Hepatic Involvement across the Metabolic Syndrome Spectrum: Non-Invasive Assessment and Risk Prediction Using Machine Learning

Adelaida Solomon; Călin Remus Cipăian; Mihai Octavian Negrea; Adrian Boicean; Romeo Mihaila; Corina Beca; Mirela Livia Popa; Sebastian Mihai Grama; Minodora Teodoru; Bogdan Neamtu

doi:10.3390/jcm12175657

Hepatic Involvement across the Metabolic Syndrome Spectrum: Non-Invasive Assessment and Risk Prediction Using Machine Learning

J Clin Med. 2023 Aug 30;12(17):5657. doi: 10.3390/jcm12175657.

Authors

Adelaida Solomon^{1

2}, Călin Remus Cipăian^{1

2}, Mihai Octavian Negrea^{1

2}, Adrian Boicean^{1

2}, Romeo Mihaila^{1

2}, Corina Beca², Mirela Livia Popa^{1

2}, Sebastian Mihai Grama¹, Minodora Teodoru^{1

2}, Bogdan Neamtu^{1

3}

Affiliations

¹ Faculty of Medicine, "Lucian Blaga" University, 550024 Sibiu, Romania.
² County Clinical Emergency Hospital of Sibiu, 2-4 Corneliu Coposu Str., 550245 Sibiu, Romania.
³ Department of Clinical Research, Pediatric Clinical Hospital Sibiu, 550166 Sibiu, Romania.

Abstract

Metabolic-dysfunction-associated steatotic liver disease (MASLD) and metabolic syndrome (MetS) are inextricably linked conditions, both of which are experiencing an upward trend in prevalence, thereby exerting a substantial clinical and economic burden. The presence of MetS should prompt the search for metabolic-associated liver disease. Liver fibrosis is the main predictor of liver-related morbidity and mortality. Non-invasive tests (NIT) such as the Fibrosis-4 index (FIB4), aspartate aminotransferase-to-platelet ratio index (APRI), aspartate aminotransferase-to-alanine aminotransferase ratio (AAR), hepatic steatosis index (HIS), transient elastography (TE), and combined scores (AGILE3+, AGILE4) facilitate the detection of liver fibrosis or steatosis. Our study enrolled 217 patients with suspected MASLD, 109 of whom were diagnosed with MetS. We implemented clinical and biological evaluations complemented by transient elastography (TE) to discern the most robust predictors for liver disease manifestation patterns. Patients with MetS had significantly higher values of FIB4, APRI, HSI, liver stiffness, and steatosis parameters measured by TE, as well as AGILE3+ and AGILE4 scores. Machine-learning algorithms enhanced our evaluation. A two-step cluster algorithm yielded three clusters with reliable model quality. Cluster 1 contained patients without significant fibrosis or steatosis, while clusters 2 and 3 showed a higher prevalence of significant liver fibrosis or at least moderate steatosis as measured by TE. A decision tree algorithm identified age, BMI, liver enzyme levels, and metabolic syndrome characteristics as significant factors in predicting cluster membership with an overall accuracy of 89.4%. Combining NITs improves the accuracy of detecting patterns of liver involvement in patients with suspected MASLD.

Keywords: cluster analysis; decision tree algorithms; liver stiffness measurement; metabolic syndrome; metabolic-associated fatty liver disease; non-alcoholic fatty liver disease; non-invasive tests; transient elastography.

Grants and funding

'Development of advanced and applied research skills in STEAM+ Health Logic,' POCU/993/6/13/153310/European Social Fund through the Operational Program Human Capital 2014-2020