Background: Despite recent decreases in the cost of sequencing, shotgun metagenome sequencing remains more expensive compared with 16S rRNA amplicon sequencing. Methods have been developed to predict the functional profiles of microbial communities based on their taxonomic composition. In this study, we evaluated the performance of three commonly used metagenome prediction tools (PICRUSt, PICRUSt2, and Tax4Fun) by comparing the significance of the differential abundance of predicted functional gene profiles to those from shotgun metagenome sequencing across different environments. Results: We selected 7 datasets of human, non-human animal, and environmental (soil) samples that have publicly available 16S rRNA and shotgun metagenome sequences. As we would expect based on previous literature, strong Spearman correlations were observed between predicted gene compositions and gene relative abundance measured with shotgun metagenome sequencing. However, these strong correlations were preserved even when the abundance of genes were permuted across samples. This suggests that simple correlation coefficient is a highly unreliable measure for the performance of metagenome prediction tools. As an alternative, we compared the performance of genes predicted with PICRUSt, PICRUSt2, and Tax4Fun to sequenced metagenome genes in inference models associated with metadata within each dataset. With this approach, we found reasonable performance for human datasets, with the metagenome prediction tools performing better for inference on genes related to “housekeeping” functions. However, their performance degraded sharply outside of human datasets when used for inference. Conclusion: We conclude that the utility of PICRUSt, PICRUSt2, and Tax4Fun for inference with the default database is likely limited outside of human samples and that development of tools for gene prediction specific to different non-human and environmental samples is warranted.

Background: Prior epidemiological and animal work has linked in utero exposure to ambient air pollutants (AAP) with accelerated postnatal weight gain, which is predictive of increased cardiometabolic risk factors in childhood and adolescence. However, few studies have assessed changes in infant body composition or multiple pollutant exposures. Therefore, the objective of this study was to examine relationships between prenatal residential AAP exposure with infant growth and adiposity. Methods: Residential exposure to AAP (particulate matter < 2.5 and 10 microns in aerodynamic diameter [PM_2.5, PM₁₀]; nitrogen dioxide [NO₂]; ozone [O₃]; oxidative capacity [O_x^wt: redox-weighted oxidative potential of O₃ and NO₂]) was modeled by spatial interpolation of monitoring stations via an inverse distance-squared weighting (IDW2) algorithm for 123 participants from the longitudinal Mother’s Milk Study, an ongoing cohort of Hispanic mother-infant dyads from Southern California. Outcomes included changes in infant growth (weight, length), total subcutaneous fat (TSF; calculated via infant skinfold thickness measures) and fat distribution (umbilical circumference, central to total subcutaneous fat [CTSF]) and were calculated by subtracting 1-month measures from 6-month measures. Multivariable linear regression was performed to examine relationships between prenatal AAP exposure and infant outcomes. Models adjusted for maternal age, pre-pregnancy body mass index, socioeconomic status, infant age, sex, and breastfeeding frequency. Sex interactions were tested, and effects are reported for each standard deviation increase in exposure. Results: NO₂ was associated with greater infant weight gain (β = 0.14, p = 0.02) and TSF (β = 1.69, p = 0.02). PM₁₀ and PM_2.5 were associated with change in umbilical circumference (β = 0.73, p = 0.003) and TSF (β = 1.53, p = 0.04), respectively. Associations of O_x^wt (p_interactions < 0.10) with infant length change, umbilical circumference, and CTSF were modified by infant sex. O_x^wt was associated with attenuated infant length change among males (β = -0.60, p = 0.01), but not females (β = 0.16, p = 0.49); umbilical circumference among females (β = 0.92, p = 0.009), but not males (β = -0.00, p = 0.99); and CTSF among males (β = 0.01, p = 0.03), but not females (β = 0.00, p = 0.51). Conclusion: Prenatal AAP exposure was associated with increased weight gain and anthropometric measures from 1-to-6 months of life among Hispanic infants. Sex-specific associations suggest differential consequences of in utero oxidative stress. These results indicate that prenatal AAP exposure may alter infant growth, which has potential to increase childhood obesity risk.

Growing evidence indicates that human milk oligosaccharides (HMOs) are important bioactive compounds that enhance health and developmental outcomes in breastfed babies. Maternal dietary intake likely contributes to variation in HMO composition, but studies identifying diet–HMO relationships are few and inconsistent. This study aimed to investigate how the maternal intake of macronutrients and micronutrients—specifically proteins, fats, vitamins, and minerals—associated with HMOs at 1 month (n = 210), 6 months (n = 131), and 12 months postpartum (n = 84). Several associations between maternal dietary factors and HMO profiles were identified utilizing partial correlation analysis. For example, maternal free sugar (rho = −0.02, p < 0.01), added sugar (rho = −0.22, p < 0.01), and sugary sweetened beverage (rho = −0.22, p < 0.01) intake were negatively correlated with the most abundant HMO, 2′-fucosyllactose (2′-FL), at 1 month, suggesting that higher sugar consumption was associated with reduced levels of 2′-FL. Further, vitamins D, C, K, and the minerals zinc and potassium were positively correlated with 2′-FL at 1 month (p_All < 0.05). For the longitudinal analysis, a mixed-effects linear regression model revealed significant associations between maternal vitamin intake and HMO profiles over time. For example, for each unit increase in niacin intake, there was a 31.355 nmol/mL increase in 2′-FL concentration (p = 0.03). Overall, the results provide additional evidence supporting a role for maternal nutrition in shaping HMO profiles, which may inform future intervention strategies with the potential of improving infant growth and development through optimal HMO levels in mothers’ milk.