[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126201-en":3,"doc-seo-126201-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":11,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126201,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting overactive bladder from inflammatory markers - A machine learning approach using NHANES 2005-2020","Overactive bladder (OAB) affects quality of life and healthcare systems, and systemic inflammation is increasingly implicated in its development. A cross-sectional analysis of 35,394 participants from NHANES 2005–2020 assessed whether CBC-derived inflammatory biomarkers are associated with OAB, defined by an OAB Symptom Score ≥3. Elevated SII, SIRI, NLR, MLR, and NMLR increased OAB risk, with nonlinear dose-response patterns and key predictors identified by random forest models. Subgroup results were mostly consistent, except effect modification by hyperlipidemia for specific indices.","RESEARCH ARTICLE  \nPredicting overactive bladder from inflammatory markers: A machine learning approach using NHANES 2005–2020  \nHaoxun Zhang , Guoling Zhang , and Chunyang Wang ∗  \nOveractive 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  \nCBC-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 \u003C 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.  \nKeywords: Overactive bladder, OAB, inflammatory biomarkers, machine learning, National Health and Nutrition Examination Survey, NHANES, predictive modeling.  \nIntroduction  \nOveractive bladder (OAB) is defined by the International Continence Society as urinary urgency, usually accompanied by frequency and nocturia, with or without urgency urinary incontinence, in the absence of urinary tract infection or other identifiable pathology [1] . Although patients typically do not present with obvious clinical abnormalities (such as urinary tract infections), common symptoms include increased daytime frequency and nocturia [2] . An epidemiological survey in China reported an overall OAB prevalence of approximately 6.0%[3] . In the United States, the prevalence among adult males and females is 16% and 16.9%, respectively, with rates increasing with age [4] . Despite its prevalence, OAB is often underdiagnosed in both men and women, with only a minority of affected individuals seeking treatment. Notably, OAB significantly diminishes quality of life, interferes with daily activities,  \nand can lead to depression or anxiety [5] . Current treatment options have notable limitations and are frequently associated with adverse effects. Additionally, the aging population contributes to a growing OAB burden, underscoring its importance as a healthcare challenge [6] . In the U.S., OAB imposesa substantial annual economic burden, with healthcare costs for patients exceeding those of non-OAB individuals by more than 2.5 times—making it a pressing public health concern [7] . Nevertheless, the risk factors and underlying pathological mechanisms of OAB remain poorly understood. Emerging research suggests that immune-inflammatory re","cbCaippzufXqGI1i","https://ap.wps.com/l/cbCaippzufXqGI1i","pdf",4728632,1,14,"English","en",105,"# Introduction\n## OAB definition, prevalence, and clinical burden\n## Immune–inflammatory mechanisms and CBC-derived biomarkers\n# Methods\n## Study design and population (NHANES 2005–2020)\n## Biomarkers and OAB definition (OAB Symptom Score ≥3)\n## Statistical and machine learning models (logistic regression, threshold analysis, RF, XGBoost)\n# Results\n## Associations between inflammatory indices and OAB risk\n## Nonlinear relationships and inflection points\n## Predictive performance and key predictors\n## Subgroup analyses and effect modification\n# Discussion\n## Interpretation of inflammatory contributions\n## Implications for risk stratification and future research","[{\"question\":\"How was overactive bladder (OAB) defined in the study?\",\"answer\":\"OAB was defined by an OAB Symptom Score of 3 or higher (≥3).\"},{\"question\":\"Which CBC-derived inflammatory biomarkers were examined?\",\"answer\":\"The study evaluated SII, SIRI, NLR, MLR, and NMLR, derived from complete blood count data.\"},{\"question\":\"What modeling approaches were used to predict OAB risk?\",\"answer\":\"The study used multivariable logistic regression with threshold analysis and machine learning models including Random Forest and Extreme Gradient Boosting.\"}]","Predicting overactive bladder from inflammatory markers - A machine learning approach using NHANES 2005-2020 | PDF",1785903772,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-overactive-bladder-from-inflammatory-markers-a-machine-learning-approach-using-nhanes-2005-2020","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-overactive-bladder-from-inflammatory-markers-a-machine-learning-approach-using-nhanes-2005-2020/126201/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How was overactive bladder (OAB) defined in the study?","Question",{"text":76,"@type":77},"OAB was defined by an OAB Symptom Score of 3 or higher (≥3).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which CBC-derived inflammatory biomarkers were examined?",{"text":81,"@type":77},"The study evaluated SII, SIRI, NLR, MLR, and NMLR, derived from complete blood count data.",{"name":83,"@type":74,"acceptedAnswer":84},"What modeling approaches were used to predict OAB risk?",{"text":85,"@type":77},"The study used multivariable logistic regression with threshold analysis and machine learning models including Random Forest and Extreme Gradient Boosting.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]