Dose–response relationship between the postload–fasting gap and the risk of developing diabetes: a cohort study from multiple centers in China

Introductive Early impairments in post-challenge glucose regulation are not fully captured by fasting measures alone. The postload-fasting gap, defined as the difference between 2-hour postload plasma glucose (2hPG) and fasting plasma glucose (FPG), may reflect dynamic dysregulation, yet its relation with glycaemic deterioration and remission in Chinese populations remains unclear. To characterise the dose-response relation between the postload-fasting gap and four glycaemic outcomes: incident diabetes, incident prediabetes, progression from prediabetes to diabetes, and reversion to normal glucose tolerance in a large multicentre Chinese cohort. 

Research design and methods We analyzed 3094 adults free of diabetes at baseline with two revisits over a mean follow-up of 3.24 years. Outcomes were ascertained at each visit by oral glucose tolerance test (OGTT) using World Health Organization (WHO) 1999 criteria, with sensitivity analyses using American Diabetes Association (ADA) definitions that include HbA1c. Primary associations were estimated on person-period data using discrete-time hazard models with a complementary log-log link, modeling the postload–fasting gap with restricted cubic splines after adjusting for demographic, clinical, and lifestyle covariates; cluster robust SEs accounted for repeated observations. Spline knots (K=3, 4, or 5) were placed at recommended percentiles and selected by Akaike information criterion, treating delta Akaike information criterion less than or equal to 2 as equivalent and favoring the more parsimonious model. Multiplicity was controlled using the false discovery rate. Internal validation used cluster bootstrap resampling. We further assessed prediction with six nested models (A–F), reporting area under the curve (AUC) with bootstrap CIs, net reclassification improvement and integrated discrimination improvement, and evaluated clinical utility by decision curve analysis. 

Results Higher postload–fasting gaps were associated with more adverse metabolic profiles at baseline and with higher risks of incident diabetes, incident pre-diabetes, and progression; lower postload–fasting gaps were associated with reversion to normal glucose tolerance. Dose–response curves showed that for incident diabetes, risk was flat close to a postload–fasting gap of 0 and increased beyond 2 mmol/L; for incident pre-diabetes, risk increased in a generally monotonic fashion; for progression, the increase was steeper; for reversion, risk decreased as postload–fasting gap increased. Findings were robust to alternative covariate sets, knot choices, and diagnostic definitions. In prediction analyses, the model that combined FPG with the postload—fasting gap (model F) provided the greatest incremental value across outcomes. For incident diabetes, the optimism-corrected AUC was 0.686, continuous net reclassification improvement was up to 0.349, and integrated discrimination improvement was 0.005; decision curve analysis indicated a higher net benefit for model F across clinically relevant thresholds. 

Conclusions The postload–fasting gap is an independent and non-linear marker of glycemic risk and remission potential. Incorporating this measure, particularly together with FPG, improves risk stratification and clinical utility, supporting its use as a practical OGTT-derived metric for early identification of people at risk of developing diabetes and targeted prevention.