[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123951-en":3,"doc-seo-123951-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},123951,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Metabolic dysfunctions predict the development of Alzheimer's disease - Statistical and machine learning analysis of EMR data","The study investigates whether factors related to metabolism, race/ethnicity, and sex are associated with Alzheimer’s disease (AD) development. Analyses were conducted on patients aged 65 and older with AD diagnoses from six University of California hospitals between January 2012 and October 2023, using matched controls without dementia. Cox proportional hazards and machine learning models assessed clinical and laboratory hazards. Results indicate higher AD risk among Hispanic/Latino and Native Hawaiian/Pacific Islander groups, while non-infectious hepatitis and alcohol abuse were significant hazards, with alcohol abuse impacting women more. Underweight increased risk, whereas overweight/obesity reduced risk.","UC Davis  \nUC Davis Previously Published Works  \nTitle  \nMetabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.  \nPermalink  \n[https://escholarship.org/uc/item/0794q2gr](https://escholarship.org/uc/item/0794q2gr)  \nAuthors  \nLiu, Rex  \nDurbin-Johnson, Blythe Paciotti, Brian  \net al.  \nPublication Date  \n2024-08-14  \nDOI  \n10.1002/alz.14101  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nReceived: 8 February 2024 Revised: 4 June 2024 Accepted: 6 June 2024  \nDOI: 10.1002/alz.14101  \nRESEARCH ARTICLE  \nMetabolic dysfunctions predict the development of Alzheimer’s disease: Statistical and machine learning analysis of EMR data  \nRex Liu1   Blythe Durbin-Johnson2  Brian Paciotti3  Albert T. Liu4  Alyssa Weakley5  Xin Liu1  Yu-Jui Yvonne Wan6   \n1 Department of Computer Science, University of California, Davis, Sacramento, California, USA  \n2 Department of Public Health Sciences, University of California, Davis, Sacramento, California, USA  \n3 Data Center of Excellence, University of California, Davis, Sacramento, California, USA  \n4 Department of Obstetrics/Gynecology, University of California, Davis, Sacramento, California, USA  \n5 Department of Neurology, University of California, Davis, Sacramento, California, USA  \n6 Department of Medical Pathology and Laboratory Medicine, University of California, Davis, Sacramento, California, USA  \nCorrespondence  \nYu-Jui Yvonne Wan, Department of Pathology and Laboratory Medicine, University of California, Davis Health, Room 3400B, Research Building III, 4645 2ndAve, Sacramento, CA 95817, USA. [Email: yjywan@ucdavis.edu](Email: yjywan@ucdavis.edu)  \nFunding information  \nCalifornia Department of Public Health; Chronic Disease Control Branch; Alzheimer’s Disease Program, Grant/Award Numbers: 18-10925, 22-10079  \nAbstract  \nINTRODUCTION: The incidence ofAlzheimer’s disease(AD)and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development.  \nMETHODS: The analyses included patients ≥ 65 years with AD diagnosis in six University of California hospitals between January 2012 and October 2023. The controls were race/ethnicity, sex, and age matched without dementia. Data analyses used the Cox proportional hazards model and machine learning (ML).  \nRESULTS: Hispanic/Latino and Native Hawaiian/Pacific Islander, but not Black subjects, had increased AD risk compared to White subjects. Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men. While underweight increased AD risk, overweight or obesity reduced risk. ML confirmed the importance of metabolic laboratory tests in predicting AD development.  \nDISCUSSION: The data stress the significance of metabolism in AD development and the need for racial/ethnic-and sex-specific preventive strategies.  \nKEYWORDS  \nalcohol abuse, metabolic liver disease, metabolism, non-infectious hepatitis, obesity  \nHighlights  \n∙ Hispanics/Latinos and Native Hawaiians/Pacific Islanders show increased hazards of Alzheimer’s disease (AD) compared to White subjects.  \n∙ Underweight individuals demonstrate a significantly higher hazard ratio for AD compared to those with normal body mass index.  \n∙ The association between obesity and AD hazard differs among racial groups, with elderly Asian subjects showing increased risk compared to White subjects.  \n∙ Alcohol consumption and non-infectious hepatitis are significant hazards for AD.  \n∙ Machine learning approaches highlight the potential of metabolic panels for AD prediction.  \nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modificatio","cbCaiue3VHNLU8QQ","https://ap.wps.com/l/cbCaiue3VHNLU8QQ","pdf",654111,1,12,"English","en",105,"# Abstract\n## Introduction\n## Methods\n## Results\n## Discussion\n## Keywords and Highlights","[{\"question\":\"What question does the study address about Alzheimer’s disease development?\",\"answer\":\"Whether metabolic factors, race/ethnicity, and sex are associated with the development of Alzheimer’s disease.\"},{\"question\":\"How were patients and controls selected in the analyses?\",\"answer\":\"The study included patients aged 65 or older with AD diagnoses from six University of California hospitals between January 2012 and October 2023, with controls matched by race/ethnicity, sex, and age and without dementia.\"},{\"question\":\"Which factors were identified as significant hazards for AD, and what sex difference was reported?\",\"answer\":\"Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men.\"}]","Metabolic dysfunctions predict the development of Alzheimer's disease - Statistical and machine learning analysis of EMR data | PDF",1785819403,30,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"metabolic-dysfunctions-predict-the-development-of-alzheimers-disease-statistical-and-machine-learning-analysis-of-emr-data","",{"@graph":36,"@context":85},[37,54,68],{"@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/metabolic-dysfunctions-predict-the-development-of-alzheimers-disease-statistical-and-machine-learning-analysis-of-emr-data/123951/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What question does the study address about Alzheimer’s disease development?","Question",{"text":75,"@type":76},"Whether metabolic factors, race/ethnicity, and sex are associated with the development of Alzheimer’s disease.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were patients and controls selected in the analyses?",{"text":80,"@type":76},"The study included patients aged 65 or older with AD diagnoses from six University of California hospitals between January 2012 and October 2023, with controls matched by race/ethnicity, sex, and age and without dementia.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors were identified as significant hazards for AD, and what sex difference was reported?",{"text":84,"@type":76},"Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]