Heritability analysis of nontraditional glycemic biomarkers in the Atherosclerosis Risk in Communities Study.

Nontraditional glycemic biomarkers, including fructosamine, glycated albumin, and 1,5-anhydroglucitol (1,5-AG) are potential alternatives or complement to traditional measures of hyperglycemia. Genetic variants are associated with these biomarkers, but the heritability, or extent to which genetics control their variation, is not known. We estimated pedigree-based, SNP-based, and bivariate heritabilities for traditional glycemic biomarkers (fasting glucose, HbA1c), and nontraditional biomarkers (fructosamine, glycated albumin, 1,5-AG) among white participants in the Atherosclerosis Risk in Communities (ARIC) Study (N = 400 first-degree relatives from sibships, N = 5,575 unrelated individuals). Pedigree-based heritabilities (representing heritability from the entire genome) for nontraditional biomarkers were substantial (0.44-0.55) and comparable to HbA1c (0.34); the fasting glucose estimate was nonsignificant. SNP-based heritabilities (representing heritability from common variants) were lower than pedigree-based heritabilities for all biomarkers. Bivariate heritabilities showed shared genetics between fructosamine and glycated albumin (0.46 pedigree-based, 1.00 SNP-based) and glycated albumin and 1,5-AG (0.50 pedigree-based, 0.47 SNP-based). Genetic factors contribute to a considerable proportion of the variance of fructosamine, glycated albumin, and 1,5-AG and a portion of this heritability likely comes from common variants.

Cross-Sectional Association of Blood Selenium with Glycemic Biomarkers among U.S. Adults with Normoglycemia in the National Health and Nutrition Examination Survey 2013-2016.

Selenium (Se) remains to have an inconsistent relationship with glycemic biomarkers and the risk of developing type 2 diabetes (T2D). Few studies have investigated the relationship between blood Se and glycemic biomarkers among people with normoglycemia. We conducted a cross-sectional analysis using the U.S. National Health and Nutrition Examination Survey 2013-2016. Multivariable linear regression models were developed to examine the associations of blood Se with glycemic biomarkers, namely, fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), insulin, and the oral glucose tolerance test (OGTT). Blood Se was treated as continuous (per log-10 increment) and categorical exposure (in quartiles) in separate regression models. We assessed the dose-response relationships by restricted cubic spline analysis. After excluding the participants with T2D or incomplete data, 2706 participants were analyzed. The highest quartile of blood Se was associated with increased FPG [adjusted ß = 0.12, 95% Confidence Intervals (CI) = 0.04, 0.20], OGTT (adjusted ß = 0.29, 95% CI = 0.02, 0.56), HbA1c (adjusted ß = 0.04, 95% CI = 0.00, 0.07), and insulin (adjusted ß = 2.50, 95% CI = 1.05, 3.95) compared with the lowest quartile. Positive associations were also observed between every log-10 increment of blood Se level and glycemic biomarkers, except for OGTT. A positive linear dose-response relationship existed between blood Se and FPG (Poverall = 0.003, Pnonlinear = 0.073) and insulin (Poverall = 0.004, Pnonlinear =0.060). BMI, age, and smoking status modified the associations of the highest quartile of Se (compared with the lowest quartile) with glycemic biomarkers. Overall, positive associations of blood Se with glycemic biomarkers were observed among U.S. adults with normoglycemia. These findings implied that people with normoglycemia need to be aware of the level of Se and other mineral intakes from diet and supplements. Further research is required to identify the mechanisms of excess Se in the progression of diabetes.

Cross-sectional and prospective associations between sleep regularity and metabolic health in the Hispanic Community Health Study/Study of Latinos.

STUDY OBJECTIVES: Sleep is an emergent, multi-dimensional risk factor for diabetes. Sleep duration, timing, quality, and insomnia have been associated with diabetes risk and glycemic biomarkers, but the role of sleep regularity in the development of metabolic disorders is less clear. METHODS: We analyzed data from 2107 adults, aged 19-64 years, from the Sueño ancillary study of the Hispanic Community Health Study/Study of Latinos, followed over a mean of 5.7 years. Multivariable-adjusted complex survey regression methods were used to model cross-sectional and prospective associations between the sleep regularity index (SRI) in quartiles (Q1-least regular, Q4-most regular) and diabetes (either laboratory-confirmed or self-reported antidiabetic medication use), baseline levels of insulin resistance (HOMA-IR), beta-cell function (HOMA-ß), hemoglobin A1c (HbA1c), and their changes over time. RESULTS: Cross-sectionally, lower SRI was associated with higher odds of diabetes (odds ratio [OR]Q1 vs. Q4 = 1.64, 95% CI: 0.98-2.74, ORQ2 vs. Q4 = 1.12, 95% CI: 0.70-1.81, ORQ3 vs. Q4 = 1.00, 95% CI: 0.62-1.62, ptrend = 0.023). The SRI effect was more pronounced in older (aged ≥ 45 years) adults (ORQ1 vs. Q4 = 1.88, 95% CI: 1.14-3.12, pinteraction = 0.060) compared to younger ones. No statistically significant associations were found between SRI and diabetes incidence, as well as baseline HOMA-IR, HOMA-ß, and HbA1c values, or their changes over time among adults not taking antidiabetic medication. CONCLUSIONS: Our results suggest that sleep regularity represents another sleep dimension relevant for diabetes risk. Further research is needed to elucidate the relative contribution of sleep regularity to metabolic dysregulation and pathophysiology.

Metrics Beyond Hemoglobin A1C in Diabetes Management: Time in Range, Hypoglycemia, and Other Parameters.

We review clinical instances in which A1C should not be used and reflect on the use of other glucose metrics that can be used, in substitution of or in combination with A1C and SMBG, to tailor an individualized approach that will result in better outcomes and patient empowerment.