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BMI over the ages of 35-60 by birth cohort for AA, TT, and AT/TA genotypes by general birth cohort (born before or during/after 1942).
Source publication
Significance
Our finding of a significant gene-by-birth-cohort interaction adds a previously unidentified dimension to gene-by-environment interaction research, suggesting that global changes in the environment over time can modify the penetrance of genetic risk factors for diverse phenotypes. This result also suggests that presence (or absence) of...
Contexts in source publication
Context 1
... this estimator has the advantage of accurately identifying the point at which there are significant changes in the impact of the genotypes based on YOB, it imposes restrictions on how the YOB affects BMI. Although we could add higher-order terms to increase the flexibility, these terms make it more difficult for the test statistics to exhibit dramatic changes as such tests will have no power in many settings. Using different sets of control variables in these models, we consistently identified breakpoints between the years of 1942 and 1945 with decidedly nonlinear changes in the magnitude of the parameter estimates after that time. Estimates of the preferred specification from the breakpoint model are depicted in Fig. 1, where we consider only a single break at 1942, although various models after that time period yield consistent ...
Context 2
... the estimates in Tables S7 and S8 also omit relevant information on how genetic effects differ across eras in which an individual grows up and, thus, it is not surprising that they differ markedly from those presented in both Table 1 and Tables S1, S2, and S4. In particular, omitting this relevant information allows one to erroneously conclude that several of the g-by-p and g-by-a interactions have a statistically significant impact. Many of these effects become statistically insignificant once we allow for g-by-c effects. Because the specifications presented in Tables S7 and S8 are restricted versions of our more general APC model presented in Eq. S2, we conducted a series of model specification tests to examine the validity of these restrictions. Irrespective of the estimator used, the test results reject these restrictions rein- forcing that researchers working with the FHS data should both allow for both main cohort effects and g-by-c interactions. This finding has implications for the interpretation of estimates from many g-by-e studies which only use interactions between gene and contemporaneous periods-which, primarily due to data limitations, have collected data on individuals for shorter durations and fewer cohorts. This also reinforces the utility of genotyping large-scale longitudinal databases thereby allowing researchers to examine whether specific g-by-e effects are sensitive to APC effects. Table S6 for the calendar time corresponding to examinations in each wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the model was estimated. Estimates from the fifth column were used to generate Fig. 1. The following indicate the statistical significance of an explanatory variable on BMI: *** P < 0.01, **P < 0.05, and *P < 0.1. Table S2. Model estimates of factors influencing BMI, where birth year is treated as a discrete variable for pre-/post-1942 as birth year Presented are estimates of the age-period-cohort model where the cohort variable is treated as discrete as indicated in Eq. S3. Each entry refers to the effect of the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ based on what factors are accounted for and the method used to estimate the statistical model. See Table S6 for the calendar time corresponding to examinations in each wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the model was estimate. The following indicate the statistical significance of each explanatory variable: ***P < 0.01, **P < 0.05, and *P < 0.1. The means and SDs are shown in parentheses of BMI for individuals with a specific FTO allele type and age range at time of examination. t tests test that there are no differences in average BMI conditional on age and FTO allele type across the birth cohorts with the 1942 breakpoint are calculated. ***P < 0.01, **P < 0.05, and *P < 0.1. Observation numbers are pre-1942 cohort + post-1942 cohort. The table clearly indicates that there are statistically significant differences for those with the AA and AT genotypes by birth cohort but there are no age ranges for those with the TT genotype where a statistically significant difference in BMI exists between cohorts. Table S4. Model estimates by sex of factors influencing BMI, where birth year is treated as a discrete variable for pre-/post-1942 as birth year Presented are estimates of the age-period-cohort model where the cohort variable is treated as discrete as indicated in Eq. S3. Each entry refers to the effect of the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ based on the sex subsample as indicated row 1 and the method used to estimate the statistical model indicated in row 2. See Table S6 for the calendar time corresponding to examinations in each wave. The following indicate the statistical significance of each explanatory variable: ***P < 0.01, **P< 0.05, and *P < 0.1. Presented is the distribution of genetic risk alleles of individuals in the Framingham Offspring Study born pre-and post-1942. A Pearson's χ 2 for the hypothesis that the rows and columns in a two-way table are independent accounting for correlations within families yields P > Χ 2 = 0.1550, χ 2 (2) = 1.88. This indicates that the distributions of genetic risk factors do not differ between cohorts born pre-and post-1942. 1920and 1925323 No. of Individuals born between 1925and 1930481 No. of Individuals born between 1930and 1935561 No. of Individuals born between 1935and 1940575 No. of Individuals born between 1940and 1945715 No. of Individuals born between 1945and 1950 Provided are the summary statistics for the measures used in the multivariate regression analysis. We only list the date of the first interview for each wave in the description above because the examinations in each wave were held over several years and the exact time could be inferred by taking the difference between age at examination and YOB. Presented are estimates of the age-period model where the cohort variable is not included and the only genetic interactions included are those with period effects allowing solely for contemporaneous gene-environment interactions. The age and period variables are treated as discrete as indicated in Eq. S3. Each entry refers to the effect of the variable listed in the first column on BMI holding all other factors constant. Robust SEs are presented in parentheses. The columns in this table differ based on what factors are accounted for and the method used to estimate the statistical model. See Table S6 for the calendar time corresponding to examinations in each wave. Note that our main results of birth cohort and genotype interactions are not sensitive to the method by which the model was estimate. The following indicate the statistical significance of each explanatory variable: ***P < 0.01, **P < 0.05, and *P < 0.1. Table S8. Model estimates of factors influencing BMI, where we ignore cohort effects and interactions of genetic factors with birth cohort ...
Context 3
... shown in Fig. 1, mean BMI evolves over the lifecycle for individuals with the same genotype, comparing the pre-and post- 1942 birth cohorts in the full dataset. However, mean BMI differs across the three genotypes in the later birth cohort compared with the pre-1942 cohort. The between-birth-cohort differences in mean BMI are statistically significant (P < 0.017) for individuals with one or two of the risk ("A") FTO allele, particularly during early middle age. This difference (and the lack of difference be- tween cohorts without the risk allele) suggests that differences between BMI growth curves from different birth cohorts are more pronounced among individuals carrying A alleles. Table 2 presents estimates from our preferred specification of the age-period-cohort regression models, allowing for differential relationships between the genetic effects and BMI on the basis of sex and APC variables (for details, see Materials and ...
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Citations
... However, the pattern of increasing obesogenic genes does support the microevolutionary hypothesis by itself. For example Rosenquist et al. (33) reported the gene pool frequency of the well known obesity promoting allele FTO pre and post 1942. Comparing the frequency of the obesity prone homozygote (AA) and heterozygote (AT), with the frequency of the obesity protective homozygote (TT) in the birth cohorts born before 1942 finds the pre 1942 frequency of the AA/AT was 63.1% compared with the post 1942 frequency of 66.7%. ...
The obesity epidemic represents potentially the largest phenotypic change in Homo sapiens since the origin of the species. Despite obesity’s high heritability, a change in the gene pool has not generally been presumed as a potential cause of 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. 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 maternal death rates (MDR) and current obesity rates. Multivariate regression showed MDR correlated more strongly with national obesity rates than GDP, calorie intake and physical inactivity. Analyses controlling for confounders via partial correlation show that MDR explains approximately 11% of the variability of obesity rate between nations. For nations with MDR above the median (>0.45%), MDR explains over 20% of obesity variance, while calorie intake, 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.
Significance Statement
Humans underwent a rapid increase in obesity in the 20 th century, and existing explanations for this trend are unsatisfactory. Here we present evidence that increases in obesity may be in large part attributable to microevolutionary changes brought about by dramatic reduction of childbirth mortality with the introduction of modern obstetrics. Given the higher relative risk of childbirth in women with obesity, obstetrics removed a strong negative selection pressure against obesity. This alteration would result in a rapid population-wide rise in obesity-promoting alleles. A cross-country analysis of earlier maternal death rates and obesity rate today found strong evidence supporting this hypothesis. These findings suggest recent medical intervention influenced the course of human evolution more profoundly than previously realized.
... Genotype-birth cohort interactions for the debute of alcohol consumption or frequent alcohol use in early age were also found with other functional gene variants such as VMAT1 (rs1390938), NRG1 (rs6994992) and OXTR (rs53576) (see for review). Birth cohort can modify even the associations between genotype and somatic measures such as body mass index (Rosenquist et al., 2015). Given that NPY is related to anxiety regulation and social behaviour, we hypothesised that functional variants of NPY may interact with the birth cohort in shaping sociability-related traits. ...
... The fact that NPY genotypes are not directly associated with personality traits but interact with birth cohort to predict Agreeableness and its facets suggests that these genetic associations relate to the variation in environmental contexts. It is likely that the impact of environmental effects is modulated by genetic pathways, causing some individuals or population groups to be differentially affected by composite changes in the environment leading to birth cohort effects (Rosenquist et al., 2015). Possibly, the NPY gene variants might have an effect either on coping styles with stress through personality-dependent choices or through modifying the interpretation of stressful events. ...
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.
... [6][7][8]15 Two previous studies in FHS had sought to examine geneby-birth cohort interactions on BMI. 15,16 One of the studies using the Offspring cohort (N=3720) detected an interaction between a FTO SNP (rs9939609, linkage disequilibrium: r 2 ≥0.9 with rs9922708 in our study) and birth cohort on BMI. 16 Another study on ≈5000 unrelated FHS participants described a gene by historical period interaction whereby genetic effects on BMI were larger after 1985 compared with before 1985. ...
... 15,16 One of the studies using the Offspring cohort (N=3720) detected an interaction between a FTO SNP (rs9939609, linkage disequilibrium: r 2 ≥0.9 with rs9922708 in our study) and birth cohort on BMI. 16 Another study on ≈5000 unrelated FHS participants described a gene by historical period interaction whereby genetic effects on BMI were larger after 1985 compared with before 1985. The authors further concluded that this genetic influence weakened over the life course. ...
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/m ² versus 26.9 [interquartile range, 23.7–30.6] kg/m ² ). 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.
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
We need better understanding of functional differences of behavioral phenotypes across cultures because cultural evolution (e.g., temporal changes in innovation within populations) is less important than culturally molded phenotypes (e.g., differences across populations) for understanding gene effects. Furthermore, changes in one behavioral domain likely have complex downstream effects in other domains, requiring careful parsing of phenotypic variability and functions.
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
Epigenetics impacts gene–culture coevolution by amplifying phenotypic variation, including clustering, and bridging the difference in timescales between genetic and cultural evolution. The dual inheritance model described by Uchiyama et al. could be modified to provide greater explanatory power by incorporating epigenetic effects.
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
We argue that heritability estimates cannot be used to make informed judgments about the populations from which they are drawn. Furthermore, predicting changes in heritability from population changes is likely impossible, and of limited value. We add that the attempt to separate human environments into cultural and non-cultural components does not advance our understanding of the environmental multiplier effect.
... Work in this area has demonstrated how effects that appear conceptually distinct can be difficult to distinguish when specified in models . Changes in genetic effects modeled in terms of cohorts (e.g., Rosenquist et al., 2015;Sanz-de-Galdeano, Terskaya, & Upegui, 2020), for example, might equivalently be framed in terms of periods (e.g., war, socioeconomic conditions, or policy eras) which are often implicitly the focus of explanation anyway. Other research suggests that apparently simple sociological concepts might require relatively complex model features to capture when considered simultaneously against the backdrop of development . ...
Uchiyama et al. rightly consider how cultural variation may influence estimates of heritability by contributing to environmental sources of variation. We disagree, however, with the idea that generalisable estimates of heritability are ever a plausible aim. Heritability estimates are always context-specific, and to suggest otherwise is to misunderstand what heritability can and cannot tell us.
... Obesity is a global pandemic with immense health consequences for individuals and societies. 1,2 Multiple factors, including genetic predispositions, 3,4 mode of delivery, 5,6 breastfeeding, 7 exercises, 8 and diet, 9 have been shown to affect the risk of development of obesity. In the last decade, the microbiome field has made tremendous progress in identifying a link between intestinal dysbiosis and obesity. ...
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 Bifidobacterium bifidum, and fatty acid metabolism-associated metabolites showed positive correlations with Alistipes 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.
... Birth cohort has been used as a proxy for exposure to obesogenic environments. A recent study demonstrated that birth cohort modified the association between FTO and BMI, suggesting that genotype-phenotype (outcome) correlations are likely highly dependent on the time period or birth cohort of individuals (Rosenquist et al. 2015). Using a polygenic score for BMI, another study found that the magnitude of associations of the polygenic score for BMI were larger for more recent cohorts (Walter et al. 2016). ...
... Available evidence suggests being overweight or obese at some point in early stages of life (e.g., childhood or adolescence) is significantly correlated with overweight, obesity, and related health outcomes in later life (Guo et al., 2000;Guo et al., 2002;Maner et al., 2017;Olsen et al., 2006;Simmonds et al., 2016;Singh et al., 2008;Stokes and Preston, 2016). Birth cohort effects have been found to shape body mass index (BMI) patterns at the country level; noteworthy examples include the United States (Reither et al., 2009;Rosenquist et al., 2015), Denmark (Olsen et al., 2006), and France (Diouf et al., 2010). The nexus between overweight/obesity and non-communicable diseases suggests that the observed historical growth and patterns in adult BMI represent an important 'canary in the coal mine' for developing a better understanding of the drivers shaping BMI in current cohorts as potential future trends and momentum in adult obesity and related health outcomes. ...
Current trends in adult obesity threaten global health. Although the implications of changes in diets, lifestyles, and food environments have been examined, the specific role of excess calorie availability (ECA)—understood as calorie availability in excess of human requirements for a healthy life—and the cohort mechanisms that underlie trends in adult body mass index (BMI) are poorly understood. We examine these relationships for 156 countries over the past century using an age-, sex-, and cohort-specific approach. We measure the association between increases in food energy supply and changes in BMI across countries and time. We find positive and significant associations between ECA and adult BMI for both males and females, and between ECA during early childhood and BMI at adulthood for males. We also find a strengthening of these correlations over successive generations. These cohort mechanisms are boosted by age effects, leading individuals in each successive cohort to reach unhealthy BMI levels at younger ages. Individuals in more recent cohorts are overweight or obese earlier and for larger proportions of their lifespan than those in earlier cohorts. Even after controlling for development dynamics, the pattern is consistent across countries and appears to be driven, in part, by availability of calories in excess of underlying requirements. Our findings provide novel insights into the role of ECA, and potential unintended health consequences of agricultural and trade policies directed at increasing calorie supplies.
