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  • 1
    In: Diabetes, American Diabetes Association, Vol. 72, No. 5 ( 2023-05-01), p. 653-665
    Abstract: Few studies have demonstrated reproducible gene–diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient–glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], −0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry–enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate–HbA1c association among major allele homozygotes only. Simulations revealed that & gt;150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry. Article Highlights We aimed to identify genetic modifiers of the dietary macronutrient–glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry–enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene–macronutrient interactions.
    Type of Medium: Online Resource
    ISSN: 0012-1797
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2023
    detail.hit.zdb_id: 1501252-9
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  • 2
    In: Diabetes, American Diabetes Association, Vol. 67, No. 8 ( 2018-08-01), p. 1684-1696
    Abstract: Fructosamine and glycated albumin are potentially useful alternatives to hemoglobin A1c (HbA1c) as diabetes biomarkers. The genetic determinants of fructosamine and glycated albumin, however, are unknown. We performed genome-wide association studies of fructosamine and glycated albumin among 2,104 black and 7,647 white participants without diabetes in the Atherosclerosis Risk in Communities (ARIC) Study and replicated findings in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Among whites, rs34459162, a novel missense single nucleotide polymorphism (SNP) in RCN3, was associated with fructosamine (P = 5.3 × 10−9) and rs1260236, a known diabetes-related missense mutation in GCKR, was associated with percent glycated albumin (P = 5.9 × 10−9) and replicated in CARDIA. We also found two novel associations among blacks: an intergenic SNP, rs2438321, associated with fructosamine (P = 6.2 × 10−9), and an intronic variant in PRKCA, rs59443763, associated with percent glycated albumin (P = 4.1 × 10−9), but these results did not replicate. Few established fasting glucose or HbA1c SNPs were also associated with fructosamine or glycated albumin. Overall, we found genetic variants associated with the glycemic information captured by fructosamine and glycated albumin as well as with their nonglycemic component. This highlights the importance of examining the genetics of hyperglycemia biomarkers to understand the information they capture, including potential glucose-independent factors.
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2018
    detail.hit.zdb_id: 1501252-9
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