Body composition as a complementary tool for detection of metabolic syndrome six years postpartum: a St. Carlos cohort follow–up

Bricia López-Plaza^1,2ANGELICA LARRAD-SAINZ¹Johanna Josefina Valerio Deogracia¹ROCIO MARTIN-O'CONNOR¹Laura Del Valle¹Ana Maria Ramos-Levi^1,3,4ANA BARABASH^1,2,4CLARA MARACUELLO^1,2Inés Jiménez Varas¹Miguel Ángel Rubio^1,2,3PILAR MATIA-MARTIN^1,2,3,4*Alfonso Luis Calle-Pascual^1,2,3,4*

• ¹Hospital Clinico San Carlos Instituto de Investigacion Sanitaria, Madrid, Spain
• ²Universidad Complutense de Madrid, Madrid, Spain
• ³Hospital Clinico San Carlos, Madrid, Spain
• ⁴Centro de Investigacion Biomedica en Red de Diabetes y Enfermedades Metabolicas Asociadas, Madrid, Spain

Background and aims: 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 six years postpartum based on prior GDM diagnosis and to identify body composition cut–off values predictive of MetS. Methods: 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 six 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. Results: 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. Conclusions: 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 Eliminado: FFM proposed cut–off values for body composition parameters may serve as effective, non– invasive tools for early MetS detection in postpartum care.