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Laboratory tests are widely available, but their prognostic value remains underused. Using two independent cohorts of older Asians (aged ≥55) from the Singapore Longitudinal Ageing Studies, the study links routine blood count and biochemistry biomarkers to all-cause mortality, deriving risk scores from a Cox model. Follow-up over 17 years identifies higher-risk groups with shorter survival (P \u003C 0.01) and robust 5- and 10-year mortality prediction. Results are validated in the NHANES cohort (concordance index 0.79 and 0.78). Biological age is elevated in deceased participants, supporting an actionable model stratifying mortality risk earlier and more accurately.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/predictive-model-for-mortality-risk-stratification-using-laboratory-biomarkers-in-singaporeans/427958/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/predictive-model-for-mortality-risk-stratification-using-laboratory-biomarkers-in-singaporeans/427958.png","ImageObject",300,407,{"name":92,"@type":93},"นรินทร์","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-07","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why are current clinical tools considered insufficient for mortality risk prediction?","Question",{"text":112,"@type":113},"They often depend on aggregate scoring or subjective assessments that may not detect early or subtle physiological changes linked to high mortality risk.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data and modeling approach were used to build the predictive tool?",{"text":117,"@type":113},"The model uses routine laboratory biomarkers, including full blood count and biochemistry variables, and derives risk scores from a Cox model trained to predict mortality.",{"name":119,"@type":110,"acceptedAnswer":120},"How was the model validated and what predictive performance was reported?",{"text":121,"@type":113},"Prediction was validated in an NHANES cohort, reporting concordance indices of 0.79 and 0.78 for ages ≥40, supporting robust risk discrimination.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},427958,1790915413,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":66,"faqs":140,"seo_title":141,"seo_description":67,"update_tm":142,"read_time":81},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","1142 Innovation in Aging, 2025, Vol. 9, No. S2  \nAbstract citation ID: igaf122.3829  \nPREDICTIVE MODEL FOR MORTALITY RISK  \nSTRATIFICATION USING LABORATORY BIOMARKERS  \nIN SINGAPOREANS  \nXinru Lim1, Jiangfeng Ye2, Denise Goh1, Jess Vo3, Roger Ho2, Min-Han Tan3, and Joe Yeong1, 1. Institute of Molecular and Cell Biology, Singapore, Singapore, 2. National University of Singapore, Singapore, Singapore, 3. Lucence Diagnostics Pte.  \nLtd., Singapore, Singapore  \nCurrent clinical decision-making tools often rely on aggregate scoring systems or subjective assessments, which may not capture early or subtle physiological changes indicating high mortality risk. While laboratory tests are ubiquitously available and inexpensive, their prognostic potential is underutilized.  \nThere is a need for an evidence-based, quantitative tool that can harness the predictive power of routine laboratory data to improve patient outcomes by identifying high-risk individuals earlier and more accurately. With two independent cohorts of older Asians, aged ≥55 years, from the Singapore Longitudinal Ageing Studies, we investigated their basic clinical laboratory biomarkers (i.e. full blood count and biochemistry variables) and its relation to all-cause mortality. Based on risk score derived from a Cox model trained to predict mortality risk, we identified a group of individuals with significantly shorter overall survival (P \u003C 0.01), using 17 years of follow-up data. Subsequent Cox Regression analysis demonstrated robust predictive performance for 5-year and 10-year mortality. This result was also validated in the National Health and Nutrition Examination Survey cohort, achieving a concordance index of 0.79 and 0.78, respectively (for aged ≥40 years) . Furthermore, we revealed that the biological age was significantly elevated in deceased individuals compared to their chronological age (P \u003C 0.0001), while surviving individuals exhibited a younger biological age relative to their chronological age (P \u003C 0.0001) . Consequently, we developed a predictive model that utilizes routinely analyzed laboratory biomarkers-including components of the immune cell count and other clinical variables-to assess and stratify mortality risk, capable of identifying high-risk individuals earlier and more accurately.","cbCaij6iUWHKkiUw","https://ap.wps.com/l/cbCaij6iUWHKkiUw","pdf",1006555,"English","[{\"question\":\"Why are current clinical tools considered insufficient for mortality risk prediction?\",\"answer\":\"They often depend on aggregate scoring or subjective assessments that may not detect early or subtle physiological changes linked to high mortality risk.\"},{\"question\":\"What data and modeling approach were used to build the predictive tool?\",\"answer\":\"The model uses routine laboratory biomarkers, including full blood count and biochemistry variables, and derives risk scores from a Cox model trained to predict mortality.\"},{\"question\":\"How was the model validated and what predictive performance was reported?\",\"answer\":\"Prediction was validated in an NHANES cohort, reporting concordance indices of 0.79 and 0.78 for ages ≥40, supporting robust risk discrimination.\"}]","Predictive Model for Mortality Risk Stratification Using Laboratory Biomarkers in Singaporeans | PDF",1790645299]