinations. Although only a subset of those combinations is correct, the subset itself is for all practical purposes infinite. Somehow you must converge on the structure of this system to use it to communicate. And you are a very young child.
This system is human language. The units are words, the materials are the small set of sounds from which they are constructed, and the combinations are the sentences into which they can be assembled. Given the complexity of this system, it seems improbable that mere children could discover its underlying structure and use it to communicate. Yet most do so with eagerness and ease, all within the first few years of life.”
It’s actually pretty wild, once you start thinking about it.
iii. The Null Ritual – What You Always Wanted to Know About Significance Testing but Were Afraid to Ask (via Gwern? I no longer remember how I found this.). An excerpt from the article:
“Question 1: What Does a Significant Result Mean?
What a simple question! Who would not know the answer? After all, psychology students spend months sitting through statistics courses, learning about null hypothesis tests (significance tests) and their featured product, the p-value. Just to be sure, consider the following problem (Haller & Krauss, 2002; Oakes, 1986):
Suppose you have a treatment that you suspect may alter performance on a certain task. You compare the means of your control and experimental groups (say, 20 subjects in each sample). Furthermore, suppose you use a simple independent means t-test and your result is signifi cant (t = 2.7, df = 18, p = .01). Please mark each of the statements below as “true” or “false.” False means that the statement does not follow logically from the above premises. Also note that several or none of the statements may be correct.
(1) You have absolutely disproved the null hypothesis (i.e., there is no difference between the population means). ® True False ®
(2) You have found the probability of the null hypothesis being true. ® True False ®
(3) You have absolutely proved your experimental hypothesis (that there is a difference between the population means). ® True False ®
(4) You can deduce the probability of the experimental hypothesis being true. ® True False ®
(5) You know, if you decide to reject the null hypothesis, the probability that you are making the wrong decision. ® True False ®
(6) You have a reliable experimental finding in the sense that if, hypothetically, the experiment were repeated a great number of
times, you would obtain a significant result on 99% of occasions. ® True False ®
Which statements are true? If you want to avoid the I-knew-it-all-along feeling, please answer the six questions yourself before continuing to read. When you are done, consider what a p-value actually is: A p-value is the probability of the observed data (or of more extreme data points), given that the null hypothesis H0 is true, defined in symbols as p(D |H0).Th is defi nition can be rephrased in a more technical form by introducing the statistical model underlying the analysis (Gigerenzer et al., 1989, chap. 3). Let us now see which of the six answers are correct:
Statements 1 and 3: Statement 1 is easily detected as being false. A significance test can never disprove the null hypothesis. Significance tests provide probabilities, not definite proofs. For the same reason, Statement 3, which implies that a significant result could prove the experimental hypothesis, is false. Statements 1 and 3 are instances of the illusion of certainty (Gigerenzer, 2002).
Statements 2 and 4: Recall that a p-value is a probability of data, not of a hypothesis. Despite wishful thinking, p(D |H0) is not the same as p(H0 |D), and a significance test does not and cannot provide a probability for a hypothesis. One cannot conclude from a p-value that a hypothesis has a probability of 1 (Statements 1 and 3) or that it has any other probability (Statements 2 and 4). Therefore, Statements 2 and 4 are false. The statistical toolbox, of course, contains tools that allow estimating probabilities of hypotheses, such as Bayesian statistics (see below). However, null hypothesis testing does not.
Statement 5: The “probability that you are making the wrong decision” is again a probability of a hypothesis. This is because if one rejects the null hypothesis, the only possibility of making a wrong decision is if the null hypothesis is true. In other words, a closer look at Statement 5 reveals that it is about the probability that you will make the wrong decision, that is, that H0 is true. Thus, it makes essentially the same claim as Statement 2 does, and both are incorrect.
Statement 6: Statement 6 amounts to the replication fallacy. Recall that a p-value is the probability of the observed data (or of more extreme data points), given that the null hypothesis is true. Statement 6, however, is about the probability of “significant” data per se, not about the probability of data if the null hypothesis were true. The error in Statement 6 is that p = 1% is taken to imply that such significant data would reappear in 99% of the repetitions. Statement 6 could be made only if one knew that the null hypothesis was true. In formal terms, p(D |H0) is confused with 1 – p(D). The replication fallacy is shared by many, including the editors of top journals. […] To sum up, all six statements are incorrect. Note that all six err in the same direction of wishful thinking: They overestimate what one can conclude from a p-value. […]
We posed the question with the six multiple-choice answers to 44 students of psychology, 39 lecturers and professors of psychology, and 30 statistics teachers […] How many students and teachers noticed that all of the statements were wrong? As Figure 1 shows, none of the students did. […] Ninety percent of the professors and lecturers also had illusions, a proportion almost as high as among their students. Most surprisingly, 80% of the statistics teachers shared illusions with their students.”
The article has much more.
“More than 25% of the U.S. population aged [>65] years has diabetes (1), and the aging of the overall population is a significant driver of the diabetes epidemic. […] The incidence of diabetes increases with age until about age 65 years, after which both incidence and prevalence seem to level off”. I should have known the first number was in that neighbourhood, but somehow I had failed to realize that it was that high; most often prevalence estimates are calculated/reported using the entire population in the denominator, but of course such estimates can be deceiving if you do not think about how they are calculated and I clearly hadn’t. At least 1 in 4 in the above-65 age bracket. That’s a lot of people. The article doesn’t have a lot of data, it’s a ‘consensus report’ handling mostly various treatment guideline suggestions and similar stuff.
v. What is the most uncomfortable situation have you ever been put in- by a guy? Any kind of unwanted flirtation- or something of that nature (Reddit). Lots of really horrible stuff; reading stuff like this makes what might be perceived of as some females’ ‘somewhat overcautious’ behaviour towards members of the opposite sex easier to understand. An example from the link:
“The last stranger-danger moment I will share tonight was at an end-of-midterms party sponsored by the student union at a local bar. I was there with my best friend, and she’s very pretty and very friendly, so we’d very quickly attracted a group of four or five men who were hanging around with us for most of the night. I hadn’t seen any of them before, so I assumed they were students from a different department, and we end up getting a table together and talking for a while. Once my friend mentions that she has a boyfriend, most of them shift their attention to me, though there’s one who still seems interested in her. As I’m talking to them, I find that they’re not students at our university, but that they’re a group of friends visiting from the a couple towns over. Nothing too creepy, so far.
My friend finishes her drink, so the guy she’s talking to goes to buy her another. She’s a little suspicious, so she starts drinking it VERY slowly. Meanwhile, I’m getting distracted talking to one of the guys who works in the same field I’ll be entering soon, and we end up talking for a while about that. He keeps telling me that I’m very beautiful, which I keep brushing off because I knew he was interested in my friend initially, and I was interested in someone else at the time, anyway. Somewhere in the middle of all this, my friend has stopped drinking the drink that was bought for her, and someone asks if she’s going to finish it. She says no.
Eventually, the guy I’m talking to apologizes for his “bad” English, saying that he hasn’t really had to use it since he was in school, which was OVER TEN YEARS AGO. At about the same time, my friend is telling the guy she’s talking to that it’s funny that they decided to visit our city on that particular weekend, because this is a student end-of-midterm party, and he answers, “I know. That’s kind of why we came here.” Someone else asks my friend if she’s going to finish her drink, and she says no, but he can have it if he wants. The drink ‘accidentally’ gets spilled in the process, and she’s signalling me to get the fuck out of there, so I take the opportunity to drag her to the bathroom. I start to notice that she’s acting really fucked up – she can usually drink a ton more than I can, and she’d only had one drink of her own and maybe a third (probably less than that, actually) of the one that guy bought for her. She says she thinks the drink they gave her was drugged, and then she gets sick. I ended up staying the night at her place to keep an eye on her, but I didn’t think to take her to the hospital or anything, so I guess we’ll never know what exactly happened…”
Of course if you’re like me you don’t engage in risky behaviours like drinking with strangers and in that case it doesn’t really matter much if you’re male or female, but then again I’m not like normal people. Most males probably significantly underestimate how risky some of their behaviours – behaviours they would not ever even think of as ‘particularly risky’ – are when a female engages in them. Note that even males that fall into the “I can’t imagine you raising your voice”-category (a female friend said this about me in a conversation I had with her earlier today) are likely to be affected by the behaviours of the (type of) males described in the link; once a female has been through situations like the ones described at the link, she’s less likely to give males the benefit of the doubt and more likely to misinterpret behaviour and the motivations driving behaviour. Reading this stuff has made me believe that the behaviour of ‘overcautious’ females may be better justified and less ‘irrational’ than males tend to think it is.
vi. I haven’t commented on the new DSM-5 – let’s just say I’ve had better things to do. Here’s one take on it (“It’s arcane, contradictory and talks about invisible entities which no-one can really prove. Yes folks, the new psychiatric bible has been finalised.”). The most ‘relevant’ change to me is the fact that they’ll remove the Asperger Syndrome diagnosis, and instead merge it with other autism spectrum disorders. If you’re asking me what I think about that, the answer is that I don’t really care.
vii. Cheetahs on the Edge (via Ed Yong). A must-see:
“Using a Phantom camera filming at 1200 frames per second while zooming beside a sprinting cheetah, the team captured every nuance of the cat’s movement as it reached top speeds of 60+ miles per hour.
The extraordinary footage that follows is a compilation of multiple runs by five cheetahs during three days of filming.”
I don’t like when the blog isn’t updated for several days, so here are some links to stuff I’ve encountered on the internet in the recent past:
i. Diabetic Autonomic Neuropathy. An overview article which covers a lot of ground; it has approximately 1000 citations and I believe it’s one of the most read articles published in Diabetes Care, a journal you incidentally should know about if you’re diabetic or are interested in diabetes.
ii. Also diabetes-related and closely related to the above paper: The EKG in Diabetes Mellitus. This article is particularly relevant to me because I had an EKG last week and will be told the results of it tomorrow where I have a doctor’s appointment – reading stuff like this first makes it easier to ask the right questions. I jokingly explained to a friend yesterday that if the results of that test come out a specific way, it will be much easier for me to make pension plans (meaning I’d most likely be dead long before the official retirement age – naturally I do not hope for that outcome to happen). I’ll also learn the results of the standard Hba-1c blood test – which is measured 3-4 times a year – as well as the annual urin-sample analysis to check for microalbuminuria (kidney damage). Also, cholesterol levels and triglycerides. So I’ll learn more from this check-up than I usually do. I hope everything is fine but there’s a reason why they perform tests like these; I have no way of knowing myself if there’s a problem here.
Anyway, a few quotes from the paper:
“Fibrotic changes, especially in the basal area of the left ventricle, have frequently been observed in diabetic patients, even when cardiac involvement is clinically not yet evident. […] The EURODIAB Insulin-Dependent Diabetes Mellitus Complications Study (EURODIAB IDDM)9 investigated 3250 type 1 diabetes patients with an average diabetes duration of >30 years; the prevalence of left ventricular hypertrophy was found to be 3 times greater than that reported in the general population of similar age. […] Baroreflex dysfunction and disturbed heart rate variability are the most commonly used methods to assess CAN [Cardiovascular autonomic neuropathy, US]. […]
Ong et al14 found the QTc to be shorter if patients had signs of neuropathy, although these patients’ heart rate was higher and their circadian patterns seemed to be preserved. Valensi et al15 found an unchanged QTc in mild neuropathy, although the circadian day/night QTc pattern was reversed. Pappachan et al16 expressed the view that the QTc interval can be used to diagnose CAN with reasonable sensitivity, specificity, and positive predictive value. Grossmann et al17 observed a prolonged QTc only in diabetic patients with CAN; late potentials were not recorded in any of these patients with CAN. CAN patients with prolonged variability in QTc, QT, or both had high incidence of sudden death.18 […]
Myocardial ischemia is more often painless in patients with diabetes mellitus.19 Resting ECG abnormalities20 as well as cardiac autonomic dysfunction21 were found to be predictors of silent ischemia in asymptomatic persons with T1D.
In otherwise healthy diabetic men during an average follow-up of 16 years, an abnormal and even an equivocal exercise ECG response was associated with a statistically significant high risk for all-cause and cardiac mortality and morbidity, independently of physical fitness and other traditional risk factors; fit men had a higher survival rate than did unfit men.22 [One more reason why I shouldn’t have that much trouble motivating myself to stay in shape.] […]
The early stage of diabetic cardiomyopathy may already be associated with a range of metabolic abnormalities and even with abnormalities in diastolic function. Frequently, no structural cardiac abnormalities can be identified at this stage; the often subtle ECG alterations may be our only way to diagnose early diabetic cardiomyopathy. […]
Even early in the course of diabetes mellitus, ECG alterations such as sinus tachycardia, long QTc, QT dispersion, changes in heart rate variability, ST-T changes, and left ventricular hypertrophy may be observed. ECG alterations help evaluate cardiac autonomic neuropathy and detect signs of myocardial ischemia even in asymptomatic patients. Prolonged myocardial fibrosis leads to diabetic cardiomyopathy, with peculiar ECG presentation. Electrocardiographic changes are already present in fetuses, children, and adolescents. The resting ECG, frequently complemented by exercise ECG, assists in cardiac screening of diabetic individuals and helps detect silent ischemia, assess prognosis, and predict mortality”
iii. Boredom Proneness: Its Relationship to Psychological- and Physical-Health Symptoms, by Sommers and Vodanovich.
“The relationship between boredom proneness and health-symptom reporting was examined. Undergraduate students (N 5 200) completed the Boredom Proneness Scale and the Hopkins Symptom Checklist. A multiple analysis of covariance indicated that individuals with high boredomproneness total scores reported significantly higher ratings on all five subscales of the Hopkins Symptom Checklist (Obsessive–Compulsive, Somatization, Anxiety, Interpersonal Sensitivity, and Depression). The results suggest that boredom proneness may be an important element to consider when assessing symptom reporting. Implications for determining the effects of boredom proneness on psychological- and physical-health symptoms, as well as the application in clinical settings, are discussed.”
I had no idea there was such a thing as a ‘Boredom Proneness Scale’! I found the literature overview in the beginning of the paper much more interesting than the study itself (one word: WEIRD). Judging from the reported results there, if you’re bored a lot and/or have a really boring job you may be well advised to do something about that – because being bored is associated with a lot of bad stuff:
“To date, the work on boredom proneness has focused on its association with negative affect, as well as problems in academic and work settings. For instance, significant positive relationships have been found between the tendency to experience boredom and depression, anxiety, hostility, anger, loneliness, and hopelessness (e.g., Ahmed, 1990; Farmer & Sundberg, 1986; Rupp & Vodanovich, 1997; Vodanovich, Verner, & Gilbride,
1991; Watt & Davis, 1991). Other researchers have reported boredom proneness to be related significantly to lower educational achievement, truancy rate, and poor work performance (e.g., Branton, 1970; Drory, 1982; Gardell, 1971; Maroldo, 1986; O’Hanlon, 1981; Robinson, 1975; Smith, 1981).
Limited work, however, has been devoted to investigating the association between boredom and psychological- and physical-health symptoms. Evidence for such a relationship can be inferred from studies reporting significant, positive correlations between boredom and substance abuse and eating disorders (e.g., Abramson & Stinson, 1977; Ganley, 1989; Johnston & O’Malley, 1986; Martin, 1989; Pascale & Sylvester, 1988).
Other researchers have established a connection between boredom and detrimental health effects in organizational settings. For instance, Smith, Cohen, and Stammerjohn (1981) found that workers in monotonous jobs reported more visual, musculoskeletal, and emotional-health complaints than those performing non-monotonous work. Samilova (1971) found that female Russian workers employed in repetitive tasks experienced higher incidence of health problems, including gastritis, peripheral neurological disorders, and joint, tendon, muscle, and cardiovascular disease, than workers in less-repetitive jobs. Ferguson (1973) found that telegraphists who complained of task monotony indicated a greater occurrence of physical-health problems, such as asthma, bronchitis, trunk myalgia, and hand tremors, as compared to other workers in less-monotonous positions.”
iv. Ideology, Motivated Reasoning, and Cognitive Reflection: An Experimental Study. I haven’t actually gotten around to reading this yet, but I bookmarked it for a reason; I probably will later during the week.
v. Media Use Among White, Black, Hispanic, and Asian American Children, by Rideout, Lauricella and Wartella. I’ve written about that stuff before but I haven’t written about this data. It’s survey data so it should be taken with a grain of salt. Even if it is, however, I think there’s some interesting information here. Some stuff from the report:
“Historically, scholars have been aware of differences in the amount of time that White and minority children spend with media, especially TV. But last year’s Generation M2 study indicated a large increase in the amount of time both Black and Hispanic youth are spending with media, to the point where they are consuming an average of 13 hours worth of media content a day (12:59 for Blacks and 13:00 for Hispanics), compared with about eight and a half hours (8:36) for White youth, a difference of about four and a half hours a day.” [my emphasis] […]
The biggest differences are in the amount of time spent with a TV (a difference of about one to two hours of TV a day between White and minority youth), music (a difference of about an hour a day), computers (up to an hour and a half difference), and video games (from 30 to 40 minutes difference).”
Here’s the ‘big picture’, click to view full size:
vi. I really, truly dislike (and that’s putting it mildly) the new format for the discover magazine blogs, but I really liked this post by Razib Khan. Then again it was posted before the switch. I like a lot of his stuff so I tend not to link to individual posts (I’d have to link to a lot of stuff…) but I figure I should remind you now and then that you should be reading his blog. Even if the new format sucks.
If you don’t know what I’m talking about, here’s the introduction.
I haven’t done as many sessions as I’d have liked, but at this point n is equal to 50 so I figured I might as well give you a scatter plot with the performance data so far:
Without the 2100+ performance at 17 mmol/l (the far right data point) R^2 would be 0,1463 – so n is still way too low to draw any conclusions. Perhaps aside from the fact that I don’t think the pattern looks completely random.
I’ve become aware of the fact that there are just loads of omitted variables here (nearby road work done with extensive use of pneumatic drills being one of the major ones in the beginning) and it would take a lot of data to take them all into account.
I’ve also realized by now that the tactics trainer performance is not a super great tool to pick up on variation in mental ability, though I maintain it’s not completely crazy to use it as a proxy. A significant number of the problems during a session are either repeats or quite similar to other problems solved in the past, and I remember those patterns just as well with a high blood glucose as with a lower one. So most of the variation in performance is around a set baseline, and how much I deviate from that baseline depends on how many ‘new’ problems – where I actually do have to think a bit – are introduced during a session. My performance is quite sensitive to the type of problems presented during a session and to which degree new problems/themes are introduced – the performance can easily vary with 200 points or more if I do two sessions ten minutes apart.
(link). Some people would say that you should formulate the hypothesis before you start gathering data – and that’s what I’ll do now.
I guess this post is mostly for people like Plamus, but other people are very welcome to read along as well. I’ll start out with some introductionary remarks. I have an account on a chess website – playchess.com. It’s a neat site, I like it. They’ve recently introduced a new featured: A so-called ‘tactics trainer’. The way the tactics trainer works is by means of tactics sessions. Each tactics session features a number of chess problems you need to solve under a time constraint. You’ll never run out of problems; each time you’ve solved one problem (or answered incorrectly) a new one will pop up. Each session lasts about 6 minutes – some problems can be solved in a second or two, others might take more than a minute. The outcome of a session will depend upon the number of problems solved correctly, the ‘toughness’ of the problems solved or not solved and probably various other factors as well. Once you’ve finished a session, you’ll get a statistic on the number of correctly and incorrectly solved problems, the average time spent on each problem and the corresponding tactics performance rating. The performance rating will impact your combined tactics rating, which is a result of all previous sessions (it’s like a standard Elo rating system with frequent updating).
But why is the tactics trainer worth blogging about? Well, here’s the thing: Solving tactics problems is hard and it’s a cognitively demanding task. It takes brain power, and if your brain isn’t working 100 % you’ll do worse than if it did. I have often thought about how to model the effects of blood glucose variations on cognitive performance. I’ve thought about it because I know that blood glucose variation impacts my performance in various areas – it’s obviously the case, in extreme cases it’s extremely obvious. But what about the non-extreme cases? Blood glucose fluctuates a lot over the course of a day, and it’s not unlikely that such fluctuations also impact performance. But can those effects be quantified? So far it’s been difficult for me to figure out how one would set about doing that – one approach I’ve contemplated in the past was to use IQ-tests to measure performance as a function of blood glucose, but that idea was basically dead in the water in terms of getting the kind of results I’d like – an IQ-test takes a lot of time, it’s not always easy to compare scores across tests and you can’t do the same test over and over because the way the test is designed the validity of the results will be impacted if you repeat the test. Another problem is that the blood glucose level wouldn’t even be exogenous – to be in a state of deep concentration for a long time under stressful circumstances impacts blood glucose. What would be much better would be a shorter version of the test – like a relatively short test where a high level of concentration is required to perform well and where even small differences in performances as a result of blood glucose fluctuations can be measured and quantified. Remember the tactics trainer I was talking about? Yeah…
It seemed to me that using the tactics trainer sessions to gauge ‘mental ability’ as a function of blood glucose actually makes a lot of sense; it’s possible to run a lot of sessions over time, so n can potentially become large enough to actually make room for some non-silly results. There are always new and different problems available, and the comparability issue across tests disappears completely. Blood glucose values can be taken as exogenous as the sessions last only a very short amount of time. Performances are precisely measured.
I should make it clear from the start that the effect of blood glucose on performance is non-linear. Extremely low values impact performance, as do extremely high values – so in theory some kind of semi-inverse-u-shaped pattern should probably be expected. The actual relationship would not look very much like an inverse u both because the scales are asymmetric in terms of symptoms/(mmol/l deviation from the desired level) – a blood glucose of somewhere between 4-10 mmol/l is often considered ‘desirable’, but whereas a value of 0 will mean that you’re dead, a value of 14 will for many diabetics probably often not give any symptoms at all – and because the left hand side is truncated (as mentioned) whereas in practice the right hand side is not for well-treated patients.
I will make a simplifying assumption here that will save me a lot of work and arguably will not be all that problematic when interpreting the results. I’ll disregard the non-linearities in the data by removing all data problems related to performance effects to the left of the lower bound of the ‘desirable level’, and by assuming that the ‘true’ non-linear relationship between performance and blood glucose on the right hand side of the distribution can be approximated by a linear function without this causing too many problems. The way to deal with the “data problems related to performance effects to the left of the lower bound of the ‘desirable level'” will be to exclude from the sample all observations with a measured blood glucose below 4.0 mmol/l. My motivation for removing the lowest values is that it will always become obvious to me within a very short amount of time, when my blood glucose is that low, that there’s a significant performance effect. I know those effects very well, and I know that it’s a bad idea to delay treatment – blood glucose levels below that can quickly turn into a medical emergency. When thinking about performance-effects here, it seems to me to make a lot of sense to implicitly employ a two-state model framework and then use separate/different models to analyze the stuff that’s going on in the two states: State one is quite simple, that’s the hypoglycemia scenario mentioned. To ‘model’ this state is easy: The effects are almost universally real and significant, to an extent where even measuring them in the manner described here becomes borderline dangerous. State two: Euglycemia or hyperglycemia. In this state, performance is likely to be at least somewhat some to linearly decreasing in the blood glucose level. I’m mostly interested in performance effects which are not obvious to me and so that makes state two the more interesting state to consider; it’s also a lot more interesting because state one is relatively (though not that…) rare, whereas state two is the default state in which I spend most of my time. Regarding using a linear approximation to model the relationship in state two rather than the ‘true’ non-linear function: This may be problematic, but I know myself well enough to know that I don’t want to bother with non-linear models when I look at this stuff later; it’s a poor and underspecified model to begin with. The kind of question I’m asking here is far more along the lines of: ‘does it even make sense to assume that your cognitive profile is affected by blood glucose variation?’ than it is a question along the lines of: ‘how will a 2,6 mmol/l difference impact your likelihood of getting an A when taking an exam in course X?’
When it comes to the specifics of the data gathering process, I’ll do it this way: Unless I have symptoms of hypoglycemia – in which case I’ll not do the session in question, but rather treat the hypoglycemia – I’ll only measure the blood glucose after I’ve finished the session. If the blood glucose is below 4.0 mmol/l the results will not be included in the sample. For all other observations, I will list the performance rating of the tactics session and the blood glucose level.
I intend to test the hypothesis that there is a significant and negative effect on performance of the blood glucose level measured (higher blood glucose level -> lower performance rating).
If I get around to it, it might also be interesting to see if there are threshold effects at play. One threshold to consider might be a blood glucose level of 15.0 mmol/l.. The precise cut-off is semi-arbitrary, but not completely; this is close to the point where you start to be able to measure beginning ketonuria, and it’s probably also around this point where symptoms start to (maybe) appear. I write ‘maybe’ because the symptoms of high blood glucose are far more unreliable than the symptoms of low blood glucose, which is also why I’m interested in the related performance effects; when I have symptoms I know I’m not ‘at my best’, but diabetics are often not ‘at their best’ without getting any signals from the body to that effect. A threshold effect also makes sense to include because it’s far from likely that a linear model will catch all the stuff that’s going on here.
As a starting point, my stopping rule will be that I’ll stop collecting data once I have 300 observations. This is completely arbitrary, but you should always have a stopping rule. I take in the neighbourhood of 8 blood tests a day and some of them aren’t taken when I sit at my computer doing tactics chess exercises. If half of them are, however, I will have 300 observations in 2,5 months, i.e. around New Year (this is close to my exams, so I’ll surely not want to do a lot of non-work statistical modelling at that point – so it will be kept simple..). Maybe it will be worth considering doing more than one session per blood test in which case the data can be gathered a lot faster than that, but then problems related to blood glucose exogeneity may start to pop up. I haven’t done multiple sessions after each other before, so I don’t know if such an approach will impact the performance rating; it might, and if it seems to do that I’ll probably disregard such ‘shortcuts’.
Potentially I might improve my tactics abilities during the survey period (in this specific setting that would be a bad thing, because the parameters would then no longer be constant over time) but unless such an effect is very noticeable early on I’ll proceed as if my skill does not improve during the survey period. I’ll write down the starting tactics rating (which is sort of ‘an average of recent past performances’) as well as the tactics rating at the end of the project and compare the difference between the two with the estimated standard deviation of the observations to at least get an idea if there’s a potential big problem here; I don’t know if I’ll really care if a big problem turns up, but I should at least pretend to care about this ‘risk’ of getting better over time (and as an added bonus this is also a simple way to try to establish if doing tactics exercises helps you improve your tactics abilities significantly). The reason why I assume the ‘improvement over time’-effect to be minor here is mostly that I’m actually a reasonably strong player by now so the learning curve is presumably a lot flatter than it was in the past, meaning that exercises like these should not be expected to have that big an effect on my performance.
Yes, I did consider including other variables in the model (number of unsolved problems, time spent/problem), but a) they don’t add much additional information, b) they’re strongly correlated with the rating variable (so I would not be comfortable including them in the same model as the rating variable), and c) the more data I need to write down the more this will feel like work, and I don’t want it to feel like work. So there’ll also be no controls included, this is all just a ‘fun (not quick) and dirty’ project to have running for a while. I’ll release the (limited) data afterwards and let people play around with it if they like to.
Ideas and suggestions (which do not involve me doing a lot of extra work), as well as questions, are of course most welcome.
Incidentally, if you want to know if you’re good at figuring out how smart people are based on how they look, here’s another small-scale project you may be interested in (I have nothing to do with it as such, but I know the guy behind it).