{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T18:08:31Z","timestamp":1772993311528,"version":"3.50.1"},"reference-count":41,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:00:00Z","timestamp":1768089600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Liver International"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Early detection of individuals with metabolic dysfunction\u2010associated steatotic liver disease (MASLD) is important as interventions to reduce steatosis can prevent progression to advanced liver disease. Identification of individuals with hepatic steatosis relies on imaging techniques, which are costly and often unavailable in large populations. Identifying hepatic steatosis through blood tests and demographics may serve as a cost\u2010effective alternative to identifying hepatic steatosis. Here, we aim to identify demographic, serum and genetic variables that can help predict hepatic steatosis in two large population\u2010based cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analysed data from 32\u2009008 participants in the UK Biobank (UKBB) with liver fat quantified using magnetic resonance proton density fat fraction (MRI\u2010PDFF). We created non\u2010invasive models to predict steatosis, the Enhanced Steatosis Indexes (ESIs), using logistic regression. Candidate predictors included variables from the literature and genetic markers. ESI\u20101 variables included demographic and serum values. ESI\u20102 included the ESI\u20101 variables plus the genetic markers. We trained and tested these models in UKBB and validated them in the Framingham Heart Study (FHS).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In UKBB, ESI\u20101 and ESI\u20102 predicted \u2265\u20095% PDFF with AUC 0.844 and 0.858, respectively, outperforming DSI, HSI, FSI and FLI (AUC 0.818\u20130.834 vs. ESI\u20101 and AUC 0.830\u20130.844 vs. ESI\u20102). In FHS, ESI\u20101 and ESI\u20102 predicted CT\u2010measured liver steatosis with AUC 0.803 and 0.808, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed a non\u2010invasive model to diagnose steatosis that outperforms existing steatosis models. Including genetic information improved the performance of ESI\u20102. Deployment of our models can facilitate non\u2010invasive screening of steatosis so that, with early intervention, disease progression can be prevented.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Impact and Implications<\/jats:title>\n                    <jats:p>We developed a non\u2010invasive model based on demographic, serum laboratory and genetic predictors that outperforms existing steatosis indices in identifying individuals with hepatic steatosis. This Enhanced Steatosis Index (ESI) enables more accurate and personalised early detection of hepatic steatosis, a highly prevalent yet underdiagnosed condition. By leveraging readily available clinical measures and genetic data, both ESI\u20101 and ESI\u20102 improve risk stratification and support large\u2010scale screening and early intervention efforts. These advances have the potential to provide more affordable, accurate and targeted screening, facilitating earlier treatment, reducing the burden of metabolic liver disease and helping to address disparities in the diagnosis and management of hepatic steatosis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/liv.70410","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:34:37Z","timestamp":1768203277000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Genetic Information Enhances a Novel Non\u2010Invasive Steatosis Index and Outperforms Existing Steatosis Indices"],"prefix":"10.1111","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1530-4317","authenticated-orcid":false,"given":"Yousif","family":"Alyousifi","sequence":"first","affiliation":[{"name":"Division of Gastroenterology and Hepatology, Department of Internal Medicine University of Michigan  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