GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Online Resource
    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
    detail.hit.zdb_id: 1501252-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Type 2 Diabetes Partitioned Polygenic Scores Associate With Disease Outcomes in 454,193 Individuals Across 13 Cohorts
    American Diabetes Association ; 2022
    In:  Diabetes Care Vol. 45, No. 3 ( 2022-03-01), p. 674-683
    Details
    In: Diabetes Care, American Diabetes Association, Vol. 45, No. 3 ( 2022-03-01), p. 674-683
    Abstract: Type 2 diabetes (T2D) has heterogeneous patient clinical characteristics and outcomes. In previous work, we investigated the genetic basis of this heterogeneity by clustering 94 T2D genetic loci using their associations with 47 diabetes-related traits and identified five clusters, termed β-cell, proinsulin, obesity, lipodystrophy, and liver/lipid. The relationship between these clusters and individual-level metabolic disease outcomes has not been assessed. RESEARCH DESIGN AND METHODS Here we constructed individual-level partitioned polygenic scores (pPS) for these five clusters in 12 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (n = 454,193) and tested for cross-sectional association with T2D-related outcomes, including blood pressure, renal function, insulin use, age at T2D diagnosis, and coronary artery disease (CAD). RESULTS Despite all clusters containing T2D risk-increasing alleles, they had differential associations with metabolic outcomes. Increased obesity and lipodystrophy cluster pPS, which had opposite directions of association with measures of adiposity, were both significantly associated with increased blood pressure and hypertension. The lipodystrophy and liver/lipid cluster pPS were each associated with CAD, with increasing and decreasing effects, respectively. An increased liver/lipid cluster pPS was also significantly associated with reduced renal function. The liver/lipid cluster includes known loci linked to liver lipid metabolism (e.g., GCKR, PNPLA3, and TM6SF2), and these findings suggest that cardiovascular disease risk and renal function may be impacted by these loci through their shared disease pathway. CONCLUSIONS Our findings support that genetically driven pathways leading to T2D also predispose differentially to clinical outcomes.
    Type of Medium: Online Resource
    ISSN: 0149-5992
    URL: Article
    DOI: 10.2337/dc21-1395
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2022
    detail.hit.zdb_id: 1490520-6
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    73-OR: Clonal Hematopoiesis of Indeterminate Potential (CHIP), Glycemic Traits, and Type 2 Diabetes (T2D) Risk
    American Diabetes Association ; 2021
    In:  Diabetes Vol. 70, No. Supplement_1 ( 2021-06-01)
    Details
    In: Diabetes, American Diabetes Association, Vol. 70, No. Supplement_1 ( 2021-06-01)
    Abstract: Objective: CHIP is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in elevated atherosclerotic heart disease (CHD) and all-cause mortality, but its association with incident T2D is unknown. We hypothesize CHIP is adversely associated with glycemic traits and T2D. Methods: We included N=16,054 participants in NHLBI Trans-omics for Precision Medicine (TOPMed), without prior T2D or chronic disease at blood draw. CHIP was derived from blood DNA whole genome sequencing using the GATK MuTECT2 somatic variant caller in pre-specified leukemogenic driver mutations. We estimated CHIP overall and candidate CHD-related mutations (DNMT3A, TET2, ASXL1, JAK2) in relation to baseline glycemic traits (fasting glucose, insulin, HOMA-IR) with linear regression. We evaluated CHIP with incident T2D using Cox regression to estimate hazard ratios and 95% confidence intervals (HR [CI]). Models adjusted for age, sex, body mass index, smoking, and family history of T2D. We combined cohort-specific estimates via random effects meta-analysis with inverse variance weighting. Results: CHIP was present in n=1,088 (7%) of participants. We observed 1,853 incident T2D cases over mean=10y follow-up. Baseline fasting glucose, insulin, and HOMA-IR did not differ by CHIP status. Participants with CHIP had a higher risk of incident T2D than those without CHIP (1.27 [1.08, 1.51]), with a significant dose-response per additional CHIP mutation (p-trend=0.005). Analyses of CHD-related CHIP vs. no CHIP mutations suggested associations with higher T2D risk for DNMT3A (1.21 [0.97, 1.51] ) and TET2 (1.35 [0.93, 1.97]), and ASXL1 (1.92 [0.71, 5.13] ), although analyses of individual mutations were underpowered. Conclusions: CHIP is associated with a higher T2D risk. This relationship may be unrelated to baseline glycemic traits. CHIP-T2D association suggests a potential role for CHD-related pathology in T2D. Disclosure D. K. Tobias: None. D. A. Dicorpo: None. B. M. Psaty: None. A. C. Morrison: None. V. S. Ramachandran: None. L. Cupples: None. L. Lange: None. A. Correa: None. C. L. Kooperberg: None. L. F. Tinker: None. B. V. Howard: None. A. Manning: Employee; Spouse/Partner; Emulate, Inc, Stock/Shareholder; Spouse/Partner; Invitae. A. Reiner: None. J. I. Rotter: None. J. M. Collins: None. J. B. Meigs: Consultant; Self; Quest Diagnostics. J. E. Manson: None. J. Wessel: Employee; Spouse/Partner; Eli Lilly and Company. K. E. Westerman: None. S. Raghavan: None. L. Raffield: None. J. Dupuis: None. A. Bick: None. P. Wu: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616 to J.B.M., J.D., D.A.D., P.W.), (U01DK105554 to J.D., D.A.D.)
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    URL: Article
    DOI: 10.2337/db21-73-OR
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2021
    detail.hit.zdb_id: 1501252-9
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...