Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects.
675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits.
Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%).
This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects.
The complete list of funders can be found in the Acknowledgement section.

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