Division of Endocrinology, Diabetes and Metabolism, Department of Internal Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
First Department of Pharmacology, Faculty of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Metabolism. 2019 Dec;101:154005. doi: 10.1016/j.metabol.2019.154005. Epub 2019 Nov 9.
Non-alcoholic fatty liver disease (NAFLD) affects 25-30% of the general population and is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to non-alcoholic steatohepatitis (NASH), liver fibrosis and cirrhosis leading to hepatocellular carcinoma. To date, liver biopsy is the gold standard for the diagnosis of NASH and for staging liver fibrosis. This study aimed to train models for the non-invasive diagnosis of NASH and liver fibrosis based on measurements of lipids, glycans and biochemical parameters in peripheral blood and with the use of different machine learning methods.
We performed a lipidomic, glycomic and free fatty acid analysis in serum samples of 49 healthy subjects and 31 patients with biopsy-proven NAFLD (15 with NAFL and 16 with NASH). The data from the above measurements combined with measurements of 4 hormonal parameters were analyzed with two different platforms and five different machine learning tools.
365 lipids, 61 glycans and 23 fatty acids were identified with mass-spectrometry and liquid chromatography. Robust differences in the concentrations of specific lipid species were observed between healthy, NAFL and NASH subjects. One-vs-Rest (OvR) support vector machine (SVM) models with recursive feature elimination (RFE) including 29 lipids or combining lipids with glycans and/or hormones (20 or 10 variables total) could differentiate with very high accuracy (up to 90%) between the three conditions. In an exploratory analysis, a model consisting of 10 lipid species could robustly discriminate between the presence of liver fibrosis or not (98% accuracy).
We propose novel models utilizing lipids, hormones and glycans that can diagnose with high accuracy the presence of NASH, NAFL or healthy status. Additionally, we report a combination of lipids that can diagnose the presence of liver fibrosis. Both models should be further trained prospectively and validated in large independent cohorts.
非酒精性脂肪性肝病（NAFLD）影响 25-30%的普通人群，其特征为存在非酒精性脂肪肝（NAFL），可进展为非酒精性脂肪性肝炎（NASH）、肝纤维化和肝硬化，导致肝细胞癌。迄今为止，肝活检是诊断 NASH 和肝纤维化分期的金标准。本研究旨在基于外周血脂质、聚糖和生化参数的测量，以及使用不同的机器学习方法，建立用于非侵入性诊断 NASH 和肝纤维化的模型。
我们对 49 名健康受试者和 31 名经活检证实的 NAFLD 患者（15 名 NAFL 和 16 名 NASH）的血清样本进行了脂质组学、聚糖组学和游离脂肪酸分析。上述测量结果与 4 种激素参数的测量结果相结合，在两个不同的平台上，用 5 种不同的机器学习工具进行了分析。
通过质谱和液相色谱法鉴定了 365 种脂质、61 种聚糖和 23 种脂肪酸。在健康、NAFL 和 NASH 受试者之间，观察到特定脂质种类浓度存在显著差异。使用包括 29 种脂质或结合聚糖和/或激素的递归特征消除（RFE）的 One-vs-Rest（OvR）支持向量机（SVM）模型（总共 20 或 10 个变量），可以非常高的准确度（高达 90%）区分三种状态。在探索性分析中，由 10 种脂质组成的模型可以稳健地区分是否存在肝纤维化（准确率为 98%）。
我们提出了利用脂质、激素和聚糖的新模型，可以高度准确地诊断 NASH、NAFL 或健康状态。此外，我们报告了一组可以诊断肝纤维化存在的脂质。这两个模型都应进一步在大型独立队列中进行前瞻性训练和验证。
