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Browsing by Author "Ma, Jiantao"
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Item Body Mass Index Trajectories, Weight Gain, and Risks of Liver and Biliary Tract Cancers(Oxford University Press, 2022-08-12) Yang, Wanshui; Zeng, Xufen; Petrick, Jessica L.; Danford, Christopher J.; Florio, Andrea A.; Lu, Bing; Nan, Hongmei; Ma, Jiantao; Wang, Liang; Zeng, Hongmei; Sudenga, Staci L.; Campbell, Peter T.; Giovannucci, Edward; McGlynn, Katherine A.; Zhang, Xuehong; Epidemiology, Richard M. Fairbanks School of Public HealthBackground: Little is known about the role of early obesity or weight change during adulthood in the development of liver cancer and biliary tract cancer (BTC). Methods: We investigated the associations of body mass index (BMI) and weight trajectories with the risk of liver cancer and BTC in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). BMI was self-reported at ages 20, 50, and at enrollment. BMI trajectories were determined using latent class growth models. Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During a median follow-up of 15.9 years among 138,922 participants, 170 liver cancer and 143 BTC cases were identified. Compared with those whose BMI does not exceed 25 kg/m2, participants with BMI exceeding 25 kg/m2 at age 20 had increased risks of liver cancer (HR = 2.03, 95% CI: 1.26-3.28) and BTC (HR = 1.99, 95% CI: 1.16-3.39). Compared to participants maintaining normal BMI until enrollment, trajectory of normal weight at age 20 to obesity at enrollment was associated with increased risk for liver cancer (HR = 2.50, 95% CI: 1.55-4.04) and BTC (HR = 1.83, 95% CI: 1.03-3.22). Compared to adults with stable weight (+/-5kg) between age 20 to 50 years, weight gain ≥20 kg between ages 20 to 50 years had higher HRs of 2.24 (95%CI: 1.40-3.58) for liver cancer and 1.86 (95% CI: 1.12-3.09) for BTC. Conclusions: Being overweight/obese at age 20, and BMI trajectories that result in being overweight and/or obese, may increase risk for both liver cancer and BTC.Item 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 HealthFew 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.