Clinical, Social, and Genetic Factors Associated with Obesity at 12 Months of Age.

OBJECTIVE: To examine genomic, social, and clinical risk factors of ≥85 weight for length percentile (WFLP) at 12 months. STUDY DESIGN: Children in this study had whole-genome sequencing, and clinical and social data were collected. WFLPs at 12 months of age were grouped as follows: (1) <85th, (2) ≥85th to <95th, (3) ≥95th to <99th, and (4) ≥99th. Whole-genome sequencing data were used to analyze rare and common variants, and association of clinical and social factors was examined. RESULTS: A total of 690 children were included; WFLPs were 422 (61.2%) <85th, 112 (16.2%) ≥85th-<95th, 89 (12.9%) ≥95th-<99th, and 67 (9.7%) ≥99th. Family-related risk factors associated with greater WFLP were greater paternal body mass index, WFLP ≥99th OR 1.10 (1.03-1.16), and greater than recommended weight gain in pregnancy, WFLP ≥85th-<95th OR 1.90 (1.09-3.26). More breast milk at 6 months was protective factor: WFLP ≥85th-<95th, OR 0.98 (0.97-0.99), WFLP ≥95th-<99th OR 0.98 (0.97-0.99), and WFLP ≥99th OR 0.98 (0.96-0.99). Although none of the variants reached genome-wide significance, there was a trend toward increased prevalence of genetic variants within or near genes previously associated with obesity in children with WFLP ≥99th. CONCLUSION: This cross-sectional study identified several modifiable factors, including increased weight gain in pregnancy and decreased breast milk at 6 months, associated with greater WFLP at 12 months. Strong genetic factors were not identified.

Prenatal exposure to PM2.5 and birth weight: A pooled analysis from three North American longitudinal pregnancy cohort studies.

A common practice when analyzing multi-site epidemiological data is to include a term for 'site' to account for unmeasured effects at each location. This practice should be carefully considered when site can have complex relationships with important demographic and exposure variables. We leverage data from three longitudinal North American pregnancy cohorts to demonstrate a novel method to assess study heterogeneity and potential combinability of studies for pooled analyses in order to better understand how to consider site in analyses. Results from linear regression and fixed effects meta-regression models run both prior to and following the proposed combinability analyses were compared. In order to exemplify this approach, we examined associations between prenatal exposure to particulate matter and birth weight. Analyses included mother-child dyads (N=1966) from the Asthma Coalition on Community Environment and Social Stress (ACCESS) Project and the PRogramming of Intergenerational Stress Mechanisms (PRISM) study in the northeastern United States, and the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study in Mexico City. Mothers' daily third trimester exposure to particulate matter≤2.5µm in diameter (PM2.5) was estimated using a validated satellite-based spatio-temporally resolved model in all studies. Fenton birth weight for gestational age z-scores were calculated. Linear regression analyses within each cohort separately did not find significant associations between PM2.5 averaged over the third trimester and Fenton z-scores. The initial meta-regression model also did not find significant associations between prenatal PM2.5 and birthweight. Next, propensity scores and log linear models were used to assess higher order interactions and determine if sites were comparable with regard to sociodemographics and other covariates; these analyses demonstrated that PROGRESS and ACCESS were combinable. Adjusted linear regression models including a 2-level site variable according to the pooling indicated by the log linear models (ACCESS and PROGRESS as one level and PRISM as another) revealed that a 5µg/m3 increase in PM2.5 was associated with a 0.075 decrease in Fenton z-score (p<0.0001); linear models including a 3-level site variable did not reveal significant associations. By assessing the combinability of heterogeneous populations prior to combining data using a method that more optimally accounts for underlying cohort differences, we were able to identify significant associations between prenatal PM2.5 exposure and birthweight that were not detected using standard methods.