TY - JOUR T1 - Explaining the variance in cardiovascular disease risk factors: A comparison of demographic, socioeconomic, and genetic predictors. JF - Epidemiology Y1 - 2022 A1 - Hamad, Rita A1 - M. Maria Glymour A1 - Calmasini, Camilla A1 - Thu T Nguyen A1 - Stefan Walter A1 - David Rehkopf KW - Cardiovascular disease KW - Demographics KW - Genetics KW - Risk Factors KW - socioeconomics AB -

BACKGROUND: Efforts to explain the burden of cardiovascular disease (CVD) often focus on genetic factors or social determinants of health. There is little evidence on the comparative predictive value of each, which could guide clinical and public health investments in measuring genetic versus social information. We compared the variance in CVD-related outcomes explained by genetic versus socioeconomic predictors.

METHODS: Data were drawn from the Health and Retirement Study (N=8,720). We examined self-reported diabetes, heart disease, depression, smoking, and body mass index, and objectively measured total and high-density lipoprotein cholesterol. For each outcome, we compared the variance explained by demographic characteristics, socioeconomic position (SEP), and genetic characteristics including a polygenic score for each outcome and principal components (PCs) for genetic ancestry. We used R-squared values derived from race-stratified multivariable linear regressions to evaluate the variance explained.

RESULTS: The variance explained by models including all predictors ranged from 3.7% to 14.3%. Demographic characteristics explained more than half this variance for most outcomes. SEP explained comparable or greater variance relative to the combination of the polygenic score and PCs for most conditions among both white and Black participants. The combination of SEP, polygenic score, and PCs performed substantially better, suggesting that each set of characteristics may independently contribute to prediction of CVD related outcomes.

CONCLUSIONS: Focusing on genetic inputs into personalized medicine predictive models, without considering measures of social context that have clear predictive value, needlessly ignores relevant information that is more feasible and affordable to collect on patients in clinical settings.

VL - 33 IS - 1 ER - TY - JOUR T1 - A data-driven prospective study of dementia among older adults in the United States. JF - PLoS One Y1 - 2020 A1 - Weiss, Jordan A1 - Puterman, Eli A1 - Aric A Prather A1 - Erin B Ware A1 - David Rehkopf AB -

BACKGROUND: Studies examining risk factors for dementia have typically focused on testing a priori hypotheses within specific risk factor domains, leaving unanswered the question of what risk factors across broad and diverse research fields may be most important to predicting dementia. We examined the relative importance of 65 sociodemographic, early-life, economic, health and behavioral, social, and genetic risk factors across the life course in predicting incident dementia and how these rankings may vary across racial/ethnic (non-Hispanic white and black) and gender (men and women) groups.

METHODS AND FINDINGS: We conducted a prospective analysis of dementia and its association with 65 risk factors in a sample of 7,908 adults aged 51 years and older from the nationally representative US-based Health and Retirement Study. We used traditional survival analysis methods (Fine and Gray models) and a data-driven approach (random survival forests for competing risks) which allowed us to account for the semi-competing risk of death with up to 14 years of follow-up. Overall, the top five predictors across all groups were lower education, loneliness, lower wealth and income, and lower self-reported health. However, we observed variation in the leading predictors of dementia across racial/ethnic and gender groups such that at most four risk factors were consistently observed in the top ten predictors across the four demographic strata (non-Hispanic white men, non-Hispanic white women, non-Hispanic black men, non-Hispanic black women).

CONCLUSIONS: We identified leading risk factors across racial/ethnic and gender groups that predict incident dementia over a 14-year period among a nationally representative sample of US aged 51 years and older. Our ranked lists may be useful for guiding future observational and quasi-experimental research that investigates understudied domains of risk and emphasizes life course economic and health conditions as well as disparities therein.

VL - 15 IS - 10 ER - TY - RPRT T1 - A data-driven prospective study of incident dementia among older adults in the United States Y1 - 2020 A1 - Weiss, Jordan A1 - Puterman, Eli A1 - Aric A Prather A1 - Erin B Ware A1 - David Rehkopf KW - data-driven KW - Dementia KW - Fine-Gray models KW - risk of death AB - We conducted a prospective analysis of incident dementia and its association with 65 sociodemographic, early-life, economic, health and behavioral, social, and genetic risk factors in a sample of 7,908 adults over the age of 50 from the nationally representative US-based Health and Retirement Study. We used traditional survival analysis methods (Fine-Gray models) and a data-driven approach (random survival forests for competing risks) which allowed us to account for the competing risk of death with up to 14 years of follow-up. Overall, the top five predictors across all groups were lower education, loneliness, lower wealth and income, and lower self-reported health. However, we observed variation in the leading predictors of dementia across racial/ethnic and gender groups. Our ranked lists may be useful for guiding future observational and quasi-experimental research that investigates understudied domains of risk and emphasizes life course economic and health conditions as well as disparities therein. PB - Cornell University CY - Ithaca, NY UR - https://arxiv.org/abs/2006.13275 ER - TY - JOUR T1 - Predicting mortality from 57 economic, behavioral, social, and psychological factors JF - Proceedings of the National Academy of Sciences Y1 - 2020 A1 - Puterman, Eli A1 - Weiss, Jordan A1 - Hives, Benjamin A. A1 - Gemmill, Alison A1 - Karasek, Deborah A1 - Mendes, Wendy Berry A1 - David Rehkopf KW - Behavioral Symptoms KW - Mortality KW - social KW - transdisciplinary AB - In our prospective study using nationally representative data from 13,611 adults in the US Health and Retirement Study, we used traditional and machine-learning statistical approaches to reveal the most important factors across the behavioral and social sciences that predict mortality in older adults. In the study, we found that top predictors of mortality spanned all investigated domains, opening up opportunities for future hypothesis generation in observational and clinical studies and the identification of potential new targets for screening and policy.Behavioral and social scientists have identified many nonbiological predictors of mortality. An important limitation of much of this research, however, is that risk factors are not studied in comparison with one another or from across different fields of research. It therefore remains unclear which factors should be prioritized for interventions and policy to reduce mortality risk. In the current investigation, we compare 57 factors within a multidisciplinary framework. These include (i) adverse socioeconomic and psychosocial experiences during childhood and (ii) socioeconomic conditions, (iii) health behaviors, (iv) social connections, (v) psychological characteristics, and (vi) adverse experiences during adulthood. The current prospective cohort investigation with 13,611 adults from 52 to 104 y of age (mean age 69.3 y) from the nationally representative Health and Retirement Study used weighted traditional (i.e., multivariate Cox regressions) and machine-learning (i.e., lasso, random forest analysis) statistical approaches to identify the leading predictors of mortality over 6 y of follow-up time. We demonstrate that, in addition to the well-established behavioral risk factors of smoking, alcohol abuse, and lack of physical activity, economic (e.g., recent financial difficulties, unemployment history), social (e.g., childhood adversity, divorce history), and psychological (e.g., negative affectivity) factors were also among the strongest predictors of mortality among older American adults. The strength of these predictors should be used to guide future transdisciplinary investigations and intervention studies across the fields of epidemiology, psychology, sociology, economics, and medicine to understand how changes in these factors alter individual mortality risk. ER - TY - JOUR T1 - Differential associations between state-level educational quality and cardiovascular health by race: Early-life exposures and late-life health. JF - SSM Population Health Y1 - 2019 A1 - Anusha M Vable A1 - Thu T Nguyen A1 - David Rehkopf A1 - M. Maria Glymour A1 - Hamad, Rita KW - Cardiovascular health KW - Education KW - Heart disease KW - Late-life Health KW - Racial/ethnic differences AB - Cardiovascular diseases (CVD) are patterned by educational attainment but educational quality is rarely examined. Educational quality differences may help explain racial disparities. Health and Retirement Study respondent data (1992-2014; born 1900-1951) were linked to state- and year-specific educational quality measures when the respondent was 6 years old. State-level educational quality was a composite of state-level school term length, student-to-teacher ratio, and per-pupil expenditure. CVD-related outcomes were self-reported (N = 24,339) obesity, heart disease, stroke, ever-smoking, high blood pressure, diabetes and objectively measured (N = 10,704) uncontrolled blood pressure, uncontrolled blood sugar, total cholesterol, high-density lipoprotein cholesterol (HDL), and C-reactive protein. Race/ethnicity was classified as White, Black, or Latino. Cox models fit for dichotomous time-to-event outcomes and generalized estimating equations for continuous outcomes were adjusted for individual and state-level confounders. Heterogeneities by race were evaluated using state-level educational quality by race interaction terms; race-pooled, race by educational quality interaction, and race-specific estimates were calculated. In race-pooled analyses, higher state-level educational quality was protective for obesity (HR = 0.92; 95%CI(0.87,0.98)). In race-specific estimates for White Americans, state-level educational quality was protective for high blood pressure (HR = 0.95; 95%CI(0.91,0.99). Differential relationships among Black compared to White Americans were observed for obesity, heart disease, stroke, smoking, high blood pressure, and HDL cholesterol. In race-specific estimates for Black Americans, higher state-level educational quality was protective for obesity (HR = 0.88; 95%CI(0.84,0.93)), but predictive of heart disease (HR = 1.07; 95%CI(1.01,1.12)), stroke (HR = 1.20; 95%CI(1.08,1.32)), and smoking (HR = 1.05; 95%CI(1.02,1.08)). Race-specific hazard ratios for Latino and Black Americans were similar for obesity, stroke, and smoking. Better state-level educational quality had differential associations with CVD by race. Among minorities, better state-level educational quality was predominately associated with poorer CVD outcomes. Results evaluate the 1900-1951 birth cohorts; secular changes in the racial integration of schools since the 1950s, means results may not generalize to younger cohorts. VL - 8 U1 - http://www.ncbi.nlm.nih.gov/pubmed/31249857?dopt=Abstract ER - TY - JOUR T1 - Educational attainment and cardiovascular disease in the United States: A quasi-experimental instrumental variables analysis. JF - PLoS Medicine Y1 - 2019 A1 - Hamad, Rita A1 - Thu T Nguyen A1 - Bhattacharya, Jay A1 - M. Maria Glymour A1 - David Rehkopf KW - Cardiovascular health KW - Education KW - NHANES AB -

BACKGROUND: There is ongoing debate about whether education or socioeconomic status (SES) should be inputs into cardiovascular disease (CVD) prediction algorithms and clinical risk adjustment models. It is also unclear whether intervening on education will affect CVD, in part because there is controversy regarding whether education is a determinant of CVD or merely correlated due to confounding or reverse causation. We took advantage of a natural experiment to estimate the population-level effects of educational attainment on CVD and related risk factors.

METHODS AND FINDINGS: We took advantage of variation in United States state-level compulsory schooling laws (CSLs), a natural experiment that was associated with geographic and temporal differences in the minimum number of years that children were required to attend school. We linked census data on educational attainment (N = approximately 5.4 million) during childhood with outcomes in adulthood, using cohort data from the 1992-2012 waves of the Health and Retirement Study (HRS; N = 30,853) and serial cross-sectional data from 1971-2012 waves of the National Health and Nutrition Examination Survey (NHANES; N = 44,732). We examined self-reported CVD outcomes and related risk factors, as well as relevant serum biomarkers. Using instrumental variables (IV) analysis, we found that increased educational attainment was associated with reduced smoking (HRS β -0.036, 95%CI: -0.06, -0.02, p < 0.01; NHANES β -0.032, 95%CI: -0.05, -0.02, p < 0.01), depression (HRS β -0.049, 95%CI: -0.07, -0.03, p < 0.01), triglycerides (NHANES β -0.039, 95%CI: -0.06, -0.01, p < 0.01), and heart disease (HRS β -0.025, 95%CI: -0.04, -0.002, p = 0.01), and improvements in high-density lipoprotein (HDL) cholesterol (HRS β 1.50, 95%CI: 0.34, 2.49, p < 0.01; NHANES β 0.86, 95%CI: 0.32, 1.48, p < 0.01), but increased BMI (HRS β 0.20, 95%CI: 0.002, 0.40, p = 0.05; NHANES β 0.13, 95%CI: 0.01, 0.32, p = 0.05) and total cholesterol (HRS β 2.73, 95%CI: 0.09, 4.97, p = 0.03). While most findings were cross-validated across both data sets, they were not robust to the inclusion of state fixed effects. Limitations included residual confounding, use of self-reported outcomes for some analyses, and possibly limited generalizability to more recent cohorts.

CONCLUSIONS: This study provides rigorous population-level estimates of the association of educational attainment with CVD. These findings may guide future implementation of interventions to address the social determinants of CVD and strengthen the argument for including educational attainment in prediction algorithms and primary prevention guidelines for CVD.

VL - 16 IS - 6 U1 - http://www.ncbi.nlm.nih.gov/pubmed/31237869?dopt=Abstract ER - TY - JOUR T1 - Machine learning approaches to the social determinants of health in the Health and Retirement study. JF - SSM Popul Health Y1 - 2018 A1 - Seligman, Benjamin A1 - Tuljapurkar, Shripad A1 - David Rehkopf KW - Biomarkers KW - Computer science KW - Machine learning KW - Neural network KW - Social Factors KW - Social Support AB -

Background: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study.

Methods: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks.

Results: All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data (between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender.

Discussion: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

VL - 4 U1 - http://www.ncbi.nlm.nih.gov/pubmed/29349278?dopt=Abstract ER - TY - JOUR T1 - Poverty dynamics, poverty thresholds and mortality: An age-stage Markovian model JF - PLOS ONE Y1 - 2018 A1 - Bernstein, Shayna Fae A1 - David Rehkopf A1 - Tuljapurkar, Shripad A1 - Horvitz, Carol C. ED - Komarova, Natalia L. KW - Life Expectancy KW - Mortality KW - Poverty KW - Socioeconomic factors AB - Recent studies have examined the risk of poverty throughout the life course, but few have considered how transitioning in and out of poverty shape the dynamic heterogeneity and mortality disparities of a cohort at each age. Here we use state-by-age modeling to capture individual heterogeneity in crossing one of three different poverty thresholds (defined as 1×, 2× or 3× the “official” poverty threshold) at each age. We examine age-specific state structure, the remaining life expectancy, its variance, and cohort simulations for those above and below each threshold. Survival and transitioning probabilities are statistically estimated by regression analyses of data from the Health and Retirement Survey RAND data-set, and the National Longitudinal Survey of Youth. Using the results of these regression analyses, we parameterize discrete state, discrete age matrix models. We found that individuals above all three thresholds have higher annual survival than those in poverty, especially for mid-ages to about age 80. The advantage is greatest when we classify individuals based on 1× the “official” poverty threshold. The greatest discrepancy in average remaining life expectancy and its variance between those above and in poverty occurs at mid-ages for all three thresholds. And fewer individuals are in poverty between ages 40-60 for all three thresholds. Our findings are consistent with results based on other data sets, but also suggest that dynamic heterogeneity in poverty and the transience of the poverty state is associated with income-related mortality disparities (less transience, especially of those above poverty, more disparities). This paper applies the approach of age-by-stage matrix models to human demography and individual poverty dynamics. In so doing we extend the literature on individual poverty dynamics across the life course. VL - 13 UR - http://dx.doi.org/10.1371/journal.pone.0195734 IS - 5 JO - PLoS ONE ER - TY - JOUR T1 - A cross-national comparison of 12 biomarkers finds no universal biomarkers of aging among individuals aged 60 and older JF - Vienna Yearbook of Population Research Y1 - 2017 A1 - David Rehkopf A1 - Rosero-Bixby, Luis A1 - William H Dow ED - Lutz, Wolfgang KW - Aging KW - Biomarkers KW - Cross-National KW - Genetics AB - There is uncertainty about whether biological and anthropometric measures that are clinical risk factors for disease are universally associated with chronological age, or whether these correlations vary depending on the social and economic context. The answer to this question has implications for the malleability of biological aging. To examine this issue, we use population-based data on individuals aged 60 and older from the Costa Rican Study on Longevity and Healthy Aging, and temporally consistent data from the United States National Health and Nutrition Examination Survey and the United States Health and Retirement Study. Our analysis focuses on 12 biomarkers that have been shown in the literature to have an association with age, and that occur prior to the clinical manifestation of disease. We find that there are few consistent patterns of association with age when these biomarkers are stratified by gender, country, and level of education. This result suggests that these measures of biological aging are highly context-dependent, and that none of the 12 biomarkers we examined are universal biomarkers of aging. Future research that investigates composite measures of biological age should test newly proposed measures across gender, social class, and country. VL - 1 UR - http://hw.oeaw.ac.at/1728-4414http://hw.oeaw.ac.at/populationyearbook2016http://hw.oeaw.ac.at?arp=0x0036e638 JO - Populationyearbook ER - TY - JOUR T1 - Crowdsourced health data: Comparability to a US national survey, 2013–2015 JF - American Journal of Public Health Y1 - 2017 A1 - Yank, Veronica A1 - Agarwal, Sanjhavi A1 - Loftus, Pooja A1 - Steven Asch A1 - David Rehkopf KW - Survey Methodology AB - To determine the generalizability of crowdsourced, electronic health data from self-selected individuals using a national survey as a reference. Using the world's largest crowdsourcing platform in 2015, we collected data on characteristics known to influence cardiovascular disease risk and identified comparable data from the 2013 Behavioral Risk Factor Surveillance System. We used age-stratified logistic regression models to identify differences among groups. Crowdsourced respondents were younger, more likely to be non-Hispanicand White, and had higher educational attainment.Those aged 40 to 59 years were similar to US adults in the rates of smoking, diabetes, hypertension, and hyperlipidemia. Those aged 18 to 39 years were less similar, whereas those aged 60 to 75 years were underrepresented among crowdsourced respondents. Crowdsourced health data might be most generalizable to adults aged 40 to 59 years, but studies of younger or older populations, racial and ethnic minorities, or those with lower educational attainment should approach crowdsourced data with caution. Policymakers, the national Precision Medicine Initiative, and others planning to use crowdsourced data should take explicit steps to define and address anticipated underrepresentation by important population subgroups. VL - 107 UR - http://ajph.aphapublications.org/doi/10.2105/AJPH.2017.303824 IS - 8 JO - Am J Public Health ER - TY - JOUR T1 - The impact of health and education on future labour force participation among individuals aged 55–74 in the United States of America: the MacArthur Foundation Research Network on an Aging Society JF - Ageing and Society Y1 - 2017 A1 - David Rehkopf A1 - Nancy E Adler A1 - John W Rowe KW - Education KW - Employment and Labor Force KW - Population Health KW - Retirement Planning and Satisfaction AB - Chronic disease, mobility limitations and low physical functioning are determinants of an earlier age of retirement. Therefore, long-term population trends in these factors may have an impact on the proportion of individuals near traditional retirement age who continue to work. Our objective is to develop a projection model that accounts for trends in these factors in order to estimate the proportion of the population aged 55–74 with the capacity to participate in the labour force. We used logistic regression models to quantify how chronic disease, mobility and functional status predict labour force participation among individuals aged 55–59. Next, we obtained estimates of the population prevalence of each of these predictors for the years 2010–2050. We then used estimated coefficients from the logistic regression models to predict the age-specific probability of capacity for work up to the age of 74. We find that population capacity for work depends on trends in disability and on level of education. Future population capacity for work depends on trends in functional limitations primarily in the population with lower levels of education. Changes in functional limitations, changes in the environment, technology and social policy targeted towards individuals with lower levels of education could result in mitigation of future decreasing capacity for work in the population near retirement age. VL - 37 UR - https://www.cambridge.org/core/product/identifier/S0144686X16000295/type/journal_articlehttps://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0144686X16000295 IS - 07 JO - Ageing and Society ER - TY - JOUR T1 - Diabetic Phenotypes and Late-Life Dementia Risk: A Mechanism-specific Mendelian Randomization Study. JF - Alzheimer Dis Assoc Disord Y1 - 2016 A1 - Stefan Walter A1 - Jessica R Marden A1 - Laura D Kubzansky A1 - Elizabeth R Mayeda A1 - Paul K Crane A1 - Chang, Shun-Chiao A1 - Marilyn C Cornelis A1 - David Rehkopf A1 - Mukherjee, Shubhabrata A1 - M. Maria Glymour KW - Alzheimer disease KW - Diabetes Mellitus, Type 2 KW - Genetic Predisposition to Disease KW - Humans KW - Insulin KW - Mendelian Randomization Analysis KW - Phenotype KW - Polymorphism, Single Nucleotide KW - Risk Factors AB -

BACKGROUND: Mendelian Randomization (MR) studies have reported that type 2 diabetes (T2D) was not associated with Alzheimer disease (AD). We adopted a modified, mechanism-specific MR design to explore this surprising result.

METHODS: Using inverse-variance weighted MR analysis, we evaluated the association between T2D and AD using data from 39 single nucleotide polymorphisms (SNPs) significantly associated with T2D in DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) and the corresponding associations of each SNP with AD risk obtained from the International Genomics of Alzheimer's Project (IGAP, n=17,008 AD cases and n=37,154 controls). We evaluated mechanism-specific genetic subscores, including β-cell function, insulin sensitivity, and adiposity, and repeated analyses in 8501 Health and Retirement Study participants for replication and model validation.

RESULTS: In IGAP, the overall T2D polygenic score did not predict AD [odds ratio (OR) for the T2D polygenic score=1.01; 95% confidence interval (CI), 0.96, 1.06] but the insulin sensitivity polygenic score predicted higher AD risk (OR=1.17; 95% CI, 1.02, 1.34). In the Health and Retirement Study, polygenic scores were associated with T2D risk; the associations between insulin sensitivity genetic polygenic score and cognitive phenotypes were not statistically significant.

CONCLUSIONS: Evidence from polygenic scores suggests that insulin sensitivity specifically may affect AD risk, more than T2D overall.

PB - 30 VL - 30 UR - http://europepmc.org/abstract/MED/26650880 IS - 1 U1 - http://www.ncbi.nlm.nih.gov/pubmed/26650880?dopt=Abstract ER - TY - JOUR T1 - The Geographic Distribution of Genetic Risk as Compared to Social Risk for Chronic Diseases in the United States. JF - Biodemography and Social Biology Y1 - 2016 A1 - David Rehkopf A1 - Benjamin W Domingue A1 - Cullen, Mark R KW - Chronic conditions KW - Genetics KW - Geography KW - Social Factors AB - There is an association between chronic disease and geography, and there is evidence that the environment plays a critical role in this relationship. Yet at the same time, there is known to be substantial geographic variation by ancestry across the United States. Resulting geographic genetic variation-that is, the extent to which single nucleotide polymorphisms (SNPs) related to chronic disease vary spatially-could thus drive some part of the association between geography and disease. We describe the variation in chronic disease genetic risk by state of birth by taking risk SNPs from genome-wide association study meta-analyses for coronary artery disease, diabetes, and ischemic stroke and creating polygenic risk scores. We compare the amount of variability across state of birth in these polygenic scores to the variability in parental education, own education, earnings, and wealth. Our primary finding is that the polygenic risk scores are only weakly differentially distributed across U.S. states. The magnitude of the differences in geographic distribution is very small in comparison to the distribution of social and economic factors and thus is not likely sufficient to have a meaningful effect on geographic disease differences by U.S. state. VL - 62 IS - 1 U1 - http://www.ncbi.nlm.nih.gov/pubmed/27050037?dopt=Abstract ER - TY - JOUR T1 - Racial and Socioeconomic Variation in Genetic Markers of Telomere Length: A Cross-Sectional Study of U.S. Older Adults. JF - EBioMedicine Y1 - 2016 A1 - Hamad, Rita A1 - Tuljapurkar, Shripad A1 - David Rehkopf KW - Age Factors KW - Aged KW - Aged, 80 and over KW - Alleles KW - Cross-Sectional Studies KW - Ethnic Groups KW - Female KW - Gene Frequency KW - Genetic Markers KW - Genome-Wide Association Study KW - Geriatric Assessment KW - Humans KW - Male KW - Middle Aged KW - Polymorphism, Single Nucleotide KW - Population Surveillance KW - Socioeconomic factors KW - Telomere Homeostasis KW - United States AB -

BACKGROUND: Shorter telomere length (TL) has been associated with stress and adverse socioeconomic conditions, yet U.S. blacks have longer TL than whites. The role of genetic versus environmental factors in explaining TL by race and socioeconomic position (SEP) remains unclear.

METHODS: We used data from the U.S. Health and Retirement Study (N=11,934) to test the hypothesis that there are differences in TL-associated SNPs by race and SEP. We constructed a TL polygenic risk score (PRS) and examined its association with race/ethnicity, educational attainment, assets, gender, and age.

RESULTS: U.S. blacks were more likely to have a lower PRS for TL, as were older individuals and men. Racial differences in TL were statistically accounted for when controlling for population structure using genetic principal components. The GWAS-derived SNPs for TL, however, may not have consistent associations with TL across different racial/ethnic groups.

CONCLUSIONS: This study showed that associations of race/ethnicity with TL differed when accounting for population stratification. The role of race/ethnicity for TL remains uncertain, however, as the genetic determinants of TL may differ by race/ethnicity. Future GWAS samples should include racially diverse participants to allow for better characterization of the determinants of TL in human populations.

VL - 11 ER - TY - JOUR T1 - Telomere length and health outcomes: A two-sample genetic instrumental variables analysis. JF - Exp Gerontol Y1 - 2016 A1 - Hamad, Rita A1 - Stefan Walter A1 - David Rehkopf KW - Aged KW - Aging KW - Coronary Artery Disease KW - Databases, Factual KW - Female KW - Humans KW - Longitudinal Studies KW - Male KW - Middle Aged KW - Molecular Epidemiology KW - Polymorphism, Single Nucleotide KW - Self Report KW - Telomere KW - Telomere Homeostasis KW - United States AB -

OBJECTIVE: Previous studies linking telomere length (TL) and health have been largely associational. We apply genetic instrumental variables (IV) analysis, also known as Mendelian randomization, to test the hypothesis that shorter TL leads to poorer health. This method reduces bias from reverse causation or confounding.

METHODS: We used two approaches in this study that rely on two separate data sources: (1) individual-level data from the Health and Retirement Study (HRS) (N=3734), and (2) coefficients from genome-wide association studies (GWAS). We employed two-sample genetic IV analyses, constructing a polygenic risk score (PRS) of TL-associated single nucleotide polymorphisms. The first approach examined the association of the PRS with nine individual health outcomes in HRS. The second approach took advantage of estimates available in GWAS databases to estimate the impact of TL on five health outcomes using an inverse variance-weighted meta-analytic technique.

RESULTS: Using individual-level data, shorter TL was marginally statistically significantly associated with decreased risk of stroke and increased risk of heart disease. Using the meta-analytic approach, shorter TL was associated with increased risk of coronary artery disease (OR 1.02 per 100 base pairs, 95%CI: 1.00, 1.03).

DISCUSSION: With the exception of a small contribution to heart disease, our findings suggest that TL may be a marker of disease rather than a cause. They also demonstrate the utility of the inverse variance-weighted meta-analytic approach when examining small effect sizes.

VL - 82 UR - http://www.ncbi.nlm.nih.gov/pubmed/27321645 U1 - http://www.ncbi.nlm.nih.gov/pubmed/27321645?dopt=Abstract ER - TY - JOUR T1 - Genetic vulnerability to diabetes and obesity: does education offset the risk? JF - Soc Sci Med Y1 - 2015 A1 - Sze Y Liu A1 - Stefan Walter A1 - Jessica R Marden A1 - David Rehkopf A1 - Laura D Kubzansky A1 - Thu T Nguyen A1 - M. Maria Glymour KW - Aged KW - Body Mass Index KW - Diabetes Mellitus, Type 2 KW - Educational Status KW - European Continental Ancestry Group KW - Female KW - Genetic Predisposition to Disease KW - Genotype KW - Glycated Hemoglobin A KW - Health Status Disparities KW - Humans KW - Male KW - Middle Aged KW - Obesity KW - Risk Factors KW - Social determinants of health AB -

The prevalence of type 2 diabetes (T2D) and obesity has recently increased dramatically. These common diseases are likely to arise from the interaction of multiple genetic, socio-demographic and environmental risk factors. While previous research has found genetic risk and education to be strong predictors of these diseases, few studies to date have examined their joint effects. This study investigates whether education modifies the association between genetic background and risk for type 2 diabetes (T2D) and obesity. Using data from non-Hispanic Whites in the Health and Retirement Study (HRS, n = 8398), we tested whether education modifies genetic risk for obesity and T2D, offsetting genetic effects; whether this effect is larger for individuals who have high risk for other (unobserved) reasons, i.e., at higher quantiles of HbA1c and BMI; and whether effects differ by gender. We measured T2D risk using Hemoglobin A1c (HbA1c) level, and obesity risk using body-mass index (BMI). We constructed separate genetic risk scores (GRS) for obesity and diabetes respectively based on the most current available information on the single nucleotide polymorphism (SNPs) confirmed as genome-wide significant predictors for BMI (29 SNPs) and diabetes risk (39 SNPs). Linear regression models with years of schooling indicate that the effect of genetic risk on HbA1c is smaller among people with more years of schooling and larger among those with less than a high school (HS) degree compared to HS degree-holders. Quantile regression models show that the GRS × education effect systematically increased along the HbA1c outcome distribution; for example the GRS × years of education interaction coefficient was -0.01 (95% CI = -0.03, 0.00) at the 10th percentile compared to -0.03 (95% CI = -0.07, 0.00) at the 90th percentile. These results suggest that education may be an important socioeconomic source of heterogeneity in responses to genetic vulnerability to T2D.

VL - 127 UR - http://www.sciencedirect.com/science/article/pii/S0277953614005760 U1 - http://www.ncbi.nlm.nih.gov/pubmed/25245452?dopt=Abstract ER - TY - JOUR T1 - The association of earnings with health in middle age: Do self-reported earnings for the previous year tell the whole story? JF - Social Science and Medicine Y1 - 2010 A1 - David Rehkopf A1 - Jencks, Christopher A1 - M. Maria Glymour KW - Health Conditions and Status KW - Healthcare KW - Income KW - Social Security AB - Research on earnings and health frequently relies on self-reported earnings (SRE) for a single year, despite repeated criticism of this measure. We use 31 years (1961-1991) of earnings recorded by the United States Social Security Administration (SSA) to predict the 1992 prevalence of disability, diabetes, stroke, heart disease, cancer, depression and death by 2002 in a subset of Health and Retirement Study participants (n = 5951). We compare odds ratios (ORs) for each health outcome associated with self-reported or administratively recorded earnings. Individuals with no 1991 SSA earnings had worse health in multiple domains than those with positive earnings. However, this association diminished as the time lag between earnings and health increased, so that the absence of earnings before approximately 1975 did not predict health in 1992. Among those with positive earnings, lengthening the lag between SSA earnings and health did not significantly diminish the magnitude of the association with diabetes, heart disease, stroke, or death. Longer lags did reduce but did not eliminate the association between earnings and both disability and depression. Despite theoretical limitations of single year SRE, there were no statistically significant differences between the ORs estimated with single-year SRE and those estimated with a 31-year average of SSA earnings. For example, a one unit increase in logged SRE for 1991 predicted a 19 reduction in the odds of dying by 2002 (OR = 0.81; 95 confidence interval: 0.72,0.90), while a similar increase in average SSA earnings for 1961-1991 had an OR of 0.72 (0.6,0.82). The point estimates for the OR associated with 31 year average SSA earnings were further from the null than the ORs associated with single year SRE for heart disease, depression, and death, and closer to the null for disability, diabetes, and stroke, but none of these differences was statistically significant. PB - 71 VL - 71 IS - 3 N1 - Using Smart Source Parsing pp. Aug Elsevier Science, Amsterdam The Netherlands U2 - PMC3345288 U4 - earnings history/Income/health outcomes/depression/diabetes/Heart disease/MORTALITY ER -