Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associ...

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Veröffentlicht in:International journal of molecular sciences Jg. 25; H. 11; S. 5965
Hauptverfasser: Mouskeftara, Thomai, Kalopitas, Georgios, Liapikos, Theodoros, Arvanitakis, Konstantinos, Germanidis, Georgios, Gika, Helen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 01.06.2024
MDPI
Schlagworte:
Accuracy
Adult
Algorithms
Biomarkers
Biomarkers - blood
Biopsy
Case-Control Studies
Classification
Enzymes
Fatty acids
Fatty liver
Female
Ferritin
Health care
Homeostasis
Humans
Inflammation
Insulin
Investigations
Lipid Metabolism
Lipidomics - methods
Lipids
Liver diseases
Low density lipoproteins
Machine Learning
Male
Metabolism
Middle Aged
Non-alcoholic Fatty Liver Disease - blood
Non-alcoholic Fatty Liver Disease - metabolism
Physiological aspects
Plasma
Triglycerides
Type 2 diabetes
Greece
Massachusetts
non-alcoholic fatty liver disease
machine learning
plasma
untargeted lipidomics
ISSN:1422-0067, 1661-6596, 1422-0067
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Zusammenfassung:Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.
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These authors contributed equally to this work.
ISSN:1422-0067
1661-6596
1422-0067
DOI:10.3390/ijms25115965