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  • 1
    Online Resource
    Online Resource
    2111-P: Effect Modification of Vitamin D Intervention by Obesity-Related Metabolites
    American Diabetes Association ; 2019
    In:  Diabetes Vol. 68, No. Supplement_1 ( 2019-06-01)
    Details
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: Background: Effects of vitamin D on biomarkers of cardiometabolic disorders among obese individuals are highly variable between studies. Comparing the effects of vitamin D supplementation in metabolically healthy (MHO) vs. metabolically unhealthy (MUHO) obesity using metabolomics may yield mechanistic insights into the observed heterogeneity. Objective: To quantify small molecular changes shortly after vitamin D intervention in MHO and MUHO individuals with sub-optimal levels of vitamin D ( & lt;75 nmol/L) using a targeted metabolomics approach. Methods: In two randomized double-blind clinical trials, 110 MHO and 105 MUHO individuals were separately and randomly assigned to receive a daily dose of vitamin D supplement (4000 IU) or placebo. These MHO/MUHO phenotypes were defined using the Adult Treatment Panel-III criteria. Obesity-related metabolites (n=104) were measured at baseline and after four months of supplementation, using liquid chromatography coupled to a triple quadrupole mass spectrometry. Multiple linear regression models were fit to assess changes in metabolite levels, adjusting for appropriate covariates as well as controlling for multiple testing. Results: In the MUHO group (n=78), we identified ten metabolites (citrulline, acyl-lysophosphatidylcholines C16:0, C16:1, C18:0 and C18:1, diacyl-phosphatidylcholines C32:0, C34:1, C38:3 and C38:4 and sphingomyelin C40:4) that were significantly altered (P=4.9×10-4 to P=0.007) in response to vitamin D therapy. In the MHO group (n=82), no significant metabolite changes were observed after the intervention. Conclusion: Findings from our study contribute to the understanding of biological variation in Vitamin D metabolism across different obesity phenotypes. Upon successful validation, these insights can increase precision of clinical approaches to metabolic derangements in obesity. This study was registered at www.irct.ir as IRCT2015061522762N1. Disclosure M. Bagheri: None. S. Aslibekyan: Employee; Self; 23andMe. A. Djazayery: None. F. Farzadfar: None. Funding Tehran University of Medical Sciences
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    URL: Article
    DOI: 10.2337/db19-2111-P
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1501252-9
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  • 2
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    Online Resource
    Investigating Gene–Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
    American Diabetes Association ; 2023
    In:  Diabetes Vol. 72, No. 5 ( 2023-05-01), p. 653-665
    Details
    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
    URL: Article
    DOI: 10.2337/db22-0851
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2023
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  • 3
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    304-OR: Polygenic Risk Score Predicts Type 2 Diabetes Susceptibility in a Diverse Consumer Genetic Database
    American Diabetes Association ; 2019
    In:  Diabetes Vol. 68, No. Supplement_1 ( 2019-06-01)
    Details
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: With the rise in the prevalence of type 2 diabetes (T2D), as well as undiagnosed cases of T2D and prediabetes (25% and 90%, respectively), early detection is imperative to minimize individual and societal burden. T2D is highly heritable, and personal genetic information is increasingly available to the general public. Studies have suggested that T2D risk reduction strategies may be more effective for individuals with high T2D genetic risk, supporting the use of genetics as a screening tool to inform cost-effective interventions. We trained a polygenic risk score (PRS) for T2D based on & gt;1,200 genotyped variants in & gt;600,000 European consented research participants from a consumer genetic database who self-reported if they had been diagnosed with T2D. We tested the PRS' performance in separate sets of participants covering five different ancestries (African-American, East-Asian, European, Latino, and South-Asian; ~ 600,000 participants). The area under the receiver-operator curve of this PRS varied from 0.65 to 0.57, performing best in European and worst in African ancestries. The PRS was calibrated separately in each ancestry to account for differences in T2D prevalence. European participants with a PRS in the top 5% of the distribution have a T2D odds ratio of more than 3, and lifetime risk for this group exceeds 65%. Our PRS is strongly correlated with an independently derived T2D PRS from (Scott et al. 2017 GWAS, Spearman rho=0.44, p & lt; 1x10^-200) but is more predictive in our dataset (AUC 0.65 vs. 0.59). Lastly, we defined an "increased likelihood" result based on the PRS threshold at which risk of T2D from genetics alone exceeds the risk of T2D due to being overweight. In our database, 19% of individuals met this criterion. We find that personalized PRS' have the potential to identify large numbers of individuals with increased T2D susceptibility equal to or greater than known risk factors and could prove useful in evaluating T2D risk at individual as well as population levels. Disclosure M.L. Multhaup: Employee; Self; 23andMe. R. Kita: Employee; Self; 23andMe. N. Eriksson: None. S. Aslibekyan: Employee; Self; 23andMe. J. Shelton: None. R.I. Tennen: Employee; Self; 23andMe. E. Kim: Employee; Self; 23andMe, inc. B. Koelsch: Employee; Self; 23andMe.
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    URL: Article
    DOI: 10.2337/db19-304-OR
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
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  • 4
    Online Resource
    Online Resource
    Epigenome-Wide Association Study of Fasting Measures of Glucose, Insulin, and HOMA-IR in the Genetics of Lipid Lowering Drugs and Diet Network Study
    American Diabetes Association ; 2014
    In:  Diabetes Vol. 63, No. 2 ( 2014-02-01), p. 801-807
    Details
    In: Diabetes, American Diabetes Association, Vol. 63, No. 2 ( 2014-02-01), p. 801-807
    Abstract: Known genetic susceptibility loci for type 2 diabetes (T2D) explain only a small proportion of heritable T2D risk. We hypothesize that DNA methylation patterns may contribute to variation in diabetes-related risk factors, and this epigenetic variation across the genome can contribute to the missing heritability in T2D and related metabolic traits. We conducted an epigenome-wide association study for fasting glucose, insulin, and homeostasis model assessment of insulin resistance (HOMA-IR) among 837 nondiabetic participants in the Genetics of Lipid Lowering Drugs and Diet Network study, divided into discovery (N = 544) and replication (N = 293) stages. Cytosine guanine dinucleotide (CpG) methylation at ∼470,000 CpG sites was assayed in CD4+ T cells using the Illumina Infinium HumanMethylation 450 Beadchip. We fit a mixed model with the methylation status of each CpG as the dependent variable, adjusting for age, sex, study site, and T-cell purity as fixed-effects and family structure as a random-effect. A Bonferroni corrected P value of 1.1 × 10−7 was considered significant in the discovery stage. Significant associations were tested in the replication stage using identical models. Methylation of a CpG site in ABCG1 on chromosome 21 was significantly associated with insulin (P = 1.83 × 10−7) and HOMA-IR (P = 1.60 × 10−9). Another site in the same gene was significant for HOMA-IR and of borderline significance for insulin (P = 1.29 × 10−7 and P = 3.36 × 10−6, respectively). Associations with the top two signals replicated for insulin and HOMA-IR (P = 5.75 × 10−3 and P = 3.35 × 10−2, respectively). Our findings suggest that methylation of a CpG site within ABCG1 is associated with fasting insulin and merits further evaluation as a novel disease risk marker.
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    URL: Article
    DOI: 10.2337/db13-1100
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2014
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