Department of Gastroenterology, Hepatology, and Infectious Diseases, Otto-von-Guericke University Magdeburg, Magdeburg, Germany.
Department of Gastroenterology and Hepatology, University Hospital, University Duisburg-Essen, Essen, Germany.
PLoS One. 2019 Mar 26;14(3):e0214436. doi: 10.1371/journal.pone.0214436. eCollection 2019.
BACKGROUND & AIMS: Current non-invasive scores for the assessment of severity of non-alcoholic fatty liver disease (NAFLD) and identification of patients with non-alcoholic steatohepatitis (NASH) have insufficient performance to be included in clinical routine. In the current study, we developed a novel machine learning approach to overcome the caveats of existing approaches.
Non-invasive parameters were selected by an ensemble feature selection (EFS) from a retrospectively collected training cohort of 164 obese individuals (age: 43.5±10.3y; BMI: 54.1±10.1kg/m2) to develop a model able to predict the histological assessed NAFLD activity score (NAS). The model was evaluated in an independent validation cohort (122 patients, age: 45.2±11.75y, BMI: 50.8±8.61kg/m2).
EFS identified age, γGT, HbA1c, adiponectin, and M30 as being highly associated with NAFLD. The model reached a Spearman correlation coefficient with the NAS of 0.46 in the training cohort and was able to differentiate between NAFL (NAS≤4) and NASH (NAS>4) with an AUC of 0.73. In the independent validation cohort, an AUC of 0.7 was achieved for this separation. We further analyzed the potential of the new model for disease monitoring in an obese cohort of 38 patients under lifestyle intervention for one year. While all patients lost weight under intervention, increasing scores were observed in 15 patients. Increasing scores were associated with significantly lower absolute weight loss, lower reduction of waist circumference and basal metabolic rate.
A newly developed model (http://CHek.heiderlab.de) can predict presence or absence of NASH with reasonable performance. The new score could be used to detect NASH and monitor disease progression or therapy response to weight loss interventions.
目前用于评估非酒精性脂肪性肝病（NAFLD）严重程度和识别非酒精性脂肪性肝炎（NASH）患者的非侵入性评分方法在性能上存在不足，无法纳入临床常规。本研究采用一种新的机器学习方法来克服现有方法的局限性。
通过回顾性收集的 164 名肥胖患者（年龄：43.5±10.3 岁；BMI：54.1±10.1kg/m2）的训练队列中使用集成特征选择（EFS）选择非侵入性参数，以开发一种能够预测组织学评估的 NAFLD 活动评分（NAS）的模型。在独立验证队列（122 名患者，年龄：45.2±11.75 岁，BMI：50.8±8.61kg/m2）中评估模型。
EFS 确定年龄、γGT、HbA1c、脂联素和 M30 与 NAFLD 高度相关。该模型在训练队列中与 NAS 的 Spearman 相关系数为 0.46，能够区分 NAFL（NAS≤4）和 NASH（NAS>4），AUC 为 0.73。在独立验证队列中，该模型也能达到 0.7 的 AUC 值。我们进一步分析了新模型在 38 名接受生活方式干预一年的肥胖患者队列中进行疾病监测的潜力。虽然所有患者在干预下体重均有所减轻，但有 15 名患者的评分增加。评分增加与绝对体重减轻量显著降低、腰围和基础代谢率降低相关。
新开发的模型（http://CHek.heiderlab.de）能够以合理的性能预测 NASH 的有无。新评分可用于检测 NASH 并监测疾病进展或对减肥干预的治疗反应。
