Frank B Hu’s research while affiliated with Joslin Diabetes Center and other places

Background Dietary strategies for type 2 diabetes (T2DM) prevention have mainly focused on solid foods and nutrients. Emanating evidence suggests that beverage consumption in adulthood may also influence T2DM development, whereas the role of beverages during adolescence remains unknow. Objective To examine adolescent beverages consumption, and their changes from adolescence to adulthood in relation to T2DM risk in adulthood. Methods This prospective cohort study, conducted within the Nurses’ Health Study II (NHS II), enrolled 41,317 women who completed a food-frequency questionnaire (FFQ) regarding their diet in high school and had no diabetes, cardiovascular disease, or cancer at baseline (1997). Beverage consumption including coffee, tea, regular or diet soda, fruit juice or milk, was assessed using the FFQ. Cox proportional hazards models were utilized to estimate hazard ratios (HRs) for the association between beverage consumption in adolescence and risk of incident type 2 diabetes (T2DM) in adulthood, adjusting for potential confounders. Results During 725,650 person-years of follow-up, 2,844 participants developed T2DM. After adjustment for demographic, lifestyle and dietary risk factors, comparing ≥ 1 serving/day with non-consumers, adolescent coffee [HR, 0.86 (95% confidence interval: 0.75 to 0.98); P-trend = 0.02)] and orange juice [HR, 0.83 (0.71 to 0.96); P-trend = 0.0008)] consumption was associated with lower T2DM risk, whereas, regular soda [HR, 1.37 (1.20 to 1.57); P-trend < 0.0001)] and iced tea [HR, 1.41 (1.21 to 1.65); P-trend < 0.0001)] intake was associated with higher T2DM risk. Increased coffee intake from adolescence to adulthood in 1991 was associated with a lower T2DM risk [HR, 0.70 (0.61 to 0.80); P-trend < 0.0001), comparing ≥ + 3 servings/day with no change], whereas the opposite was observed for increased regular soda [HR, 1.20 (1.06 to 1.35); P-trend = 0.004), comparing ≥ + 1 or more servings/week with no change)] and diet soda consumption [HR, 1.59 (1.41 to 1.80); P-trend = 0.0002), comparing ≥ + 2 servings/day with no change]. Conclusion Adolescent consumption of coffee or orange juice intake was associated with a lower risk of T2DM, whereas the opposite was observed for intake of regular soda or iced tea. In addition, increased coffee intake was associated with a lower diabetes risk, whereas the opposite was observed for regular or diet soda intake. These data highlight a potentially important role of beverage intake at early life in the etiology of diabetes during adulthood.

Since dietary intake is challenging to directly measure in large-scale cohort studies, we often rely on self-reported instruments (e.g., food frequency questionnaires, 24-hour recalls, and diet records) developed in nutritional epidemiology. Those self-reported instruments are prone to measurement errors, which can lead to inaccuracies in the calculation of nutrient profiles. Currently, few computational methods exist to address this problem. In the present study, we introduce a deep-learning approach—Microbiome-based nutrient profile corrector (METRIC), which leverages gut microbial compositions to correct random errors in self-reported dietary assessments using 24-hour recalls or diet records. We demonstrate the excellent performance of METRIC in minimizing the simulated random errors, particularly for nutrients metabolized by gut bacteria in both synthetic and three real-world datasets. Additionally, we find that METRIC can still correct the random errors well even without including gut microbial compositions. Further research is warranted to examine the utility of METRIC to correct actual measurement errors in self-reported dietary assessment instruments.

Background Gestational age (GEAA) estimated by newborn DNA methylation (GAmAge) is associated with maternal prenatal exposures and immediate birth outcomes. However, the association of GAmAge with long-term overweight or obesity (OWO) trajectories is yet to be determined. Methods GAmAge was calculated for 831 children from a US predominantly urban, low-income, multi-ethnic birth cohort based on cord blood DNA methylation profile using Illumina EPIC array. Repeated anthropometric measurements aligned with pediatric primary care schedule allowed us to calculate body-mass-index percentiles (BMIPCT) at specific age and to define long-term weight trajectories from birth to 18 years. Results GAmAge was associated with BMIPCT trajectories, defined by 4 groups: stable (consistent OWO: “early OWO”; constant normal weight: “NW”) or non-stable (OWO by year 1 of follow-up: “late OWO”; OWO by year 6 of follow-up: “NW to very late OWO”). GAmAge differentiated between the group with consistently normal BMIPCT pattern and the non-stable groups with late and very late OWO development. Such differentiation was observed in the age periods of birth to 1year, 3years, 6years, 10years, and 14years (p < 0.05 for all). The findings persisted after adjusting for GEAA, maternal smoking, delivery method, and child’s sex in multivariate models. Birth weight was a mediator for the GAmAge effect on OWO status for specific groups at multiple age periods. Conclusions GAmAge is associated with BMIPCT trajectories from birth to age 18 years, independent of GEAA and birth weight. If further confirmed, GAmAge may serve as an early biomarker for predicting BMI trajectory to inform early risk assessment and prevention of OWO. Trial registration ClinicalTrials.gov (NCT03228875).

Pubertal timing varies considerably and is associated with later health outcomes. We performed multi-ancestry genetic analyses on ~800,000 women, identifying 1,080 signals for age at menarche. Collectively, these explained 11% of trait variance in an independent sample. Women at the top and bottom 1% of polygenic risk exhibited ~11 and ~14-fold higher risks of delayed and precocious puberty, respectively. We identified several genes harboring rare loss-of-function variants in ~200,000 women, including variants in ZNF483, which abolished the impact of polygenic risk. Variant-to-gene mapping approaches and mouse gonadotropin-releasing hormone neuron RNA sequencing implicated 665 genes, including an uncharacterized G-protein-coupled receptor, GPR83, which amplified the signaling of MC3R, a key nutritional sensor. Shared signals with menopause timing at genes involved in DNA damage response suggest that the ovarian reserve might signal centrally to trigger puberty. We also highlight body size-dependent and independent mechanisms that potentially link reproductive timing to later life disease.

Importance Sleep duration and moderate-to-vigorous physical activity (MVPA) are associated with healthy aging, but the associations of sedentary behaviors and light-intensity physical activity (LPA) with healthy aging are still unclear. Objective To examine the independent association of sedentary behaviors and LPA with healthy aging, and to estimate the theoretical association of replacing sedentary behavior with LPA, MVPA, or sleep with healthy aging. Design, Setting, and Participants In this cohort study using data from the Nurses’ Health Study, participants aged 50 years or older and free of major chronic diseases in 1992 were prospectively followed up for 20 years. Data were analyzed from January to May 2022. Exposures Three measures for sedentary behaviors (hours watching television, sitting at work, and other sitting at home) and 2 measures for LPA (hours of standing or walking around at home [LPA-Home] and at work [LPA-Work]). Main Outcomes and Measures Healthy aging was defined as survival to at least age 70 years with maintenance of 4 health domains (ie, no major chronic diseases and no impairment in subjective memory, physical function, or mental health). The isotemporal substitution model was used to evaluate the potential impact on healthy aging of replacing 1 hour of 1 behavior with equivalent duration of another. Results Among 45 176 participants (mean [SD] age, 59.2 [6.0] years), 3873 (8.6%) women achieved healthy aging. After adjustment for covariates including MVPA, each increment of 2 hours per day in sitting watching television was associated with a 12% (95% CI, 7%-17%) reduction in the odds of healthy aging. In contrast, each increase of 2 hours per day in LPA-Work was associated with a 6% (95% CI, 3%-9%) increase in the odds of healthy aging. Replacing 1 hour of sitting watching television with LPA-Home (OR, 1.08; 95% CI, 1.05-1.12), LPA-Work (OR, 1.10; 95% CI, 1.07-1.14), or MVPA (OR, 1.28; 95% CI, 1.23-1.34) was associated with increased odds of healthy aging. Among participants who slept 7 hours per day or less, replacing television time with sleep was also associated with increased odds of healthy aging. Conclusions and Relevance In this cohort study, longer television watching time decreased odds of healthy aging, whereas LPA and MVPA increased odds of healthy aging and replacing sitting watching television with LPA or MVPA, or with sleep in those who slept 7 hours per day or less, was associated with increased odds of healthy aging, providing evidence for rearranging 24-hour behavior to promote overall health.