- Design
- Cross-sectional pooled analysis of nationally representative surveys
- Population
- 598,883 adults aged ≥25 with BMI 18.5–29.9 in 82 low- and middle-income countries
- Primary outcome
- Classification of diabetes status (AUC)
- Effect
- AUC waist 0.69 vs BMI 0.64 in men; 0.69 vs 0.63 in women
A cross-sectional analysis pooled nationally representative surveys from 82 low- and middle-income countries: 598,883 adults aged 25 and over, all with a BMI between 18.5 and 29.9. It compared waist circumference, relative fat mass and BMI as classifiers of diabetes.
Pooled diabetes prevalence was 9.1%. Waist circumference and relative fat mass each classified diabetes status better than BMI, with an AUC of 0.69 against 0.64 in men and 0.69 against 0.63 in women. Relative fat mass matched or beat waist circumference across regions.
This is the population in which BMI-led screening most often misses diabetes, and it describes the typical Indian patient with central adiposity and a 'normal' BMI. Adding a waist measurement to screening decisions costs nothing.
- Measure waist circumference in every adult at a diabetes risk assessment, whatever the BMI
- Offer glucose testing to adults with a normal BMI but a raised waist
- Relative fat mass (from height and waist) is an option where it is easy to calculate
- Do not let a BMI under 23 end the risk conversation
Why it matters
BMI-based screening under-calls diabetes in exactly the lean, centrally obese adults common in South Asian practice.
Don't overread it
This was cross-sectional; it shows better classification, not that waist-based screening improves outcomes, and an AUC of 0.69 is still modest.
The statistics, in plain English
An AUC is the chance that a randomly chosen person with diabetes has a higher value than a randomly chosen person without it. 0.5 is a coin toss and 1.0 is perfect. Moving from 0.64 to 0.69 is a real but modest gain, enough to change who gets tested, not enough to replace a glucose test.
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