Browsing by Author "Pettinger, Mary"

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    Correction: Taking action to advance the study of race and ethnicity: the Women’s Health Initiative (WHI)
    (Springer Nature, 2022-11-25) Garcia, Lorena; Follis, Shawna; Thomson, Cynthia A.; Breathett, Khadijah; Wiley Cené, Crystal; Jimenez, Monik; Kooperberg, Charles; Masaki, Kamal; Paskett, Electra D.; Pettinger, Mary; Aragaki, Aaron; Dilworth‑Anderson, Peggye; Stefanick, Marcia L.; Medicine, School of Medicine
    Correction: Women’s Midlife Health 8, 1 (2021) https://doi.org/10.1186/s40695-021-00071-6
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    Investigating Gene-Diet Interactions Impacting the Association Between Macronutrient Intake and Glycemic Traits
    (American Diabetes Association, 2023) 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.; Epidemiology, Richard M. Fairbanks School of Public Health
    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 >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.
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    Taking action to advance the study of race and ethnicity: the Women’s Health Initiative (WHI)
    (Springer Nature, 2022) Garcia, Lorena; Follis, Shawna; Thomson, Cynthia A.; Breathett, Khadijah; Wiley Cené, Crystal; Jimenez, Monik; Kooperberg, Charles; Masaki, Kamal; Paskett, Electra D.; Pettinger, Mary; Aragaki, Aaron; Dilworth‑Anderson, Peggye; Stefanick, Marcia L.; Medicine, School of Medicine
    “Race” and “ethnicity” are socially constructed terms, not based on biology - in contrast to biologic ancestry and genetic admixture - and are flexible, contested, and unstable concepts, often driven by power. Although individuals may self-identify with a given race and ethnic group, as multidimensional beings exposed to differential life influencing factors that contribute to disease risk, additional social determinants of health (SDOH) should be explored to understand the relationship of race or ethnicity to health. Potential health effects of structural racism, defined as “the structures, policies, practices, and norms resulting in differential access to goods, services, and opportunities of society by “race,” have been largely ignored in medical research. The Women’s Health Initiative (WHI) was expected to enroll a racially and ethnically diverse cohort of older women at 40 U.S. clinical centers between 1993 and 1998; yet, key information on the racial and ethnic make-up of the WHI cohort of 161,808 women was limited until a 2020–2021 Task Force was charged by the WHI Steering Committee to better characterize the WHI cohort and develop recommendations for WHI investigators who want to include “race” and/or “ethnicity” in papers and presentations. As the lessons learned are of relevance to most cohorts, the essence of the WHI Race and Ethnicity Language and Data Interpretation Guide is presented in this paper. Recommendations from the WHI Race and Ethnicity Language and Data Interpretation Guide include: Studies should be designed to include all populations and researchers should actively, purposefully and with cultural-relevance, commit to recruiting a diverse sample; Researchers should collect robust data on race, ethnicity and SDOH variables that may intersect with participant identities, such as immigration status, country of origin, acculturation, current residence and neighborhood, religion; Authors should use appropriate terminology, based on a participant’s self-identified “race” and “ethnicity”, and provide clear rationale, including a conceptual framework, for including race and ethnicity in the analytic plan; Researchers should employ appropriate analytical methods, including mixed-methods, to study the relationship of these sociocultural variables to health; Authors should address how representative study participants are of the population to which results might apply, such as by age, race and ethnicity.