TY - JOUR
T1 - Clusters of longitudinal risk profile trajectories are associated with cardiometabolic diseases
T2 - Results from the population-based KORA cohort
AU - Niedermayer, Fiona
AU - Schauberger, Gunther
AU - Rathmann, Wolfgang
AU - Klug, Stefanie J.
AU - Thorand, Barbara
AU - Peters, Annette
AU - Rospleszcz, Susanne
N1 - Publisher Copyright:
© 2024 Niedermayer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2024/3
Y1 - 2024/3
N2 - Background 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. Methods We analyzed population-based data comprising three examinations (Exam 1:1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) 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. Results 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)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). Conclusions 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.
AB - Background 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. Methods We analyzed population-based data comprising three examinations (Exam 1:1999-2001, Exam 2: 2006-2008, Exam 3: 2013-2014) 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. Results 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)ClusterII = 2.0, 95% confidence interval: (0.9-4.5); ORClusterIII = 10.5 (4.8-22.9); women: ORClusterII = 1.7 (0.6-4.7); ORClusterIII = 5.8 (2.6-12.9)). Trajectory clusters were furthermore associated with incident cardiometabolic cases after Exam 3 (men: ORClusterII = 3.5 (1.1-15.6); ORClusterIII = 7.5 (2.4-32.7); women: ORClusterII = 5.0 (1.1-34.1); ORClusterII = 8.0 (2.2-51.7)). Associations remained significant after adjusting for a single time point cardiovascular risk score (Framingham). Conclusions 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.
UR - http://www.scopus.com/inward/record.url?scp=85189105762&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0300966
DO - 10.1371/journal.pone.0300966
M3 - Article
C2 - 38547172
AN - SCOPUS:85189105762
SN - 1932-6203
VL - 19
JO - PLoS ONE
JF - PLoS ONE
IS - 3 March
M1 - e0300966
ER -
