{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T06:21:36Z","timestamp":1787638896567,"version":"build-2736575974"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Biological ages capture aging signatures associated with aging-related outcomes, but most models use systemic and unimodal indices. The added value of organ-specific and multimodal information for assessing organ-specific disease associations remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled 1,830 participants (77.81% male; age 55.87\u2009\u00b1\u200911.07 years) undergoing blood chemistry testing and echocardiography. Cardiac, systemic, and multimodal biological age models were trained using supervised machine learning with 20-fold cross-validation to predict chronological age from LASSO-selected variables among 15 echocardiographic indices, 73 blood-based indices, or their combination. Biological age acceleration (BAA) was derived using modality-specific age-bias correction functions fitted in the apparently healthy reference cohort and standardized before analysis. Associations between BAA and prevalent cardiovascular disease (CVD; ICD-10 I00\u2013I99) were evaluated using multivariable logistic regression. Models were developed in 937 apparently healthy participants; CVD analyses compared 810 cases with 1,020 CVD-negative participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Cardiac biological age showed moderate accuracy (mean absolute error [MAE]\u2009=\u20096.08 years;\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.34), with A1 (late diastolic transmitral flow velocity) and septal e\u2032 (early diastolic mitral annular velocity) as the leading SHAP contributors. Systemic biological age performed better (MAE\u2009=\u20094.60 years;\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.65), with estimated glomerular filtration rate and creatinine as the leading contributors. Cardiac BAA showed a numerically larger association with prevalent CVD than systemic BAA (odds ratio [OR]\u2009=\u20091.36, 95% confidence interval [CI] 1.23\u20131.52 vs. OR\u2009=\u20091.31, 95% CI 1.20\u20131.43). Cardiac and systemic biological ages showed partial overlap (\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.25). The multimodal model achieved the best age-prediction performance (MAE\u2009=\u20094.29 years;\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009=\u20090.70) and was associated with prevalent CVD (OR\u2009=\u20091.40, 95% CI 1.28\u20131.53). Subtype-specific associations were broadly directionally consistent; significance was reached for hypertension, whereas estimates for smaller subtypes remained imprecise.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Cardiac and systemic biological age models captured complementary aging information and showed distinct strengths in chronological-age estimation and cross-sectional CVD assessment. The multimodal model achieved the lowest MAE and highest\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      , supporting the complementary value of multimodal integration for biological aging assessment.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1877589","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:34:54Z","timestamp":1787636094000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cardiac and systemic biological age capture complementary aging signals associated with cardiovascular disease"],"prefix":"10.3389","volume":"13","author":[{"given":"Yiming","family":"Wang","sequence":"first","affiliation":[{"name":"Division of Geriatric Endocrinology, the First Affiliated Hospital of Nanjing Medical University","place":["Nanjing, 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Medical University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Endocrinology, the First Affiliated Hospital of Nanjing Medical University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Duan","sequence":"additional","affiliation":[{"name":"Division of Geriatric Endocrinology, the First Affiliated Hospital of Nanjing Medical University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1016\/j.cell.2013.05.039","article-title":"The hallmarks of 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