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. 2019 Nov 18;9(1):16972.
doi: 10.1038/s41598-019-53546-y.

Association of longitudinal risk profile trajectory clusters with adipose tissue depots measured by magnetic resonance imaging

Susanne Rospleszcz  1 , Roberto Lorbeer  2   3 , Corinna Storz  4 , Christopher L Schlett  5 , Christa Meisinger  6   7 , Barbara Thorand  6 , Wolfgang Rathmann  8   9 , Fabian Bamberg  2   5 , Wolfgang Lieb  10 , Annette Peters  6   3   11
Affiliations

Association of longitudinal risk profile trajectory clusters with adipose tissue depots measured by magnetic resonance imaging

Susanne Rospleszcz et al. Sci Rep. .

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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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
MRI-based assessment of visceral (VAT; red) and subcutaneous (SAT; yellow) adipose tissue in a 46-year-old male (VAT 6.57 l), displayed in coronar (A), sagittal (B) and axial (C) slices.
Figure 2
Figure 2
Exemplary MRI images of ectopic fat quantification. (A) Quantification of hepatic fat fraction. The dual-echo Dixon sequence shows the region of interest (orange square) placed in the liver parenchyma on the level of the portal vein. Results of the multi-echo spectroscopy are displayed as graph and colored bar. (B) Quantification of pancreatic fat fraction. Using a multi-echo Dixon-VIBE sequence, circular regions of interest were drawn into the pancreatic caput (B1), corpus (B2) cauda (B3) and resulting proton-density fat fractions were averaged. (C) Quantification of renal sinus fat fraction. Displayed is the overlay of renal sinus segmentation with Water-Only (C1) and Fat-Only (C2) Dixon images.
Figure 3
Figure 3
Goodness-of-Fit of the linear regression models estimating the association of single-point risk profiles with adipose tissue outcomes. On the x-axis: single time points at which risk profiles were obtained: Exam 1, Exam 2, Exam 3. On the y-axis: Goodness-of-Fit as measured by explained variance in outcome (adjusted R2). The single time points are connected by lines for visual aid only. The risk factor profiles included systolic blood pressure, diastolic blood pressure, BMI, WC, Total Cholesterol, HDL, LDL and HbA1c whereas the outcome variables comprised TAT, SAT, VAT, RSFF, log (HFF) and log (PFF).TAT: Total adipose tissue, VAT: Visceral adipose tissue, SAT: Subcutaneous adipose tissue, RSFF: Renal sinus fat fraction, HFF: Hepatic fat fraction, PFF: Pancreatic fat fraction
Figure 4
Figure 4
Mean risk factor levels at Exam 1, Exam 2 and Exam 3 according to cluster membership of participants. Cluster membership in either Cluster I, Cluster II or Cluster III was determined by multivariate k-means clustering based on individual longitudinal risk profile trajectories.
Figure 5
Figure 5
Box plots illustrating the distribution of adipose tissue depots, measured at Exam 3, according to cluster membership of participants. Cluster membership in either Cluster I, Cluster II or Cluster III was determined by multivariate k-means clustering based on individual longitudinal risk profile trajectories. TAT: Total adipose tissue, VAT: Visceral adipose tissue, SAT: Subcutaneous adipose tissue, RSFF: Renal sinus fat fraction, HFF: Hepatic fat fraction, PFF: Pancreatic fat fraction.
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