{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:41:18Z","timestamp":1786700478741,"version":"build-2736575974"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>Metabolic dysfunction-associated steatotic liver disease (MASLD) is increasingly recognized as a systemic disorder that extends beyond hepatic steatosis to involve multiple extrahepatic organs, including the cardiovascular system, kidneys, brain, pancreas, and other organ systems. This multisystem involvement is accompanied by marked heterogeneity in clinical presentation and disease progression, supporting the concept that MASLD comprises distinct phenotypes rather than a uniform entity. Conventional classifications based solely on hepatic features are insufficient to capture this complexity, underscoring the need for phenotyping grounded in comprehensive clinical data. Published phenotyping attempts, however, differ in cohort definition, variable selection, and validation, and their subgroups are not directly comparable. Data-driven stratification frameworks use unsupervised clustering to identify latent patient subgroups characterized by distinct comorbidity profiles and disease trajectories, followed by phenotype-specific supervised modeling to refine risk prediction. Such approaches enable the identification of clinically meaningful MASLD phenotypes and support the implementation of precision medicine through personalized surveillance strategies, targeted therapeutic interventions, and prioritization of high-risk individuals. Ultimately, this paradigm improves prognostic accuracy, enhances resource allocation, and facilitates the prevention of extrahepatic complications, advancing personalized disease management in MASLD.<\/jats:p>","DOI":"10.3389\/fendo.2026.1873215","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T09:25:55Z","timestamp":1786699555000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Metabolic dysfunction-associated steatotic liver disease as a systemic disorder: extrahepatic manifestations and the need for data-driven phenotyping"],"prefix":"10.3389","volume":"17","author":[{"given":"Sergejs","family":"Lobanovs","sequence":"first","affiliation":[{"name":"University of Latvia, Faculty of Medicine","place":["Riga, Latvia"]},{"name":"Department of Gastroenterology and Hepatology, Pauls Stradins Clinical University Hospital","place":["Riga, Latvia"]},{"name":"Institute of Applied 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