{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T12:14:50Z","timestamp":1768565690604,"version":"3.49.0"},"posted":{"date-parts":[[2024,8,6]]},"group-title":"Cardiovascular Medicine","reference-count":81,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,8,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration. We hypothesized that a video-based DL model provided with heart-masked MRI data would capture a rich yet cardiac-specific representation of cardiac aging.<\/jats:p>\n                <jats:p>In 61,691 UK Biobank participants, we excluded noncardiac pixels from cardiac MRI and trained a video-based DL model to predict age from one cardiac cycle in the 4-chamber view. We then computed cardiac age acceleration as the bias-corrected prediction of heart age minus the calendar age. Predicted heart age explained 71.1% of variance in calendar age, with a mean absolute error of 3.3 years. Cardiac age acceleration was linked to unfavorable cardiac geometry and systolic and diastolic dysfunction. We also observed links between cardiac age acceleration and diet, decreased physical activity, increased alcohol and tobacco use, and altered levels of 239 serum proteins, as well as adverse brain MRI characteristics.<\/jats:p>\n                <jats:p>\n                  We found cardiac age acceleration to be heritable (h2g 26.6%); a genome-wide association study identified 8 loci related to linked to cardiomyopathy (near\n                  <jats:italic>TTN, TNS1, LSM3, PALLD, DSP, PLEC, ANKRD1<\/jats:italic>\n                  and\n                  <jats:italic>MYO18B<\/jats:italic>\n                  ) and an additional 16 loci (near\n                  <jats:italic>MECOM, NPR3, KLHL3, HDGFL1, CDKN1A, ELN, SLC25A37, PI15, AP3M1, HMGA2, ADPRHL1, PGAP3, WNT9B, UHRF1 and DOK5<\/jats:italic>\n                  ). Of the discovered loci, 21 were not previously associated with cardiac age acceleration. Mendelian randomization revealed that lower genetically mediated levels of 6 circulating proteins (MSRA most strongly), as well as greater levels of 5 proteins (LXN most strongly) were associated with cardiac age acceleration, as were greater blood pressure and Lp(a). A polygenic score for cardiac age acceleration predicted earlier onset of arrhythmia, heart failure, myocardial infarction, and mortality.\n                <\/jats:p>\n                <jats:p>These findings provide a thematic understanding of cardiac age acceleration and suggest that heart- and vascular-specific factors are key to cardiac age acceleration, predominating over a more global aging program.<\/jats:p>","DOI":"10.1101\/2024.08.02.24310874","type":"posted-content","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T14:42:57Z","timestamp":1722955377000},"source":"Crossref","is-referenced-by-count":1,"title":["Genetics of Cardiac Aging Implicate Organ-Specific Variation"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1603-6406","authenticated-orcid":false,"given":"James","family":"Brundage","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua P.","family":"Barrios","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Geoffrey H.","family":"Tison","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6088-4037","authenticated-orcid":false,"given":"James P.","family":"Pirruccello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2024080803551019000_2024.08.02.24310874v1.1","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-017-0367-1"},{"key":"2024080803551019000_2024.08.02.24310874v1.2","doi-asserted-by":"publisher","DOI":"10.1186\/s12968-017-0327-9"},{"key":"2024080803551019000_2024.08.02.24310874v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/s41569-023-00881-3"},{"key":"2024080803551019000_2024.08.02.24310874v1.4","doi-asserted-by":"crossref","first-page":"e13083","DOI":"10.1111\/acel.13083","article-title":"Cellular senescence and chronological age in various human tissues: A systematic review and meta-analysis","volume":"19","year":"2020","journal-title":"Aging Cell"},{"key":"2024080803551019000_2024.08.02.24310874v1.5","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-018-0004-3"},{"key":"2024080803551019000_2024.08.02.24310874v1.6","doi-asserted-by":"publisher","DOI":"10.1186\/gb-2013-14-10-r115"},{"key":"2024080803551019000_2024.08.02.24310874v1.7","doi-asserted-by":"publisher","DOI":"10.1101\/2021.06.09.21258645"},{"key":"2024080803551019000_2024.08.02.24310874v1.8","doi-asserted-by":"crossref","first-page":"12805","DOI":"10.1038\/s41598-022-16639-9","article-title":"Estimation of biological heart age using cardiovascular magnetic resonance radiomics","volume":"12","year":"2022","journal-title":"Sci Rep"},{"key":"2024080803551019000_2024.08.02.24310874v1.9","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-40566-6"},{"key":"2024080803551019000_2024.08.02.24310874v1.10","doi-asserted-by":"crossref","first-page":"22625","DOI":"10.1038\/s41598-022-27254-z","article-title":"Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes","volume":"12","year":"2022","journal-title":"Sci Rep"},{"key":"2024080803551019000_2024.08.02.24310874v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06802-1"},{"key":"2024080803551019000_2024.08.02.24310874v1.12","doi-asserted-by":"crossref","unstructured":"Cetin, I. et al. 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