Modeling childhood body mass index (BMI) trajectories, versus estimating change in BMI between specific ages, may improve prediction of later body-size-related outcomes. Prior studies of BMI trajectories are limited by restricted age periods and insufficient use of trajectory information.

Gestational diabetes mellitus (GDM) is associated with increased risk of pregnancy complications and adverse perinatal outcomes. GDM often reoccurs and is associated with increased risk of subsequent diagnosis of type 2 diabetes (T2D). To improve our understanding of the aetiological factors and molecular processes driving the occurrence of GDM, including the extent to which these overlap with T2D pathophysiology, the GENetics of Diabetes In Pregnancy Consortium assembled genome-wide association studies of diverse ancestry in a total of 5485 women with GDM and 347 856 without GDM. Through multi-ancestry meta-analysis, we identified five loci with genome-wide significant association (P < 5 × 10⁻⁸) with GDM, mapping to/near MTNR1B (P = 4.3 × 10⁻⁵⁴), TCF7L2 (P = 4.0 × 10⁻¹⁶), CDKAL1 (P = 1.6 × 10⁻¹⁴), CDKN2A-CDKN2B (P = 4.1 × 10⁻⁹) and HKDC1 (P = 2.9 × 10⁻⁸). Multiple lines of evidence pointed to the shared pathophysiology of GDM and T2D: (i) four of the five GDM loci (not HKDC1) have been previously reported at genome-wide significance for T2D; (ii) significant enrichment for associations with GDM at previously reported T2D loci; (iii) strong genetic correlation between GDM and T2D and (iv) enrichment of GDM associations mapping to genomic annotations in diabetes-relevant tissues and transcription factor binding sites. Mendelian randomization analyses demonstrated significant causal association (5% false discovery rate) of higher body mass index on increased GDM risk. Our results provide support for the hypothesis that GDM and T2D are part of the same underlying pathology but that, as exemplified by the HKDC1 locus, there are genetic determinants of GDM that are specific to glucose regulation in pregnancy.

Given that it is not feasible to use dual x-ray absorptiometry (DXA) or other reference methods to measure adiposity in all pediatric clinical and research settings, it is important to identify reasonable alternatives. Therefore, we sought to determine the extent to which other adiposity measures were correlated with DXA fat mass in school-aged children. In 1110 children aged 6.5-10.9 years in the pre-birth cohort Project Viva, we calculated Spearman correlation coefficients between DXA (n=875) and other adiposity measures including body mass index (BMI), skinfold thickness, circumferences, and bioimpedance. We also computed correlations between lean body mass measures. 50.0% of the children were female and 36.5% were non-white. Mean (SD) BMI was 17.2 (3.1) and total fat mass by DXA was 7.5 (3.9) kg. DXA total fat mass was highly correlated with BMI (rs=0.83), bioimpedance total fat (rs=0.87), and sum of skinfolds (rs=0.90), and DXA trunk fat was highly correlated with waist circumference (rs=0.79). Correlations of BMI with other adiposity indices were high, e.g., with waist circumference (rs=0.86) and sum of subscapular plus triceps skinfolds (rs=0.79). DXA fat-free mass and bioimpedance fat-free mass were highly correlated (rs=0.94). In school-aged children, BMI, sum of skinfolds, and other adiposity measures were strongly correlated with DXA fat mass. Although these measurement methods have limitations, BMI and skinfolds are adequate surrogate measures of relative adiposity in children when DXA is not practical.