Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects.

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.

代谢综合征（MetS）与内脏脂肪组织（VAT）的过度堆积密切相关。在临床实践中，内脏脂肪组织的定量测量通常用于代谢风险的评估；然而，内脏脂肪组织的质地是否能够评估内脏肥胖程度、对代谢综合征进行分层以及预测手术引起的减肥效果，在很大程度上仍然未知。 675名中国成年志愿者和63名肥胖患者（接受减肥手术）参与了研究。从计算机断层扫描（CT）的内脏脂肪组织图像中提取质地特征，并应用机器学习来识别与代谢相关特征有关的重要影像生物标志物。 结合性别，10种内脏脂肪组织质地特征在预测胰岛素抵抗、代谢综合征、中心性肥胖和内脏肥胖的患病率时，曲线下面积（AUCs）分别达到0.872、0.888、0.961和0.947。一种新的影像生物标志物——游程熵（RunEntropy）被确定与主要代谢结果显著相关，并且对338名志愿者进行的3.5年随访证明了其长期有效性。更重要的是，术前影像生物标志物在估计手术反应方面具有较高的曲线下面积和准确性，包括多余体重减少百分比（%EWL）（0.867和74.6%）、术后体重指数（BMI）分组（0.930和76.1%）、术后胰岛素抵抗（0.947和88.9%）以及内脏脂肪过度减少（内脏脂肪减少比例超过50%；0.928和84.1%）。 这项研究表明，内脏脂肪组织的质地特征在评估代谢紊乱和预测手术引起的减肥效果方面具有重要的临床意义。 资助者的完整名单可在致谢部分找到。