Prognostics factors for regression from prediabetes to normoglycaemia: individual participant data meta-analysis

Najmeh Davoodian; Mojtaba Lotfaliany; Rachel Huxley; Crystal Lee; Julie Pasco; Mohammadreza Mohebbi

doi:10.1530/endoabs.99.P461

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Endocrine Abstracts (2024) 99 P461 | DOI: 10.1530/endoabs.99.P461

ECE2024 Poster Presentations Diabetes, Obesity, Metabolism and Nutrition (130 abstracts)

Prognostics factors for regression from prediabetes to normoglycaemia: individual participant data meta-analysis

Najmeh Davoodian ¹ , Mojtaba Lotfaliany ¹ , Rachel Huxley ² , Crystal Lee ³ , Julie Pasco ⁴ & Mohammadreza Mohebbi ⁵

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Author affiliations

¹IMPACT, the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong, Australia; ²Faculty of Health, Deakin University, Melbourne, Australia; ³School of Population Health, Curtin University, Perth, Australia; ⁴IMPACT, the Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia; ⁵Biostatistics Unit, Faculty of Health, Deakin University, Melbourne, Australia

Background: Prediabetes, a subclinical precursor to type 2 diabetes mellitus (T2DM) that currently affects approximately 374 million adults worldwide, is a risk factor for the development of cardiovascular disease and stroke, in addition to T2DM. Prediabetes can be reversed to normoglycemia; hence, we aimed to quantify the role of metabolic risk factors in prediabetes regression to normoglycemia.

Methods: We utilized the Obesity, Diabetes, and Cardiovascular Disease Collaboration database for our individual participant data meta-analysis. This database includes 19 prospective cohort studies involving 113,296 adults across various ethnicities and age groups. We included individuals with prediabetes with at least one follow-up in the analysis. Discrete-Time Hidden Markov Models were used to estimate hazard ratios for prognostic factors of prediabetes regression in each cohort study. These estimations were then pooled in the random-effects meta-analysis model.

Results: We included 19,255 participants with prediabetes at baseline, with a median follow-up of 9.8 years (IQR 5.8–12.3); 53% were women, with a mean age of 51 years for both sexes. Former smoking (hazard ratio 0.98, 95% CI 0.89-1.06), higher waist-to-hip ratio (0.86, 0.79-0.93), higher waist-to-height ratio (0.83, 0.75-0.92), higher value of waist circumferences (0.87, 0.71-1.06), overweight (0.88, 0.81-0.96), and obesity (0.86, 0.71-1.04), high diastolic (0.93, 0.87-0.99) and systolic (0.96, 0.91-1.01) blood pressure, low serum HDL-cholesterol (0.87, 0.81-0.92), and high serum triglycerides (0.88, 0.81-0.96) were associated with a lower likelihood of individuals with prediabetes achieving normoglycemia.

Conclusion: The role of metabolic risk factors in prediabetes regression underscores the importance of lifestyle modification in the prediabetes state, not only to reduce T2DM development but also to attain normoglycemia.