The role of omics in the pathophysiology, diagnosis and treatment of non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a multifaceted metabolic disorder, whose spectrum covers clinical, histological and pathophysiological developments ranging from simple steatosis to non-alcoholic steatohepatitis (NASH) and liver fibrosis, potentially evolving into cirrhosis, hepatocellular carcinoma and liver failure. Liver biopsy remains the gold standard for diagnosing NAFLD, while there are no specific treatments. An ever-increasing number of high-throughput Omics investigations on the molecular pathobiology of NAFLD at the cellular, tissue and system levels produce comprehensive biochemical patient snapshots. In the clinical setting, these applications are considerably enhancing our efforts towards obtaining a holistic insight on NAFLD pathophysiology. Omics are also generating non-invasive diagnostic modalities for the distinct stages of NAFLD, that remain though to be validated in multiple, large, heterogenous and independent cohorts, both cross-sectionally as well as prospectively. Finally, they aid in developing novel therapies. By tracing the flow of information from genomics to epigenomics, transcriptomics, proteomics, metabolomics, lipidomics and glycomics, the chief contributions of these techniques in understanding, diagnosing and treating NAFLD are summarized herein.

Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: A proof of concept study

BACKGROUND

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.

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.

RESULTS

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).

CONCLUSION

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.

Serum Total Sialic Acid Level is Elevated in Hypothyroid Patients as an Atherosclerotic Risk Factor

BACKGROUND

Serum total sialic acid (TSA) concentration is regarded as an indicator of the risks of atherosclerosis and cardiovascular diseases. The association between SA levels and atherosclerosis risk factors has not been assessed in patients with thyroid diseases.

METHODS

Sixty newly diagnosed treatment-naive hypothyroid patients, 35 with subclinical and 25 with overt hypothyroidism, and 30 euthyroid individuals were analyzed. SA was measured in fasting blood samples, as were routine biochemical parameters, some atherosclerosis markers and carotid artery intima media thickness (CIMT).

RESULTS

Mean SA (38.1 ± 12.0 vs. 46.0 ±15.8; P = 0.019) and CIMT (0.57 ± 0.06 vs. 0.62 ± 0.12; P = 0.013) were found to be higher in the patient group compared with the control group. Mean sialic acid was higher in overt hypothyroidism patients compared with subclinical hypothyroidism patients and the control group. No difference was found between the subclinical hypothyroidism group and the control group. Sialic acid level and CIMT had a positive correlation in both the entire population and the hypothyroidism group. The linear regression model established for mean CIMT level in the entire population showed that risk factors of LDL (B ± SE = 0.454 ± 0.206; P = 0.030), uric acid (B ± SE = 1.902 ± 0.686; P = 0.007), hs-CRP (B ± SE = 1.003 ± 0.380; P =0.010), and SA (B ± SE = 2.419 ± 0.450; P < 0.001) were independent predictors of CIMT level.

CONCLUSION

Sialic acid level is elevated in hypothyroid patients. However, this elevation is not related to thyroid hormone levels and autoantibodies. Correlations between SA and atherosclerosis indicators, such as CIMT, LDL, hs-CRP, and uric acid, in hypothyroid individuals suggest that SA may be an indicator of atherogenesis in these patients.