Coffee Consumption and Risk of Chronic Kidney Disease: Evidence from a Nationally Representative U.S. Sample
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Background : Chronic kidney disease (CKD) affects over 35 million U.S. adults and is a leading cause of morbidity and mortality. Coffee, widely consumed worldwide, contains bioactive compounds with potential renal benefits, yet epidemiologic findings on its association with CKD remain inconclusive. Objective : To examine the association between caffeinated coffee intake and CKD prevalence in a nationally representative U.S. adult sample, using advanced statistical methods to assess dose–response and non-linear relationships. Methods : We analyzed data from 22,283 adults aged ≥18 years participating in the 2015–2023 NHANES cycles. Usual caffeinated coffee intake (grams/day) was estimated using two 24-hour dietary recalls and a two-part National Cancer Institute model. CKD was defined as eGFR <60 mL/min/1.73m² or albumin-to-creatinine ratio ≥30 mg/g. We applied survey-weighted logistic regression models incorporating stabilized inverse probability of treatment weights (IPTWs) to adjust for confounding. Coffee intake was modeled continuously, categorically (quartiles), and using generalized additive models (GAMs) to explore non-linearity. Sensitivity analyses included a synthetic trial framework and E-value calculation. Results : Higher coffee intake was associated with lower CKD odds. Each 240 mL cup/day corresponded to a 1.9% reduction in CKD odds (adjusted OR per gram = 0.99992; 95% CI: 0.99987–0.99996; p = 0.0009). Compared to non-drinkers, individuals in the highest quartile had 52% lower odds of CKD (OR 0.477; 95% CI: 0.371–0.613; p < 0.001). GAMs revealed a plateau in risk reduction beyond ~1,500 g/day. The E-value for the quartile association was 2.25. Conclusions : Caffeinated coffee consumption was inversely associated with CKD in a dose-responsive and non-linear manner. These findings suggest potential renal benefits of moderate coffee intake. Longitudinal studies are needed to confirm temporality and assess causality.
