{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:05:03Z","timestamp":1791223503946,"version":"4.1.0"},"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Background<\/jats:title>\n<jats:p>Multiple risk factors contribute jointly to the development and progression of cardiometabolic diseases. Therefore, joint longitudinal trajectories of multiple risk factors might represent different degrees of cardiometabolic risk.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>We analyzed population-based data comprising three examinations (Exam 1: 1999\u20132001, Exam 2: 2006\u20132008, Exam 3: 2013\u20132014) of 976 male and 1004 female participants of the KORA cohort (Southern Germany). Participants were followed up for cardiometabolic diseases, including cardiovascular mortality, myocardial infarction and stroke, or a diagnosis of type 2 diabetes, until 2016. Longitudinal multivariate k-means clustering identified sex-specific trajectory clusters based on nine cardiometabolic risk factors (age, systolic and diastolic blood pressure, body-mass-index, waist circumference, Hemoglobin-A1c, total cholesterol, high- and low-density lipoprotein cholesterol). Associations between clusters and cardiometabolic events were assessed by logistic regression models.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Results<\/jats:title>\n<jats:p>We identified three trajectory clusters for men and women, respectively. Trajectory clusters reflected a distinct distribution of cardiometabolic risk burden and were associated with prevalent cardiometabolic disease at Exam 3 (men: odds ratio (OR)<jats:sub>ClusterII<\/jats:sub> = 2.0, 95% confidence interval: (0.9\u20134.5); OR<jats:sub>ClusterIII<\/jats:sub> = 10.5 (4.8\u201322.9); women: OR<jats:sub>ClusterII<\/jats:sub> = 1.7 (0.6\u20134.7); OR<jats:sub>ClusterIII<\/jats:sub> = 5.8 (2.6\u201312.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: OR<jats:sub>ClusterII<\/jats:sub> = 3.5 (1.1\u201315.6); OR<jats:sub>ClusterIII<\/jats:sub> = 7.5 (2.4\u201332.7); women: OR<jats:sub>ClusterII<\/jats:sub> = 5.0 (1.1\u201334.1); OR<jats:sub>ClusterIII<\/jats:sub> = 8.0 (2.2\u201351.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>On a population-based level, distinct longitudinal risk profiles over a 14-year time period are differentially associated with cardiometabolic events. Our results suggest that longitudinal data may provide additional information beyond single time-point measures. Their inclusion in cardiometabolic risk assessment might improve early identification of individuals at risk.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0300966","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T18:06:47Z","timestamp":1711649207000},"page":"e0300966","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases: Results from the population-based KORA cohort"],"prefix":"10.1371","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-2630","authenticated-orcid":true,"given":"Fiona","family":"Niedermayer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gunther","family":"Schauberger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wolfgang","family":"Rathmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanie J.","family":"Klug","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8416-6440","authenticated-orcid":true,"given":"Barbara","family":"Thorand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annette","family":"Peters","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanne","family":"Rospleszcz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"issue":"1","key":"pone.0300966.ref001","doi-asserted-by":"crossref","first-page":"107","DOI":"10.2991\/jegh.k.191028.001","article-title":"Epidemiology of Type 2 Diabetes\u2014Global Burden of Disease and Forecasted Trends.","volume":"10","author":"MAB Khan","year":"2020","journal-title":"J Epidemiol Glob Health."},{"issue":"25","key":"pone.0300966.ref002","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1016\/j.jacc.2020.11.021","article-title":"The Global Burden of Cardiovascular Diseases and Risks: A Compass for Global Action","volume":"76","author":"GA Roth","year":"2020","journal-title":"J Am Coll Cardiol"},{"issue":"11","key":"pone.0300966.ref003","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1038\/s41591-019-0643-8","article-title":"Sex differences in cardiometabolic disorders","volume":"25","author":"E Gerdts","year":"2019","journal-title":"Nat Med"},{"issue":"19","key":"pone.0300966.ref004","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1161\/CIRCULATIONAHA.110.968792","article-title":"Sex\/gender differences in cardiovascular disease prevention: what a difference a decade makes","volume":"124","author":"L Mosca","year":"2011","journal-title":"Circulation"},{"issue":"6","key":"pone.0300966.ref005","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: the Framingham Heart Study","volume":"117","author":"RB, Sr. D\u2019Agostino","year":"2008","journal-title":"Circulation"},{"issue":"3","key":"pone.0300966.ref006","doi-asserted-by":"crossref","first-page":"725","DOI":"10.2337\/diacare.26.3.725","article-title":"The diabetes risk score: a practical tool to predict type 2 diabetes risk","volume":"26","author":"J Lindstrom","year":"2003","journal-title":"Diabetes Care"},{"issue":"1","key":"pone.0300966.ref007","doi-asserted-by":"crossref","first-page":"e000280","DOI":"10.1136\/bmjdrc-2016-000280","article-title":"Validation of the German Diabetes Risk Score among the general adult population: findings from the German Health Interview and Examination Surveys","volume":"4","author":"R Paprott","year":"2016","journal-title":"BMJ Open Diabetes Res Care"},{"issue":"7","key":"pone.0300966.ref008","article-title":"Trends in Cardiovascular Health Metrics in Obese Adults: National Health and Nutrition Examination Survey (NHANES), 1988\u20132014.","volume":"5","author":"F Guo","year":"2016","journal-title":"J Am Heart Assoc"},{"issue":"5","key":"pone.0300966.ref009","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1161\/CIRCULATIONAHA.107.705947","article-title":"How much of the recent decline in the incidence of myocardial infarction in British men can be explained by changes in cardiovascular risk factors? 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