Abstract
BackgroundLimited studies have investigated the correlation between fat distribution and the risk of diabetic retinopathy (DR) in the general population with diabetes. The relationship between obesity and DR remains inconclusive, possibly due to using simple anthropometric measures to define obesity. This study investigates the relationships between the android-to-gynoid fat ratio (A/G ratio, measured using dual-energy X-ray absorptiometry) and DR within the US population with diabetes.MethodsThe study used a population-based, cross-sectional approach based on the 2003–2006 and 2011–2018 data of the National Health and Nutrition Examination Survey (NHANES). Multivariable logistic regression analyses were performed on participants with diabetes to evaluate the contribution of body mass index (BMI), waist-to-height ratio (WHtR), and A/G ratio to the prevalence of DR.ResultsThe prevalence of DR was 22.2, 21.2, and 17.6% among participants with A/G ratios <1.0, 1.0–1.2, and ≥1.2, respectively. After adjusting sex, age, ethnicity, diabetes duration, hemoglobin A1c level, blood pressure level, and non-high-density lipoprotein cholesterol level, a higher A/G ratio (≥1.2) was independently associated with decreased odds of DR (odds ratio [OR], 0.565; 95% CI: 0.372–0.858) compared with the A/G ratio of 1.0–1.2. Associations between a higher A/G ratio and DR remained statistically significant after adjusting for BMI (OR, 0.567; 95% CI: 0.373–0.861) and WHtR (OR, 0.586; 95% CI: 0.379–0.907). Moreover, these associations remained statistically significant in analyses using the ethnic-specific tertiles for the A/G ratio. In sex-stratified models, these correlations remained in males. There was a significant inverse association between the A/G ratio and diabetes duration in males, which persisted after multivariable adjustments (p < 0.05).ConclusionsA novel finding indicates that a higher A/G ratio is associated with a reduced likelihood of DR in males with diabetes. The results from NHANES underscore the importance of considering imaging-based fat distribution as a critical indicator in clinical practice.
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