Investigating Gene–Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
Authors: Westerman Kenneth E. , Walker Maura E. , Gaynor Sheila M. , Wessel Jennifer , DiCorpo Daniel , Ma Jiantao , Alonso Alvaro , Aslibekyan Stella , Baldridge Abigail S. , Bertoni Alain G. , Biggs Mary L. , Brody Jennifer A. , Chen Yii-Der Ida , Dupuis Joseé , Goodarzi Mark O. , Guo Xiuqing , Hasbani Natalie R. , Heath Adam , Hidalgo Bertha , Irvin Marguerite R. , Johnson W. Craig , Kalyani Rita R. , Lange Leslie , Lemaitre Rozenn N. , Liu Ching-Ti , Liu Simin , Moon Jee-Young , Nassir Rami , Pankow James S. , Pettinger Mary , Raffield Laura M. , Rasmussen-Torvik Laura J. , Selvin Elizabeth , Senn Mackenzie K. , Shadyab Aladdin H. , Smith Albert V. , Smith Nicholas L. , Steffen Lyn , Talegakwar Sameera , Taylor Kent D. , de Vries Paul S. , Wilson James G. , Wood Alexis C. , Yanek Lisa R. , Yao Jie , Zheng Yinan , Boerwinkle Eric , Morrison Alanna C. , Fornage Miriam , Russell Tracy P. , Psaty Bruce M. , Levy Daniel , Heard-Costa Nancy L. , Ramachandran Vasan S. , Mathias Rasika A. , Arnett Donna K. , Kaplan Robert , North Kari E. , Correa Adolfo , Carson April , Rotter Jerome I. , Rich Stephen S. , Manson JoAnn E. , Reiner Alexander P. , Kooperberg Charles , Florez Jose C. , Meigs James B. , Merino Jordi , Tobias Deirdre K. , Chen Han , Manning Alisa K. 
Published: February 2023

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.