Association of longitudinal risk profile trajectory clusters with adipose tissue depots measured by magnetic resonance imaging - Rospleszcz, Susanne (Sci Rep, 2019)
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: β(ClusterII) = 1.30 l, 95%-CI:[0.84 l;1.75 l], β(ClusterIII) = 3.32 l[2.74 l;3.90 l]; intrahepatic: Estimate(ClusterII) = 1.54[1.27,1.86], Estimate(ClusterIII) = 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.