Changes in fetal DNA methylation (DNAm) of the leptin (LEP) gene have been associated with exposure to maternal hyperglycemia, but their links with childhood obesity risk are still unclear. We investigated the association between maternal hyperglycemia, placental LEP DNAm (25 5′-C-phosphate-G-3′ (CpG) sites), neonatal leptinemia, and adiposity (i.e., BMI and skinfold thickness (ST) (subscapular (SS) + triceps (TR) skinfold measures, and the ratio of SS:TR) at 3-years-old, in 259 mother–child dyads, from Gen3G birth cohort. We conducted multivariate linear analyses adjusted for gestational age at birth, sex of the child, age at follow-up, and cellular heterogeneity. We assessed the causal role of DNAm in the association between maternal glycemia and childhood outcomes, using mediation analysis. We found three CpGs associated with neonatal leptinemia (p ≤ 0.002). Of these, cg05136031 and cg15758240 were also associated with BMI (β = −2.69, p = 0.05) and fat distribution (β = −0.581, p = 0.05) at 3-years-old, respectively. Maternal glycemia was associated with DNAm at cg15758240 (β = −0.01, p = 0.04) and neonatal leptinemia (β = 0.19, p = 0.004). DNAm levels at cg15758240 mediates 0.8% of the association between maternal glycemia and neonatal leptinemia (p < 0.001). Our results support that DNAm regulation of the leptin pathway in response to maternal glycemia might be involved in programming adiposity in childhood.

Background During pregnancy, maternal metabolism undergoes substantial changes to support the developing fetus. Such changes are finely regulated by different mechanisms carried out by effectors such as microRNAs (miRNAs). These small non-coding RNAs regulate numerous biological functions, mostly through post-transcriptional repression of gene expression. miRNAs are also secreted in circulation by numerous organs, such as the placenta. However, the complete plasmatic microtranscriptome of pregnant women has still not been fully described, although some miRNA clusters from the chromosome 14 (C14MC) and the chromosome 19 (C19MC and miR-371-3 cluster) have been proposed as being specific to pregnancy. Our aims were thus to describe the plasma microtranscriptome during the first trimester of pregnancy, by assessing the differences with non-pregnant women, and how it varies between the 4th and the 16th week of pregnancy. Methods Plasmatic miRNAs from 436 pregnant (gestational week 4 to 16) and 15 non-pregnant women were quantified using Illumina HiSeq next-generation sequencing platform. Differentially abundant miRNAs were identified using DESeq2 package (FDR q-value ≤ 0.05) and their targeted biological pathways were assessed with DIANA-miRpath. Results A total of 2101 miRNAs were detected, of which 191 were differentially abundant (fold change < 0.05 or > 2, FDR q-value ≤ 0.05) between pregnant and non-pregnant women. Of these, 100 miRNAs were less and 91 miRNAs were more abundant in pregnant women. Additionally, the abundance of 57 miRNAs varied according to gestational age at first trimester, of which 47 were positively and 10 were negatively associated with advancing gestational age. miRNAs from the C19MC were positively associated with both pregnancy and gestational age variation during the first trimester. Biological pathway analysis revealed that these 191 (pregnancy-specific) and 57 (gestational age markers) miRNAs targeted genes involved in fatty acid metabolism, ECM-receptor interaction and TGF-beta signaling pathways. Conclusion We have identified circulating miRNAs specific to pregnancy and/or that varied with gestational age in first trimester. These miRNAs target biological pathways involved in lipid metabolism as well as placenta and embryo development, suggesting a contribution to the maternal metabolic adaptation to pregnancy and fetal growth.

IntroductionGestational diabetes mellitus (GDM) is a consequence of an imbalance between insulin sensitivity (IS) and secretion during pregnancy. MicroRNAs (miRNAs) are small and secreted RNA molecules stable in blood and known to regulate physiological processes including glucose homeostasis. The aim of this study was to identify plasmatic miRNAs detectable in early pregnancy predicting IS at 24th-29th week of pregnancy.Research design and methodsWe quantified circulating miRNAs in 421 women in plasma collected at 9.6±2.2 weeks of pregnancy using next-generation sequencing.Resultswe detected 2170 miRNAs: 39 (35 positively and 4 negatively) were associated with IS as estimated by the Matsuda Index at 26.4±1.0 weeks of pregnancy. Lasso regression identified 18 miRNAs independently predicting Matsuda Index-estimated IS. Together with gestational age, maternal age and body mass index at first trimester, they explain 36% of IS variance in late second trimester of pregnancy. These miRNAs regulate fatty acid biosynthesis and metabolism among other pathways.ConclusionsIn summary, we have identified first trimester plasmatic miRNAs predictive of Matsuda Index-estimated IS in late second trimester of pregnancy. These miRNAs could also contribute to initiate and support IS adaptation to pregnancy potentially through lipid metabolism regulation.

Studies have linked maternal pre-pregnancy obesity and hyperglycaemia with metabolic and neurodevelopmental complications in childhood. DNA methylation (DNAm) might enable foetal adaptations to environmental adversities through important gene loci. NEGR1 is involved in both energy balance and behaviour regulation. The aim of this study was to investigate associations between placental DNAm at the NEGR1 gene locus and childhood anthropometric and neurodevelopmental profiles in preschoolers. We analysed 276 mother-child dyads from Gen3G, a prospective birth cohort from Sherbrooke. At 3yo (40.4 ± 3.0 months), we measured body mass index (BMI) and the mothers reported on offspring neurobehavior using the Strengths and Difficulties Questionnaire (SDQ). We quantified DNAm levels at 30 CpGs at the NEGR1 locus using the MethylationEPIC Array in placental biopsies. DNAm at four CpGs located before NEGR1 second exon predicted child's BMI z-score (cg26153364: β=−0.16 ± 0.04; p=0.008, cg23166710: β=0.14 ± 0.08; p=0.03) and SDQ total score (cg04932878: β=0.22 ± 1.0; p= 3.0x10 −4 , cg16525738: β=−0.14 ± 0.18; p=0.01, cg23166710: β=−0.13 ± 0.36; p= 0.04), explaining 4.2% (p=0.003) and 7.3% (p= 1.3 x 10 −4 ) of BMI-z and SDQ variances. cg23166710 was associated with both childhood phenotypes and correlated with NEGR1 placental expression (r= −0.22, p=0.04), suggesting its possible functional role. Together, maternal metabolic characteristics during pregnancy with NEGR1 DNAm levels explained 7.4% (p=4.2 x 10 −4 ) of BMI-z and 14.2% (p=2.8 x 10 −7 ) of SDQ variance at 3yo. This longitudinal study suggests that placental NEGR1 DNAm is associated with adiposity and neurodevelopment in preschool children and highlights its potential role in their comorbidity.

AimsOur objective is to identify first-trimester plasmatic miRNAs associated with and predictive of GDM.MethodsWe quantified miRNA using next-generation sequencing in discovery (Gen3G: n = 443/GDM = 56) and replication (3D: n = 139/GDM = 76) cohorts. We have diagnosed GDM using a 75-g oral glucose tolerance test and the IADPSG criteria. We applied stepwise logistic regression analysis among replicated miRNAs to build prediction models.ResultsWe identified 17 miRNAs associated with GDM development in both cohorts. The prediction performance of hsa-miR-517a-3p|hsa-miR-517b-3p, hsa-miR-218-5p, and hsa-let7a-3p was slightly better than GDM classic risk factors (age, BMI, familial history of type 2 diabetes, history of GDM or macrosomia, and HbA1c) (AUC 0.78 vs. 0.75). MiRNAs and GDM classic risk factors together further improved the prediction values [AUC 0.84 (95% CI 0.73–0.94)]. These results were replicated in 3D, although weaker predictive values were obtained. We suggest very low and higher risk GDM thresholds, which could be used to identify women who could do without a diagnostic test for GDM and women most likely to benefit from an early GDM prevention program.ConclusionsIn summary, three miRNAs combined with classic GDM risk factors provide excellent prediction values, potentially strong enough to improve early detection and prevention of GDM.