Association of a Genetic Risk Score With Body Mass Index Across Different Birth Cohorts
Open accessAbstract
IMPORTANCE: Many genetic variants are associated with body mass index (BMI). Associations may have changed with the 20th century obesity epidemic and may differ for black vs white individuals. OBJECTIVE: Using birth cohort as an indicator for exposure to obesogenic environment, to evaluate whether genetic predisposition to higher BMI has a larger magnitude of association among adults from more recent birth cohorts, who were exposed to the obesity epidemic at younger ages. DESIGN, SETTING, AND PARTICIPANTS: Observational study of 8788 adults in the US national Health and Retirement Study who were aged 50 years and older, born between 1900 and 1958, with as many as 12 BMI assessments from 1992 to 2014. EXPOSURES: A multilocus genetic risk score for BMI (GRS-BMI), calculated as the weighted sum of alleles of 29 single nucleotide polymorphisms associated with BMI, with weights equal to the published per-allele effects. The GRS-BMI represents how much each person's BMI is expected to differ, based on genetic background (with respect to these 29 loci), from the BMI of a sample member with median genetic risk. The median-centered GRS-BMI ranged from -1.68 to 2.01. MAIN OUTCOMES AND MEASURES: BMI based on self-reported height and weight. RESULTS: GRS-BMI was significantly associated with BMI among white participants (n = 7482; mean age at first assessment, 59 years; 3373 [45%] were men; P <.001) and among black participants (n = 1306; mean age at first assessment, 57 years; 505 [39%] were men; P <.001) but accounted for 0.99% of variation in BMI among white participants and 1.37% among black participants. In multilevel models accounting for age, the magnitude of associations of GRS-BMI with BMI were larger for more recent birth cohorts. For example, among white participants, each unit higher GRS-BMI was associated with a difference in BMI of 1.37 (95% CI, 0.93 to 1.80) if born after 1943, and 0.17 (95% CI, -0.55 to 0.89) if born before 1924 (P = .006). For black participants, each unit higher GRS-BMI was associated with a difference in BMI of 3.70 (95% CI, 2.42 to 4.97) if born after 1943, and 1.44 (95% CI, -1.40 to 4.29) if born before 1924. CONCLUSIONS AND RELEVANCE: For participants born between 1900 and 1958, the magnitude of association between BMI and a genetic risk score for BMI was larger among persons born in later cohorts. This suggests that associations of known genetic variants with BMI may be modified by obesogenic environments.
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Among US adults aged >50 years, an interaction of birth cohort and known genetic variants with BMI suggested that genetic risks might be exacerbated by obesogenic exposures.
“Among US adults aged >50 years, a multilocus genetic risk score for BMI was estimated for participants born between 1900 and 1958; an interaction of birth cohort and known genetic variants with BMI suggested that genetic risks might be exacerbated by obesogenic exposures [53].”
From Advantages and Limitations of the Body Mass Index (BMI) to Assess Adult Obesity (2024) · cites this paper as [53]
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“Previous studies have shown that a higher genetic risk score is associated with a greater BMI and increased risk of obesity in nonpregnant populations (Gonzalez et al., 2014; Walter et al., 2016).”
Genetic Predisposition to Obesity and Medicare Expenditures
Wehby, G., Domingue, B., Ullrich, F., & Wolinsky, F. (2017) · The journals of gerontology. Series A, Biological sciences and medical sciences · doi:10.1093/gerona/glx062 · Cited by 11
“Recent work has suggested that genetic effects on obesity are intensified in environments predisposing to increasing obesity rates (15,16).”
BackgroundHao, L., Nie, Y.-H., Chen, C.-Y., Li, X.-Y., Kaliannan, K., & Kang, J. X. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911903 · Cited by 10 · see citing works
“The exact causes of obesity remain controversial and are still under investigation [4]; however, it is believed that multi-factors, including genetic makeup [5], physical activity [6], diet, and some environmental and social factors [4], contribute to the development of obesity [7].”
BackgroundJacobsen, B. K., Melhus, M., Kvaløy, K., Siri, S. R. A., Michalsen, V. L., & Broderstad, A. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229234 · Cited by 11 · see citing works
“This results in higher BMI in later birth cohorts compared to earlier cohorts less exposed to environmental obesogenic factors [19,20].”
ResultWeight Trends in Veterans With and Without Diabetes, 2000-2014
Tamas, M. J., Khakharia, A., Rothenberg, R., & Phillips, L. S. (2018) · Obesity · doi:10.1002/oby.22337 · Cited by 10 · see citing works
“The prevalence of obesity and diabetes is known to be increasing in United States populations (1-4), and veterans make up an estimated 14.”
BackgroundMethodObesity and Cardiometabolic Disease: Insights From Genetic Studies
Dash, S. (2025) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2025.01.034 · Cited by 9 · see citing works
Chen, Z., Liang, S., Bai, Y., Lin, J., Li, M., Mo, Z., Xie, S., Huang, S., & Long, J. (2022) · European Journal of Clinical Nutrition · doi:10.1038/s41430-022-01165-8 · Cited by 9 · see citing works
“Summarily, the GRS is calculated using GWAS summary data, and weighted by the published per-allele effects, and has a more robust predictive ability [37, 38].”
MethodKim, R., Lippert, A. M., Wedow, R., Jimenez, M. P., & Subramanian, S. V. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa058 · Cited by 12 · see citing works
“Prior studies have reported heterogeneity in some of the SNPs associated with obesity phenotype by age (45) as well as heterogeneity in the association between BMI and genetic risk score depending on the demographic characteristics of the cohort (46).”
BackgroundFernández-Gutiérrez, F., Kennedy, J., Cooksey, R., Atkinson, M., Choy, E. H. S., Brophy, S., Huo, L., & Zhou, S.-M. (2021) · Diagnostics · doi:10.3390/diagnostics11101908 · Cited by 8 · see citing works
“Larger cohorts are a key contribution to novel discoveries in genetic medicine [3].”
BackgroundStephan, Y., Sutin, A., Luchetti, M., Caille, P., & Terracciano, A. (2020) · Psychology and Health · doi:10.1080/08870446.2020.1743839 · Cited by 8 · see citing works
Genetic variants of VDR and CYP2R1 affect BMI independently of serum vitamin D concentrations
Bakos, B., Szili, B., Szabó, B., Horváth, P., Kirschner, G., Kósa, J., Toldy, E., Lakatos, P., Tabák, Á., & Takács, I. (2020) · BMC Medical Genetics · doi:10.1186/s12881-020-01065-3 · Cited by 7 · see citing works
Association of genetic and behavioral characteristics with the onset of diabetes
Ng, C. D., & Weiss, J. (2019) · BMC Public Health · doi:10.1186/s12889-019-7618-z · Cited by 8 · see citing works
“The GWASs in the meta-analysis used to estimate SNP weights were derived from analyses based on European ancestry groups; in other words, the SNP weights that were developed from the European GWAS were applied to the African ancestry PGS, which may affect the predictive power and interpretation of the diabetes PGS for…”
MethodInvited Commentary: Gestational Hypertension and Diabetes—A Major Public Health Concern
Kuller, L. H., & Catov, J. M. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx265 · Cited by 8 · see citing works
Nakamura, S., Fang, X.-M., Saito, Y., Narimatsu, H., Ota, A., Ikezaki, H., Shimanoe, C., Tanaka, K., Kubo, Y., Tsukamoto, M., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279169 · Cited by 6 · see citing works
Maternal smoking, genetic susceptibility, and birth-to-adulthood body weight
Sun, D., Zhou, T., Li, X., Ley, S. H., Heianza, Y., & Qi, L. (2019) · International Journal of Obesity · doi:10.1038/s41366-019-0509-7 · Cited by 7 · see citing works
The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study
de Toro-Martín, J., Guénard, F., Bouchard, C., Tremblay, A., Pérusse, L., & Vohl, M. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00994 · Cited by 6 · see citing works
“Accordingly, another study focused on the contribution of polygenic risk to obesity reported different effects of the genetic score on BMI across different ethnics and birth cohorts (Walter et al., 2016).”
BackgroundShrestha, D., Rahman, M. L., Workalemahu, T., Zhu, C., & Tekola-Ayele, F. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00511 · Cited by 6 · see citing works
Exploring the Fetal Origins Hypothesis Using Genetic Data
Trejo, S. (2024) · Social Forces · doi:10.1093/sf/soae018 · Cited by 8 · see citing works
Gene–environment interactions and the case of body mass index and obesity: How much do they matter?
Huangfu, Y., Palloni, A., Beltrán‐Sánchez, H., & McEniry, M. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad213 · Cited by 5 · see citing works
Kim, J., Bhupathiraju, S. N., Harrington, L. B., Hagan, K. A., Lindström, S., Manson, J. E., Kraft, P. L., & Kabrhel, C. (2022) · PubMed · doi:10.1097/gme.0000000000001924 · Cited by 3 · see citing works
“As a large number of genome-wide association studies (GWAS) have identified thousands of common genetic polymorphisms associated with human diseases, genetic risk scores (GRS) aggregating common disease-related variants have emerged as useful tools to estimate genetic predisposition or detect gene and environment inte…”
BackgroundHazewinkel, A.-D., Richmond, R. C., Wade, K., & Dixon, P. (2021) · Economics and Human Biology · doi:10.1016/j.ehb.2021.101088 · Cited by 5 · see citing works
Genetic and Genomic Advances in Developmental Models: Applications for Nutrition Research.
Chowanadisai, W., Hart, M. D., Strong, M. D., Graham, D. M., Rucker, R., Smith, B. J., Keen, C., & Messerli, M. (2020) · Advances in Nutrition · doi:10.1093/advances/nmaa022 · Cited by 5
“For example, genetic scoring of disease risk or treatment outcomes, such as in developmental disorders like autism (91) or metabolic conditions such as obesity (92), relies on the identification of specific, causal gene polymorphisms.”
BackgroundWright, L., Davies, N. M., Shireby, G., Williams, D. M., Morris, T. T., & Bann, D. (2024) · medRxiv · doi:10.1371/journal.pgen.1012138 · Cited by 4 · see citing works
Gholami, F., Lesani, A., Soveid, N., Rasaei, N., Samadi, M., Bahrampour, N., Javdan, G., & Mirzaei, K. (2024) · Aspects of Molecular Medicine · doi:10.1016/j.amolm.2024.100044 · Cited by 5 · see citing works
Microevolutionary hypothesis of the obesity epidemic
Fraiman, J., Baver, S., & Henneberg, M. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305255 · Cited by 4 · see citing works
Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations
Sarnowski, C., Conomos, M. P., Vasan, R., Meigs, J., Dupuis, J., Liu, C.-T., & Leong, A. (2023) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.122.003858 · Cited by 4 · see citing works
Barry, C.-J. S., Carslake, D., Wade, K. H., Sanderson, E. C. M., & Smith, G. D. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac159 · Cited by 7 · see citing works
“This assumption depends on the genetic associations with BMI remaining similar in successive generations, but there is evidence that they have increased.(55) This would result in the overestimation of associations between the instrument and exposure, thus underestimating the average causal effect of BMI in the PGMR an…”
BackgroundThe Impact of Late-Career Job Loss and Genotype on Body Mass Index
Schmitz, L. L., Goodwin, J., Miao, J.-C., Lu, Q.-S., & Conley, D. (2020) · medRxiv · doi:10.1101/2020.11.20.20235895 · Cited by 4
Genetic scores for BMI related to metabolically unhealthy obesity in an older Chinese population
Yang, S.-S., Wang, Y.-Y., He, Y., Xu, L., Jin, Y.-L., Zhang, W., Jiang, C., Cheng, K., & Lam, T. (2019) · Clinical Endocrinology · doi:10.1111/cen.14077 · Cited by 4
Langevin, S., Mascheretti, S., Côté, S. M., Vitaro, F., Boivin, M., Turecki, G., Tremblay, R. E., & Ouellet‐Morin, I. (2018) · The British Journal of Psychiatry · doi:10.1192/bjp.2018.251 · Cited by 6 · see citing works
The origins of early childhood anthropometric persistence
Denteh, A., Millimet, D. L., & Tchernis, R. (2018) · Empirical Economics · doi:10.1007/s00181-018-1421-z · Cited by 4
Genetics and epigenetics of food addiction
Davis, C. A., & Bonder, R. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816207-1.00010-x · Cited by 5 · see citing works
Li, Y., Gu, R., Chen, L., Zhao, Q., & Wang, Y. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1653764 · Cited by 3
Younis, J., Wang, L., Zhang, K.-X., Jebril, M., Jiang, H., Fan, Y., Li, Z., Ma, M., Ma, L., Hui, Z.-Z., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-076577 · Cited by 3 · see citing works
Hazewinkel, A.-D., Richmond, R., Wade, K., & Dixon, P. (2020) · medRxiv · doi:10.1101/2020.07.14.20153742 · Cited by 3
“There is some evidence from other sources to suggest that SNPs exert a greater influence on BMI for those born in more recent decades, possibly because of an increasingly obesogenic environment [55,56].”
BackgroundCardiovascular disease risk factor profile of male Gaelic Athletic Association sports referees
O’Riordan, C. N., Newell, M., & Flaherty, G. T. (2018) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-018-1792-3 · Cited by 3 · see citing works
“risk of mortality [23], genetic variants with a change in BMI due to the obesity epidemic [24] and the inability to distinguish between body fat and muscle mass [24].”
BackgroundKONES, R. J., & Rumana, U. (2016) · Journal of Clinical & Experimental Cardiology · doi:10.4172/2155-9880.1000e145 · Cited by 2 · see citing works
“While genetic predisposition is important, diet, lifestyle choices, and other modifiable factors account for about 70% to 90% of CHD pathology [2-5], and are reviewed elsewhere [6-8].”
BackgroundDesign and model choices shape inference of age-varying genetic effects on complex traits
Schoeler, T., Wiegrebe, S., Winkler, T. W., & Kutalik, Z. (2026) · Nature Aging · doi:10.1038/s43587-026-01232-w · Cited by 1 · see citing works
“Supporting this, most gene-by-cohort effects detected here were observed for obesogenic traits and health behaviors, plausibly reflecting generational influences stemming from the 20th-century obesity epidemic, alongside improved public health initiatives and medical care 37–39 .”
Zheng, B., Fletcher, J. M., Song, J., & Lu, Q. (2023) · Journal of Health and Social Behavior · doi:10.1177/00221465231188166 · Cited by 2 · see citing works
“Walter et al. (2016) found that the PGS coefficients on BMI are larger for younger cohorts, suggesting increasing genetic effects.”
Heteroscedastic regression modeling elucidates gene-by-environment interaction
Domingue, B., Kanopka, K., Mallard, T., Trejo, S., & Tucker-Drob, E. (2020) · bioRxiv · doi:10.1101/2020.09.08.287888 · Cited by 2
“, π1 > 0); this is consistent with earlier findings indicating secular increases in the penetrance of a BMI PGS [9, 10, 11].”
BackgroundMethodResultInfluentialAbadi, A., Alyass, A., du Pont, S. R., Bolker, B., Singh, P., Mohan, V., Diaz, R., Engert, J., Gerstein, H., Anand, S., et al. (2017) · bioRxiv · doi:10.1101/225128 · Cited by 2 · see citing works
Weight-related quality of life in obese, pregnant women in South Africa
Tisane, M., van der Merwe, J. L., & Hall (2017) · Journal of Endocrinology Metabolism and Diabetes of South Africa · doi:10.1080/16089677.2017.1368959 · Cited by 2 · see citing works
Evidence that lower socioeconomic position accentuates genetic susceptibility to obesityPreprint
Tyrrell, J., Wood, A., Ames, R. M., Yaghootkar, H., Beaumont, R., Jones, S., Tuke, M., Ruth, K., Freathy, R., Smith, G., et al. (2016) · bioRxiv · doi:10.1101/074054 · Cited by 2 · see citing works
Myles, E. M., O’Leary, M. E., Romkey, I. D., Hamm, S. I., Dauphinee, L., Piano, A., Bronner, S., & Perrot, T. S. (2025) · Physiology & Behavior · doi:10.1016/j.physbeh.2025.115030 · Cited by 1 · see citing works
“Anxiety disorders and obesity are common and chronic conditions with associated environmental and genetic risks [1,2].”
Arvelig fedme - hvor mye vekt skal man legge på gener?
Øvretveit, K. (2020) · Norsk tidsskrift for ernæring · doi:10.18261/ntfe.18.4.2 · Cited by 1 · see citing works
Kim, S., Oh, J., Heo, J., Lee, H.-Y., Lee, J.-K., Subramanian, S., & Kang, D. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195091 · Cited by 1
Translating Polygenic Analysis for Prevention: From “Who” to “How”
Belsky, D. W. (2017) · Circulation: Cardiovascular Genetics · doi:10.1161/circgenetics.117.001798 · Cited by 1
- Authors
- Walter, S., Mejía-Guevara, I., Estrada, K., Liu, S. Y., & Glymour, M. M.
- Affiliations
- University of California, San Francisco (US)
- Harvard Center for Population and Development Studies, Harvard University, Cambridge, Massachusetts
- Broad Institute (US)
- Harvard University (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2016-07-05
- Volume
- 316
- Issue
- 1
- Pages
- 63
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2466503433
- Cited by
- 130
- References
- 0
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 8 · 2023: 11 · 2022: 13 · 2021: 8 · 2020: 18 · 2019: 14 · 2018: 21 · 2017: 17 · 2016: 8 · 2013: 1
- FWCI
- 16.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (U01AG009740, RC2AG036495, and RC4AG039029, RC2AG036495, and RC4AG039029, RC4AG039029, U01AG009740)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Physical Performance
- Keywords
- polygenic risk score, body mass index, obesity, birth cohort, obesogenic environment, obesity epidemic, single nucleotide polymorphisms, genetic predisposition to obesity, racial differences, Black and White adults, older adults, Health and Retirement Study, gene-environment interaction
- MeSH terms
- Age Factors, Aged, Aged, 80 and over, Alleles, Black or African American, Female, Humans, Male, Middle Aged, Obesity, Risk Factors, United States, Genetic Variation, Cohort Studies, Body Mass Index, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Black People, White People, Genome-Wide Association Study, Multilocus Sequence Typing
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