First-Trimester Leukoglycemic Index and the Risk of Gestational Diabetes Mellitus: Diagnostic Performance and Predictive Value (Analytical-Retrospective Cohort Study)
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Date
Journal Title
Journal ISSN
Volume Title
dc.identifier.issn
2619-9467
dc.identifier.doi
10.5336/jcog.2026-117624
dc.identifier.other
dc.description.volume
36
dc.description.issue
2
Publisher
TURKIYE KLINIKLERI
DOI
10.5336/jcog.2026-117624
10.5336/jcog.2026-117624
10.5336/jcog.2026-117624
Abstract
Objective: To assess whether the first-trimester Leukoglycemic Index (LGI) predicts the later development of gestational diabetes mellitus (GDM) and what its diagnostic performance is, as well as the incremental predictive value beyond conventional clinical risk factors. Material and Methods: This single-center retrospective cohort study included 986 pregnant women who had first-trimester leukocyte count and fasting plasma glucose values available, with documented 75-g oral glucose tolerance test results at 24-28 weeks. LGI was calculated by multiplying leukocyte count by fasting plasma glucose. Diagnostic performance was assessed by receiver operating characteristic analysis and area under the curve (AUC) for independent predictors identified through multivariable logistic regression. Model improvement will be assessed using net reclassification improvement (NRI) and integrated discrimination improvement (IDI), while clinical usefulness can be determined through decision curve analysis. Results: GDM was developed in 24.9% of participants. LGI was significantly higher in women who developed GDM (p<0.001) and remained an independent predictor together with age and body mass index. LGI has a good discriminatory ability (AUC=0.74), outperforming fasting glucose and leukocyte count. Adding LGI to the clinical model increased the AUC from 0.73 to 0.81 with meaningful reclassification gains (NRI=0.17; IDI=0.04). Conclusion: First-trimester LGI is a simple index reflecting both inflammatory burden and glycemic load that significantly improves early GDM prediction when added to routine clinical models; hence, supporting it as a promising adjunct biomarker but requiring prospective multicenter validation for wide implementation.
