Higher Maternal Body Mass Index Is Associated With Lower Placental Expression of EPYC: A Genome-Wide Transcriptomic Study

CONTEXT

Elevated body mass index (BMI) in pregnancy is associated with adverse maternal and fetal outcomes. The placental transcriptome may elucidate molecular mechanisms underlying these associations.

OBJECTIVE

We examined the association of first-trimester maternal BMI with the placental transcriptome in the Gen3G prospective cohort.

METHODS

We enrolled participants at 5 to 16 weeks of gestation and measured height and weight. We collected placenta samples at delivery. We performed whole-genome RNA sequencing using Illumina HiSeq 4000 and aligned RNA sequences based on the GTEx v8 pipeline. We conducted differential gene expression analysis of over 15 000 genes from 450 placental samples and reported the change in normalized gene expression per 1-unit increase in log2 BMI (kg/m2) as a continuous variable using Limma Voom. We adjusted models for maternal age, fetal sex, gestational age at delivery, gravidity, and surrogate variables accounting for technical variability. We compared participants with BMI of 18.5 to 24.9 mg/kg2 (N = 257) vs those with obesity (BMI ≥30 kg/m2, N = 82) in secondary analyses.

RESULTS

Participants' mean ± SD age was 28.2 ± 4.4 years and BMI was 25.4 ± 5.5 kg/m2 in early pregnancy. Higher maternal BMI was associated with lower placental expression of EPYC (slope = -1.94, false discovery rate [FDR]-adjusted P = 7.3 × 10-6 for continuous BMI; log2 fold change = -1.35, FDR-adjusted P = 3.4 × 10-3 for BMI ≥30 vs BMI 18.5-24.9 kg/m2) and with higher placental expression of IGFBP6, CHRDL1, and CXCL13 after adjustment for covariates and accounting for multiple testing (FDR < 0.05).

CONCLUSION

Our genome-wide transcriptomic study revealed novel genes potentially implicated in placental biologic response to higher maternal BMI in early pregnancy.

IL-6 Trans-Signaling Is Increased in Diabetes, Impacted by Glucolipotoxicity, and Associated With Liver Stiffness and Fibrosis in Fatty Liver Disease

Many people living with diabetes also have nonalcoholic fatty liver disease (NAFLD). Interleukin-6 (IL-6) is involved in both diseases, interacting with both membrane-bound (classical) and circulating (trans-signaling) soluble receptors. We investigated whether secretion of IL-6 trans-signaling coreceptors are altered in NAFLD by diabetes and whether this might associate with the severity of fatty liver disease. Secretion patterns were investigated with use of human hepatocyte, stellate, and monocyte cell lines. Associations with liver pathology were investigated in two patient cohorts: 1) biopsy-confirmed steatohepatitis and 2) class 3 obesity. We found that exposure of stellate cells to high glucose and palmitate increased IL-6 and soluble gp130 (sgp130) secretion. In line with this, plasma sgp130 in both patient cohorts positively correlated with HbA1c, and subjects with diabetes had higher circulating levels of IL-6 and trans-signaling coreceptors. Plasma sgp130 strongly correlated with liver stiffness and was significantly increased in subjects with F4 fibrosis stage. Monocyte activation was associated with reduced sIL-6R secretion. These data suggest that hyperglycemia and hyperlipidemia can directly impact IL-6 trans-signaling and that this may be linked to enhanced severity of NAFLD in patients with concomitant diabetes.

ARTICLE HIGHLIGHTS

IL-6 and its circulating coreceptor sgp130 are increased in people with fatty liver disease and steatohepatitis. High glucose and lipids stimulated IL-6 and sgp130 secretion from hepatic stellate cells. sgp130 levels correlated with HbA1c, and diabetes concurrent with steatohepatitis further increased circulating levels of all IL-6 trans-signaling mediators. Circulating sgp130 positively correlated with liver stiffness and hepatic fibrosis. Metabolic stress to liver associated with fatty liver disease might shift the balance of IL-6 classical versus trans-signaling, promoting liver fibrosis that is accelerated by diabetes.

Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features

Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta1-42, geriatric depression scale (GDS) scores, and anxiety scores. Interestingly, many of the predictive features in our model have previously been associated with Alzheimer's disease, showing the importance of assessing Alzheimer's disease pathology in patients with Parkinson's disease.