Gestational diabetes mellitus (GDM) is a well-established precursor of type 2 diabetes mellitus (T2DM), representing a critical metabolic transition period in women’s health. This study presents a retrospective risk assessment and predictive modeling framework for identifying the progression from GDM to T2DM among women treated at the University Hospital of Kinshasa. Using evidence synthesized from epidemiological and meta-analytical literature, the study constructs a structured risk stratification model integrating clinical, behavioral, and metabolic determinants. Prior research indicates that women with a history of GDM exhibit significantly elevated long-term risk of T2DM, influenced by postpartum metabolic dysfunction, intrauterine hyperglycemia, and lifestyle factors (Kim, Newton & Knopp, 2002; Rayanagoudar et al., 2016). Furthermore, systematic evidence highlights that physical inactivity, obesity, and postpartum glucose intolerance significantly accelerate disease progression (Bao et al., 2014; Bellou et al., 2018).

The predictive framework developed in this study incorporates demographic variables, obstetric history, and metabolic indicators to estimate individualized diabetes risk scores. Retrospective analysis from clinical cohorts demonstrates that early postpartum glycemic abnormalities strongly correlate with future T2DM incidence (Kugishima et al., 2015). Additionally, intergenerational metabolic transmission further amplifies risk profiles, reinforcing the need for long-term surveillance strategies (McLean et al., 2006). The findings emphasize that GDM should not be viewed as a transient pregnancy complication but as an early marker of chronic metabolic disease susceptibility.

This study contributes to clinical epidemiology by proposing an integrated predictive approach tailored to low-resource healthcare systems. The results underscore the importance of early screening, structured follow-up, and lifestyle interventions in mitigating long-term diabetic risk. The University Hospital of Kinshasa context provides valuable insights into GDM outcomes in sub-Saharan populations, where epidemiological transitions are accelerating.