Racial and ethnic disparities in predictors of glycemia: a moderated mediation analysis of inflammation-related predictors of diabetes in the NHANES 2007–2010

Abstract

Background/ObjectiveRacial/ethnic disparities in type 2 diabetes (T2D) outcomes exist, and could be explained by nutrition- and inflammation-related differences. The objective of this study is to identify associations between race/ethnicity and glucose control among participants from NHANES 2007–2010, as influenced by diet quality, body mass, and inflammation and grouped by T2D status.Subjects/MethodsThe following is a cross-sectional, secondary data analysis of two NHANES data cycles spanning 2007–2010. The association between race/ethnicity and hemoglobin A1c (HbA1c) as mediated by dietary intake score, body mass index (BMI), and C-reactive protein (CRP) was assessed, as was the strength of the difference of that association, or moderation, by T2D status. The sample included n = 7850 non-pregnant adult participants ≥ 20 years of age who had two days of reliable dietary recall data, and no missing data on key variables included in the analysis. The primary outcome examined was HbA1c.ResultsThe model accurately explained the variation in HbA1c measures in participants without T2D, as mediated by diet quality, BMI, and CRP. However, significant variation in HbA1c remained after accounting for aforementioned mediators when contrasting non-Hispanic White to non-Hispanic Black participants without T2D. The model was not a good fit for explaining racial/ethnic disparities in HbA1c in participants with T2D. A test of the index of moderated mediation for this model was not significant for the differences in the effect of race/ethnicity on HbA1c by T2D status (moderator).ConclusionsThis study demonstrated that diet quality, BMI, and CRP mediated the effect of race/ethnicity on HbA1c in persons without T2D, but not in persons with T2D. Further research should include additional inflammatory markers, and other inflammation- and T2D-related health outcomes, and their association with racial/ethnic disparities in diabetes.

Metabolic Factors Mediate the Association Between Serum Uric Acid to Serum Creatinine Ratio and Cardiovascular Disease

“…This statistical approach was conducted in accordance with that described by Valeri and VanderWeele and has been applied successfully in previous studies to demonstrate the role of mediators. 27 , 28 , 29 We included age, sex, education, income, smoking status, and drinking status as covariates to adjust for the mediating effect of these biomarkers on the association between SUA/SCr and CVD because when using mediation models, it is necessary that baseline covariates are sufficient to control for exposure‐outcome, mediator‐outcome, and exposure‐mediator confounding. 30 …”

Association of serum uric acid to lymphocyte ratio, a novel inflammatory biomarker, with risk of stroke: A prospective cohort study

“…Once the temporal relationships of ULR with stroke and its subtypes had been established, mediation analysis was performed to examine whether the associations between ULR (X) and stroke (Y) were mediated by other metabolic factors (M), using the method described by Valeri and VanderWeele. [24][25][26] In general, four steps are involved in the mediation analysis 1 : demonstrating that the predictor is associated with the outcome (Model Y = β Tol X, β Tol = total effect) 2 ; demonstrating that the predictor is associated with the mediator (Model M = β 1 X, β 1 = indirect effect 1) 3 ; demonstrating which part of the outcome is explained by controlling for the predictor (Model Y = β 2 M + β Dir X, β 2 = indirect effect 2, β Dir = direct effect); and 4 calculating the proportion of mediation: mediation effect (%) = (β 1 × β 2 /β Tol ) × 100%. We adjusted age, sex, education, income, smoking status, and drinking status in the mediation analysis, as it is necessary for mediation models in which baseline covariates are sufficient to control for exposure-outcome, mediator-outcome, and exposure-mediator confounding.…”

Ethnicity, Age, and Gender Differences in Glycated Hemoglobin (HbA1c) Levels among Adults in Northern and Eastern Sudan: A Community-Based Cross-Sectional Study

“…In addition, variation in insulin resistance was reported among ethnicities [25]. Race and ethnicity could influence HbA1c via the inflammatory processes that mediate via diet quality, BMI, and C-reactive protein (CRP) [26]. The high HbA1c levels in eastern Sudan compared to those in northern Sudan could be attributed not only to ethnicity but also to other sociodemographic factors.…”