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Prediabetes Phenotypes and Adiposity Patterns: Findings From a Population-Based Study
Jincheng Rong1, Mandy Ho1, Sarah Garnett2,3
1School of Nursing, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China, hku.hk.
Journal of Diabetes Research
|July 20, 2026
Summary
Adiposity patterns vary across prediabetes types. Understanding these differences in body fat distribution can help tailor interventions for preventing type 2 diabetes.
Area of Science:
- Endocrinology
- Metabolic Health
- Public Health
Background:
- Prediabetes presents as heterogeneous phenotypes: isolated impaired fasting glucose (i-IFG), isolated impaired glucose tolerance (i-IGT), and combined IFG+IGT.
- Distinct pathophysiological mechanisms underlie these prediabetes phenotypes.
- Understanding the role of body composition and fat distribution is crucial for effective diabetes prevention strategies.
Purpose of the Study:
- To investigate the associations between various measures of body composition and adiposity with distinct prediabetes phenotypes.
- To determine how body mass index (BMI), general adiposity (fat mass index [FMI]), lean mass (lean mass index [LMI]), and body fat distribution (waist circumference [WC], DEXA-derived fat depots) relate to i-IFG, i-IGT, and IFG+IGT.
Main Methods:
- A cross-sectional analysis of 3225 adults without diabetes from the National Health and Nutrition Examination Survey (2011-2016).
- Data included glycemic status, anthropometric measures (BMI, WC), and DEXA-derived body composition (FMI, LMI, regional fat percentages).
- Logistic regression and restricted cubic spline analyses were used to assess associations between adiposity measures and prediabetes phenotypes.
Main Results:
- Higher BMI (overweight/obesity) was linked to increased odds of i-IFG and IFG+IGT. A U-shaped relationship was observed between BMI and i-IGT.
- Increased fat mass index (FMI) and lean mass index (LMI) were associated with higher odds of all three prediabetes phenotypes.
- Central obesity (high WC) and higher proportions of abdominal/visceral fat were associated with increased odds of i-IFG and IFG+IGT, while gynoid fat showed an inverse association with i-IGT.
Conclusions:
- Adiposity patterns significantly differ across prediabetes phenotypes.
- These findings highlight the importance of considering specific body composition and fat distribution profiles when developing targeted interventions.
- Tailoring diabetes prevention strategies based on individual prediabetes phenotype and associated adiposity patterns may optimize outcomes.
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