Abstract
BACKGROUND: This study aimed to investigate the association between kidney stones and novel indicators for inflammation and metabolism, including the neutrophil percentage-to-albumin ratio (NPAR), the neutrophil count-to-albumin ratio (NAR), the uric acid-to-high-density lipoprotein cholesterol ratio (UHR), and the lymphocyte count-to-high-density lipoprotein cholesterol ratio (LHR). METHODS: The study analyzed 59,842 participants from the National Health and Nutrition Examination Survey spanning from 2007 to 2018, with 27,420 remaining after exclusions. The determination of kidney stones was determined by the personal history of kidney stones reported through the questionnaire. All indicators were calculated based on the relevant data extracted from the database. Weighted logistic regression and restricted cubic spline (RCS) analyses were performed to evaluate the association between indicators and kidney stones. Furthermore, subgroup analyses and interaction tests were conducted to assess the robustness and generalizability of the results. RESULTS: In this study, 2,636 of the enrolled participants were identified as having kidney stones. Following adjustment for all covariates, multivariate logistic regression analysis demonstrated a significant positive relationship between kidney stones and NPAR (OR: 1.63, 95% CI: 1.22-2.18, p = 0.001), NAR (OR: 1.20, 95% CI: 1.04-1.40, p = 0.015), UHR (OR: 1.43, 95% CI: 1.15-1.79, p = 0.002), and LHR (OR: 1.15, 95% CI: 1.02-1.30, p = 0.026). However, the significant association of NAR was no longer observed in quartile analysis and trend analysis. RCS analysis demonstrated a nonlinear association between NAR, UHR, LHR, and kidney stones. Furthermore, sex was the key factor in subgroup analysis of NPAR and LHR, whereas age was the key factor for UHR and NAR. CONCLUSION: This study found that higher NPAR, NAR, UHR, and LHR levels were linked to increased kidney stone risk. UHR exhibits greater stability in comparison to other indicators. Subgroup analyses emphasized the need to consider demographic factors when interpreting these indicators. Further prospective studies are essential to confirm the results.