Why the study?
Abnormal accumulation of adipose tissue alters metabolic profiles and causes cardiovascular complications, but conventional measures only provide global whole-body measurements.
Does MRI-based quantification of truncal adipose tissue sub-types differentiate patients with metabolic diseases from controls?
Does MRI-based quantification of truncal adipose tissue sub-types differentiate patients with metabolic diseases from controls?
Automated 3D MRI quantification of truncal adipose tissue sub-types, particularly organ AT infiltration, can successfully characterize and differentiate patients with various metabolic diseases.
oVAT may support MRI phenotyping of metabolic disease; leaves open prospective validation before clinical adoption.
INTRODUCTION: Abnormal accumulation of adipose tissue (AT) alters the metabolic profile and underlies cardiovascular complications. Conventional measures provide global measurements for the entire body. The purpose of this study was to propose a new approach to quantify the amount and type of truncal AT automatically from MRI in metabolic patients and controls. MATERIALS AND METHODS: DIXON acquisitions were performed at 1.5 T in 30 metabolic syndrome (MS) (59 ± 6 years), 12 obese (50 ± 11 years), 35 type 2 diabetes (T2DM) patients (56 ± 11 years) and 19 controls (52 ± 11 years). AT was segmented into: subcutaneous AT "SAT", visceral AT "VAT", deep VAT "dVAT", peri-organ VAT "pVAT" using active contours and k-means clustering algorithms. Subsequently, organ AT infiltration index "oVAT" was calculated as the normalized fat signal magnitude in organs. RESULTS: Excellent intra- and inter-operator reproducibility was obtained for AT segmentation. MS and obese patients had the highest amount of total AT. SAT increased in MS (1144 ± 621 g) and T2DM patients (1024 ± 634 g), and twice the level of SAT in controls (505 ± 238 g), and further increased in obese patients (1429 ± 621 g). While VAT, pVAT and dVAT increased to a similar degree in the metabolic patients compared to controls, the oVAT index was able to differentiate controls from MS and T2DM patients and to discriminate the three metabolic patient groups (p < 0.01). Local AT sub-types were not related to BMI in all groups except for SAT in controls (p = 0.03). CONCLUSION: Reproducible truncal AT sub-types quantification using 3D MRI was able to characterize patients with metabolic diseases. It may serve in the future as a non-invasive predictor of cardiovascular complications in such patients.
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Bouazizi et al. (2021) studied this question.
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