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An examination of potential mediators of the relationship between polygenic scores of BMI and waist circumference and phenotypic adiposity
Abstract
Objective: The present study examined whether physical activity, personality, cognition, education, and depressive symptoms mediate the association between polygenic scores (PGS) for body mass index (BMI) and waist circumference and the corresponding phenotypic adiposity measures.
Design: Participants were 9,139 individuals aged 50 to 107 years (57% women; Mean Age: 68.17, SD: 10.06) from the Health and Retirement Study who were genotyped. Trained staff measured their height, weight, and waist circumference, and participants answered questions on physical activity, personality, education, cognitive function, and depressive symptoms.
Main Outcome Measures: BMI and waist circumference.
Results: A higher PGS for both BMI and waist circumference were related to higher phenotypic BMI and waist circumference, respectively, in part through their association with lower physical activity, conscientiousness, education, and higher depressive symptoms but not cognition. The mediators accounted for 6.6% of the association between PGS and BMI and 9.6% of the association between PGS and waist circumference.
Conclusion: The present study provides new evidence on the multiple, distinct pathways through which genetic propensity to higher BMI and waist circumference may lead to higher adiposity in adulthood. Individuals with a higher genetic predisposition to obesity may gain more weight through less adaptive behavioral, personality and educational profiles.
... These earlier studies have used PRS-BMI based on a small number of variants (32 or fewer) and thus had a lower predictive power for capturing the obesity genetic risk [7]. Three previous PRS-BMI mediation studies have included all available common genetic variants [20][21][22], but they did not examine the temporal direction of appetite traits and obesity. Longitudinal studies using genetically sensitive designs to investigate the direction of pathways between eating behaviors and weight gain are needed to advance our understanding in the field [23]. ...
... Obesity susceptibility genes are not only expressed in the brain [53], but also in the adipose tissue [58], suggesting that there may be pathways other than eating behaviors by which obesity susceptibility genes might exert their influence. A recent American study of adults suggested physical activity, conscientiousness, education and depression as potential pathways by which genetic susceptibility to obesity may lead to weight gain [20]. As a novel aspect, we found that obesity indices mediate the prospective association between the PRS-BMI and parental concern of overeating. ...
Background
Many genes and molecular pathways are associated with obesity, but the mechanisms from genes to obesity are less well known. Eating behaviors represent a plausible pathway, but because the relationships of eating behaviors and obesity may be bi-directional, it remains challenging to resolve the underlying pathways. A longitudinal approach is needed to assess the contribution of genetic risk during the development of obesity in childhood. In this study we aim to examine the relationships between the polygenic risk score for body mass index (PRS-BMI), parental concern of overeating and obesity indices during childhood.
Methods
The IDEFICS/I.Family study is a school-based multicenter pan-European cohort of children observed for 6 years (mean ± SD follow-up 5.8 ± 0.4). Children examined in 2007/2008 (wave 1) (mean ± SD age: 4.4 ± 1.1, range: 2–9 years), in 2009/2010 (wave 2) and in 2013/2014 (wave 3) were included. A total of 5112 children (49% girls) participated at waves 1, 2 and 3. For 2656 children with genome-wide data we constructed a PRS based on 2.1 million single nucleotide polymorphisms. Z-score BMI and z-score waist circumference (WC) were assessed and eating behaviors and relevant confounders were reported by parents via questionnaires. Parental concern of overeating was derived from principal component analyses from an eating behavior questionnaire.
Results
In cross-lagged models, the prospective associations between z-score obesity indices and parental concern of overeating were bi-directional. In mediation models, the association between the PRS-BMI and parental concern of overeating at wave 3 was mediated by baseline z-BMI ( β = 0.16, 95% CI: 0.10, 0.21) and baseline z-WC ( β = 0.17, 95% CI: 0.11, 0.23). To a lesser extent, baseline parental concern of overeating also mediated the association between the PRS-BMI and z-BMI at wave 3 ( β = 0.10, 95% CI: 0.07, 0.13) and z-WC at wave 3 ( β = 0.09, 95% CI: 0.07, 0.12).
Conclusions
The findings suggest that the prospective associations between obesity indices and parental concern of overeating are likely bi-directional, but obesity indices have a stronger association with future parental concern of overeating than vice versa. The findings suggest parental concern of overeating as a possible mediator in the genetic susceptibility to obesity and further highlight that other pathways are also involved. A better understanding of the genetic pathways that lead to childhood obesity can help to prevent weight gain.
Trial registration
Registry number: ISRCTN62310987 Retrospectively registered 17 September 2018.
... In other words, mediation studies may be useful for identifying targets for intervention, whereas interaction studies may identify subgroups that are responsive to a particular intervention [4]. Prior studies have shown that genetic risk to obesity was mostly mediated by eating behaviors [5] although the genetic susceptibility to obesity was also mediated by other factors such as gray matter volume [6,7], early life stress [8], leptin levels [9], or education, physical activity, conscientiousness, and depressive symptoms [10]. ...
Objective: The study's objective was to examine whether adherence to three plant-based dietary indices (PDIs) mediated or moderated genetic susceptibility to obesity.
Methods: Baseline participants were 7037 adults (57% women, aged 55.6 ± 7.7 years) from the CARTaGENE cohort of Quebec adults. Two polygenic risk scores for BMI (PRS-BMI), 92 single-nucleotide polymorphisms and 2 million single-nucleotide polymorphisms, and three plant-based scores were calculated (overall, healthy, and unhealthy). Follow-up participants were 2258 adults with data on obesity outcomes, measured 6 years later. General linear models were used to examine the relationships between PRSs and PDI scores on obesity outcomes. Causal mediation analyses were conducted to assess mediation and interaction models.
Results: The overall- and healthy-PDIs and PRSs were significantly associated with obesity outcomes. Adherence to PDIs did not mediate or moderate genetic susceptibility to obesity. Associations between PRSs and obesity outcomes were partly mediated by meat intake cross-sectionally and whole grains intake among males both cross-sectionally and longitudinally. Higher meat intake had a positive association with obesity outcomes, whereas higher whole grains intake had an inverse association.
Conclusions: These findings suggest that components of a plant-based diet and a shift away from animal products, specifically meat, might be beneficial for nutrition interventions, particularly among individuals with higher genetic risk of obesity.
... Genetic risk can lead to activity behaviour through multiple distinct social-behavioural factors [21]. In addition, biological determinants may also play a significant role in regulating physical activity levels [22]. ...
Purpose
It has been suggested that genetic pleiotropy, in which the same genes affect two or more traits, may partially explain the frequently observed associations between high physical activity (PA) and later reduced morbidity or mortality. However, the evidence about pleiotropy from human studies is limited. This study investigated associations between PA polygenic risk scores (PRSs) and cardiometabolic diseases among the Finnish population.
Methods
PRSs for device-measured overall PA were adapted to a FinnGen study cohort of 218,792 individuals with genome-wide genotyping and extensive digital longitudinal health register data. Associations between PA PRS and body mass index (BMI), diseases, and mortality were analysed with linear and logistic regression models. The number of different disease endpoints varied between 894 and 111,108 in FinnGen cohort.
Results
A high PA PRS predicted a lower BMI (β −0.025 kg/m ² per one standard deviation (SD) change in PA PRS, SE 0.013, p=1.87×10 ⁻⁸⁰ ). The PA PRS also predicted a lower risk for diseases that typically develop later in life or not at all among highly active individuals. A lower disease risk was systematically observed for cardiovascular diseases [odds ratio, OR per 1 SD change in PA PRS 0.95, p=9.5*10 ⁻¹⁹ ) and, for example, hypertension [OR 0.93, p=2.7*10 ⁻⁴⁴ ), type 2 diabetes (OR 0.91, p=4.1*10 ⁻⁴² ), and coronary heart disease (OR 0.95 p=1.2*10 ⁻⁹ ). Participants with high PA PRS had also lower mortality risk (OR 0.97, p=0.0003). We did not observe statistically significant associations with hypothetical control conditions, such as osteoarthritis and osteoporosis.
Conclusions
Genetically less active persons are at a higher risk of developing cardiometabolic diseases, which may partly explain the previously observed associations between low PA and higher disease and mortality risk. The same inherited physical fitness and metabolism related mechanisms may be associated both with PA levels and with cardiometabolic disease risk.
Introduction:
Genetic pleiotropy, in which the same genes affect two or more traits, may partially explain the frequently observed associations between high physical activity (PA) and later reduced morbidity or mortality. This study investigated associations between PA polygenic risk scores (PRSs) and cardiometabolic diseases among the Finnish population.
Methods:
PRSs for device-measured overall PA were adapted to a FinnGen study cohort of 218,792 individuals with genome-wide genotyping and extensive digital longitudinal health register data. Associations between PA PRS and body mass index (BMI), diseases, and mortality were analysed with linear and logistic regression models.
Results:
A high PA PRS predicted a lower BMI (β -0.025 kg/m2 per one standard deviation (SD) change in PA PRS, SE 0.013, p = 1.87x10-80). The PA PRS also predicted a lower risk for diseases that typically develop later in life or not at all among highly active individuals. A lower disease risk was systematically observed for cardiovascular diseases [odds ratio, OR per 1 SD change in PA PRS 0.95, p = 9.5*10-19) and, for example, hypertension [OR 0.93, p = 2.7*10-44), type 2 diabetes (OR 0.91, p = 4.1*10-42), and coronary heart disease (OR 0.95 p = 1.2*10-9). Participants with high PA PRS had also lower mortality risk (OR 0.97, p = 0.0003).
Conclusions:
Genetically less active persons are at a higher risk of developing cardiometabolic diseases, which may partly explain the previously observed associations between low PA and higher disease and mortality risk. The same inherited physical fitness and metabolism related mechanisms may be associated both with PA levels and with cardiometabolic disease risk.
Previous genome-wide association studies on anthropometric measurements have identified more than 100 related loci, but only a small portion of heritability in obesity was explained. Here we present a bivariate twin study to look for the genetic variants associated with body mass index and waist-hip ratio, and to explore the obesity-related pathways in Northern Han Chinese. Cholesky decomposition model for 242 monozygotic and 140 dizygotic twin pairs indicated a moderate genetic correlation (r = 0.53, 95%CI: 0.42–0.64) between body mass index and waist-hip ratio. Bivariate genome-wide association analysis in 139 dizygotic twin pairs identified 26 associated SNPs with p < 10⁻⁵. Further gene-based analysis found 291 nominally associated genes (P < 0.05), including F12, HCRTR1, PHOSPHO1, DOCK2, DOCK6, DGKB, GLP1R, TRHR, MMP1, GPR55, CCK, and OR2AK2, as well as 6 enriched gene-sets with FDR < 0.05. Expression quantitative trait loci analysis identified rs2242044 as a significant cis-eQTL in both the normal adipose-subcutaneous (P = 1.7 × 10⁻⁹) and adipose-visceral (P = 4.4 × 10⁻¹⁵) tissue. These findings may provide an important entry point to unravel genetic pleiotropy in obesity traits.
Introduction:
Higher midlife body mass index (BMI) is suggested to increase the risk of dementia, but weight loss during the preclinical dementia phase may mask such effects.
Methods:
We examined this hypothesis in 1,349,857 dementia-free participants from 39 cohort studies. BMI was assessed at baseline. Dementia was ascertained at follow-up using linkage to electronic health records (N = 6894). We assumed BMI is little affected by preclinical dementia when assessed decades before dementia onset and much affected when assessed nearer diagnosis.
Results:
Hazard ratios per 5-kg/m(2) increase in BMI for dementia were 0.71 (95% confidence interval = 0.66-0.77), 0.94 (0.89-0.99), and 1.16 (1.05-1.27) when BMI was assessed 10 years, 10-20 years, and >20 years before dementia diagnosis.
Conclusions:
The association between BMI and dementia is likely to be attributable to two different processes: a harmful effect of higher BMI, which is observable in long follow-up, and a reverse-causation effect that makes a higher BMI to appear protective when the follow-up is short.
Background: Many genetic variants show highly robust associations with body mass index (BMI). However, the mechanisms through which genetic susceptibility to obesity operates are not well understood. Potentially modifiable mechanisms, including eating behaviors, are of particular interest to public health.
Objective: Here we explore whether eating behaviors mediate or modify genetic susceptibility to obesity.
Design: Genetic risk scores for BMI (BMI-GRSs) were calculated for 3515 and 2154 adults in the Fenland and EDEN (Etude des déterminants pré et postnatals de la santé et du développement de l'enfant) population-based cohort studies, respectively. The eating behaviors—emotional eating, uncontrolled eating, and cognitive restraint—were measured through the use of a validated questionnaire. The mediating effect of each eating behavior on the association between the BMI-GRS and measured BMI was assessed by using the Sobel test. In addition, we tested for interactions between each eating behavior and the BMI-GRS on BMI.
Results: The association between the BMI-GRS and BMI was mediated by both emotional eating (EDEN: P-Sobel = 0.01; Fenland: P-Sobel = 0.02) and uncontrolled eating (EDEN: P-Sobel = 0.04; Fenland: P-Sobel = 0.0006) in both sexes combined. Cognitive restraint did not mediate this association (P-Sobel > 0.10), except among EDEN women (P-Sobel = 0.0009). Cognitive restraint modified the relation between the BMI-GRS and BMI among men (EDEN: P-interaction = 0.0001; Fenland: P-interaction = 0.04) and Fenland women (P-interaction = 0.0004). By tertiles of cognitive restraint, the association between the BMI-GRS and BMI was strongest in the lowest tertile of cognitive restraint, and weakest in the highest tertile.
Conclusions: Genetic susceptibility to obesity was partially mediated by the “appetitive” eating behavior traits (uncontrolled and emotional eating) and, in 3 of the 4 population groups studied, was modified by cognitive restraint. High levels of cognitive control over eating appear to attenuate the genetic susceptibility to obesity. Future research into interventions designed to support restraint may help to protect genetically susceptible individuals from weight gain.
Objectives
The objective of this study was to examine if, in the general population, physically active adults have less body fat after taking body mass index (BMI) into account.
Design
A cross-sectional analysis of participants recruited into UK Biobank in 2006–2010.
Setting
UK Biobank assessment centres throughout the UK.
Participants
119 230 men and 140 578 women aged 40–69 years, with complete physical activity information, and without a self-reported long-term illness, disability or infirmity.
Exposures
Physical activity measured as excess metabolic equivalent (MET)-hours per week, estimated from a combination of walking, and moderate and vigorous physical activity. BMI from measured height and weight.
Main outcome measure
Body fat percentage estimated from bioimpedance.
Results
BMI and body fat percentage were highly correlated (r=0.85 in women; r=0.79 in men), and both were inversely associated with physical activity. Compared with <5 excess MET-hours/week at baseline, ≥100 excess MET-hours/week were associated with a 1.1 kg/m² lower BMI (27.1 vs 28.2 kg/m²) and 2.8 percentage points lower body fat (23.4% vs 26.3%) in men, and 2.2 kg/m² lower BMI (25.6 vs 27.7 kg/m²) and 4.0 percentage points lower body fat (33.9% vs 37.9%) in women. For a given BMI, greater physical activity was associated with lower average body fat percentage (for a BMI of 22.5–24.99 kg/m²: 2.0 (95% CI 1.8 to 2.2), percentage points lower body fat in men and 1.8 (95% CI 1.6 to 2.0) percentage points lower body fat in women, comparing ≥100 excess MET-hours per week with <5 excess MET-hours/week).
Conclusions
In this sample of middle-aged adults, drawn from the general population, physical activity was inversely associated with BMI and body fat percentage. For people with the same BMI, those who were more active had a lower body fat percentage.
Obesity and low cognitive function are associated with multiple adverse health outcomes across the life course. They have a small phenotypic correlation (r=-0.11; high body mass index (BMI)-low cognitive function), but whether they have a shared genetic aetiology is unknown. We investigated the phenotypic and genetic correlations between the traits using data from 6815 unrelated, genotyped members of Generation Scotland, an ethnically homogeneous cohort from five sites across Scotland. Genetic correlations were estimated using the following: same-sample bivariate genome-wide complex trait analysis (GCTA)-GREML; independent samples bivariate GCTA-GREML using Generation Scotland for cognitive data and four other samples (n=20 806) for BMI; and bivariate LDSC analysis using the largest genome-wide association study (GWAS) summary data on cognitive function (n=48 462) and BMI (n=339 224) to date. The GWAS summary data were also used to create polygenic scores for the two traits, with within- and cross-trait prediction taking place in the independent Generation Scotland cohort. A large genetic correlation of -0.51 (s.e. 0.15) was observed using the same-sample GCTA-GREML approach compared with -0.10 (s.e. 0.08) from the independent-samples GCTA-GREML approach and -0.22 (s.e. 0.03) from the bivariate LDSC analysis. A genetic profile score using cognition-specific genetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using BMI-specific variants accounts for 0.42% (P=1.9 × 10(-7)) of the variance in cognitive function. Seven common genetic variants are significantly associated with both traits at P<5 × 10(-5), which is significantly more than expected by chance (P=0.007). All these results suggest there are shared genetic contributions to BMI and cognitive function.Molecular Psychiatry advance online publication, 9 February 2016; doi:10.1038/mp.2015.205.
Physical activity (PA) has been shown to reduce the impact of FTO variation and obesity genetic risk scores (GRS) on BMI. We examined this interaction using a quantitative measure of PA and two adiposity indexes in a longitudinal multi-ethnic study. We analyzed the impact of PA on the association between 14 obesity predisposing variants (analyzed independently and as a GRS) and baseline/follow-up obesity measures in the multi-ethnic prospective cohort EpiDREAM (17423 participants from six ethnic groups). PA was analyzed using basic (low-moderate-high) and quantitative measures (metabolic equivalents (METS)), while BMI and the body adiposity index (BAI) were used to measure obesity. Increased PA was associated with decreased BMI/BAI at baseline/follow-up. FTO rs1421085, CDKAL1 rs2206734, TNNl3K rs1514176, GIPR rs11671664 and the GRS were associated with obesity measures at baseline and/or follow-up. Risk alleles of three SNPs displayed nominal associations with increased (NTRK2 rs1211166, BDNF rs1401635) or decreased (NPC1 rs1805081) basic PA score independently of BMI/BAI. Both basic and quantitative PA measures attenuated the association between FTO rs1421085 risk allele and BMI/BAI at baseline and follow-up. Our results show that physical activity can blunt the genetic effect of FTO rs1421085 on adiposity by 36-75% in a longitudinal multi-ethnic cohort.
The mechanisms through which genes influence body weight are not well understood, but appetite has been implicated as one mediating pathway. Here we use data from two independent population-based Finnish cohorts (4632 adults aged 25–74 years from the DILGOM study and 1231 twin individuals aged 21–26 years from the FinnTwin12 study) to investigate whether two appetitive traits mediate the associations between known obesity-related genetic variants and adiposity. The results from structural equation modelling indicate that the effects of a polygenic risk score (90 obesity-related loci) on measured body mass index and waist circumference are partly mediated through higher levels of uncontrolled eating (βindirect = 0.030–0.032, P < 0.001 in DILGOM) and emotional eating (βindirect = 0.020–0.022, P < 0.001 in DILGOM and βindirect = 0.013–0.015, P = 0.043–0.044 in FinnTwin12). Our findings suggest that genetic predispositions to obesity may partly exert their effects through appetitive traits reflecting lack of control over eating or eating in response to negative emotions. Obesity prevention and treatment studies should examine the impact of targeting these eating behaviours, especially among individuals having a high genetic predisposition to obesity.
Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 x 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10(-8)), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis
Objective
To test transethnic replication of a genetic risk score for obesity in white and black young adults using a national sample with longitudinal data.
Design and Methods
A prospective longitudinal study using the National Longitudinal Study of Adolescent Health Sibling Pairs (n = 1,303). Obesity phenotypes were measured from anthropometric assessments when study members were aged 18–26 and again when they were 24–32. Genetic risk scores were computed based on published genome-wide association study discoveries for obesity. Analyses tested genetic associations with body-mass index (BMI), waist-height ratio, obesity, and change in BMI over time.
Results
White and black young adults with higher genetic risk scores had higher BMI and waist-height ratio and were more likely to be obese compared to lower genetic risk age-peers. Sibling analyses revealed that the genetic risk score was predictive of BMI net of risk factors shared by siblings. In white young adults only, higher genetic risk predicted increased risk of becoming obese during the study period. In black young adults, genetic risk scores constructed using loci identified in European and African American samples had similar predictive power.
Conclusion
Cumulative information across the human genome can be used to characterize individual level risk for obesity. Measured genetic risk accounts for only a small amount of total variation in BMI among white and black young adults. Future research is needed to identify modifiable environmental exposures that amplify or mitigate genetic risk for elevated BMI.
Importance:
A better understanding of the cause of obesity is a clinical priority. Obesity is highly heritable, and specific genes are being identified. Discovering the mechanisms through which obesity-related genes influence weight would help pinpoint novel targets for intervention. One potential mechanism is satiety responsiveness. Lack of satiety characterizes many monogenic obesity disorders, and lower satiety responsiveness is linked with weight gain in population samples.
Objective:
To test the hypothesis that satiety responsiveness is an intermediate behavioral phenotype associated with genetic predisposition to obesity in children.
Design, setting, and participants:
Cross-sectional observational study of a population-based cohort of twins born January 1, 1994, to December 31, 1996 (Twins Early Development Study). Participants included 2258 unrelated children (53.3% female; mean [SD] age, 9.9 [0.8] years), one randomly selected from each twin pair.
Exposure:
Genetic predisposition to obesity. We created a polygenic risk score (PRS) comprising 28 common obesity-related single-nucleotide polymorphisms identified in a meta-analysis of obesity-related genome-wide association studies.
Main outcomes and measures:
Satiety responsiveness was indexed with a standard psychometric scale (Child Eating Behavior Questionnaire). Using 1990 United Kingdom reference data, body mass index SD scores and waist SD scores were calculated from parent-reported anthropometric data for each child. Information on satiety responsiveness, anthropometrics, and genotype was available for 2258 children. We examined associations among the PRS, adiposity, and satiety responsiveness.
Results:
The PRS was negatively related to satiety responsiveness (β coefficient, -0.060; 95% CI, -0.019 to -0.101) and positively related to adiposity (β coefficient, 0.177; 95% CI, 0.136-0.218 for body mass index SD scores and β coefficient, 0.167; 95% CI, 0.126-0.208 for waist SD scores). More children in the top 25% of the PRS were overweight than in the lowest 25% (18.5% vs 7.2%; odds ratio, 2.90; 95% CI, 1.98-4.25). Associations between the PRS and adiposity were significantly mediated by satiety responsiveness (P = .006 for body mass index SD scores and P = .005 for waist SD scores).
Conclusions and relevance:
These results support the hypothesis that low satiety responsiveness is one of the mechanisms through which genetic predisposition leads to weight gain in an environment rich with food. Strategies to enhance satiety responsiveness could help prevent weight gain in genetically at-risk children.
Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
Estimates of the relative mortality risks associated with normal weight, overweight, and obesity may help to inform decision making in the clinical setting.
To perform a systematic review of reported hazard ratios (HRs) of all-cause mortality for overweight and obesity relative to normal weight in the general population.
PubMed and EMBASE electronic databases were searched through September 30, 2012, without language restrictions.
Articles that reported HRs for all-cause mortality using standard body mass index (BMI) categories from prospective studies of general populations of adults were selected by consensus among multiple reviewers. Studies were excluded that used nonstandard categories or that were limited to adolescents or to those with specific medical conditions or to those undergoing specific procedures. PubMed searches yielded 7034 articles, of which 141 (2.0%) were eligible. An EMBASE search yielded 2 additional articles. After eliminating overlap, 97 studies were retained for analysis, providing a combined sample size of more than 2.88 million individuals and more than 270,000 deaths.
Data were extracted by 1 reviewer and then reviewed by 3 independent reviewers. We selected the most complex model available for the full sample and used a variety of sensitivity analyses to address issues of possible overadjustment (adjusted for factors in causal pathway) or underadjustment (not adjusted for at least age, sex, and smoking).
Random-effects summary all-cause mortality HRs for overweight (BMI of 25-<30), obesity (BMI of ≥30), grade 1 obesity (BMI of 30-<35), and grades 2 and 3 obesity (BMI of ≥35) were calculated relative to normal weight (BMI of 18.5-<25). The summary HRs were 0.94 (95% CI, 0.91-0.96) for overweight, 1.18 (95% CI, 1.12-1.25) for obesity (all grades combined), 0.95 (95% CI, 0.88-1.01) for grade 1 obesity, and 1.29 (95% CI, 1.18-1.41) for grades 2 and 3 obesity. These findings persisted when limited to studies with measured weight and height that were considered to be adequately adjusted. The HRs tended to be higher when weight and height were self-reported rather than measured.
Relative to normal weight, both obesity (all grades) and grades 2 and 3 obesity were associated with significantly higher all-cause mortality. Grade 1 obesity overall was not associated with higher mortality, and overweight was associated with significantly lower all-cause mortality. The use of predefined standard BMI groupings can facilitate between-study comparisons.
Personality is thought to affect obesity risk but before such information can be incorporated into prevention and intervention plans, robust and converging evidence concerning the most relevant personality traits is needed. We performed a meta-analysis based on individual-participant data from nine cohort studies to examine whether broad-level personality traits predict the development and persistence of obesity (n = 78,931 men and women; mean age 50 years). Personality was assessed using inventories of the Five-Factor Model (extraversion, neuroticism, agreeableness, conscientiousness and openness to experience). High conscientiousness - reflecting high self-control, orderliness and adherence to social norms - was associated with lower obesity risk across studies (pooled odds ratio [OR] = 0.84; 95% confidence interval [CI] = 0.80-0.88 per 1 standard deviation increment in conscientiousness). Over a mean follow-up of 5.4 years, conscientiousness predicted lower obesity risk in initially non-obese individuals (OR = 0.88, 95% CI = 0.85-0.92; n = 33,981) and was associated with greater likelihood of reversion to non-obese among initially obese individuals (OR = 1.08, 95% CI = 1.01-1.14; n = 9,657). Other personality traits were not associated with obesity in the pooled analysis, and there was substantial heterogeneity in the associations between studies. The findings indicate that conscientiousness may be the only broad-level personality trait of the Five-Factor Model that is consistently associated with obesity across populations.
Evidence for a major role of genetic factors in the determination of body mass index (BMI) comes from studies of related individuals. Despite consistent evidence for a heritable component of BMI, estimates of BMI heritability vary widely between studies and the reasons for this remain unclear. While some variation is natural due to differences between populations and settings, study design factors may also explain some of the heterogeneity. We performed a systematic review that identified 88 independent estimates of BMI heritability from twin studies (total 140,525 twins) and 27 estimates from family studies (42,968 family members). BMI heritability estimates from twin studies ranged from 0.47 to 0.90 (5th/50th/95th centiles: 0.58/0.75/0.87) and were generally higher than those from family studies (range: 0.24–0.81; 5th/50th/95th centiles: 0.25/0.46/0.68). Meta-regression of the results from twin studies showed that BMI heritability estimates were 0.07 (P = 0.001) higher in children than in adults; estimates increased with mean age among childhood studies (+0.012/year, P = 0.002), but decreased with mean age in adult studies (−0.002/year, P = 0.002). Heritability estimates derived from AE twin models (which assume no contribution of shared environment) were 0.12 higher than those from ACE models (P < 0.001), whilst lower estimates were associated with self reported versus DNA-based determination of zygosity (−0.04, P = 0.02), and with self reported versus measured BMI (−0.05, P = 0.03). Although the observed differences in heritability according to aspects of study design are relatively small, together, the above factors explained 47% of the heterogeneity in estimates of BMI heritability from twin studies. In summary, while some variation in BMI heritability is expected due to population-level differences, study design factors explained nearly half the heterogeneity reported in twin studies. The genetic contribution to BMI appears to vary with age and may have a greater influence during childhood than adult life.
The present study addresses issues of measurement invariance and comparability of factor parameters of Big Five personality adjective items across age. Data from the Midlife in the United States (MIDUS) survey were used to investigate age-related developmental psychometrics of the MIDUS personality adjective items in 2 large cross-sectional samples (exploratory sample: N = 862; analysis sample: N = 3,000). After having established and replicated a comprehensive 5-factor structure of the measure, increasing levels of measurement invariance were tested across 10 age groups. Results indicate that the measure demonstrates strict measurement invariance in terms of number of factors and factor loadings. Also, we found that factor variances and covariances were equal across age groups. By contrast, a number of age-related factor mean differences emerged. The practical implications of these results are discussed, and future research is suggested.
This study examines the similarity of cognitive assessments using 1 interview in a large population study, the Health and Retirement Study (HRS), and a subsample in which a detailed neuropsychiatric assessment has been performed (Aging, Demographics, and Memory Study [ADAMS]).
Respondents are diagnosed in ADAMS as demented, cognitively impaired without dementia (CIND), or as having normal cognitive function. Multinomial logistic analysis is used to predict diagnosis using a variety of cognitive and noncognitive measures from the HRS and additional measures and information from ADAMS.
The cognitive tests in HRS predict the ADAMS diagnosis in 74% of the sample able to complete the HRS survey on their own. Proxy respondents answer for a large proportion of HRS respondents who are diagnosed as demented in ADAMS. Classification of proxy respondents with some cognitive impairment can be predicted in 86% of the sample. Adding a small number of additional tests from ADAMS can increase each of these percentages to 84% and 93%, respectively.
Cognitive assessment appropriate for diagnosis of dementia and CIND in large population surveys could be improved with more targeted information from informants and additional cognitive tests targeting other areas of brain function.
We tested the hypothesis that the previously reported association between a higher body mass index (BMI) and poorer cognition in later adulthood is an artifact of confounding by previous cognitive ability and socioeconomic status. Participants were 1,079 adults aged about 70 years in the Lothian Birth Cohort 1936 Study, on whom there are IQ data from age 11. Cognitive outcome measures included: IQ at age 70 using the same test that was administered at age 11; composite measures of general cognitive ability (g factor), speed of information processing, and memory; and two tests of verbal ability. People classified as overweight or obese in later adulthood had significantly lower scores on tests of childhood IQ, age 70 IQ, g factor, and verbal ability. There was no significant association with processing speed or memory performance. After adjusting for childhood IQ and social class in general linear models, associations with age 70 IQ and g factor were nonsignificant or attenuated. However, throughout the models, there was a persistent (inverse) relationship between BMI and performance on the National Adult Reading Test (NART) and Wechsler Test of Adult Reading (WTAR), which remained significant after full adjustment for all sociodemographic and health covariates (for the NART, p = .025; for the WTAR, p = .011). The findings suggest that the previously reported BMI–cognition associations in later adulthood could be largely accounted for by prior ability and socioeconomic status, and by the possible influence of these factors on the adoption of health behaviors in adulthood.
Genetics provides two major opportunities for understanding human disease—as a transformative line of etiological inquiry and as a biomarker for heritable diseases. In psychiatry, biomarkers are very much needed for both research and treatment, given the heterogenous populations identified by current phenomenologically based diagnostic systems. To date, however, useful and valid biomarkers have been scant owing to the inaccessibility and complexity of human brain tissue and consequent lack of insight into disease mechanisms. Genetic biomarkers are therefore especially promising for psychiatric disorders. Genome-wide association studies of common diseases have matured over the last decade, generating the knowledge base for increasingly informative individual-level genetic risk prediction. In this review, we discuss fundamental concepts involved in computing genetic risk with current methods, strengths and weaknesses of various approaches, assessments of utility, and applications to various psychiatric disorders and related traits. Although genetic risk prediction has become increasingly straightforward to apply and common in published studies, there are important pitfalls to avoid. At present, the clinical utility of genetic risk prediction is still low; however, there is significant promise for future clinical applications as the ancestral diversity and sample sizes of genome-wide association studies increase. We discuss emerging data and methods aimed at improving the value of genetic risk prediction for disentangling disease mechanisms and stratifying subjects for epidemiological and clinical studies. For all applications, it is absolutely critical that polygenic risk prediction is applied with appropriate methodology and control for confounding to avoid repeating some mistakes of the candidate gene era.
Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.
Purpose:
Improving the understanding of the role of genetic risk on disordered eating (DE).
Methods:
A case-control study including 1757 (F: 979, M: 778) adolescents (aged 13-19 years) from the Nord-Trøndelag Health Study (HUNT), an ethnically homogenous Norwegian population based study. Cases and controls were defined using a shortened version of the Eating Attitude Test. Logistic regression was employed to test for associations between DE phenotypes and 24 obesity and eating disorder susceptibility SNPs, and the joint effect of a subset of these in a genetic risk score (GRS).
Results:
COMT was shown to be associated with poor appetite/undereating (OR: 0.6, CI 95%: 0.43-0.83, p = 0.002). Independent of obesity associations, the weighted GRS was associated to overeating in 13-15 year old females (OR: 2.07, CI 95%: 1.14-3.76, p = 0.017). Additionally, a significant association was observed between the GRS and loss of control over eating in the total sample (OR: 1.62, CI 95%: 1.01-2.61, p = 0.046).
Conclusions:
The COMT variant (rs4680) was associated with poor appetite/undereating. Our study further confirms prior findings that obesity risk also confers risk for loss of control over eating; and overeating amongst girls.
Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.Molecular Psychiatry advance online publication, 28 March 2017; doi:10.1038/mp.2017.51.
Importance:
Many genetic variants are associated with body mass index (BMI). Associations may have changed with the 20th century obesity epidemic and may differ for black vs white individuals.
Objective:
Using birth cohort as an indicator for exposure to obesogenic environment, to evaluate whether genetic predisposition to higher BMI has a larger magnitude of association among adults from more recent birth cohorts, who were exposed to the obesity epidemic at younger ages.
Design, setting, and participants:
Observational study of 8788 adults in the US national Health and Retirement Study who were aged 50 years and older, born between 1900 and 1958, with as many as 12 BMI assessments from 1992 to 2014.
Exposures:
A multilocus genetic risk score for BMI (GRS-BMI), calculated as the weighted sum of alleles of 29 single nucleotide polymorphisms associated with BMI, with weights equal to the published per-allele effects. The GRS-BMI represents how much each person's BMI is expected to differ, based on genetic background (with respect to these 29 loci), from the BMI of a sample member with median genetic risk. The median-centered GRS-BMI ranged from -1.68 to 2.01.
Main outcomes and measures:
BMI based on self-reported height and weight.
Results:
GRS-BMI was significantly associated with BMI among white participants (n = 7482; mean age at first assessment, 59 years; 3373 [45%] were men; P <.001) and among black participants (n = 1306; mean age at first assessment, 57 years; 505 [39%] were men; P <.001) but accounted for 0.99% of variation in BMI among white participants and 1.37% among black participants. In multilevel models accounting for age, the magnitude of associations of GRS-BMI with BMI were larger for more recent birth cohorts. For example, among white participants, each unit higher GRS-BMI was associated with a difference in BMI of 1.37 (95% CI, 0.93 to 1.80) if born after 1943, and 0.17 (95% CI, -0.55 to 0.89) if born before 1924 (P = .006). For black participants, each unit higher GRS-BMI was associated with a difference in BMI of 3.70 (95% CI, 2.42 to 4.97) if born after 1943, and 1.44 (95% CI, -1.40 to 4.29) if born before 1924.
Conclusions and relevance:
For participants born between 1900 and 1958, the magnitude of association between BMI and a genetic risk score for BMI was larger among persons born in later cohorts. This suggests that associations of known genetic variants with BMI may be modified by obesogenic environments.
Educational attainment is strongly influenced by social and other environmental factors, but genetic factors are estimated to account for at least 20% of the variation across individuals1. Here we report the results of a genome-wide association study (GWAS) for educational attainment that extends our earlier discovery sample1, 2 of 101,069 individuals to 293,723 individuals, and a replication study in an independent sample of 111,349 individuals from the UK Biobank. We identify 74 genome-wide significant loci associated with the number of years of schooling completed. Single-nucleotide polymorphisms associated with educational attainment are disproportionately found in genomic regions regulating gene expression in the fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the prenatal period, and enriched for biological pathways involved in neural development. Our findings demonstrate that, even for a behavioural phenotype that is mostly environmentally determined, a well-powered GWAS identifies replicable associated genetic variants that suggest biologically relevant pathways. Because educational attainment is measured in large numbers of individuals, it will continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of related phenotypes, including cognition and neuropsychiatric diseases.
Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual-level genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique-cross-trait LD Score regression-for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and educational attainment and several diseases. These results highlight the power of genome-wide analyses, as there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
There is considerable evidence for both environmental and genetic causes of obesity. Increased availability of cheap, palatable food plays a role, but despite the ubiquity of the 'obesogenic' environment there is still substantial variation in weight - in fact, weight variability has gone up over recent decades. Twin and adoption studies show that adiposity is highly heritable (50-90%), and genome-wide association studies have started to identify single nucleotide polymorphisms (SNPs) associated with weight. We have proposed that genetic susceptibility to obesity is partly attributable to appetitive phenotypes; called behavioral susceptibility theory (BST). BST proposes that individuals who inherit a more avid appetite or lower sensitivity to satiety are more likely to overeat in response to the food environment. Our laboratory has provided considerable evidence for BST using a variety of research approaches. We have used prospective epidemiological studies to demonstrate that appetite plays a causal role in the development of weight, twin designs to show that appetitive phenotypes are highly heritable and have genetic overlap with adiposity, genomic analyses to show that obesity-related SNPs are associated with appetite and that appetite mediates some the SNP-adiposity association. BST has helped to resolve the seeming paradox of both genetic and environmental determination of weight, and points to appetite as a useful target for pharmacological and behavioral interventions in the prevention and management of obesity.
Copyright © 2015. Published by Elsevier Inc.
Although previous systematic reviews considered the relationship between socioeconomic status and obesity, almost 200 peer-reviewed articles have been published since the last review on that topic, and this paper focuses specifically on education, which has different implications. The authors systematically review the peer-reviewed literature from around the world considering the association between educational attainment and obesity. Databases from public health and medicine, education, psychology, economics, and other social sciences were searched, and articles published in English, French, Portuguese and Spanish were included. This paper includes 289 articles that report on 410 populations in 91 countries. The relationship between educational attainment and obesity was modified by both gender and the country's economic development level: an inverse association was more common in studies of higher-income countries and a positive association was more common in lower-income countries, with stronger social patterning among women. Relatively few studies reported on lower-income countries, controlled for a comprehensive set of potential confounding variables and/or attempted to assess causality through the use of quasi-experimental designs. Future research should address these gaps to understand if the relationship between educational attainment and obesity may be causal, thus supporting education policy as a tool for obesity prevention.
The causal role of obesity in the development of depression remains uncertain. We applied instrumental-variables regression (Mendelian randomization) to examine the association of adolescent and adult body mass index (BMI) with adult depressive symptoms. Participants were from the Young Finns prospective cohort study (n = 1731 persons, 2844 person-observations), with repeated measurements of BMI and depressive symptoms (modified Beck's Depression Inventory). Genetic risk score of 31 single nucleotide polymorphisms previously identified as robust genetic markers of body weight was used as a proxy for variation in BMI. In standard linear regression analysis, higher adult depressive symptoms were predicted by higher adolescent BMI (B = 0.33, CI = 0.06-0.60, P = 0.017) and adult BMI (B = 0.47, CI = 0.32-0.63, P < 0.001). These associations were replicated in instrumental-variables analysis with genetic risk score as instrument (B = 1.96, CI = 0.03-3.90, P = 0.047 for adolescent BMI; B = 1.08, CI = 0.11-2.04, P = 0.030 for adult BMI). The association for adolescent BMI was significantly stronger in the instrumented analysis compared to standard regression (P = 0.04). These findings provide additional evidence to support a causal role for high BMI in increasing symptoms of depression. However, the present analysis also demonstrates potential limitations of applying Mendelian randomization when using complex phenotypes.
The objective of this study was to evaluate the evidence on whether childhood obesity is a risk factor for adult disease, independent of adult body mass index (BMI). Ovid MEDLINE (1948-May 2011), EMBASE (1980-2011 week 18) and the Cochrane Library (1990-2011) were searched for published studies of BMI from directly measured weight and height in childhood (2-19 years) and disease outcomes in adulthood. Data were synthesized in a narrative fashion. Thirty-nine studies (n 181-1.1 million) were included in the review. There was evidence for associations between childhood BMI and type 2 diabetes, hypertension and coronary heart disease. Few studies examined associations independent of adult BMI; these showed that effect sizes were attenuated after adjustment for adult BMI in standard regression analyses. Although there is a consistent body of evidence for associations between childhood BMI and cardiovascular outcomes, there is a lack of evidence for effects independent of adult BMI. Studies have attempted to examine independent effects using standard adjustment for adult BMI, which is subject to over-adjustment and problems with interpretation. Studies that use more robust designs and analytical techniques are needed to establish whether childhood obesity is an independent risk factor for adult disease.
Association between obesity and depression has repeatedly been established. For treatment and prevention purposes, it is important to acquire more insight into their longitudinal interaction.
To conduct a systematic review and meta-analysis on the longitudinal relationship between depression, overweight, and obesity and to identify possible influencing factors.
Studies were found using PubMed, PsycINFO, and EMBASE databases and selected on several criteria.
Studies examining the longitudinal bidirectional relation between depression and overweight (body mass index 25-29.99) or obesity (body mass index > or =30) were selected.
Unadjusted and adjusted odds ratios (ORs) were extracted or provided by the authors.
Overall, unadjusted ORs were calculated and subgroup analyses were performed for the 15 included studies (N = 58 745) to estimate the effect of possible moderators (sex, age, depression severity). Obesity at baseline increased the risk of onset of depression at follow-up (unadjusted OR, 1.55; 95% confidence interval [CI], 1.22-1.98; P < .001). This association was more pronounced among Americans than among Europeans (P = .05) and for depressive disorder than for depressive symptoms (P = .05). Overweight increased the risk of onset of depression at follow-up (unadjusted OR, 1.27; 95% CI, 1.07-1.51; P < .01). This association was statistically significant among adults (aged 20-59 years and > or =60 years) but not among younger persons (aged <20 years). Baseline depression (symptoms and disorder) was not predictive of overweight over time. However, depression increased the odds for developing obesity (OR, 1.58; 95% CI, 1.33-1.87; P < .001). Subgroup analyses did not reveal specific moderators of the association.
This meta-analysis confirms a reciprocal link between depression and obesity. Obesity was found to increase the risk of depression, most pronounced among Americans and for clinically diagnosed depression. In addition, depression was found to be predictive of developing obesity.
Documentation of affective functioning measures in the health and retirement study
- R Wallace
- A R Herzog
- M B Ofstedal
- D Steffick
- S Fonda
- K Langa
HRS polygenic scores release 2 2006-2012 genetic data
- E Ware
- L Schmitz
- A Gard
- J Faul