DataSheet1_Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.PDF

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.</p

Image1_Metabolic syndrome traits exhibit genotype-by-environment interaction in relation to socioeconomic status in the Mexican American family heart study.jpeg

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.</p

Improved clinical prediction of incident type 2 diabetes based on sICAM-1 and sVCAM-1 as independent biomarkers^*.

<p>Improved clinical prediction of incident type 2 diabetes based on sICAM-1 and sVCAM-1 as independent biomarkers^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0151177#t004fn001" target="_blank">*</a>}.</p

Median HOMA-IR values based on serum concentrations of sICAM-1 and sVCAM-1.

<p>Median HOMA-IR values based on serum concentrations of sICAM-1 and sVCAM-1.</p

Description of the study cohort and distribution of the serum concentration of adhesion molecules in study participants.

<p><b>(A)</b> Selection criteria. White colored boxes indicate the cross-sectional component of the study while the blue colored boxes indicate the prospective component of the study. Color-coded numbered boxes represent the four study groups for which distribution of adhesion molecules is given in panels B and C. AM, adhesion molecules <b>(B and C)</b> Box and whisker plots showing the distribution of sICAM-1 (B) and sVCAM-1 (C) concentration in serum. The width of the boxes is proportional to the number of individuals within the group. The boxes are color-coded to match the numbered boxes in panel A.</p

Survival analyses for time to onset of type 2 diabetes from the day of enrolment into the study based on the serum concentrations of adhesion molecules.

<p><b>(A and B)</b> Association of sICAM-1 concentration with incident type 2 diabetes, <b>(C and D)</b> association of sVCAM-1 concentration with incident type 2 diabetes, and <b>(E and F)</b> association of a combination of the serum concentrations of sICAM-1 and sVCAM-1 with incident type 2 diabetes. The group identifiers are as follows: I_LO, sICAM-1 ≤ 336.21 ng/ml; I_HI, sICAM-1 > 336.21 ng/ml; V_LO, sVCAM-1 ≤ 670.42 ng/ml; V_HI, sVCAM-1 > 670.42 ng/ml. Panels E and F use a combination of these groups as indicated in the Fig. All panels show Kaplan-Meier survival plots for the indicated groups. Inset in each panel are results from mixed effects Cox proportional hazards regression. All regression models used following variables as fixed effects covariates in addition to kinship as random effects: age, sex, age*sex interaction, waist circumference, body mass index, systolic and diastolic blood pressures, total serum cholesterol, serum triglycerides, serum HDL-cholesterol, use of anti-lipid medications and use of antihypertensive medication. PH, proportional hazards; RH, relative hazards; CI, confidence interval; NGT, normal glucose tolerance.</p

Mixed effects Cox regression for prediction of incident type 2 diabetes in women in reproductive age group (WRAG) and the remainder of the cohort.

<p>Mixed effects Cox regression for prediction of incident type 2 diabetes in women in reproductive age group (WRAG) and the remainder of the cohort.</p