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June 7, 2026Diabetes

Relative Fat Mass had the highest HR, waist-to-hip ratio had the highest increase in AUC, and waist-to-height ratio and roundness index had the highest NRI for predicting T2D risk, with significant heterogeneity across cohorts.

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Why the study?

Do specific adiposity indexes improve the prediction of Type 2 Diabetes risk beyond BMI in diverse US cohorts?

Population

19,006 individuals from 5 US cohorts

Comparison

7 adiposity indexes calculated from… vs BMI (models adjusted for age, sex, and BMI)

Design

Cohort

Follow-up

301,000 person-years total

Authors

FKFEHMIDA F. KHANRHRobert L. HansonMNMURUGASU NAGUL

Discussion

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Overview

Integrating simple adiposity indexes with BMI enhances the predictive accuracy of Type 2 Diabetes risk by capturing central adiposity and body fat distribution.

Key Points

  • The study aims to evaluate how different adiposity indexes predict the risk of developing Type 2 Diabetes across diverse U.S. cohorts.
  • Analyzed data from five cohorts including 2627 American Indians, 2156 Blacks and 2109 Whites from CARDIA, and 2602 Blacks and 9512 Whites from ARIC.
  • Calculated seven adiposity indexes based on anthropometric measures to assess predictive performance.
  • Evaluated predictive performance using hazard ratios, changes in AUC, and NRI from Cox models controlled for age, sex, and BMI.
  • Total of 3942 T2D cases identified over 301K years of follow-up.
  • Relative Fat Mass (RFM) showed the highest HR for T2D risk, while waist-to-hip ratio had the greatest increase in AUC.
  • Waist-to-height ratio and roundness index provided the highest NRI, indicating better risk reclassification.

Structured PICO

Do specific adiposity indexes improve the prediction of Type 2 Diabetes risk beyond BMI in diverse US cohorts?

P
Population
19,006 individuals from 5 US cohorts (Southwest US American Indians n=2627; CARDIA Blacks n=2156 and Whites n=2109; ARIC Blacks n=2602 and Whites n=9512)
I
Intervention
7 adiposity indexes calculated from anthropometric measures (including Relative Fat Mass, waist-to-hip ratio, waist-to-height ratio, and roundness index)
C
Comparator
BMI (models adjusted for age, sex, and BMI)
O
Outcome
Type 2 Diabetes incidencehard clinical

Integrating simple adiposity indexes with BMI enhances the predictive accuracy of Type 2 Diabetes risk by capturing central adiposity and body fat distribution.

Limitations

  • Heterogeneity between studies for all predictive performance measures

Cite This Study

KHAN et al. (2026) studied this question.

synapsesocial.com/papers/6a250ac07def13d035e1adc1https://doi.org/10.2337/db26-2254-p
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 12235-P: Comparison of Surrogate Indexes of Insulin Resistance as Predictors of Diabetes: Analysis of Five U.S. Cohorts2026
  2. 2A Comprehensive Evaluation of Adiposity Indices on 20‐Year Cumulative Incidence of Type 2 Diabetes: The ATTICA Cohort Study (2002–2022)2026
  3. 3Anthropometric Indices and Diabetes Disease: Based on the Rafsanjan Cohort Study2025
  4. 41655-P: Where Fat Is Stored Matters More than How Much: A Large-Scale Population-Based Study Integrating DXA Phenotyping and Causal Inference2026
  5. 5Epidemiological associations between obesity, metabolism and disease risk: are body mass index and waist-hip ratio all you need?2025 · 7 citations