Cohort of birth modifies the association between FTO genotype and BMI

Microevolutionary hypothesis of the obesity epidemic

The obesity epidemic represents potentially the largest phenotypic change in Homo sapiens since the origin of the species. Despite obesity's high heritability, it is generally presumed a change in the gene pool could not have caused the obesity epidemic. Here we advance the hypothesis that a rapid change in the obesogenic gene pool has occurred second to the introduction of modern obstetrics dramatically altering evolutionary pressures on obesity-the microevolutionary hypothesis of the obesity epidemic. Obesity is known to increase childbirth-related mortality several fold. Prior to modern obstetrics, childbirth-related mortality occurred in over 10% of women in their lifetime. After modern obstetrics, this mortality reduced to a fraction of a percent, thereby lifting a strong negative selection pressure. Regression analysis of data for ~ 190 countries was carried out to examine associations between 1990 lifetime maternal death rates (LMDR) and current obesity rates. Multivariate regression showed LMDR correlated more strongly with national obesity rates than GDP, calorie intake and physical inactivity. Analyses controlling for confounders via partial correlation show that LMDR explains approximately 11% of the variability of obesity rate between nations. For nations with LMDR above the median (>0.45%), LMDR explains 33% of obesity variance, while calorie intake, GDP and physical inactivity show no association with obesity in these nations. The microevolutionary hypothesis offers a parsimonious explanation of the global nature of the obesity epidemic.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Neuropeptide Y gene variants and Agreeableness: interaction effect with the birth cohort and the serotonin transporter promoter polymorphism

OBJECTIVE

Neuropeptide Y (NPY) is a powerful regulator of anxious states, including social anxiety, but evidence from human genetic studies is limited. Associations of common gene variants with behaviour have been described as subject to birth cohort effects, especially if the behaviour is socially motivated. This study aimed to examine the association of NPY rs16147 and rs5574 with personality traits in highly representative samples of two birth cohorts of young adults, the samples having been formed during a period of rapid societal transition.

METHODS

Both birth cohorts (original n = 1238) of the Estonian Children Personality Behaviour and Health Study (ECPBHS) self-reported personality traits of the five-factor model at 25 years of age.

RESULTS

A significant interaction effect of the NPY rs16147 and rs5574 and birth cohort on Agreeableness was found. The T/T genotype of NPY rs16147 resulted in low Agreeableness in the older cohort (born 1983) and in high Agreeableness in the younger cohort (born 1989). The C/C genotype of NPY rs5574 was associated with higher Agreeableness in the younger but not in the older cohort. In the NPY rs16147 T/T homozygotes, the deviations from average in Agreeableness within the birth cohort were dependent on the serotonin transporter promoter polymorphism.

CONCLUSIONS

The association between the NPY gene variants and a personality domain reflecting social desirability is subject to change qualitatively in times of rapid societal changes, serving as an example of the relationship between the plasticity genes and environment. The underlying mechanism may involve the development of the serotonergic system.

Overweight grandsons and grandfathers' starvation exposure

Much of the increase in the prevalence of overweight and obesity has been in developing countries with a history of famines and malnutrition. This paper is the first to examine overweight among adult grandsons of grandfathers exposed to starvation during developmental ages. I study grandsons born to grandfathers who served in the Union Army during the US Civil War (1861-5) where some grandfathers experienced severe net malnutrition because they suffered a harsh POW experience. I find that male-line but not female-line grandsons of grandfathers who survived a severe captivity during their growing years faced a 21% increase in mean overweight and a 2% increase in mean BMI compared to grandsons of non-POWs. Male-line grandsons descended from grandfathers who experienced a harsh captivity faced a 22%-28% greater risk of dying every year after age 45 relative to grandsons descended from non-POWs, with overweight accounting for 9%-14% of the excess risk.

Exercise and Experiments of Nature

In this article, we highlight the contributions of passive experiments that address important exercise-related questions in integrative physiology and medicine. Passive experiments differ from active experiments in that passive experiments involve limited or no active intervention to generate observations and test hypotheses. Experiments of nature and natural experiments are two types of passive experiments. Experiments of nature include research participants with rare genetic or acquired conditions that facilitate exploration of specific physiological mechanisms. In this way, experiments of nature are parallel to classical "knockout" animal models among human research participants. Natural experiments are gleaned from data sets that allow population-based questions to be addressed. An advantage of both types of passive experiments is that more extreme and/or prolonged exposures to physiological and behavioral stimuli are possible in humans. In this article, we discuss a number of key passive experiments that have generated foundational medical knowledge or mechanistic physiological insights related to exercise. Both natural experiments and experiments of nature will be essential to generate and test hypotheses about the limits of human adaptability to stressors like exercise. © 2023 American Physiological Society. Compr Physiol 13:4879-4907, 2023.

Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations

BACKGROUND

Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today's obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year.

METHODS

We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493).

RESULTS

Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m2 versus 26.9 [interquartile range, 23.7-30.6] kg/m2). The effect of the 941-variant BMI GRS on BMI and CVD risk was stronger in people who were born in later years (GRS-birth year interaction: P=0.0007 and P=0.04 respectively).

CONCLUSIONS

The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences.

Multi-omic analyses identify mucosa bacteria and fecal metabolites associated with weight loss after fecal microbiota transplantation

Fecal microbiota transplantation (FMT) has shown promising results in animal models of obesity, while results in human studies are inconsistent. We aimed to determine factors associated with weight loss after FMT in nine obese subjects using serial multi-omics analysis of the fecal and mucosal microbiome. The mucosal microbiome, fecal microbiome, and fecal metabolome showed individual clustering in each subject after FMT. The colonic microbiome in patients showed more marked variance after FMT compared with the duodenal microbiome, characterized by an increased relative abundance of Bacteroides. Subjects who lost weight after FMT sustained enrichment of Bifidobacterium bifidum and Alistipes onderdonkii in the duodenal, colonic mucosal, and fecal microbiome and increased levels of phosphopantothenate biosynthesis and fecal metabolite eicosapentaenoic acid (EPA), compared with those without weight loss. Fecal levels of amino acid metabolism-associated were positively correlated with the fecal abundance of B. bifidum, and fatty acid metabolism-associated metabolites showed positive correlations with A. onderdonkii. We report for the first time the individualized response of fecal and mucosa microbiome to FMT in obese subjects and highlight that FMT is less capable of shaping the small intestine microbiota. These findings contribute to personalized microbe-based therapies for obesity.

Cultural evolution and behavior genetic modeling: The long view of time

We advocate for an integrative long-term perspective on time, noting that culture changes on timescales amenable to behavioral genetic study with appropriate design and modeling extensions. We note the need for replications of behavioral genetic studies to examine model invariance across long-term timescales, which would afford examination of specified as well as unspecified cultural moderators of behavioral genetic effects.

Modeling Interaction and Dispersion Effects in the Analysis of Gene-by-Environment Interaction

Genotype-by-environment interaction (GxE) studies probe heterogeneity in response to risk factors or interventions. Popular methods for estimation of GxE examine multiplicative interactions between individual genetic and environmental measures. However, risk factors and interventions may modulate the total variance of an epidemiological outcome that itself represents the aggregation of many other etiological components. We expand the traditional GxE model to directly model genetic and environmental moderation of the dispersion of the outcome. We derive a test statistic, [Formula: see text], for inferring whether an interaction identified between individual genetic and environmental measures represents a more general pattern of moderation of the total variance in the phenotype by either the genetic or the environmental measure. We validate our method via extensive simulation, and apply it to investigate genotype-by-birth year interactions for Body Mass Index (BMI) with polygenic scores in the Health and Retirement Study (N = 11,586) and individual genetic variants in the UK Biobank (N = 380,605). We find that changes in the penetrance of a genome-wide polygenic score for BMI across birth year are partly representative of a more general pattern of expanding BMI variation across generations. Three individual variants found to be more strongly associated with BMI among later born individuals, were also associated with the magnitude of variability in BMI itself within any given birth year, suggesting that they may confer general sensitivity of BMI to a range of unmeasured factors beyond those captured by birth year. We introduce an expanded GxE regression model that explicitly models genetic and environmental moderation of the dispersion of the outcome under study. This approach can determine whether GxE interactions identified are specific to the measured predictors or represent a more general pattern of moderation of the total variance in the outcome by the genetic and environmental measures.

Insights into the Genetic Underpinnings of Endocrine Traits from Large-Scale Genome-Wide Association Studies

Great strides have been made in genetic association studies of endocrine traits and diseases, with hundreds or thousands of variants associated with height, body mass index, bone density, pubertal timing, and diabetes in recent years. The common variants associated with these traits explain up to half of the trait variation owing to genetic factors, and when aggregated into polygenic risk scores, can also impact clinically relevant phenotypes at the tail ends of the trait distributions. However, pediatric studies tend to lag behind, and it is often unclear how adult-associated variants behave across life.

Association of a genetic risk score with BMI along the life-cycle: Evidence from several US cohorts

We use data from the National Longitudinal Study of Adolescent to Adult Health and from the Health and Retirement Study to explore how the effect of individuals’ genetic predisposition to higher BMI —measured by BMI polygenic scores— changes over the life-cycle for several cohorts. We find that the effect of BMI polygenic scores on BMI increases significantly as teenagers transition into adulthood (using the Add Health cohort, born 1974-83). However, this is not the case for individuals aged 55+ who were born in earlier HRS cohorts (1931-53), whose life-cycle pattern of genetic influence on BMI is remarkably stable as they move into old-age.

Interactions between Polygenic Scores and Environments: Methodological and Conceptual Challenges

Interest in the study of gene-environment interaction has recently grown due to the sudden availability of molecular genetic data-in particular, polygenic scores-in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene-environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene-environment interaction studies.

Causal Association of Haptoglobin With Obesity in Mexican Children: A Mendelian Randomization Study

CONTEXT

Little is known about the association between haptoglobin level and cardiometabolic traits. A previous genome-wide association study identified rs2000999 in the HP gene as the stronger genetic contributor to serum haptoglobin level in European populations.

OBJECTIVE AND DESIGN

We investigated the association of HP rs2000999 with serum haptoglobin and childhood and adult obesity in up to 540/697 and 592/691 Mexican cases and controls, respectively. Anthropometric and biochemical data were collected. Serum haptoglobin was measured by an immunoturbidimetry assay. HP rs2000999 was genotyped using the TaqMan technology. Mendelian randomization analysis was performed using the Wald and inverse variance weighting methods.

RESULTS

Haptoglobin level was positively associated with childhood and adult obesity. HP rs2000999 G allele was positively associated with haptoglobin level in children and adults. HP rs2000999 G allele was positively associated with childhood but not adult obesity. The association between HP rs2000999 and childhood obesity was removed after adjusting for haptoglobin level. In a Mendelian randomization analysis, haptoglobin level genetically predicted by HP rs2000999 showed a significant causal effect on childhood obesity by the Wald and inverse variance weighting methods.

CONCLUSION

Our data provide evidence for the first time for a causal positive association between serum haptoglobin level and childhood obesity in the Mexican population. Our study contributes to the genetic elucidation of childhood obesity and proposes haptoglobin as an important biomarker and treatment target for obesity.

The polygenic architecture of left ventricular mass mirrors the clinical epidemiology

Left ventricular (LV) mass is a prognostic biomarker for incident heart disease and all-cause mortality. Large-scale genome-wide association studies have identified few SNPs associated with LV mass. We hypothesized that a polygenic discovery approach using LV mass measurements made in a clinical population would identify risk factors and diseases associated with adverse LV remodeling. We developed a polygenic single nucleotide polymorphism-based predictor of LV mass in 7,601 individuals with LV mass measurements made during routine clinical care. We tested for associations between this predictor and 894 clinical diagnoses measured in 58,838 unrelated genotyped individuals. There were 29 clinical phenotypes associated with the LV mass genetic predictor at FDR q < 0.05. Genetically predicted higher LV mass was associated with modifiable cardiac risk factors, diagnoses related to organ dysfunction and conditions associated with abnormal cardiac structure including heart failure and atrial fibrillation. Secondary analyses using polygenic predictors confirmed a significant association between higher LV mass and body mass index and, in men, associations with coronary atherosclerosis and systolic blood pressure. In summary, these analyses show that LV mass-associated genetic variability associates with diagnoses of cardiac diseases and with modifiable risk factors which contribute to these diseases.

Mendelian Randomization analysis of the causal effect of adiposity on hospital costs

Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.

Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms

Background: While the academic genetic literature has clearly shown that common genetic single nucleotide polymorphisms (SNPs), and even large polygenic SNP risk scores, cannot reliably be used to determine risk of disease or to personalize interventions, a significant industry of companies providing SNP-based recommendations still exists. Healthcare practitioners must therefore be able to navigate between the promise and reality of these tools, including being able to interpret the literature that is associated with a given risk or suggested intervention. One significant hurdle to this process is the fact that most population studies of common SNPs only provide average (+/- error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves or their patients might truly be affected. Methods: We generated synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using simple statistical theory and full graphical representation of the generated data, we developed a method by which anybody can better understand phenotypic heterogeneity in a population, as well as the degree to which common SNPs truly drive disease risk. Results: Individual risk SNPs had a <10% likelihood of effecting the associated phenotype (bodyweight, fasting glucose, or homocysteine levels). Example polygenic risk scores including the SNPs most associated with obesity and type 2 diabetes only explained 2% and 5% of the final phenotype, respectively. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to support the pursuit of lifestyle-based interventions that are likely to be beneficial regardless of genetics.

The FTO genetic variants are associated with dietary intake and body mass index amongst Emirati population

BACKGROUND

The risk of obesity is determined by complex interactions between genetic and environmental factors. Little research to date has investigated the interaction between gene and food intake. The aim of the current study is to explore the potential effect of fat mass and obesity-associated protein gene (FTO) rs9939609 and rs9930506 single nucleotide polymorphism (SNP) on the pattern of food intake in the Emirati population.

METHODS

Adult healthy Emirati subjects with Body mass index (BMI) of 16-40 kg/m2 were included in the study. Genotyping for FTO rs9939609(A>T) and rs9930506(A>G) was performed using DNA from saliva samples. Subjects were categorized according to the WHO classification by calculating the BMI to compare different classes. Dietary intake was assessed by a sixty-one-item FFQ that estimated food and beverage intakes over the past year. The daily energy, macronutrient, and micronutrient consumption were computed.

RESULTS

We included 169 subjects in the final analysis (mean age 30.49± 9.1years, 57.4% females). The mean BMI of the study population was 26.19 kg/m2. Both SNPs were in Hardy Weinberg Equilibrium. The rs9939609 AA genotype was significantly associated with higher BMI (p = 0.004); the effect was significant in females (p = 0.028), but not in males (p = 0.184). Carbohydrate intake was significantly higher in AA subjects with a trend of lower fat intake compared to other genotypes. The odds ratio for the AA was 3.78 in the fourth quartile and 2.67 for the A/T in the second quartile of total carbohydrate intake, considering the first quartile as a reference (95% CI = 1.017-14.1 and 1.03-6.88, respectively). Fat intake was significantly lower in the FTO rs9930506 GG subjects. The presence of FTO rs9930506 GG genotype decreased the fat intake in subjects with FTO rs9939609 AA (p = 0.037).

CONCLUSIONS

The results of this study highlight the interaction of the FTO risk alleles on the food intake in Emirati subjects. The FTO rs9939609 AA subjects had higher carbohydrate and lower fat intake. The latter was accentuated in presence of rs9930506 GG genotype.

Development and change in attachment: A multiwave assessment of attachment and its correlates across childhood and adolescence

This research examines the contextual factors that facilitate development and change in attachment during later childhood, adolescence, and early adulthood using a longitudinal cohort design involving 690 children (7-19 years old) and their parents. At each wave, a variety of interpersonal variables (e.g., parent-child stress) were measured. We examined alternative developmental processes (i.e., long-term, catalytic, and short-term processes) that have not been previously distinguished in attachment research. Preregistered analyses revealed that nondevelopmental processes can explain the associations between almost all of the interpersonal variables of interest and attachment security, suggesting that previous research using traditional longitudinal methods may have misattributed nondevelopmental processes for developmental ones. For example, we found that friendship quality, although prospectively associated with attachment both in prior work and in the current study, was not developmentally associated with attachment. However, after controlling for nondevelopmental sources of covariation, we identified a number of developmental processes that may help explain change in attachment. For example, we found that initial levels of parental depression, as well as growth in parent-child stress, were related to growth in adolescent insecurity over 3 years. We also examined 12 genetic variants studied in previous research and found that they were not related to average levels or changes in attachment. These results highlight how distinguishing unique kinds of developmental processes allows for a more comprehensive understanding of attachment. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

The "All of Us" Research Program

Knowledge gained from observational cohort studies has dramatically advanced the prevention and treatment of diseases. Many of these cohorts, however, are small, lack diversity, or do not provide comprehensive phenotype data. The All of Us Research Program plans to enroll a diverse group of at least 1 million persons in the United States in order to accelerate biomedical research and improve health. The program aims to make the research results accessible to participants, and it is developing new approaches to generate, access, and make data broadly available to approved researchers. All of Us opened for enrollment in May 2018 and currently enrolls participants 18 years of age or older from a network of more than 340 recruitment sites. Elements of the program protocol include health questionnaires, electronic health records (EHRs), physical measurements, the use of digital health technology, and the collection and analysis of biospecimens. As of July 2019, more than 175,000 participants had contributed biospecimens. More than 80% of these participants are from groups that have been historically underrepresented in biomedical research. EHR data on more than 112,000 participants from 34 sites have been collected. The All of Us data repository should permit researchers to take into account individual differences in lifestyle, socioeconomic factors, environment, and biologic characteristics in order to advance precision diagnosis, prevention, and treatment.

Quantifying the impact of genes on body mass index during the obesity epidemic: longitudinal findings from the HUNT Study

OBJECTIVES

To study the trajectories of body mass index (BMI) in Norway over five decades and to assess the differential influence of the obesogenic environment on BMI according to genetic predisposition.

DESIGN

Longitudinal study.

SETTING

General population of Nord-Trøndelag County, Norway.

PARTICIPANTS

118 959 people aged 13-80 years who participated in a longitudinal population based health study (Nord-Trøndelag Health Study, HUNT), of whom 67 305 were included in analyses of association between genetic predisposition and BMI.

MAIN OUTCOME MEASURE

BMI.

RESULTS

Obesity increased in Norway starting between the mid-1980s and mid-1990s and, compared with older birth cohorts, those born after 1970 had a substantially higher BMI already in young adulthood. BMI differed substantially between the highest and lowest fifths of genetic susceptibility for all ages at each decade, and the difference increased gradually from the 1960s to the 2000s. For 35 year old men, the most genetically predisposed had 1.20 kg/m² (95% confidence interval 1.03 to 1.37 kg/m²) higher BMI than those who were least genetically predisposed in the 1960s compared with 2.09 kg/m² (1.90 to 2.27 kg/m²) in the 2000s. For women of the same age, the corresponding differences in BMI were 1.77 kg/m² (1.56 to 1.97 kg/m²) and 2.58 kg/m² (2.36 to 2.80 kg/m²).

CONCLUSIONS

This study provides evidence that genetically predisposed people are at greater risk for higher BMI and that genetic predisposition interacts with the obesogenic environment resulting in higher BMI, as observed between the mid-1980s and mid-2000s. Regardless, BMI has increased for both genetically predisposed and non-predisposed people, implying that the environment remains the main contributor.

Testing for family influences on obesity: The role of genetic nurture

A large literature has documented strong positive correlations among siblings in health, including body mass index (BMI) and obesity. This paper tests whether that is explained by a specific type of peer effect in obesity: genetic nurture. Specifically, we test whether an individual's weight is affected by the genes of their sibling, controlling for the individual's own genes. Using genetic data in Add Health, we find no credible evidence that an individual's BMI is affected by the polygenic risk score for BMI of their full sibling when controlling for the individual's own polygenic risk score for BMI. Thus, we find no evidence that the positive correlations in BMI between siblings are attributable to genetic nurture within families.

BMI-associated gene variants in FTO and cardiometabolic and brain disease: obesity or pleiotropy?

Obesity is a causal risk factor for the development of age-related disease conditions, which includes Type 2 diabetes mellitus, cardiovascular disease, and dementia. In genome-wide association studies, genetic variation in FTO is strongly associated with obesity and has been described across different ethnic backgrounds and life stages. To date, much work has been devoted on determining the biological mechanisms via which FTO affects body weight regulation and ultimately contributes to age-related cardiometabolic and brain disease. The main hypotheses of the involved biological mechanisms include the involvement of FTO in habitual food intake and energy expenditure. In this narrative review, our overall aim is to provide an overview on how FTO gene variants could increase the risk of developing age-related disease conditions. Specifically, we will discuss the state of the literature based on the different hypotheses how FTO regulates body weight and ultimately contributes to cardiometabolic disease and brain disease.

Gut microbiota and obesity: An opportunity to alter obesity through faecal microbiota transplant (FMT)

Obesity is a global pandemic with immense health consequences for individuals and societies. Multiple factors, including environmental influences and genetic predispositions, are known to affect the development of obesity. Despite an increasing understanding of the factors driving the obesity epidemic, therapeutic interventions to prevent or reverse obesity are limited in their impact. Manipulation of the human gut microbiome provides a new potential therapeutic approach in the fight against obesity. Specific gut bacteria and their metabolites are known to affect host metabolism and feeding behaviour, and dysbiosis of this biosystem may lead to metabolic syndrome. Potential therapies to alter the gut microbiota to treat obesity include dietary changes, supplementation of the diet with probiotic organisms and prebiotic compounds that influence bacterial growth, and the use of faecal microbiota transplant, in which gut microbiota from healthy individuals are introduced into the gut. In this review, we examine the growing scientific evidence supporting the mechanisms by which the human gut microbiota may influence carbohydrate metabolism and obesity, and the various possible therapies that may utilize the gut microbiota to help correct metabolic dysfunction.

Genetic determinants of BMI from early childhood to adolescence: the Santiago Longitudinal Study

BACKGROUND

While the genetic contribution to obesity is well established, few studies have examined how genetic variants influence standardized body mass index Z-score (BMIz) in Hispanics/Latinos, especially across childhood and adolescence.

OBJECTIVES

We estimated the effect of established BMIz loci in Chilean children of the Santiago Longitudinal Study (SLS).

METHODS

We examined associations with BMIz at age 10 for 15 loci previously identified in European children. For significant loci, we performed association analyses at ages 5 and 16 years, for which we have smaller sample sizes. We tested associations of unweighted genetic risk scores (GRSs) for previously identified tag variants (GRS_EUR) and from the most significant variants in SLS at each locus (GRS_SLS).

RESULTS

We generalized five variants at age 10 (P < 0.05 and directionally consistent), including rs543874 that reached Bonferroni-corrected significance. The effect on BMIz was greatest at age 10 for all significant loci, except FTO, which exhibited an increase in effect from ages 5 to 16. Both GRSs were associated with BMIz (P < 0.0001), but GRS_SLS explained a much greater proportion of the variation (13.63%).

CONCLUSION

Our results underscore the importance of conducting genetic investigations across life stages and selecting ancestry appropriate tag variants in future studies for disease prediction and clinical evaluation.

Bidirectional Mendelian randomization to explore the causal relationships between body mass index and polycystic ovary syndrome

STUDY QUESTION

What are the causal relationships between polycystic ovary syndrome (PCOS) and body mass index (BMI)?

SUMMARY ANSWER

Bidirectional Mendelian randomization analyses suggest that increased BMI is causal for PCOS while the reverse is not the case.

WHAT IS KNOWN ALREADY

The contribution of obesity to the pathogenesis of PCOS is controversial. To date, published genetic studies addressing this question have generated conflicting results and have not utilized the full extent of known single nucleotide polymorphisms associated with body mass index (BMI).

STUDY DESIGN, SIZE, DURATION

This cross-sectional Mendelian randomization (MR) and genetic association study was conducted in 750 individuals of European origin and with PCOS and 1567 BMI-matched controls.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Cases and controls were matched for BMI as well as for distribution of weight categories (normal weight, overweight, obese). Two-sample MR using inverse variance weighting (IVW) was conducted using a 92-SNP instrument variable for BMI with PCOS as the outcome, followed by two-sample MR with a 16-SNP instrument variable for PCOS with BMI as the outcome. Sensitivity analyses included MR-Egger and maximum likelihood methods. Secondary analyses assessed associations of genetic risk scores and individual SNPs with PCOS, BMI and quantitative androgen-related and glucose homeostasis-related traits.

MAIN RESULTS AND THE ROLE OF CHANCE

Each standard deviation genetically higher BMI was associated with a 4.89 (95% CI 1.46-16.32) higher odds of PCOS. Conversely, genetic risk of PCOS did not influence BMI. Sensitivity analyses yielded directionally consistent results. The genetic risk score of 92 BMI SNPs was associated with the diagnosis of PCOS (OR 1.043, 95% CI 1.009-1.078, P = 0.012). Of the 92 BMI risk variants evaluated, none were associated individually with PCOS after considering multiple testing. The association of FTO SNP rs1421085 with BMI was stronger in women with PCOS (β = 0.071, P = 0.0006) than in controls (β = 0.046, P = 0.065).

LIMITATIONS, REASONS FOR CAUTION

The current sample size, while providing good power for MR and genetic risk score analyses, had limited power to demonstrate association of individual SNPs with PCOS. Cases and controls were not matched for age; however, this was mitigated by adjusting analyses for age. Dietary and lifestyle data, which could have been used to explore the greater association of the FTO SNP with BMI in women with PCOS, was not available.

WIDER IMPLICATIONS OF THE FINDINGS

Increasing BMI appears to be causal for PCOS but having PCOS does not appear to affect BMI. This study used the most comprehensive set of SNPs for BMI currently available. Prior studies using fewer SNPs had yielded conflicting results and may have been confounded because cases and controls were not matched for weight categories. The current results highlight the potential utility of weight management in the prevention and treatment of PCOS.

STUDY FUNDING/COMPETING INTEREST(S)

National Institutes of Health Grants R01-HD29364 and K24-HD01346 (to R.A.), Grant R01-DK79888 (to M.O.G.), Grant U54-HD034449 (to R.S.L.), Grant U19-HL069757 (to R.M.K.). The funders had no influence on the data collection, analyses or conclusions of the study. No conflict of interests to declare.

TRIAL REGISTRATION NUMBER

N/A.

Interpretation and Potential Biases of Mendelian Randomization Estimates With Time-Varying Exposures

Mendelian randomization (MR) is used to answer a variety of epidemiologic questions. One stated advantage of MR is that it estimates a "lifetime effect" of exposure, though this term remains vaguely defined. Instrumental variable analysis, on which MR is based, has focused on estimating the effects of point or time-fixed exposures rather than "lifetime effects." Here we use an empirical example with data from the Rotterdam Study (Rotterdam, the Netherlands, 2009-2013) to demonstrate how confusion can arise when estimating "lifetime effects." We provide one possible definition of a lifetime effect: the average change in outcome measured at time t when the entire exposure trajectory from conception to time t is shifted by 1 unit. We show that MR only estimates this type of lifetime effect under specific conditions-for example, when the effect of the genetic variants used on exposure does not change over time. Lastly, we simulate the magnitude of bias that would result in realistic scenarios that use genetic variants with effects that change over time. We recommend that investigators in future MR studies carefully consider the effect of interest and how genetic variants whose effects change with time may impact the interpretability and validity of their results.

The Fat Mass- and Obesity-Associated (FTO) Gene to Obesity: Lessons from Mouse Models

OBJECTIVE

Genetic variants at the fat mass- and obesity-associated (FTO) locus are strongly associated with obesity-related traits by regulating neighboring genes. Nevertheless, it is possible that FTO protein is directly involved in mechanisms regulating body composition and adiposity. Here, the in vivo biological functions of FTO in the risk for obesity were studied by reviewing murine models.

METHODS

The effects of the locus-specific manipulations of the murine Fto gene on metabolic-related phenotypes in genetically modified mouse models were reviewed and summarized into the following three categories: growth and body composition, eating behaviors, and metabolic homeostasis.

RESULTS

The mouse models showed different phenotypes depending on target tissues and methods for gene manipulation. Mice harboring deletions or point mutations at the Fto locus had high metabolic rates, while FTO-overexpressing mice showed dyslipidemia. Both deletion and overexpression of the Fto gene led to abnormal eating behaviors. Intriguingly, several phenotypes were differently expressed depending on developmental timing of the genetic manipulations. For instance, a germ line deletion decreased total body fat mass, while the deletion in adult mice increased it.

CONCLUSIONS

The results highlight that FTO is critical not only for body composition but also normal development, and its function might differ depending on the stage of development.

Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: mendelian randomisation study

OBJECTIVES

To investigate whether the association between subjective wellbeing (subjective happiness and life satisfaction) and cardiometabolic health is causal.

DESIGN

Two sample, bidirectional mendelian randomisation study.

SETTING

Genetic data taken from various cohorts comprised of the general population (mostly individuals of European ancestry, plus a small proportion of other ancestries); follow-up analysis included individuals from the United Kingdom.

PARTICIPANTS

Summary data were used from previous genome wide association studies (number of participants ranging from 83 198 to 339 224), which investigated traits related to cardiovascular or metabolic health, had the largest sample sizes, and consisted of the most similar populations while minimising sample overlap. A follow-up analysis included 337 112 individuals from the UK Biobank (54% female (n=181 363), mean age 56.87 years (standard deviation 8.00) at recruitment).

MAIN OUTCOME MEASURES

Subjective wellbeing and 11 measures of cardiometabolic health (coronary artery disease; myocardial infarction; total, high density lipoprotein, and low density lipoprotein cholesterol; diastolic and systolic blood pressure; body fat; waist to hip ratio; waist circumference; and body mass index).

RESULTS

Evidence of a causal effect of body mass index on subjective wellbeing was seen; each 1 kg/m2 increase in body mass index caused a -0.045 (95% confidence interval -0.084 to -0.006, P=0.02) standard deviation reduction in subjective wellbeing. Follow-up analysis of this association in an independent sample from the UK Biobank provided strong evidence of an effect of body mass index on satisfaction with health (β=-0.035 unit decrease in health satisfaction (95% confidence interval -0.043 to -0.027) per standard deviation increase in body mass index, P<0.001). No clear evidence of a causal effect was seen between subjective wellbeing and the other cardiometabolic health measures, in either direction.

CONCLUSIONS

These results suggest that a higher body mass index is associated with a lower subjective wellbeing. A follow-up analysis confirmed this finding, suggesting that the effect in middle aged people could be driven by satisfaction with health. Body mass index is a modifiable determinant, and therefore, this study provides further motivation to tackle the obesity epidemic because of the knock-on effects of higher body mass index on subjective wellbeing.

Pervasive Modulation of Obesity Risk by the Environment and Genomic Background

The prevalence of the so-called diseases of affluence, such as type 2 diabetes or hypertension, has increased dramatically in the last two generations. Although genome-wide association studies (GWAS) have discovered hundreds of genes involved in disease etiology, the sudden increase in disease incidence suggests a major role for environmental risk factors. Obesity constitutes a case example of a modern trait shaped by contemporary environment, although with considerable debates about the extent to which gene-by-environment (G×E) interactions accentuate obesity risk in individuals following obesogenic lifestyles. Although interaction effects have been robustly confirmed at the FTO locus, accumulating evidence at the genome-wide level implicates a role for polygenic risk-by-environment interactions. Through a variety of analyses using the UK Biobank, we confirm that the genomic background plays a major role in shaping the expressivity of alleles that increase body mass index (BMI).

Socioeconomic inequalities in the prevalence of biomarkers of cardio-metabolic disease in South Korea: Comparison of the Health Examinees Study to a nationally representative survey

BACKGROUND/OBJECTIVES

This study aimed to examine socioeconomic inequalities in the prevalence of biomarkers of cardiovascular disease and diabetes in the newly developed large-scale genomic cohort study of Korean adults, the Health Examinees-Gem (HEXA-G), with a comparison of the nationally representative cross-sectional study, the Korea National Health and Nutrition Examination Survey (K-NHANES).

SUBJECTS/METHODS

Using the HEXA-G and the K-NHANES from 2007-2012, we analyzed the age-adjusted relative risk (RR) and prevalence of enlarged waist circumference (EWC), elevated triglycerides (ET), low HDL cholesterol (LHC), elevated blood pressure (EBP) and elevated blood glucose (EBG) by income and educational groups for adults at age 40-69.

RESULTS

For men, the prevalence of risk factors was similar across different income and educational groups (p>0.1), and between the K-NHANES and the HEXA-G. Among five risk factors, EBG showed the greatest discrepancy by 7 to 11 percentage points (i.e., the prevalence of 0.43 and 0.36 for college graduates, respectively, in K-NHANES and HEXA-G). For women, socioeconomic inequalities appeared for the five risk factors. Prevalence of risk factors was mostly lower in the HEXA-G than the K-NHANES, by approximately 11.0 percentage points. Especially, the discrepancy between K-NHANES and HEXA-G was largest in EBG (i.e., the prevalence of 0.31 and 0.20 for the lowest income groups, respectively).

CONCLUSION

The HEXA-G shows broadly similar socioeconomic inequality in prevalence of cardio-metabolic risk factors to the nationally representative sample with more modest socioeconomic inequality among women in the HEXA-G than the K-NHANES.

The importance of cohort studies in the post-GWAS era

The past decade has seen enormous success of wide-scale genetic studies in identifying genetic variants that modify individuals' predisposition to common diseases. However, the interpretation and functional understanding of these variants lag far behind. In this Perspective, we discuss opportunities for using large-scale cohort studies to investigate the downstream molecular effects of SNPs at different 'omics' data levels. We point to the pivotal role of population cohorts in establishing causality and advancing drug discovery. In particular, we focus on the breadth-versus-depth concepts of population studies, on data harmonization, and on the challenges, ethical aspects and future perspectives of cohort studies.

Genetics of obesity: what genetic association studies have taught us about the biology of obesity and its complications

Genome-wide association studies (GWAS) for BMI, waist-to-hip ratio, and other adiposity traits have identified more than 300 single-nucleotide polymorphisms (SNPs). Although there is reason to hope that these discoveries will eventually lead to new preventive and therapeutic agents for obesity, this will take time because such developments require detailed mechanistic understanding of how an SNP influences phenotype (and this information is largely unavailable). Fortunately, absence of functional information has not prevented GWAS findings from providing insights into the biology of obesity. Genes near loci regulating total body mass are enriched for expression in the CNS, whereas genes for fat distribution are enriched in adipose tissue itself. Gene by environment and lifestyle interaction analyses have revealed that our increasingly obesogenic environment might be amplifying genetic risk for obesity, yet those at highest risk could mitigate this risk by increasing physical activity and possibly by avoiding specific dietary components. GWAS findings have also been used in mendelian randomisation analyses probing the causal association between obesity and its many putative complications. In supporting a causal association of obesity with diabetes, coronary heart disease, specific cancers, and other conditions, these analyses have clinical relevance in identifying which outcomes could be preventable through weight loss interventions.

The statistical mechanics of human weight change

Over the past 35 years there has been a near doubling in the worldwide prevalence of obesity. Body Mass Index (BMI) distributions in high-income societies have increasingly shifted rightwards, corresponding to increases in average BMI that are due to well-studied changes in the socioeconomic environment. However, in addition to this shift, BMI distributions have also shown marked changes in their particular shape over time, exhibiting an ongoing right-skewed broadening that is not well understood. Here, we compile and analyze the largest data set so far of year-over-year BMI changes. The data confirm that, on average, heavy individuals become lighter while light individuals become heavier year-over-year, and also show that year-over-year BMI evolution is characterized by fluctuations with a magnitude that is linearly proportional to BMI. We find that the distribution of human BMIs is intrinsically dynamic-due to the short-term variability of human weight-and its shape is determined by a balance between deterministic drift towards a natural set point and diffusion resulting from random fluctuations in, e.g., diet and physical activity. We formulate a stochastic mathematical model for BMI dynamics, deriving a theoretical shape for the BMI distribution and offering a mechanism that may explain the right-skewed broadening of BMI distributions over time. An extension of the base model investigates the hypothesis that peer-to-peer social influence plays a role in BMI dynamics. While including this effect improves the fit with the data, indicating that correlations in the behavior of individuals with similar BMI may be important for BMI dynamics, testing social transmission against other plausible unmodeled effects and interpretations remains the subject of future work. Implications of our findings on the dynamics of BMI distributions for public health interventions are discussed.

Trends in the distribution of body mass index and waist circumference among South Korean adults, 1998-2014

BACKGROUND/OBJECTIVES

An increase in mean body mass index (BMI) or prevalence of obesity may be accompanied by changes in the population BMI distribution. This study aimed to examine how the distributions of BMI and waist circumference (WC) have changed in South Korea over a 16-year interval (1998-2014).

SUBJECTS/METHODS

Using the Korea National Health and Nutrition Examination Survey, we analyzed changes in mean, standard deviation (SD), 5th, and 95th percentile values of BMI and WC distributions for 46,343 (BMI) and 46,327 (WC) adults aged 25-64 years.

RESULTS

For men, mean BMI increased at an annual rate of 0.060 units (95% CI: 0.047-0.073) with annual increases of 0.029 units in SD (95% CI: 0.019-0.039), 0.121 units in the 95th percentile (95% CI: 0.097-0.145) and 0.042 units in the 5th percentile (95% CI: 0.021-0.062). The 95th percentile and SD of the WC distribution increased, but not mean WC and the 5th percentile. For women, mean BMI decreased at an annual rate of 0.030 units (95% CI: 0.010-0.049) with a 0.030-unit increase in SD (95% CI: 0.012-0.048) and a 0.049-unit decrease in the 5th percentile (95% CI: 0.029-0.070). Mean WC also decreased with an increase in SD and a decrease in the 5th percentile.

CONCLUSIONS

These findings suggest increasing dispersion in the distribution of BMI and WC derived from significant shifts in the upper tails for Korean men, but not women. Future research needs to identify the factors that underlie the increasing dispersion of obesity measures.

Lessons Learned From Past Gene-Environment Interaction Successes

Genetic and environmental factors are both known to contribute to susceptibility to complex diseases. Therefore, the study of gene-environment interaction (G×E) has been a focus of research for several years. In this article, select examples of G×E from the literature are described to highlight different approaches and underlying principles related to the success of these studies. These examples can be broadly categorized as studies of single metabolism genes, genes in complex metabolism pathways, ranges of exposure levels, functional approaches and model systems, and pharmacogenomics. Some studies illustrated the success of studying exposure metabolism for which candidate genes can be identified. Moreover, some G×E successes depended on the availability of high-quality exposure assessment and longitudinal measures, study populations with a wide range of exposure levels, and the inclusion of ethnically and geographically diverse populations. In several examples, large population sizes were required to detect G×Es. Other examples illustrated the impact of accurately defining scale of the interactions (i.e., additive or multiplicative). Last, model systems and functional approaches provided insights into G×E in several examples. Future studies may benefit from these lessons learned.

A mediational model of obesity related disordered eating: The roles of childhood emotional abuse and self-perception

The extant literature indicates negative self-perceptions are a risk factor for disordered eating (DE) and DE is a risk factor for overweight and obesity. While childhood emotional abuse (EA) is often linked to DE and obesity, it is typically not included in comprehensive models of these health problems. Further investigation of interactions among EA, self-perception, and DE is needed to refine treatments for overweight, obesity, and DE. This study evaluated a model of DE and weight difficulties in which negative self-perception mediate the relationship between EA and DE, and DE predicts body mass index (BMI) in a population of emerging adults. Further, this study investigated the utility of history of EA for prediction of DE and classification of individuals with and without DE. Self-report questionnaires on childhood trauma, psychopathology, and eating behaviors were administered to 598 undergraduate students. Latent variable analysis confirmed the hypothesized model. Recursive partitioning determined that individuals reporting a high level of EA likely meet criteria for night eating syndrome (NES) or binge eating disorder (BED), and history of EA has a moderate to high level of specificity as a predictor of BED and NES. These findings confirm the necessity of evaluating EA and DE in emerging adults with weight difficulties, and the importance of assessing self-perception and DE in individuals with a history of EA. Future studies should investigate the utility of addressing EA and self-perception in interventions for DE and obesity and to determine whether these findings can be generalized to a clinical population.

Genotype-covariate interaction effects and the heritability of adult body mass index

Obesity is a worldwide epidemic, with major health and economic costs. Here we estimate heritability for body mass index (BMI) in 172,000 sibling pairs and 150,832 unrelated individuals and explore the contribution of genotype-covariate interaction effects at common SNP loci. We find evidence for genotype-age interaction (likelihood ratio test (LRT) = 73.58, degrees of freedom (df) = 1, P = 4.83 × 10^-18), which contributed 8.1% (1.4% s.e.) to BMI variation. Across eight self-reported lifestyle factors, including diet and exercise, we find genotype-environment interaction only for smoking behavior (LRT = 19.70, P = 5.03 × 10^-5 and LRT = 30.80, P = 1.42 × 10^-8), which contributed 4.0% (0.8% s.e.) to BMI variation. Bayesian association analysis suggests that BMI is highly polygenic, with 75% of the SNP heritability attributable to loci that each explain <0.01% of the phenotypic variance. Our findings imply that substantially larger sample sizes across ages and lifestyles are required to understand the full genetic architecture of BMI.

The importance of gene-environment interactions in human obesity

The worldwide obesity epidemic has been mainly attributed to lifestyle changes. However, who becomes obese in an obesity-prone environment is largely determined by genetic factors. In the last 20 years, important progress has been made in the elucidation of the genetic architecture of obesity. In parallel with successful gene identifications, the number of gene-environment interaction (GEI) studies has grown rapidly. This paper reviews the growing body of evidence supporting gene-environment interactions in the field of obesity. Heritability, monogenic and polygenic obesity studies provide converging evidence that obesity-predisposing genes interact with a variety of environmental, lifestyle and treatment exposures. However, some skepticism remains regarding the validity of these studies based on several issues, which include statistical modelling, confounding, low replication rate, underpowered analyses, biological assumptions and measurement precision. What follows in this review includes (1) an introduction to the study of GEI, (2) the evidence of GEI in the field of obesity, (3) an outline of the biological mechanisms that may explain these interaction effects, (4) methodological challenges associated with GEI studies and potential solutions, and (5) future directions of GEI research. Thus far, this growing body of evidence has provided a deeper understanding of GEI influencing obesity and may have tremendous applications in the emerging field of personalized medicine and individualized lifestyle recommendations.

Does neighbourhood deprivation affect the genetic influence on body mass?

Most research into the role of gene-environment interactions in the etiology of obesity has taken environment to mean behaviours such as exercise and diet. While interesting, this is somewhat at odds with research into the social determinants of obesity, in which the focus has shifted away from individuals and behaviours to the types of wider obesogenic environments in which individuals live, which influence and produce these behaviours. This study combines these two strands of research by investigating how the genetic influence on body mass index (BMI), used as a proxy for obesity, changes across different neighbourhood environments measured by levels of deprivation. Genetics are incorporated using a classical twin design with data from Twins UK, a longitudinal study of UK twins running since 1992. A multilevel modelling approach is taken to decompose variation between individuals into genetic, shared environmental, and non-shared environmental components. Neighbourhood deprivation is found to be a statistically significant predictor of BMI after conditioning on individual characteristics, and a heritability of 0.75 is estimated for the entire sample. This heritability estimate is shown, however, to be higher in more deprived neighbourhoods and lower in less deprived ones, and this relationship is statistically significant. While this research cannot say anything directly about the mechanisms behind the relationship, it does highlight how the relative importance of genetic factors can vary across different social environments, and therefore the value of considering both genetic and social determinants of health simultaneously.

Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.

Detection of gene-environment interaction in pedigree data using genome-wide genotypes

Heritability may be estimated using phenotypic data collected in relatives or in distantly related individuals using genome-wide single nucleotide polymorphism (SNP) data. We combined these approaches by re-parameterizing the model proposed by Zaitlen et al and extended this model to include moderation of (total and SNP-based) genetic and environmental variance components by a measured moderator. By means of data simulation, we demonstrated that the type 1 error rates of the proposed test are correct and parameter estimates are accurate. As an application, we considered the moderation by age or year of birth of variance components associated with body mass index (BMI), height, attention problems (AP), and symptoms of anxiety and depression. The genetic variance of BMI was found to increase with age, but the environmental variance displayed a greater increase with age, resulting in a proportional decrease of the heritability of BMI. Environmental variance of height increased with year of birth. The environmental variance of AP increased with age. These results illustrate the assessment of moderation of environmental and genetic effects, when estimating heritability from combined SNP and family data. The assessment of moderation of genetic and environmental variance will enhance our understanding of the genetic architecture of complex traits.

Assortative mating and differential fertility by phenotype and genotype across the 20th century

This study asks two related questions about the shifting landscape of marriage and reproduction in US society over the course of the last century with respect to a range of health and behavioral phenotypes and their associated genetic architecture: (i) Has assortment on measured genetic factors influencing reproductive and social fitness traits changed over the course of the 20th century? (ii) Has the genetic covariance between fitness (as measured by total fertility) and other traits changed over time? The answers to these questions inform our understanding of how the genetic landscape of American society has changed over the past century and have implications for population trends. We show that husbands and wives carry similar loadings for genetic factors related to education and height. However, the magnitude of this similarity is modest and has been fairly consistent over the course of the 20th century. This consistency is particularly notable in the case of education, for which phenotypic similarity among spouses has increased in recent years. Likewise, changing patterns of the number of children ever born by phenotype are not matched by shifts in genotype-fertility relationships over time. Taken together, these trends provide no evidence that social sorting is becoming increasingly genetic in nature or that dysgenic dynamics have accelerated.

Stability and Change in Genetic and Environmental Influences on Well-Being in Response to an Intervention

Genetic and environmental influences on complex traits can change in response to developmental and environmental contexts. Here we explore the impact of a positive activity intervention on the genetic and environmental influences on well-being and mental health in a sample of 750 adolescent twins. Twins completed a 10-week online well-being intervention, consisting of kindness and gratitude tasks and matched control activities. The results showed significant improvements both in well-being and in internalizing symptoms in response to the intervention activities. We used multivariate twin analyses of repeated measures, tracking stability and change in genetic and environmental influences, to assess the impact of this environmental intervention on these variance components. The heritability of well-being remained high both before and after the intervention, and the same genetic effects were important at each stage, even as well-being increased. The overall magnitude of environmental influences was also stable across the intervention; however, different non-shared environmental influences emerged during the intervention. Our study highlights the value of exploring the innovations in non-shared environmental influences that could provide clues to the mechanisms behind improvements in well-being. The findings also emphasize that even traits strongly influenced by genetics, like well-being, are subject to change in response to environmental interventions.

Miniature Swine for Preclinical Modeling of Complexities of Human Disease for Translational Scientific Discovery and Accelerated Development of Therapies and Medical Devices

Noncommunicable diseases, including cardiovascular disease, diabetes, chronic respiratory disease, and cancer, are the leading cause of death in the world. The cost, both monetary and time, of developing therapies to prevent, treat, or manage these diseases has become unsustainable. A contributing factor is inefficient and ineffective preclinical research, in which the animal models utilized do not replicate the complex physiology that influences disease. An ideal preclinical animal model is one that responds similarly to intrinsic and extrinsic influences, providing high translatability and concordance of preclinical findings to humans. The overwhelming genetic, anatomical, physiological, and pathophysiological similarities to humans make miniature swine an ideal model for preclinical studies of human disease. Additionally, recent development of precision gene-editing tools for creation of novel genetic swine models allows the modeling of highly complex pathophysiology and comorbidities. As such, the utilization of swine models in early research allows for the evaluation of novel drug and technology efficacy while encouraging redesign and refinement before committing to clinical testing. This review highlights the appropriateness of the miniature swine for modeling complex physiologic systems, presenting it as a highly translational preclinical platform to validate efficacy and safety of therapies and devices.

The obese gut microbiome across the epidemiologic transition

The obesity epidemic has emerged over the past few decades and is thought to be a result of both genetic and environmental factors. A newly identified factor, the gut microbiota, which is a bacterial ecosystem residing within the gastrointestinal tract of humans, has now been implicated in the obesity epidemic. Importantly, this bacterial community is impacted by external environmental factors through a variety of undefined mechanisms. We focus this review on how the external environment may impact the gut microbiota by considering, the host’s geographic location ‘human geography’, and behavioral factors (diet and physical activity). Moreover, we explore the relationship between the gut microbiota and obesity with these external factors. And finally, we highlight here how an epidemiologic model can be utilized to elucidate causal relationships between the gut microbiota and external environment independently and collectively, and how this will help further define this important new factor in the obesity epidemic.

Cohort Effects in the Genetic Influence on Smoking

We examine the hypothesis that the heritability of smoking has varied over the course of recent history as a function of associated changes in the composition of the smoking and non-smoking populations. Classical twin-based heritability analysis has suggested that genetic basis of smoking has increased as the information about the harms of tobacco has become more prevalent-particularly after the issuance of the 1964 Surgeon General's Report. In the present paper we deploy alternative methods to test this claim. We use data from the Health and Retirement Study to estimate cohort differences in the genetic influence on smoking using both genomic-relatedness-matrix restricted maximum likelihood and a modified DeFries-Fulker approach. We perform a similar exercise deploying a polygenic score for smoking using results generated by the Tobacco and Genetics consortium. The results support earlier claims that the genetic influence in smoking behavior has increased over time. Emphasizing historical periods and birth cohorts as environmental factors has benefits over existing GxE research. Our results provide additional support for the idea that anti-smoking policies of the 1980s may not be as effective because of the increasingly important role of genotype as a determinant of smoking status.