Linking artificial sweetener intake with kidney function: insights from NHANES 2003–2006 and findings from Mendelian randomization research

BackgroundThe current investigation examines the association between artificial sweetener (AS) consumption and the likelihood of developing chronic kidney disease (CKD), along with its impact on kidney function.MethodsWe utilized data from the National Health and Nutrition Examination Survey from 2003–2006 to conduct covariance analysis and weighted adjusted logistic regression, aiming to assess the association between artificial sweetener intake and CKD risk, as well as kidney function indicators. Subsequently, we employed Mendelian randomization methods to validate the causal relationship between the intake of artificial sweeteners, CKD risk, and kidney function indicators. Instrumental variable analysis using inverse-variance weighting and Robust adjusted profile score were the primary analytical methods employed.ResultsA total of 20,470 participants were included in the study, with 1,257 participants ultimately included in the analysis. In all adjusted logistic regression models, no significant association was found between the intake of artificial sweeteners and CKD risk. Similarly, the summary odds ratios (OR) for each unit change in genetically predicted CKD risk were 2.14 (95% CI: 0.83, 5.21, p = 0.092), 1.41 (95% CI: 0.54, 3.63, p = 0.482), and 1.50 (95% CI: 0.50, 4.52, p = 0.468) for the impact of artificial sweeteners added to cereals, tea, and coffee, respectively. It was only observed that adding artificial sweeteners to coffee was associated with a modest reduction in urinary albumin-to-creatinine ratio (OR = 0.94, 95% CI: −0.108, −0.022, p = 0.003), the effect appeared to be relatively small and may not directly impact the individual level.ConclusionOur study does not support a causal relationship between artificial sweetener intake and the risk of CKD. However, due to the limitations and potential confounding factors, these findings need to be further validated through larger sample sizes in observational studies and Mendelian randomization analyses.