# Gestational Diabetes Mellitus and Biomarker Profiles: A BMI-Stratified Analysis of Gremlin 1 and BMP 4—A Cross-Sectional Study

## TL;DR

This study explores how Gremlin 1 and BMP 4 levels differ in pregnant women with gestational diabetes compared to healthy controls, and how these levels relate to BMI and other metabolic factors.

## Contribution

The study provides a BMI-stratified analysis of Gremlin 1 and BMP 4 in gestational diabetes mellitus, highlighting their potential roles in metabolic adaptations during pregnancy.

## Key findings

- BMI, triglycerides, HOMA-IR, and TyG were significantly higher in the GDM group compared to controls.
- Gremlin 1 and BMP 4 levels showed a strong positive correlation, suggesting coordinated regulatory pathways.
- BMI and gestational age correlated negatively with both biomarkers, indicating potential metabolic influences.

## Abstract

Background/Objectives: We sought to examine serum concentrations of Gremlin 1 and BMP 4 in pregnant women diagnosed with gestational diabetes mellitus (GDM) compared to healthy pregnant controls while also exploring potential associations with body mass index (BMI) and gestational age. Methods: Our cohort comprised 72 pregnant women—35 with GDM and 37 healthy controls. We measured serum levels of Gremlin 1 and BMP 4 and stratified participants according to BMI categories. Statistical comparisons employed appropriate tests for group differences, and we used Spearman’s correlation to evaluate relationships among BMI, gestational age, fetal birth weight, HOMA-IR, Triglyceride-Glucose Index (TyG), QUICKI and biomarker levels. Results: BMI, triglyceride, HOMA-IR, and TyG were significantly higher, and QUICKI was lower in the GDM group compared with controls. Although Gremlin 1 levels were lower and BMP 4 levels and fetal birth weight were higher in the GDM group, these differences were not statistically significant. In BMI stratified analysis, both biomarkers were higher in the normal weight group, without significant differences. BMI correlated negatively with Gremlin 1 and BMP 4, and gestational age correlated negatively with both biomarkers. A strong positive correlation was observed between Gremlin 1 and BMP 4. Conclusions: The biomarker patterns observed in GDM appear distinct from those reported in diabetes mellitus, possibly reflecting pregnancy-related physiological weight gain and shifts in body composition. The strong positive relationship between Gremlin 1 and BMP 4 lends support to the notion of coordinated regulatory pathways, potentially indicating cellular resistance to BMP 4’s pro-adipogenic actions. Larger longitudinal investigations incorporating detailed body composition assessments will be essential to elucidate their roles in gestational metabolic adaptations and their potential utility for GDM risk stratification.