{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Carrillo-Larco RM"],"funding":["Wellcome Trust"],"pagination":["e067203"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9685238"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(11)"],"pubmed_abstract":["<h4>Objectives</h4>Non-alcoholic fatty liver disease (NAFLD) is a non-communicable disease with a rising prevalence worldwide and with large burden for patients and health systems. To date, the presence of unique phenotypes in patients with NAFLD has not been studied, and their identification could inform precision medicine and public health with pragmatic implications in personalised management and care for patients with NAFLD.<h4>Design</h4>Cross-sectional and prospective (up to 31 December 2019) analysis of National Health and Nutrition Examination Survey III (1988-1994).<h4>Primary and secondary outcomes measures</h4>NAFLD diagnosis was based on liver ultrasound. The following predictors informed an unsupervised machine learning algorithm (k-means): body mass index, waist circumference"],"journal":["BMJ open"],"pubmed_title":["Phenotypes of non-alcoholic fatty liver disease (NAFLD) and all-cause mortality: unsupervised machine learning analysis of NHANES III."],"pmcid":["PMC9685238"],"funding_grant_id":["214185/Z/18/Z"],"pubmed_authors":["Alvizuri-Gomez C","Alqahtani S","Garcia-Larsen V","Castillo-Cara M","Carrillo-Larco RM","Guzman-Vilca WC"],"additional_accession":[]},"is_claimable":false,"name":"Phenotypes of non-alcoholic fatty liver disease (NAFLD) and all-cause mortality: unsupervised machine learning analysis of NHANES III.","description":"<h4>Objectives</h4>Non-alcoholic fatty liver disease (NAFLD) is a non-communicable disease with a rising prevalence worldwide and with large burden for patients and health systems. To date, the presence of unique phenotypes in patients with NAFLD has not been studied, and their identification could inform precision medicine and public health with pragmatic implications in personalised management and care for patients with NAFLD.<h4>Design</h4>Cross-sectional and prospective (up to 31 December 2019) analysis of National Health and Nutrition Examination Survey III (1988-1994).<h4>Primary and secondary outcomes measures</h4>NAFLD diagnosis was based on liver ultrasound. The following predictors informed an unsupervised machine learning algorithm (k-means): body mass index, waist circumference","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2025-04-05T15:22:20.899Z","creation":"2025-04-05T15:22:20.899Z"},"accession":"S-EPMC9685238","cross_references":{"pubmed":["36418130"],"doi":["10.1136/bmjopen-2022-067203"]}}