JMIR research protocols ·11 Nov 2026 ·Added 12 Nov 2026

Identifying Metabolic Pathways That Drive Gestational Diabetes Mellitus: Protocol for a Prospective Observational Study.

Felix Jebasingh, Lakshmi Marri Harika, Shajith Anoop, Mandisa Christian, Sherin Daniel, Santhosh Joseph Benjamin, Swati Rathore, Reeta Vijaya Selvi, Manisha Madhai Beck, Anuja Abraham, R N Preethi, Ullas Kolthur-Seetharam, Mahendra Sonawane, Sarah Price, Elif Ilhan Ekinci, Nihal Thomas

DOI ↗

Abstract

BACKGROUND: Gestational diabetes mellitus (GDM) is a disorder characterized by hyperglycemia that is first recognized during pregnancy due to a mismatch between placental insulin production and increased maternal need, exacerbating insulin resistance. Few studies have systematically examined the metabolic markers and pathways underlying GDM from diagnosis through the postnatal period, including placental metabolic changes through metabolomics studies. OBJECTIVE: This study aims to evaluate the metabolite profiles identified through metabolomics, as well as the associated pathways responsible for hyperglycemia during pregnancy and the postpartum period, in mothers with GDM compared with mothers without diabetes during pregnancy. METHODS: We plan to recruit an equal number of patients with GDM and age- and BMI-matched mothers without GDM. Anthropometric data are collected at the first visit. Samples are collected at 3 time points: serum or plasma samples at the time of GDM diagnosis or at the first visit after diagnosis (24-28 weeks of gestation), placental and cord blood samples during delivery, and postnatal serum samples 4 to 12 weeks after delivery. Macroscopic and microscopic features of the placenta will be noted, and a histological score will be derived. Using liquid chromatography tandem mass spectrometry (LC-MS) with an untargeted approach, metabolite profiles will be identified in serum samples collected at diagnosis and postpartum, cord blood samples, and placental samples from both groups. The raw mass spectrometry (MS) data will be analyzed using the company-provided MS operator software. After data normalization, univariate analyses will be performed. Subsequently, all statistical analyses will be performed using MetaboAnalyst. Through these analyses, differences in the metabolite profiles and their pathways between mothers with and without GDM across pregnancy, starting from the diagnosis of GDM, will be identified. Data will be analyzed using R software. Clinical and demographic data will be presented as percentages, means, or medians, as appropriate. Logistic regression will be performed to assess the association between biomarkers and the placental histological score. RESULTS: Ethics approval for the study was obtained from the Christian Medical College Vellore Ethics Committee, along with an institutional research grant in January 2025 and an Endocrine Society of Tamil Nadu grant in May 2026. Recruitment for the study began in June 2025 and was completed by June 2026; as of January 2026, a total of 35 patients had been recruited. The analysis of the samples using LC-MS, along with further statistical analysis, is expected to be completed in the first half of 2027. CONCLUSIONS: The findings from this study will provide insight into the metabolites or biomarkers, as well as the metabolic pathways involved, in the pathogenesis of GDM across the life course of mothers, compared with those of normoglycemic mothers, and will offer potential insight into the role of the placenta in GDM.