Predicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005-2020.

0 MEDICINE, RESEARCH & EXPERIMENTAL
Haoxun Zhang, Guoling Zhang, Chunyang Wang
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引用次数: 0

Abstract

Overactive bladder (OAB), a prevalent condition characterized by urgency and nocturia, imposes significant burdens on both quality of life and healthcare systems. Emerging evidence implicates systemic inflammation in OAB pathogenesis; however, the role of complete blood count (CBC)-derived inflammatory biomarkers remains underexplored. This cross-sectional study analyzed data from 35,394 participants in the National Health and Nutrition Examination Survey (NHANES, 2005-2020) to evaluate associations between CBC-derived biomarkers-such as the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and Neutrophil-to-Lymphocyte Ratio (NLR)-and OAB (defined by an OAB Symptom Score ≥3). Multivariable logistic regression, threshold analysis, and machine learning models (Random Forest [RF], Extreme Gradient Boosting) were employed, adjusting for sociodemographic, lifestyle, and clinical covariates. Elevated levels of SII, SIRI, NLR, Monocyte-to-Lymphocyte Ratio (MLR), and Neutrophil-MLR (NMLR) were significantly associated with increased OAB risk (all P < 0.05), with adjusted odds ratios for the highest quartiles ranging from 1.21 (SII; 95% CI: 1.10-1.34) to 1.31 (NMLR; 1.19-1.44). Nonlinear associations were observed, with inflection points (e.g., NLR = 1.071, MLR = 0.174) marking abrupt increases in risk. RF models showed strong predictive performance (area under the curve = 0.89 for training; 0.76 for testing), identifying SII and SIRI as key predictors. Subgroup analyses demonstrated consistent associations across most demographic groups, with the exception of hyperlipidemia, which modified the effects of SIRI, NLR, and NMLR. These findings highlight the role of systemic inflammation in OAB and suggest that CBC-derived biomarkers could serve as cost-effective tools for risk stratification. The integration of epidemiological analysis and machine learning enhances our understanding of OAB's inflammatory underpinnings, although longitudinal studies are needed to establish causal relationships and therapeutic implications.

从炎症标志物预测膀胱过度活动:使用NHANES 2005-2020的机器学习方法。
膀胱过动症(OAB)是一种以尿急和夜尿为特征的常见疾病,对生活质量和医疗保健系统都造成了重大负担。新的证据表明OAB的发病机制与全身性炎症有关;然而,全血细胞计数(CBC)衍生的炎症生物标志物的作用仍未得到充分探索。这项横断面研究分析了全国健康与营养调查(NHANES, 2005-2020)中35,394名参与者的数据,以评估cbc衍生的生物标志物(如全身免疫炎症指数(SII)、全身炎症反应指数(SIRI)和中性粒细胞与淋巴细胞比率(NLR))与OAB(由OAB症状评分≥3定义)之间的关系。采用多变量逻辑回归、阈值分析和机器学习模型(随机森林[RF]、极端梯度增强),调整社会人口统计学、生活方式和临床协变量。SII、SIRI、NLR、单核-淋巴细胞比(MLR)和中性粒细胞-MLR (NMLR)水平升高与OAB风险增加显著相关(均P < 0.05),最高四分位数的调整优势比为1.21 (SII;95% CI: 1.10-1.34)至1.31 (NMLR;1.19 - -1.44)。观察到非线性关联,拐点(例如,NLR = 1.071, MLR = 0.174)标志着风险的突然增加。RF模型表现出较强的预测性能(训练曲线下面积= 0.89;0.76测试),确定SII和SIRI为关键预测因子。亚组分析表明,除高脂血症外,大多数人口统计学组的相关性一致,高脂血症改变了SIRI、NLR和NMLR的效果。这些发现强调了全身性炎症在OAB中的作用,并表明cbc衍生的生物标志物可以作为风险分层的成本效益工具。流行病学分析和机器学习的整合增强了我们对OAB炎症基础的理解,尽管需要纵向研究来建立因果关系和治疗意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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