[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124572-en":3,"doc-seo-124572-105":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},124572,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",7,"Healthcare","Using machine learning to estimate the calendar age based on autonomic cardiovascular function","Aging is accompanied by measurable changes in cardiovascular regulation that can be captured using autonomic cardiovascular indices. The study applies machine learning to resting-state electrocardiogram and continuous blood pressure recordings from healthy participants to estimate calendar age. Cardiovascular indices, including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics, support four modeling approaches. In five-fold cross validation, Gaussian process regression best estimates age (r≈0.81, MAE≈5.6 years) and shows sex-related differences and distinct performance in an obesity evaluation setting.","TYPE Original Research PUBLISHED 23 January 2023 DOI 10.3389/fnagi.2022.899249  \nOPEN ACCESS  \nEDITED BY  \nYang Jiang, University of Kentucky, United States  \nREVIEWED BY  \nNadia Solaro,  \nUniversity of Milano-Bicocca, Italy Edward Lakatta,  \nNational Institute on Aging (NIH), United States  \nFrederic Roche, Université Jean Monnet, France  \nChih-Cheng Huang,  \nKaohsiung Chang Gung Memorial Hospital, Taiwan  \n*CORRESPONDENCE  \nAndy Schumann  \n [andy.schumann@med.uni-jena.de](andy.schumann@med.uni-jena.de)  \nSPECIALTY SECTION  \nThis article was submitted to Neurocognitive Aging and Behavior, a section of the journal  \nFrontiers in Aging Neuroscience  \nRECEIVED 18 March 2022  \nACCEPTED 20 December 2022  \nPUBLISHED 23 January 2023  \nCITATION  \nSchumann A, Gaser C, Sabeghi R, Schulze PC, Festag S, Spreckelsen C and Bär K-J (2023) Using machine learning to estimate the calendar age based on autonomic cardiovascular function.  \nFront. Aging Neurosci. 14:899249.  \ndoi: 10.3389/fnagi.2022.899249  \nCOPYRIGHT  \n© 2023 Schumann, Gaser, Sabeghi, Schulze, Festag, Spreckelsen and Bär. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nUsing machine learning to estimate the calendar age based on autonomic cardiovascular function  \nAndy Schumann 1*, Christian Gaser 2, 3, Rassoul Sabeghi 1,  \nP. Christian Schulze4, Sven Festag 5, 6, Cord Spreckelsen 5, 6 and Karl-Jürgen Bär 1  \n1 Lab for Autonomic Neuroscience, Imaging and Cognition (LANIC), Department of Psychosomatic Medicine and Psychotherapy, Jena University Hospital, Jena, Germany, 2 Hans Berger Department of Neurology, Jena University Hospital, Jena, Germany, 3 Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany, 4 Department of Internal Medicine I, Division of Cardiology, Jena University Hospital, Jena, Germany, 5 Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital, Jena, Germany, 6SMITH Consortium of the German Medical Informatics Initiative, Leipzig, Germany  \nIntroduction: Aging is accompanied by physiological changes in cardiovascular regulation that can be evaluated using a variety of metrics. In this study, we employ machine learning on autonomic cardiovascular indices in order to estimate participants’ age.  \nMethods: We analyzed a database including resting state electrocardiogram and continuous blood pressure recordings of healthy volunteers. A total of 884 data sets met the inclusion criteria. Data of 72 other participants with an BMI indicating obesity (>30 kg/m²) were withheld as an evaluation sample. For all participants, 29 different cardiovascular indices were calculated including heart rate variability, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics. Based on cardiovascular indices, sex and device, four different approaches were applied in order to estimate the calendar age of healthy subjects, i.e., relevance vector regression (RVR), Gaussian process regression (GPR), support vector regression (SVR), and linear regression (LR) . To estimate age in the obese group, we drew normal-weight controls from the large sample to build a training set and a validation set that had an age distribution similar to the obesity test sample.  \nResults: In a five-fold cross validation scheme, we found the GPR model to be suited best to estimate calendar age, with a correlation of r=0 .81 and a mean absolute error of MAE=5 .6 years. In men, the error (MAE=5 .4 years) seemed to be lower than that in women (MAE=6 .0 years) . In comparison to normalweight subjects, GPR and SVR sign","cbCaisNuxw5kC61H","https://ap.wps.com/l/cbCaisNuxw5kC61H","pdf",936334,1,10,"English","en",105,"# Introduction\n# Methods\n# Results\n# Discussion\n# Keywords","[{\"question\":\"What data and cardiovascular indices were used to estimate calendar age?\",\"answer\":\"Resting-state electrocardiogram and continuous blood pressure recordings from healthy volunteers were analyzed. Twenty-nine cardiovascular indices were calculated, including HRV, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics.\"},{\"question\":\"Which machine learning model performed best for calendar age estimation?\",\"answer\":\"Gaussian process regression (GPR) was best in five-fold cross validation, yielding a correlation of about 0.81 and a mean absolute error around 5.6 years.\"},{\"question\":\"How did the model perform differently for men versus women and for obese participants?\",\"answer\":\"The age estimation error appeared lower in men (MAE≈5.4 years) than in women (MAE≈6.0 years). Compared with normal-weight controls, GPR and support vector regression overestimated age in obese participants, with the largest indicated cardiovascular aging gap around 5.7 years.\"}]","Using machine learning to estimate the calendar age based on autonomic cardiovascular function | PDF",1785893045,25,{"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},"using-machine-learning-to-estimate-the-calendar-age-based-on-autonomic-cardiovascular-function","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-to-estimate-the-calendar-age-based-on-autonomic-cardiovascular-function/124572/",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","2026-08-05",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 data and cardiovascular indices were used to estimate calendar age?","Question",{"text":75,"@type":76},"Resting-state electrocardiogram and continuous blood pressure recordings from healthy volunteers were analyzed. Twenty-nine cardiovascular indices were calculated, including HRV, blood pressure variability, baroreflex function, pulse wave dynamics, and QT interval characteristics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best for calendar age estimation?",{"text":80,"@type":76},"Gaussian process regression (GPR) was best in five-fold cross validation, yielding a correlation of about 0.81 and a mean absolute error around 5.6 years.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the model perform differently for men versus women and for obese participants?",{"text":84,"@type":76},"The age estimation error appeared lower in men (MAE≈5.4 years) than in women (MAE≈6.0 years). Compared with normal-weight controls, GPR and support vector regression overestimated age in obese participants, with the largest indicated cardiovascular aging gap around 5.7 years.","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},[93,97,101,105,110,115,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]