# Body composition as a complementary tool for detection of metabolic syndrome 6 years postpartum: a St. Carlos Cohort follow-up

## TL;DR

This study shows that body composition measures can help detect metabolic syndrome in women six years after pregnancy, especially those with a history of gestational diabetes.

## Contribution

The study identifies novel body composition cut-off values for predicting metabolic syndrome in postpartum women.

## Key findings

- Women with prior gestational diabetes had twice the risk of developing metabolic syndrome.
- Body composition parameters like fat mass and visceral fat showed high predictive value for metabolic syndrome.
- Diagnostic models using body composition had high negative predictive values, making them effective for excluding metabolic syndrome.

## Abstract

Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication associated with long-term cardiometabolic risk, including metabolic syndrome (MetS). This study aimed to assess differences in body composition and metabolic health 6 years postpartum based on prior GDM diagnosis and to identify body composition cut-off values predictive of MetS.

This cross-sectional analysis included 604 women from the prospective St. Carlos Cohort in Spain, who had no subsequent pregnancies and complete body composition data 6 years postpartum. Body composition was assessed using bioelectrical impedance analysis (BIA), and MetS was diagnosed per harmonized criteria. Statistical analyses included ROC curves to establish diagnostic accuracy and optimal cut-off points.

Women with prior GDM had a twofold increased risk of developing MetS (26.6 vs. 14.6%). However, waist circumference or elevated BMI and waist-to-height ratio were not significantly different between groups. ROC analysis identified that body composition parameters, particularly fat mass (FM), visceral fat, and FM/Fat Free Mass ratio, as having high predictive value for MetS, regardless of GDM history (AUC ≥ 0.8). Women with MetS showed significantly higher FM and lower relative muscle mass and function. Diagnostic models showed high negative predictive values (≥90%) for most body composition parameters making them effective for excluding MetS.

GDM is a significant predictor of MetS. However, body composition, especially increased adiposity and reduced relative muscle mass, provides valuable clinical insights beyond traditional anthropometric measures in postpartum women. The proposed cut-off values for body composition parameters may serve as effective, non-invasive tools for early MetS detection in postpartum care.