Discrete placental gene expression signatures accompany diabetic disease classifications during pregnancy.

BACKGROUND: Gestational diabetes mellitus affects up to 10% of pregnancies and is classified into subtypes gestational diabetes subtype A1 (GDMA1) (managed by lifestyle modifications) and gestational diabetes subtype A2 (GDMA2) (requiring medication). However, whether these subtypes are distinct clinical entities or more reflective of an extended spectrum of normal pregnancy endocrine physiology remains unclear. OBJECTIVE: Integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-sequencing (scRNA-seq), and spatial transcriptomics harbors the potential to reveal disease gene signatures in subsets of cells and tissue microenvironments. We aimed to combine these high-resolution technologies with rigorous classification of diabetes subtypes in pregnancy. We hypothesized that differences between preexisting type 2 and gestational diabetes subtypes would be associated with altered gene expression profiles in specific placental cell populations. STUDY DESIGN: In a large case-cohort design, we compared validated cases of GDMA1, GDMA2, and type 2 diabetes mellitus (T2DM) to healthy controls by bulk RNA-seq (n=54). Quantitative analyses with reverse transcription and quantitative PCR of presumptive genes of significant interest were undertaken in an independent and nonoverlapping validation cohort of similarly well-characterized cases and controls (n=122). Additional integrated analyses of term placental single-cell, single-nuclei, and spatial transcriptomics data enabled us to determine the cellular subpopulations and niches that aligned with the GDMA1, GDMA2, and T2DM gene expression signatures at higher resolution and with greater confidence. RESULTS: Dimensional reduction of the bulk RNA-seq data revealed that the most common source of placental gene expression variation was the diabetic disease subtype. Relative to controls, we found 2052 unique and significantly differentially expressed genes (-22 thresholds; q<0.05 Wald Test) among GDMA1 placental specimens, 267 among GDMA2, and 1520 among T2DM. Several candidate marker genes (chorionic somatomammotropin hormone 1 [CSH1], period circadian regulator 1 [PER1], phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta [PIK3CB], forkhead box O1 [FOXO1], epidermal growth factor receptor [EGFR], interleukin 2 receptor subunit beta [IL2RB], superoxide dismutase 3 [SOD3], dedicator of cytokinesis 5 [DOCK5], suppressor of glucose, and autophagy associated 1 [SOGA1]) were validated in an independent and nonoverlapping validation cohort (q<0.05 Tukey). Functional enrichment revealed the pathways and genes most impacted for each diabetes subtype, and the degree of proximal similarity to other subclassifications. Surprisingly, GDMA1 and T2DM placental signatures were more alike by virtue of increased expression of chromatin remodeling and epigenetic regulation genes, while albumin was the top marker for GDMA2 with increased expression of placental genes in the wound healing pathway. Assessment of these gene signatures in single-cell, single-nuclei, and spatial transcriptomics data revealed high specificity and variability by placental cell and microarchitecture types. For example, at the cellular and spatial (eg, microarchitectural) levels, distinguishing features were observed in extravillous trophoblasts (GDMA1) and macrophages (GDMA2). Lastly, we utilized these data to train and evaluate 4 machine learning models to estimate our confidence in predicting the control or diabetes status of placental transcriptome specimens with no available clinical metadata. CONCLUSION: Consistent with the distinct association of perinatal outcome risk, placentae from GDMA1, GDMA2, and T2DM-affected pregnancies harbor unique gene signatures that can be further distinguished by altered placental cellular subtypes and microarchitectural niches.

Shift work and sleep duration are associated with adverse pregnancy outcomes in a predominantly Latinx population with high rates of obesity.

BACKGROUND: Shift work has been associated with adverse pregnancy outcomes. The objective of this study was to evaluate the association between sleep disturbances and adverse pregnancy outcomes. METHODS AND FINDINGS: This was a secondary analysis of a prospective study of participants enrolled in a prospective observational study wherein gravidae were screened for sleep apnea (2010-2012). A screening questionnaire with standard sleep apnea questionnaires as well as novel items about shift work and nocturnal sleep duration was administered at a prenatal care visit. Short sleep duration was defined as less than 7 hours. Prolonged sleep duration was defined as greater than 9 hours. In a cohort of 1125 pregnant people, 9.4% reported shift work at the time of screening. Gravidae who reported shift work were more likely than gravidae who reported no shift work to develop preeclampsia (28.3% versus 13.0%, P<0.001), preeclamspsia with severe features (16.0% versus 8.5%, P = 0.010), gestational diabetes (28.3% versus 19.9%, P = 0.041), and a composite of adverse obstetric outcomes (61.3% versus 47.8%, P = 0.008). After adjusting for potentially confounding variables, shift work was associated with an increased risk for preeclampsia with (adjusted relative risk (aRR) 1.70, 95% CI 1.03-2.79, p = 0.036) and without (aRR 2.03, 95% CI 1.43-2.90, p<0.001) severe features, and gestational diabetes mellitus class A1 (aRR 1.47, 95% CI 1.05-2.05, p = 0.023) and class A2 (aRR 1.67, 95% CI 1.13-2.44, p = 0.009). Sleep duration was associated with gestational diabetes (31.3% among those with short sleep duration, 25.2% among those with normal sleep duration and 14.0% among those with prolonged sleep duration, P<0.001) and gestational diabetes class A2 (29.5%, 17.9%, and 10.1%, respectively, P<0.001). Gravidae with prolonged sleep duration experienced less composite adverse pregnancy outcomes at 42.6% compared to 57.4% for those with short sleep duration or 52.5% for those with normal sleep duration, P = 0.002. CONCLUSIONS: Shift work and sleep duration are both associated with adverse pregnancy outcomes. Further research on the impact of sleep disturbance on pregnancy outcomes is warranted.

Increasing maternal obesity is associated with alterations in both maternal and neonatal thyroid hormone levels.

OBJECTIVE: Obesity is associated with alterations in thyroid hormone (TH) levels in obese, pregnant individuals. The maintenance of TH levels throughout gestation is important for proper foetal development. The aim of this study was to measure levels of fT3, fT4 and TSH in maternal and matched cord blood serum from normal weight, overweight and obese gravidae to determine alterations in maternal and neonatal TH levels by virtue of maternal obesity. DESIGN, SETTING, SUBJECTS, OUTCOME MEASURES: ELISA was utilized to measure fT3, fT4 and TSH levels from banked, matched maternal and neonatal (cord blood) serum (N = 205 matched pairs). Data were stratified according to prepregnancy or first trimester BMI. RESULTS: Both maternal and neonatal fT3 levels consistently increased with increasing maternal obesity, and maternal and neonatal fT3 were significantly correlated (r = 0·422, P < 0·001). Maternal and neonatal fT3 were also significantly associated with birthweight (ß = 0·155, P = 0·027 and ß = 0·171, P = 0·018, respectively). Both the maternal and neonatal fT3 to fT4 ratio significantly increased with increasing maternal obesity. We further found that excess gestational weight gain was associated with a decrease in maternal fT4 compared with gravidae who had insufficient gestational weight gain (0·86 ± 0·17 vs 0·95 ± 0·22, P < 0·01). CONCLUSION: Maternal obesity is not only associated with maternal alterations in TH, but with accompanying neonatal changes. Because both maternal obesity and alterations in TH levels are associated with childhood obesity, based on these findings and our prior analyses in a nonhuman primate model, we propose that changes in fT3 levels in the offspring of obese mothers may be a potential molecular mediator of foetal overgrowth and childhood obesity.