Cross-sectional associations between central and general adiposity with albuminuria: observations from 400,000 people in UK Biobank

Background Whether measures of central adiposity are more or less strongly associated with risk of albuminuria than body mass index (BMI), and by how much diabetes/levels of glycosylated haemoglobin (HbA1c) explain or modify these associations, is uncertain. Methods Ordinal logistic regression was used to estimate associations between values of central adiposity (waist-to-hip ratio) and, separately, general adiposity (BMI) with categories of urinary albumin-to-creatinine ratio (uACR) in 408,527 UK Biobank participants. Separate central and general adiposity-based models were initially adjusted for potential confounders and measurement error, then sequentially, models were mutually adjusted (e.g. waist-to-hip ratio adjusted for BMI, and vice versa), and finally they were adjusted for potential mediators. Results Levels of albuminuria were generally low: 20,425 (5%) had a uACR ≥3 mg/mmol. After adjustment for confounders and measurement error, each 0.06 higher waist-to-hip ratio was associated with a 55% (95%CI 53–57%) increase in the odds of being in a higher uACR category. After adjustment for baseline BMI, this association was reduced to 32% (30–34%). Each 5 kg/m² higher BMI was associated with a 47% (46–49%) increase in the odds of being in a higher uACR category. Adjustment for baseline waist-to-hip ratio reduced this association to 35% (33–37%). Those with higher HbA1c were at progressively higher odds of albuminuria, but positive associations between both waist-to-hip ratio and BMI were apparent irrespective of HbA1c. Altogether, about 40% of central adiposity associations appeared to be mediated by diabetes, vascular disease and blood pressure. Conclusions Conventional epidemiological approaches suggest that higher waist-to-hip ratio and BMI are independently positively associated with albuminuria. Adiposity-albuminuria associations appear strong among people with normal HbA1c, as well as people with pre-diabetes or diabetes.

Pengfei Zhu, Sarah Lewington, Richard Haynes, Jonathan Emberson, Martin J. Landray, David Cherney, Mark Woodward, Colin Baigent, William G. Herrington, Natalie Staplin