{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14"],"submitter":["Schumann A"],"pubmed_abstract":["<h4>Introduction</h4>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.<h4>Methods</h4>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/m<sup>²</sup>) 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"],"journal":["Frontiers in aging neuroscience"],"pagination":["899249"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9899796"],"repository":["biostudies-literature"],"pubmed_title":["Using machine learning to estimate the calendar age based on autonomic cardiovascular function."],"pmcid":["PMC9899796"],"pubmed_authors":["Sabeghi R","Schulze PC","Schumann A","Bar KJ","Festag S","Gaser C","Spreckelsen C"],"additional_accession":[]},"is_claimable":false,"name":"Using machine learning to estimate the calendar age based on autonomic cardiovascular function.","description":"<h4>Introduction</h4>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.<h4>Methods</h4>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/m<sup>²</sup>) 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","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-05-28T20:16:07.93Z","creation":"2025-04-05T11:24:12.283Z"},"accession":"S-EPMC9899796","cross_references":{"pubmed":["36755773"],"doi":["10.3389/fnagi.2022.899249"]}}