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Abstract
The objective of the study was to identify associations of longitudinal trajectories of traditional cardiometabolic risk factors with abdominal and ectopic adipose tissue depots measured by magnetic resonance imaging (MRI). We measured total abdominal, visceral, and subcutaneous adipose tissue in liter and intrahepatic, intrapancreatic and renal sinus fat as fat fractions by MRI in 325 individuals free of cardiovascular disease at Exam 3 of a population-based cohort. We related these MRI measurements at Exam 3 to longitudinal risk profile trajectory clusters, based on risk factor measurements from Exam 3, Exam 2 (seven years prior to MRI) and Exam 1 (14 years prior to MRI). Based on the levels and longitudinal trajectories of several risk factors (blood pressure, lipid profile, anthropometric measurements, HbA1c), we identified three different trajectory clusters. These clusters displayed a graded association with all adipose tissue traits after adjustment for potential confounders (e.g. visceral adipose tissue: βClusterII = 1.30 l, 95%-CI:[0.84 l;1.75 l], βClusterIII = 3.32 l[2.74 l;3.90 l]; intrahepatic: EstimateClusterII = 1.54[1.27,1.86], EstimateClusterIII = 2.48[1.93,3.16]. Associations remained statistically significant after additional adjustment for the risk factor levels at Exam 1 or Exam 3, respectively. Trajectory clusters provided additional information in explaining variation in the different fat compartments beyond risk factor profiles obtained at individual exams. In conclusion, sustained high risk factor levels and unfavorable trajectories are associated with high levels of adipose tissue; however, the association with cardiometabolic risk factors varies substantially between different ectopic adipose tissues. Trajectory clusters, covering longitudinal risk profiles, provide additional information beyond single-point risk profiles. This emphasizes the need to incorporate longitudinal information in cardiometabolic risk estimation.
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Details
1 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
2 Department of Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany; German Centre for Cardiovascular Research (DZHK e.V.), Munich, Germany
3 Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany
4 Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
5 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Chair of Epidemiology, Ludwig-Maximilians-University München, UNIKA-T Augsburg, Augsburg, Germany
6 German Center for Diabetes Research (DZD), München-Neuherberg, Neuherberg, Germany; Institute for Biometrics and Epidemiology, German Diabetes Center, Duesseldorf, Germany
7 Department of Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany; Department of Diagnostic and Interventional Radiology, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
8 Institute of Epidemiology and Biobank PopGen, Kiel University, Kiel, Germany
9 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Centre for Cardiovascular Research (DZHK e.V.), Munich, Germany; Chair of Epidemiology, Ludwig-Maximilians-University München, Munich, Germany