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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Vydáno v:	International journal of molecular sciences Ročník 25; číslo 11; s. 5965
Hlavní autoři:	Mouskeftara, Thomai, Kalopitas, Georgios, Liapikos, Theodoros, Arvanitakis, Konstantinos, Germanidis, Georgios, Gika, Helen
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.06.2024 MDPI
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work.
ISSN:	1422-0067 1661-6596 1422-0067
DOI:	10.3390/ijms25115965