Browsing by Author "Ma, Jiantao"

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    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 Health
    Background: 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.
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    Epigenetic age acceleration and cognitive resilience in the Framingham Heart Study
    (Wiley, 2025-01-03) Dacey, Ryan; Durape, Shruti; Wang, Mengyao; Hwang, Phillip H.; Gurnani, Ashita S.; Ang, Ting Fang Alvin; Devine, Sherral A.; Choi, Seo-Eun; Lee, Michael L.; Scollard, Phoebe; Gibbons, Laura E.; Mukherjee, Shubhabrata; Trittschuh, Emily H.; Sherva, Richard; Dumitrescu, Logan C.; Hohman, Timothy J.; Cuccaro, Michael L.; Saykin, Andrew J.; Crane, Paul K.; Li, Yi; Levy, Daniel; Ma, Jiantao; Liu, Chunyu; Lunetta, Kathryn L.; Au, Rhoda; Farrer, Lindsay A.; Mez, Jesse; Radiology and Imaging Sciences, School of Medicine
    Background: There is growing evidence that epigenetic age acceleration may predict late life cognitive decline and dementia, but it is unknown whether this is due to accelerated neurodegeneration or reduction in cognitive resilience. We examined the relationship between epigenetic clocks and domain specific neuropsychological (NP) factor scores, mild cognitive impairment (MCI), Alzheimer’s Disease (AD), and all‐cause dementia, before and after accounting for plasma total tau (t‐tau), a marker of neurodegeneration. Method: DNA methylation and plasma t‐tau (Simoa assay; Quanterix) data from 2091 Framingham Heart Study Offspring cohort participants were generated from blood at the same Exam 8 visit (2005‐2008). Three epigenetic clock measures: DunedinPACE, PC PhenoAge, and PC GrimAge were estimated from the DNA methylation data. Longitudinal NP factor scores were previously derived for memory, language, and executive function using confirmatory factor analysis. We tested the association of epigenetic age acceleration with cognitive trajectories using linear mixed effects models and with time to MCI, all‐cause dementia and AD using Cox‐proportional hazard models. Models were run with and without adjustment for plasma t‐tau. All models included APOE ε4‐carrier status, education, smoking, age, and sex as covariates. Epigenetic measures were standardized in all models. Result: At Exam 8, the sample was, on average, 66.3 (SD = 9.0) years of age, 54.8% female, and had 16.4 (SD = 2.7) years of education. DundeinPACE was significantly associated with faster decline in executive function (βtimeXepi_age = ‐0.005, 95% CI:[‐0.009,‐0.002], p = 0.0020), but not with baseline executive function. Older PhenoAge (βepi_age = ‐0.041, 95% CI:[‐0.067,‐0.014], p = 0.0028) and GrimAge (βepi_age = ‐0.042, 95% CI:[‐0.073,‐0.011], p = 0.0084) were significantly associated with worse baseline executive function, but not with rate of decline. Older PhenoAge also was significantly associated with worse baseline memory (βepi_age = ‐0.037, 95% CI:[‐0.061,‐0.012], p = 0.0036). DunedinPACE was significantly associated with time to MCI (HR = 1.20, 95% CI:[1.06,1.35], p = 0.0034), AD (HR = 1.30, 95% CI:[1.07,1.57], p = 0.0068) and all‐cause dementia (HR = 1.30, 95% CI:[1.10,1.53], p = 0.0017). Results remained similar after adjustment for plasma t‐tau. Conclusion: Epigenetic age acceleration may be a marker of cognitive resilience, particularly in executive function. Of the three epigenetic clocks examined, DundedinPACE showed the most robust associations with cognitive resilience, with lower DunedinPACE associated with greater cognitive resilience.
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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.