Objective To identify metabolite patterns associated with childhood obesity, to examine relations of these patterns with measures of adiposity and cardiometabolic risk, and to evaluate associations with maternal peripartum characteristics. Design and Methods We employed untargeted metabolomic profiling to quantify metabolites in plasma of 262 children (6–10 years). We used principal components analysis to consolidate 345 metabolites into 18 factors and identified two that differed between obese (BMI ≥95%ile; n=84) and lean children (BMI<85%ile; n=150). We then investigated relations of these factors with adiposity (fat mass, BMI, skinfold thicknesses) and cardiometabolic biomarkers (HOMA-IR, triglycerides, leptin, adiponectin, hsCRP, IL-6) using multivariable linear regression. Finally, we examined associations of maternal pre-pregnancy obesity, gestational weight gain, and gestational glucose tolerance with the offspring metabolite patterns. Results A branched-chain amino acid (BCAA)-related pattern and an androgen hormone pattern were higher in obese vs. lean children. Both patterns were associated with adiposity and worse cardiometabolic profiles. For example, each increment in the BCAA and androgen pattern scores corresponded with 6% (95% CI: 1%, 13%) higher HOMA-IR. Children of obese mothers had 0.61 (0.13, 1.08) higher BCAA score than their counterparts. Conclusions BCAA and androgen metabolites were associated with adiposity and cardiometabolic risk during mid-childhood. Maternal obesity may contribute to altered offspring BCAA metabolism.

Purpose To examine the relations of maternal pre-pregnancy body mass index (ppBMI) and gestational weight gain (GWG) with offspring cardiometabolic health. Design We studied 1,090 mother-child pairs in Project Viva, a Boston-area pre-birth cohort. We measured overall (DXA total fat; BMI z-score) and central adiposity (DXA trunk fat), and SBP in offspring at 6–10 years. Fasting bloods (n=687) were assayed for insulin and glucose (for calculation of HOMA-IR), triglycerides, leptin, adiponectin, hsCRP and IL-6. Using multivariable linear regression, we examined differences in offspring outcomes per 1 SD maternal ppBMI and GWG. Results After adjustment for confounders, each 5 kg/m2 higher ppBMI corresponded with 0.92 (95% CI: 0.70, 1.14) kg higher total fat, 0.27 (0.21, 0.32) BMI z-score, and 0.39 (0.29, 0.49) kg trunk fat. ppBMI was also positively associated with HOMA-IR, leptin, hsCRP, IL-6, and SBP; and lower adiponectin. Each 5 kg of GWG predicted greater adiposity (0.33 [0.11, 0.54] kg total fat; 0.14 [0.04, 0.23] kg trunk fat) and higher leptin (6% [0%, 13%]) in offspring after accounting for confounders and ppBMI. Conclusions Children born to heavier mothers have more overall and central fat and greater cardiometabolic risk. Offspring of women with higher GWG had greater adiposity and higher leptin.

A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data.