Analysis of early-pregnancy metabolome in early- and late-onset gestational diabetes reveals distinct associations with maternal overweight

Author:

Masalin SenjaORCID,Klåvus Anton,Rönö KristiinaORCID,Koistinen Heikki A.ORCID,Koistinen VilleORCID,Kärkkäinen OlliORCID,Jääskeläinen Tiina J.ORCID,Klemetti Miira M.ORCID

Abstract

Abstract Aims/hypothesis It is not known whether the early-pregnancy metabolome differs in patients with early- vs late-onset gestational diabetes mellitus (GDM) stratified by maternal overweight. The aims of this study were to analyse correlations between early-pregnancy metabolites and maternal glycaemic and anthropometric characteristics, and to identify early-pregnancy metabolomic alterations that characterise lean women (BMI <25 kg/m2) and women with overweight (BMI ≥25 kg/m2) with early-onset GDM (E-GDM) or late-onset GDM (L-GDM). Methods We performed a nested case–control study within the population-based prospective Early Diagnosis of Diabetes in Pregnancy cohort, comprising 210 participants with GDM (126 early-onset, 84 late-onset) and 209 normoglycaemic control participants matched according to maternal age, BMI class and primiparity. Maternal weight, height and waist circumference were measured at 8–14 weeks’ gestation. A 2 h 75 g OGTT was performed at 12–16 weeks’ gestation (OGTT1), and women with normal results underwent repeat testing at 24–28 weeks’ gestation (OGTT2). Comprehensive metabolomic profiling of fasting serum samples, collected at OGTT1, was performed by untargeted ultra-HPLC-MS. Linear models were applied to study correlations between early-pregnancy metabolites and maternal glucose concentrations during OGTT1, fasting insulin, HOMA-IR, BMI and waist circumference. Early-pregnancy metabolomic features for GDM subtypes (participants stratified by maternal overweight and gestational timepoint at GDM onset) were studied using linear and multivariate models. The false discovery rate was controlled using the Benjamini–Hochberg method. Results In the total cohort (n=419), the clearest correlation patterns were observed between (1) maternal glucose concentrations and long-chain fatty acids and medium- and long-chain acylcarnitines; (2) maternal BMI and/or waist circumference and long-chain fatty acids, medium- and long-chain acylcarnitines, phospholipids, and aromatic and branched-chain amino acids; and (3) HOMA-IR and/or fasting insulin and l-tyrosine, certain long-chain fatty acids and phospholipids (q<0.001). Univariate analyses of GDM subtypes revealed significant differences (q<0.05) for seven non-glucose metabolites only in overweight women with E-GDM compared with control participants: linolenic acid, oleic acid, docosapentaenoic acid, docosatetraenoic acid and lysophosphatidylcholine 20:4/0:0 abundances were higher, whereas levels of specific phosphatidylcholines (P-16:0/18:2 and 15:0/18:2) were lower. However, multivariate analyses exploring the early-pregnancy metabolome of GDM subtypes showed differential clustering of acylcarnitines and long-chain fatty acids between normal-weight and overweight women with E- and L-GDM. Conclusions/interpretation GDM subtypes show distinct early-pregnancy metabolomic features that correlate with maternal glycaemic and anthropometric characteristics. The patterns identified suggest early-pregnancy disturbances of maternal lipid metabolism, with most alterations observed in overweight women with E-GDM. Our findings highlight the importance of maternal adiposity as the primary target for prevention and treatment. Graphical Abstract

Funder

Jalmari ja Rauha Ahokkaan Säätiö

Päivikki ja Sakari Sohlbergin Säätiö

Juho Vainion Säätiö

Paulon Säätiö

Suomen Kulttuurirahasto

Maud Kuistilan Muistosäätiö

South Karelia medical association

Biomedicum Helsinki-säätiö

Diabetestutkimussäätiö

Research Foundation of South Karelia Central Hospital

Finska Läkaresällskapet

Finnish State Funding for University-level Health Research

Viipuri Tuberculosis Foundation

Research Foundation for Obstetrics and Gynecology

University of Helsinki

Publisher

Springer Science and Business Media LLC

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