Age-related bone loss is well established in humans and is known to occur in nonhuman primates. There is little information, however, on the effect of dietary interventions, such as caloric restriction (CR), on age-related bone loss. This study examined the effects of long-term, moderate CR on skeletal parameters in rhesus monkeys. Thirty adult male rhesus monkeys were subjected to either a restricted (R, n = 15) or control (C, n = 15) diet for 20 years and examined throughout for body composition and biochemical markers of bone turnover. Total body, spine, and radius bone mass and density were assessed by dual-energy X-ray absorptiometry. Assessment of biochemical markers of bone turnover included circulating serum levels of osteocalcin, carboxyterminal telopeptide of type I collagen, cross-linked aminoterminal telopeptide of type I collagen, parathyroid hormone, and 25(OH)vitamin D. Overall, we found that bone mass and density declined over time with generally higher levels in C compared to R animals. Circulating serum markers of bone turnover were not different between C and R with nonsignficant diet-by-time interactions. We believe the lower bone mass in R animals reflects the smaller body size and not pathological osteopenia.
Caloric restriction; Bone; Aging; Osteoporosis; Dietary restriction; Dual-energy x-ray absorptiometry
To show how the variance of the measurement error (ME) associated with individual ancestry proportion estimates can be estimated, especially when the number of ancestral populations (k) is greater than 2.
We extend existing internal consistency measures to estimate the ME variance, and we compare these estimates with the ME variance estimated by use of the repeated measurement (RM) approach. Both approaches work by dividing the genotyped markers into subsets. We examine the effect of the number of subsets and of the allocation of markers to each subset on the performance of each approach. We used simulated data for all comparisons.
Independently of the value of k, the measures of internal reliability provided less biased and more precise estimates of the ME variance than did those obtained with the RM approach. Both methods tend to perform better when a large number of subsets of markers with similar sizes are considered.
Our results will facilitate the use of ME correction methods to address the ME problem in individual ancestry proportion estimates. Our method will improve the ability to control for type I error inflation and loss of power in association tests and other genomic research involving ancestry estimates.
Population stratification; admixture; type I error inflation; reliability; Cronbach’s alpha; measurement errors; measurement error variance
Asthma and chronic obstructive pulmonary disease (COPD) are major worldwide health problems. Pulmonary function testing is a useful diagnostic tool for these diseases, and is known to be influenced by genetic and environmental factors. Previous studies have demonstrated that a substantial proportion of the variation in pulmonary function phenotypes can be explained by familial relationships. The availability of whole-genome single nucleotide polymorphism (SNP) data enables us to further evaluate the extent to which genetic factors account for variation in pulmonary function and to compare pedigree- to SNP-based estimates of heritability. Here, we employ methods developed in the animal breeding field to estimate the heritability of forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the ratio of these two measures (FEV1/FVC) among subjects in the Framingham Heart Study dataset. We compare heritability estimates based on pedigree-based relationships to those based on genome-wide SNPs. We find that, in a family-based study, estimates of heritability using SNP data are nearly identical to estimates based on pedigree information, and range from 0.50 for FEV1 to 0.66 for FEV1/FVC. Therefore, we conclude that genetic factors account for a sizable proportion of inter-individual differences in pulmonary function, and that estimates of heritability based on SNP data are nearly identical to estimates based on pedigree data. Finally, our findings suggest a higher heritability for FEV1/FVC compared to either FEV1 or FVC.
FEV1; FVC; FEV1/FVC; heritability; pulmonary function; genetic
n-3 Polyunsaturated fatty acids (n-3 PUFAs) have anti-obesity effects that may modulate risk of obesity, in part, through interactions with genetic factors. Genome-wide association studies (GWAS) have identified genetic variants associated with body mass index (BMI); however, the extent to which these variants influence adiposity through interactions with n-3 PUFAs remains unknown. We evaluated 10 highly replicated obesity GWAS single nucleotide polymorphisms (SNPs) for individual and cumulative associations with adiposity phenotypes in a cross-sectional sample of Yup’ik people (n = 1,073) and evaluated whether genetic associations with obesity were modulated by n-3 PUFA intake. A genetic risk score (GRS) was calculated by adding the BMI-increasing alleles across all 10 SNPs. Dietary intake of n-3 PUFAs was estimated using nitrogen stable isotope ratio (δ15N) of red blood cells, and genotype–phenotype analyses were tested in linear models accounting for familial correlations. GRS was positively associated with BMI (p = 0.012), PBF (p = 0.022), ThC (p = 0.025), and waist circumference (p = 0.038). The variance in adiposity phenotypes explained by the GRS included BMI (0.7 %), PBF (0.3 %), ThC (0.7 %), and WC (0.5 %). GRS interactions with n-3 PUFAs modified the association with adiposity and accounted for more than twice the phenotypic variation (~1–2 %), relative to GRS associations alone. Obesity GWAS SNPs contribute to adiposity in this study population of Yup’ik people and interactions with n-3 PUFA intake potentiated the risk of fat accumulation among individuals with high obesity GRS. These data suggest the anti-obesity effects of n-3 PUFAs among Yup’ik people may, in part, be dependent upon an individual’s genetic predisposition to obesity.
Electronic supplementary material
The online version of this article (doi:10.1007/s12263-013-0340-z) contains supplementary material, which is available to authorized users.
BMI; Adiposity; Alaska Native; SNP; δ15N; rs9939609; rs7647305; FTO; ETV5; Genetic risk score; CANHR; Gene-by-environment interactions
The analysis of longevity as a function of risk factors such as body mass index (BMI; kg/m2), activity levels, and dietary factors is a mainstay of obesity research. Modeling survival through hazard functions, relative risks, or odds of dying with methods such as Cox proportional hazards or logistic regression are the most common approaches and have many advantages. However, they also have disadvantages in terms of the ease of interpretability, especially for non-statisticians; the need for additional data to convert parameter estimates to estimates of years of life lost (YLL); and debates about the appropriate time scale in the model. Parametric survival models are able to provide more direct answers, and in our analysis of an obesity-related data set, gave consistent YLL estimates regardless of the distribution used. Additionally, we offer alternative approaches to the analyses of censored survival data including a modified or ‘compressed’ Gaussian distribution. We therefore recommend increased consideration of parametric survival models in chronic disease and risk factor epidemiology.
statistics; longitudinal; BMI; biostatistics; epidemiology
Heightened stress reactivity is associated with hippocampal atrophy, age-related cognitive deficits, and increased risk for Alzheimer s disease. This temperament predisposition may aggravate age-associated brain pathology or be reflective of it. This association may be mediated through repeated activation of the stress hormone axis over time. Dietary interventions, such as calorie restriction (CR), affect stress biology and may moderate the pathogenic relationship between stress reactivity and brain in limbic and prefrontal regions.
Rhesus monkeys (Macaca mulatta) aged 19–31 years consumed either a standard diet (N=18) or were maintained on 30% CR relative to baseline intake (N=26) for 13–19 years. Behavior was rated in both normative and aversive contexts. Urinary cortisol was collected. Animals underwent magnetic resonance imaging and diffusion tensor imaging (DTI) to acquire volumetric and tissue microstructure data respectively. Voxel-wise statistics regressed a global stress reactivity factor, cortisol, and their interaction on brain indices across and between dietary groups.
CR significantly reduced stress reactivity during aversive contexts without affecting activity, orientation, or attention behavior. Stress reactivity was associated with less volume and tissue density in areas important for emotional regulation and the endocrine axis including prefrontal cortices, hippocampus, amygdala, and hypothalamus. CR reduced these relationships. A Cortisol by Stress Reactivity voxel-wise interaction indicated that only monkeys with high stress reactivity and high basal cortisol demonstrated lower brain volume and tissue density in prefrontal cortices, hippocampus, and amygdala.
High stress reactivity predicted lower volume and microstructural tissue density in regions involved in emotional processing and modulation. A CR diet reduced stress reactivity and regional associations with neural modalities. High levels of cortisol appear to mediate some of these relationships.
aging; stress; prefrontal cortex; hippocampus; calorie restriction; monkey; cortisol
It has been suggested that poor immunogenicity may explain the lack of vaccine efficacy in preventing or controlling HIV infection in the Step trial. To investigate this issue we vaccinated eight Indian rhesus macaques with a trivalent replication-incompetent adenovirus serotype 5 vaccine expressing SIV Gag, Pol, and Nef using a regimen similar to that employed in the Step trial. We detected broad vaccine-induced CD8+ (2–7 pool-specific responses) and CD4+ (5–19 pool-specific responses) T-cell responses in IFN-γ ELISPOT assays at one week post-boost using fresh PBMC. However, using cryopreserved cells at one and four weeks post-boost we observed a reduction in both the number and magnitude of most vaccine-induced responses. This demonstrates that the time points and conditions chosen to perform immune assays may influence the observed breadth and frequency of vaccine-induced T-cell responses. To evaluate protective efficacy, we challenged the immunized macaques, along with naïve controls, with repeated, limiting doses of the heterologous swarm isolate SIVsmE660. Vaccination did not significantly affect acquisition or control of virus replication in vaccinees compared to naïve controls. Post-infection we observed an average of only two anamnestic CD8+ T-cell responses per animal, which may not have been sufficiently broad to control heterologous virus replication. While the trivalent vaccine regimen induced relatively broad T-cell responses in rhesus macaques, it failed to protect against infection or control viral replication. Our results are consistent with those observed in the Step trial and indicate that SIV immunization and challenge studies in macaque models of HIV infection can be informative in assessing pre-clinical HIV vaccines.
HIV vaccine; Adenovirus serotype 5; Simian Immunodeficiency Virus; CD8+ T cells; CD4+ T cells; Step trial
Plausibility of high variability in treatment effects across individuals has been recognized as an important consideration in clinical studies. Surprisingly, little attention has been given to evaluating this variability in design of clinical trials or analyses of resulting data. High variation in a treatment’s efficacy or safety across individuals (referred to herein as treatment heterogeneity) may have important consequences because the optimal treatment choice for an individual may be different from that suggested by a study of average effects. We call this an individual qualitative interaction (IQI), borrowing terminology from earlier work - referring to a qualitative interaction (QI) being present when the optimal treatment varies across a“groups” of individuals. At least three techniques have been proposed to investigate treatment heterogeneity: techniques to detect a QI, use of measures such as the density overlap of two outcome variables under different treatments, and use of cross-over designs to observe “individual effects.” We elucidate underlying connections among them, their limitations and some assumptions that may be required. We do so under a potential outcomes framework that can add insights to results from usual data analyses and to study design features that improve the capability to more directly assess treatment heterogeneity.
Causation; Crossover interaction; Individual effects; Potential outcomes; Probability of similar response; Subject-treatment interaction
The perception that all high-fat snacks are unhealthy may be wrong.
We aimed to assess whether replacing low-fat and high-fat snacks with snacks rich in polyunsaturated fatty acids (PUFAs) and low in saturated and trans fatty acids would improve cardiovascular health.
Thirty-three adults participated in a randomized crossover trial of 3 controlled feeding phases of 25 d each in which a different type of snack was provided: low-fat (30.8% of energy from fat, 5.2% of energy from PUFAs), high-PUFA (36.3% of energy from fat, 9.7% of energy from PUFAs), or high-fat (37.9% of energy from fat, 5.8% of energy from PUFAs) snack.
Each diet reduced LDL- and total cholesterol concentrations, but reductions were greater with the low-fat and the high-PUFA diets than with the high-fat diet: LDL cholesterol (11.8% and 12.5% compared with 8.8%, respectively; P = 0.03 and 0.01), total cholesterol (10.5% and 10.7% compared with 7.9%, respectively; P = 0.03 and 0.02). The high-PUFA diet tended to reduce triacylglycerol concentrations (9.4%; P = 0.06), and this change was greater than that with the low-fat (P = 0.028) and high-fat (P = 0.0008) diets.
These data show that snack type affects cardiovascular health. Consuming snack chips rich in PUFA and low in saturated or trans fatty acids instead of high-saturated fatty acid and trans fatty acid or low-fat snacks leads to improvements in lipid profiles concordant with reductions in cardiovascular disease risk. Am J Clin Nutr 2007;85:1503–10.
Snacks; polyunsaturated fat; trans fat; saturated fat; cholesterol; cardiovascular disease; corn oil
Insulin signaling dysregulation is related to neural atrophy in hippocampus and other areas affected by neurovascular and neurodegenerative disorders. It is not known if long-term calorie restriction (CR) can ameliorate this relationship through improved insulin signaling or if such an effect might influence task learning and performance. To model this hypothesis, magnetic resonance imaging was conducted on 27 CR and 17 control rhesus monkeys aged 19–31 years from a longitudinal study. Voxel-based regression analyses were used to associate insulin sensitivity with brain volume and microstructure cross-sectionally. Monkey motor assessment panel (mMAP) performance was used as a measure of task performance. CR improved glucoregulation parameters and related indices. Higher insulin sensitivity predicted more gray matter in parietal and frontal cortices across groups. An insulin sensitivity × dietary condition interaction indicated that CR animals had more gray matter in hippocampus and other areas per unit increase relative to controls, suggesting a beneficial effect. Finally, bilateral hippocampal volume adjusted by insulin sensitivity, but not volume itself, was significantly associated with mMAP learning and performance. These results suggest that CR improves glucose regulation and may positively influence specific brain regions and at least motor task performance. Additional studies are warranted to validate these relationships.
The objective of this study was to determine the accuracy, precision, bias, and reliability of percent fat (%fat) determined by air-displacement plethysmography (ADP) with the Pediatric Option against the 4-compartment model in 31 children (4.1 ± 1.2 yr., 103.3 ± 10.2 cm., 17.5 ± 3.4 kg.). %fat was determined by (BOD POD® Body Composition System, COSMED USA, Inc., Concord, CA) with the Pediatric Option. Total body water was determined by isotope dilution (2H2O; 0.2 g/kg) while bone mineral was determined by DXA (Lunar iDXA v13.31; GE, Fairfield, CT and analyzed using enCore 2010 software). The 4-compartment model by Lohman was used as the criterion measure of %fat. The regression for %fat by ADP versus %fat by the 4-compartment model did not deviate from the line of identity where: y = 0.849(x) + 4.291. ADP explained 75.2% of the variance in %fat by the 4-compartment model while the SEE was 2.09 %fat. The Bland Altman analysis showed %fat by ADP did not exhibit any bias across the range of fatness (r = 0.04; p = 0.81). The reliability of ADP was assessed by the coefficient of variation (CV), within-subject SD, and Cronbach’s alpha. The CV was 3.5%, within-subject SD was 0.9%, and Cronbach’s alpha was 0.95. In conclusion, ADP with the Pediatric Option is accurate, precise, reliable, and without bias in estimating %fat in children 2–6 years old.
It has been shown that caloric restriction (CR) delays aging and possibly delays the development of Alzheimer's disease (AD). We conjecture that the mechanism may involve interoceptive cues, rather than reduced energy intake per se. We determined that hunger alone, induced by a ghrelin agonist, reduces AD pathology and improves cognition in the APP-SwDI mouse model of AD. Long-term treatment with a ghrelin agonist was sufficient to improve the performance in the water maze. The treatment also reduced levels of amyloid beta (Aβ) and inflammation (microglial activation) at 6 months of age compared to the control group, similar to the effect of CR. Thus, a hunger-inducing drug attenuates AD pathology, in the absence of CR, and the neuroendocrine aspects of hunger also prevent age-related cognitive decline.
Odds ratios (ORs) are widely used in scientific research to demonstrate associations between outcome variables and covariates (risk factors) of interest and are often described in language suitable for risks or probabilities, but odds and probabilities are related, not equivalent. In situations where the outcome is not rare (e.g., obesity), ORs no longer approximate the relative risk ratio and may be misinterpreted. Our study examines the extent of misinterpretation of ORs in Obesity and International Journal of Obesity. We reviewed all 2010 issues of these journals to identify all articles that presented ORs. Included articles were then primarily reviewed for correct presentation and interpretation of ORs; and secondarily reviewed for article characteristics that may have been associated with how ORs are presented and interpreted. Of the 855 articles examined, 62 (7.3%) presented ORs. ORs were presented incorrectly in 23.2% of these articles. Clinical articles were more likely to present ORs correctly than social science or basic science articles. Studies with outcome variables that had higher relative prevalence were less likely to present ORs correctly. Overall, almost a quarter of the studies presenting ORs in two leading journals on obesity misinterpreted them. Furthermore, even when researchers present ORs correctly, the lay media may misinterpret them as relative risk ratios. Therefore, we suggest that when the magnitude of associations is of interest, researchers should carefully and accurately present interpretable measures of association -- including risk ratios and risk differences -- to minimize confusion and misrepresentation of research results.
Obesity; Research Method; Prevalence; Clinical Research; Statistics
Current clinical guidelines and public health statements generically prescribe body mass index (BMI;
kgm2) categories regardless of the individual’s situation (age, risk for diseases, etc.). However, regarding BMI and mortality rate (MR), two well-established observations are (1) there is a U-shaped (i.e., concave) association - people with intermediate BMIs tend to outlive people with higher or lower BMIs; and (2) the nadirs of these curves tend to increase monotonically with age. Multiple hypotheses have been advanced to explain either of these two observations. Here we introduce a new hypothesis that may explain both phenomena, by drawing on the so-called obesity paradox: the unexpected finding that obesity is often associated with increased survival time among people who have some serious injury or illness despite being associated with reduced survival time among the general population.
We establish that the obesity paradox offers one potential explanation for two curious but consistently observed phenomena in the obesity field.
Further research is needed to determine the extent to which the obesity paradox is actually an explanation for these phenomena, but if our hypothesis proves true the common practice of prescribing overweight patients to lower their BMI should currently be applied with caution. In addition, the statistical modeling technique employed here could be applied in such other areas involving survival analysis of disjoint subgroups, in order to explain possible interacting causal associations and to determine clinical practice.
Obesity Paradox; Aging; Mortality Rate; Statistics; Mathematical Modeling; Longevity
To estimate fruit and vegetable (FV) intake levels of US adult population and evaluate the association between FV intake and BMI status after controlling for confounding demographic, socioeconomic and lifestyle factors. We also sought to identify moderating factors.
We used 2007 Behavior Risk Factors Surveillance System (N > 400,000) data. FV intake was dichotomized as ≥5 servings (FV5+) versus <5 servings/day. BMI status was categorized as normal, overweight, and obese. Identification of moderators was performed by testing interactions between BMI status and other variables using bivariate analyses followed by multiple logistic regression analysis incorporating complex survey sampling design features.
Only 24.6% of US adults consumed ≥5 servings per day and less than 4% consumed 9 or more servings. Overweight (% FV5+ = 23.9%) and obese (21.9%) groups consumed significantly less FV than the normal-weight (27.4%) group (p < 0.0001). This inverse association remained significant even after controlling for potential confounding factors. Multivariate analysis identified five significant moderators (p < 0.0001) after controlling for all evaluated variables: race, sex, smoking status, health coverage, and physical activity. Notably, physically inactive obese males tended to consume the least FV (% FV5+ = 14.7%).
Current US population FV intake level is below recommended levels. The inverse association between FV intake and obesity was significant and was moderated by demographic, socioeconomic status, and lifestyle factors. These factors should be considered when developing policies and interventions to increase FV intake.
Fruit and vegetable; Obesity; US adults; Health policy; BRFSS
Randomized controlled trials (RCTs) in obesity are plagued by missing data due to participant drop-outs. Most methodologists and regulatory bodies agree that the primary analysis of such RCTs should be based on the intent-to-treat (ITT) principle, such that all randomized subjects are included in the analysis, even those who dropped out. Unfortunately, some authors do not include an ITT analysis in their published reports. Here we show that one form of ITT analysis, baseline observation carried forward (BOCF), can be performed utilizing only information available in a published complete case (CC) analysis, permitting readers, editors, meta-analysts, and regulators to easily conduct their own ITT analyses when the original authors do not report one.
We mathematically derive a simple method for estimating and testing treatment effects using the BOCF to allow a more conservative comparison of treatment effects when there are drop outs in a clinical trial. We provide two examples of this method using available CC analysis data from reported obesity trials to illustrate the application for readers who wish to determine a range of treatment effects based on published summary statistics.
Commonly used CC analyses may lead to inflated Type I error rates and/or treatment effect estimates. The method described herein can be useful for researchers who wish to estimate a conservative range of plausible treatment effects based on limited reported data. Limitations of this method are discussed.
baseline observation carried forward; complete cases; obesity interventions; treatment effects; randomized controlled trials; intention-to-treat
Prediction of genetic risk for disease is needed for preventive and personalized medicine. Genome-wide association studies have found unprecedented numbers of variants associated with complex human traits and diseases. However, these variants explain only a small proportion of genetic risk. Mounting evidence suggests that many traits, relevant to public health, are affected by large numbers of small-effect genes and that prediction of genetic risk to those traits and diseases could be improved by incorporating large numbers of markers into whole-genome prediction (WGP) models. We developed a WGP model incorporating thousands of markers for prediction of skin cancer risk in humans. We also considered other ways of incorporating genetic information into prediction models, such as family history or ancestry (using principal components, PCs, of informative markers). Prediction accuracy was evaluated using the area under the receiver operating characteristic curve (AUC) estimated in a cross-validation. Incorporation of genetic information (i.e., familial relationships, PCs, or WGP) yielded a significant increase in prediction accuracy: from an AUC of 0.53 for a baseline model that accounted for nongenetic covariates to AUCs of 0.58 (pedigree), 0.62 (PCs), and 0.64 (WGP). In summary, prediction of skin cancer risk could be improved by considering genetic information and using a large number of single-nucleotide polymorphisms (SNPs) in a WGP model, which allows for the detection of patterns of genetic risk that are above and beyond those that can be captured using family history. We discuss avenues for improving prediction accuracy and speculate on the possible use of WGP to prospectively identify individuals at high risk.
skin cancer risk; whole-genome prediction; prediction of complex traits and diseases; pedigree predictions; genomic predictions; GenPred; shared data resources
The role of telomere attrition in limiting the replicative capacity of cells in culture is well established. In humans, epidemiologic evidence suggests telomere length (TL) in leukocytes is highly variable at birth and inversely related to age. Although calorie restriction (CR) significantly increases life span in most rodent models, its association with TL is unknown. Using linear regression analysis, TLs (as measured by Southern blot analysis) of skeletal muscle (a postmitotic tissue that largely represents early development TL), fat, leukocytes, and skin were tested for effects of age, sex, and diet in 48 control and 23 calorie restriction rhesus monkeys. After controlling for the individual's muscle mean TL, differences between leukocytes muscle and skin muscle were significantly associated with age (p = .002; p = .002) and sex (p = .003; p = .042), but not calorie restriction (p = .884; p = .766). Despite an age-dependent shortening of TL in leukocytes and skin, calorie restriction did not significantly affect TL dynamics in these samples.
Telomere; Macaca mulatta; Aging; Caloric restriction; Rhesus monkeys
Much has been written regarding p-values below certain thresholds (most notably 0.05) denoting statistical significance and the tendency of such p-values to be more readily publishable in peer-reviewed journals. Intuition suggests that there may be a tendency to manipulate statistical analyses to push a “near significant p-value” to a level that is considered significant. This article presents a method for detecting the presence of such manipulation (herein called “fiddling”) in a distribution of p-values from independent studies. Simulations are used to illustrate the properties of the method. The results suggest that the method has low type I error and that power approaches acceptable levels as the number of p-values being studied approaches 1000.
In many circumstances, individuals do not respond identically to the same treatment. This phenomenon, which is called treatment response heterogeneity (TRH), appears to be present in treatments for many conditions, including obesity. Estimating the total amount of TRH, predicting an individual’s response, and identifying the mediators of TRH are of interest to biomedical researchers. Clinical investigators and physicians commonly postulate that some of these mediators could be genetic. Current designs can estimate TRH as a function of specific, measurable observed factors; however, they cannot estimate the total amount of TRH, nor provide reliable estimates of individual persons’ responses. We propose a new repeated randomizations design (RRD), which can be conceived as a generalization of the Balaam design, that would allow estimates of that variability and facilitate estimation of the total amount of TRH, prediction of an individual’s response, and identification of the mediators of TRH. In a pilot study, we asked 118 subjects entering a weight loss trial for their opinion of the RRD, and they stated a preference for the RRD over the conventional two-arm parallel groups design. Research is needed as to how the RRD will work in practice and its relative statistical properties, and we invite dialog about it.
treatment response heterogeneity; crossover design; Balaam design
Genetic factors are believed to account for 25% of the interindividual differences in Years of Life (YL) among humans. However, the genetic loci that have thus far been found to be associated with YL explain a very small proportion of the expected genetic variation in this trait, perhaps reflecting the complexity of the trait and the limitations of traditional association studies when applied to traits affected by a large number of small-effect genes. Using data from the Framingham Heart Study and statistical methods borrowed largely from the field of animal genetics (whole-genome prediction, WGP), we developed a WGP model for the study of YL and evaluated the extent to which thousands of genetic variants across the genome examined simultaneously can be used to predict interindividual differences in YL. We find that a sizable proportion of differences in YL—which were unexplained by age at entry, sex, smoking and BMI—can be accounted for and predicted using WGP methods. The contribution of genomic information to prediction accuracy was even higher than that of smoking and body mass index (BMI) combined; two predictors that are considered among the most important life-shortening factors. We evaluated the impacts of familial relationships and population structure (as described by the first two marker-derived principal components) and concluded that in our dataset population structure explained partially, but not fully the gains in prediction accuracy obtained with WGP. Further inspection of prediction accuracies by age at death indicated that most of the gains in predictive ability achieved with WGP were due to the increased accuracy of prediction of early mortality, perhaps reflecting the ability of WGP to capture differences in genetic risk to deadly diseases such as cancer, which are most often responsible for early mortality in our sample.
Background: The genetic predisposition to obesity may have contributed to the obesity epidemic through assortative mating. We investigated whether spouses were positively assorted by body mass index (BMI; = kg/m2) in late childhood, and whether changes in assorted marriage by upper BMI-percentiles occurred during the obesity epidemic. Methods: In the Copenhagen School Health Records Register (CSHRR) boys and girls with measures of BMI at age 13 years later became 37,792 spousal-pairs who married between 1945 and 2010. Trends in the spousal BMI correlations using sex-, age-, and birth cohort-specific BMI z-scores across time were investigated. Odds ratios (ORs) of marriage among spouses both with BMI z-scores >90th or >95th percentile compared with marriage among spouses ≤90th percentile were analyzed for marriages entered during the years prior to (1945–1970), and during the obesity epidemic (1971–2010). Findings: Spousal BMI correlations were around 0.05 and stayed similar across time. ORs of marriage among spouses with BMIs >90th percentile at age 13 were 1.21, 1.05–1.39, in 1945–1970, and increased to 1.63, 1.40–1.91, in 1971–2010 (p = 0.006). ORs of marriage among spouses both >95th BMI percentile were higher and increased more; from 1.39, 1.10–1.81, to 2.39, 1.85–3.09 (p = 0.004). Interpretation: Spousal correlations by pre-marital BMIs were small and stable during the past 65 years. Yet, there were assorted marriages between spouses with high BMI at age 13 years and the tendency increased alongside with the obesity epidemic which may increase the offsprings' predisposition to obesity.
assortative mating; body mass index; childhood; obesity; overweight; phenotype; human genetics
`White hat bias' (WHB) (bias leading to distortion of information in the service of what may be perceived to be righteous ends) is documented via quantitative data and anecdotal evidence from the research record regarding the postulated predisposing and protective effects respectively of nutritively-sweetened beverages and breastfeeding on obesity. Evidence of an apparent WHB is found in a degree sufficient to mislead readers. WHB bias may be conjectured to be fuelled by feelings of righteous zeal, indignation toward certain aspects of industry, or other factors. Readers should beware of WHB and our field should seek methods to minimize it.
Although offering many benefits for several psychiatric disorders, antipsychotic drugs (APDs) as a class have a major liability in their tendency to promote adiposity, obesity, and metabolic dysregulation in an already metabolically vulnerable population. The past decade has witnessed substantial research aimed at investigating the mechanisms of these adverse effects and mitigating them. On July 11 and 12, 2011, with support from 2 NIH institutes, leading experts convened to discuss current research findings and to consider future research strategies. Five areas where significant advances are being made emerged from the conference: (1) methodological issues in the study of APD effects; (2) unique characteristics and needs of pediatric patients; (3) genetic components underlying susceptibility to APD-induced metabolic effects; (4) APD effects on weight gain and adiposity in relation to their acute effects on glucose regulation and diabetes risk; and (5) the utility of behavioral, dietary, and pharmacological interventions in mitigating APD-induced metabolic side effects. This paper summarizes the major conclusions and important supporting data from the meeting.
antipsychotic drugs; obesity; diabetes; schizophrenia; adiposity; pediatric populations; pharmacologic interventions; behavioral interventions