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尿镉和外周血端粒长度预测肾功能损害风险:中国山西 547 名社区居民的研究。

Urinary cadmium and peripheral blood telomere length predict the risk of renal function impairment: a study of 547 community residents of Shanxi, China.

机构信息

Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, 030001, China.

出版信息

Environ Sci Pollut Res Int. 2022 Oct;29(47):71427-71438. doi: 10.1007/s11356-022-20923-6. Epub 2022 May 21.

Abstract

Few reports have investigated the predictive value of urinary cadmium (UCd) and telomere length on renal function impairment. Therefore, we constructed nomogram models, using a cross-sectional survey to analyze the potential function of UCd and telomere length in renal function impairment risk. We randomly selected two community populations in Shanxi, China, and general information of the subjects was collected through face-to-face questionnaire surveys. Venous blood of subjects was collected to detect absolute telomere length (ATL) by real-time quantitative chain reaction (RT-PCR). Collecting urinary samples detected UCd and urinary N-acetyl-β-d-glucosaminidase (UNAG). Estimated glomerular filtration rate (eGFR) was obtained based on serum creatinine (SCr). Nomogram models on risk prediction analysis of renal function impairment was constructed. After adjusting for other confounding factors, UCd (β = 0.853, 95% confidence interval (CI): 0.739 ~ 0.986) and ATL (β = 1.803, 95%CI: 1.017 ~ 1.154) were independent risk influencing factors for increased UNAG levels, and the risk factors for eGFR reduction were UCd (β = 1.011, 95%CI: 1.187 ~ 1.471), age (β = 1.630, 95%CI: 1.303 ~ 2.038), and sex (β = 0.181, 95%CI: 0.105 ~ 0.310). Using UCd, ATL, sex, and age to construct the nomogram, and the C-statistics 0.584 (95%CI: 0.536 ~ 0.632) and 0.816 (95%CI: 0.781 ~ 0.851) were obtained by internal verification of the calibration curve, C-statistics revealed nomogram model validation was good and using decision curve analysis (DCA) confirmed a good predictive value of the nomogram models. In a nomogram model, ATL, UCd, sex, and age were detected as independent risk factors for renal function impairment, with UCd being the strongest predictor.

摘要

鲜有研究调查尿镉(UCd)和端粒长度对肾功能损害的预测价值。因此,我们构建了列线图模型,使用横断面研究分析 UCd 和端粒长度在肾功能损害风险中的潜在功能。我们随机选择了中国山西的两个社区人群,并通过面对面的问卷调查收集了受试者的一般信息。采集静脉血,通过实时定量聚合酶链反应(RT-PCR)检测绝对端粒长度(ATL)。收集尿样检测 UCd 和尿 N-乙酰-β-D-氨基葡萄糖苷酶(UNAG)。根据血清肌酐(SCr)估算肾小球滤过率(eGFR)。构建肾功能损害风险预测分析的列线图模型。在调整其他混杂因素后,UCd(β=0.853,95%置信区间(CI):0.7390.986)和 ATL(β=1.803,95%CI:1.0171.154)是 UNAG 水平升高的独立危险因素,eGFR 降低的危险因素是 UCd(β=1.011,95%CI:1.1871.471)、年龄(β=1.630,95%CI:1.3032.038)和性别(β=0.181,95%CI:0.1050.310)。使用 UCd、ATL、性别和年龄构建列线图,校准曲线的内部验证得到 C 统计量为 0.584(95%CI:0.5360.632)和 0.816(95%CI:0.781~0.851),C 统计量表明列线图模型验证良好,使用决策曲线分析(DCA)证实了列线图模型的良好预测价值。在列线图模型中,ATL、UCd、性别和年龄被检测为肾功能损害的独立危险因素,其中 UCd 是最强的预测因子。

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