No abstract

The association between dietary selenium intake and kidney stones remains unclear. The purpose of this study was to explore the correlation between dietary selenium intake and kidney stones in older adults. A total of 6669 adults aged ≥ 60 years who had participated in the National Health and Nutrition Examination Survey (NHANES) during 2011–2018 were enrolled in the current study. The correlation between dietary selenium intake and kidney stones was assessed by the logistic regression analysis. Smooth curve fitting was used to explore the potential non-linear relationship and subgroup analyses were further adopted. After adjustment for multiple confounding factors, the odds ratio (OR) with 95% confidence interval (CI) of kidney stones for per standard deviation increment in dietary selenium intake was 0.92 (0.85, 1.00) overall. Compared with the lowest quartile, the ORs (95% CIs) with increasing quartiles were 0.88 (0.71, 1.08), 0.82 (0.66, 1.02), and 0.79 (0.64, 0.97). In addition, smooth curve fitting and stratified analyses showed that there was a non-linear and stable correlation between dietary selenium intake and the occurrence of kidney stones respectively. For adults aged over 60, dietary selenium intake was inversely correlated with kidney stones, and this relationship remained after adjusting for other confounding variables. Further researches are needed to explore the potential mechanism between dietary selenium intake and kidney stones.

The purpose of this study is to construct a new prediction model to evaluate the recurrence risk of upper urinary tract stones in patients. We retrospectively reviewed the clinical data of 657 patients with upper urinary tract stones and divided them into stone recurrence group and non-recurrence group. Blood routine, urine routine, biochemical and urological CT examinations were searched from the electronic medical record, relevant clinical data were collected, including age, BMI, stones number and location, hyperglycemia, hypertension, and relevant blood and urine parameters. Then, independent sample t-test, Wilcoxon rank sum test, and Chi-square test were used to preliminarily analyze the data of two groups, and then LASSO and Logistic regression analysis were used to find out the significant difference indicators. Finally, R software was used to draw a nomogram to construct the model, and ROC curve was drawn to evaluate the sensitivity and specificity. The results showed that multiple stones (OR:1.832,95%CI:1.240–2.706), bilateral stones (OR:1.779,95%CI: 1.226–2.582), kidney stones (OR: 3.268, 95% CI: 1.638–6.518) and kidney ureteral stone (OR: 3.375, 95% CI:1.649–6.906) were high risks factors. And the stone recurrence risk was positively correlated with creatinine (OR:1.012,95%CI:1.006–1.018), urine pH (OR:1.967, 95%CI:1.343–2.883), Apo B (OR:4.189, 95%CI:1.985–8.841) and negatively correlated with serum phosphorus (OR:0.282, 95%CI:0.109–0.728). In addition, the sensitivity and specificity of the prediction model were 73.08% and 61.25%, diagnosis values were greater than any single variable. It means the model can effectively evaluate the recurrence risk of upper urinary stones, especially suitable for stone postoperative patients, to help reduce the possibility of postoperative stone recurrence.