Genotype-by-socioeconomic status interaction influences heart disease risk scores and carotid artery thickness in Mexican Americans: the predominant role of education in comparison to household income and socioeconomic index

Statistical Genetic Approaches to Investigate Genotype-by-Environment Interaction: Review and Novel Extension of Models

Statistical genetic models of genotype-by-environment (G×E) interaction can be divided into two general classes, one on G×E interaction in response to dichotomous environments (e.g., sex, disease-affection status, or presence/absence of an exposure) and the other in response to continuous environments (e.g., physical activity, nutritional measurements, or continuous socioeconomic measures). Here we develop a novel model to jointly account for dichotomous and continuous environments. We develop the model in terms of a joint genotype-by-sex (for the dichotomous environment) and genotype-by-social determinants of health (SDoH; for the continuous environment). Using this model, we show how a depression variable, as measured by the Beck Depression Inventory-II survey instrument, is not only underlain by genetic effects (as has been reported elsewhere) but is also significantly determined by joint G×Sex and G×SDoH interaction effects. This model has numerous applications leading to potentially transformative research on the genetic and environmental determinants underlying complex diseases.

Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study

Background: Socioeconomic Status (SES) is a potent environmental determinant of health. To our knowledge, no assessment of genotype-environment interaction has been conducted to consider the joint effects of socioeconomic status and genetics on risk for metabolic disease. We analyzed data from the Mexican American Family Studies (MAFS) to evaluate the hypothesis that genotype-by-environment interaction (GxE) is an essential determinant of variation in risk factors for metabolic syndrome (MS). Methods: We employed a maximum likelihood estimation of the decomposition of variance components to detect GxE interaction. After excluding individuals with diabetes and individuals on medication for diabetes, hypertension, or dyslipidemia, we analyzed 12 MS risk factors: fasting glucose (FG), fasting insulin (FI), 2-h glucose (2G), 2-h insulin (2I), body mass index (BMI), waist circumference (WC), leptin (LP), high-density lipoprotein-cholesterol (HDL-C), triglycerides (TG), total serum cholesterol (TSC), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our SES variable used a combined score of Duncan's socioeconomic index and education years. Heterogeneity in the additive genetic variance across the SES continuum and a departure from unity in the genetic correlation coefficient were taken as evidence of GxE interaction. Hypothesis tests were conducted using standard likelihood ratio tests. Results: We found evidence of GxE for fasting glucose, 2-h glucose, 2-h insulin, BMI, and triglycerides. The genetic effects underlying the insulin/glucose metabolism component of MS are upregulated at the lower end of the SES spectrum. We also determined that the household variance for systolic blood pressure decreased with increasing SES. Conclusion: These results show a significant change in the GxE interaction underlying the major components of MS in response to changes in socioeconomic status. Further mRNA sequencing studies will identify genes and canonical gene pathways to support our molecular-level hypotheses.

Bridging Bone Health: Osteoporosis Disparities in the Rio Grande Valley

INTRODUCTION

Osteoporosis is characterized by decreased bone mass and decreased bone quality, leading to increased bone fragility and risk of fractures. The number of fractures due to osteoporosis is projected to increase to over three million by the year 2025 and cost $25.3 billion annually. It ranks highly among diseases that cause patients to become bedridden with serious complications and reduced quality of life. Additionally, osteoporosis disproportionately affects Hispanics, which comprise most of the Rio Grande Valley (RGV) population. Therefore, our primary objective was to determine the prevalence of osteoporosis within the RGV. Additionally, we had secondary objectives to determine the screening rates of osteoporosis in the RGV and identify other potential risk factors associated with osteoporosis. We hypothesize that individuals residing in the RGV have higher rates of osteoporosis and lower rates of osteoporosis screening than the national average.

METHODS

This retrospective observational cross-sectional study utilized Medicare beneficiary data via the "Mapping Medicare Disparities by Population" interactive tool. Osteoporosis data were compared within the RGV (comprising Starr, Hidalgo, Cameron, and Willacy counties) and compared with national averages between the years 2016 and 2021. Statistical analysis included prevalence ratios with 95% confidence intervals and chi-square values when applicable.

RESULTS

Among Medicare beneficiaries residing in the RGV, there are higher rates of osteoporosis compared to the national average (11.5% vs. 7.20%; p < .00001). Screening for osteoporosis within the RGV is above the national average (9.29% vs. 6.67%, p < .00001). Hispanics residing in the RGV have higher overall rates of osteoporosis than Caucasians residing in the RGV (12.3% vs. 8.60%, p < .00001). Females residing in the RGV have nearly twice the rate of osteoporosis compared to the national average (19.1% vs. 11.8%, p < .00001) and 6.58 times the rate of males residing in the RGV (19.1% vs. 2.9%, p < .00001).

CONCLUSION

Individuals residing in the RGV are disproportionately affected by osteoporosis. Despite increased screening rates seen among Medicare beneficiaries, we also suspect many individuals, uninsured or undocumented, have not received any appropriate osteoporosis screening. Risk factors in the RGV associated with higher rates of osteoporosis could include low education levels, socioeconomic status, physical activity, and mineral intake. These results demonstrate a need to address osteoporosis health literacy, promote earlier interventions to treat osteoporosis and increase healthcare accessibility in the RGV.