Routine biochemical indices for hepatometabolic stratification across the dysglycaemia spectrum: insulin resistance, fibrosis scores, and machine-learning integration.

OBJECTIVES: To evaluate the behaviour of insulin resistance indices and non-invasive liver fibrosis scores across the dysglycaemia spectrum using routine laboratory data. METHODS: This retrospective observational study included 1,949 outpatients with complete biochemical profiles. Patients were classified into five groups according to fasting plasma glucose and HbA1c: group A (normoglycaemia), group B (isolated impaired fasting glucose), group C (isolated elevated HbA1c), group D (combined impairment), and group E (type 2 diabetes mellitus, T2DM). Insulin resistance indices (HOMA-IR, HOMA-β, QUICKI, TyG), liver fibrosis scores (FIB-4, APRI, m-APRI, Forns), and inflammatory markers (CRP, ferritin) were compared across groups. Discriminative performance was assessed using ROC analysis for the identification of T2DM (group E vs. groups A-D) and early dysglycaemic impairment (group D vs. group A). Support vector machine (SVM) models integrating the best-performing indices were constructed with adjustment for age and sex. RESULTS: Insulin resistance indices, fibrosis scores, and CRP showed progressive deterioration across worsening glycaemic stages (p<0.001). TyG showed the highest individual discriminative performance for identifying T2DM (AUC=0.90), whereas QUICKI performed best for detecting combined early glycaemic impairment (AUC=0.78). Among fibrosis scores, the Forns index showed the strongest discriminative capacity (AUC=0.67-0.74) and the broadest correlations with metabolic and inflammatory parameters. SVM models integrating insulin resistance and fibrosis indices improved classification performance compared with individual markers alone (AUC=0.87-0.93). CONCLUSIONS: TyG and the Forns index support laboratory-based hepatometabolic stratification across the dysglycaemia spectrum using routine analytical data.