Pancreatic Beta Cell Function in Infants Varies by Maternal Weight.

The Asian Indian Beta Cell function (ABCs) in Infants Study examined the associations of maternal weight on infant pancreatic beta cell function across 7 months postpartum. Pregnant women aged 18-35 years were recruited in Hyderabad, India. Women were classified by first trimester weight as underweight (UW), BMI < 18.5 kg/m2; normal weight (NW), BMI 18.5-22.9 kg/m2; or overweight (OW), BMI 23.0 through <28.5 kg/m2. At age > 7 months, infants had an oral glucose tolerance test (OGTT, 1.75 g glucose/kg bodyweight) following a 3 h fast. Infant blood samples were assayed for C-peptide and glucose. Infant beta cell function (HOMA2-B; disposition index, DI) and insulin resistance (HOMA2-IR) were compared across maternal weight groups. Mothers (UW n = 63; NW n = 43; OW n = 29) had similar age at delivery and second trimester 50 g glucose challenge test results. Cord HOMA2-B values were 51% greater for IUW (83.5, SD 55.2) and 44% greater for IOW (79.9, SD 60.8) vs. INW (55.4, SD 51.5), forming a U-shaped relationship between maternal weight and HOMA2-B. No qualitative differences in HOMA2-IR were found at birth. However, at 7 months postpartum, HOMA2-IR changed most within IUW (-64% median reduction) and changed the least in IOW (-7% median reduction). At seven months postpartum, DI was higher in IUW vs. the other groups (geometric mean IUW 1.9 SD 2.5; INW 1.3 SD 2.6 or vs. IOW mean 1.2 SD 3.7), reflecting a +49% difference in DI. Evidence from this study illustrates adaptations in the pancreatic functional response of infants associated with the maternal nutritional environment.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 µg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 µg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 µg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 µg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.