{"id":367608,"date":"2026-03-27T07:01:20","date_gmt":"2026-03-27T07:01:20","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/367608\/"},"modified":"2026-03-27T07:01:20","modified_gmt":"2026-03-27T07:01:20","slug":"new-algorithm-enables-precise-subtyping-of-metabolic-liver-disease","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/367608\/","title":{"rendered":"New algorithm enables precise subtyping of metabolic liver disease"},"content":{"rendered":"<p>Metabolic-associated steatotic liver disease (MASLD) is a clinically heterogeneous condition with highly variable outcomes affecting more than 30% individuals globally. The disease is conventionally staged by histological progression, ranging from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH) and ultimately fibrosis or cirrhosis. Beyond liver-related outcomes, MASLD significantly elevates the risk of extrahepatic complications, including cardiovascular disease (CVD), type 2 diabetes mellitus (T2DM), and chronic kidney disease (CKD). Currently, personalized management strategies are lacking, underscoring an urgent need for a prognostic stratification system that integrates both hepatic and extrahepatic risks to guide clinical decision-making. A groundbreaking study led by Professor Yan Bi from Drum Tower Hospital, Medical School of Nanjing University, has developed a novel algorithm enabling precise MASLD subtyping for individualized intervention. This study was published online on January 28, 2026, in the Chinese Medical Journal.<\/p>\n<p>The study analyzed 1,111 individuals who underwent liver <a href=\"https:\/\/www.news-medical.net\/health\/What-is-Biopsy.aspx\" class=\"linked-term\" rel=\"nofollow noopener\" target=\"_blank\">biopsy<\/a> and developed a multi-task deep LASSO algorithm for feature selection. This model identified six core clinical indicators: age, BMI, HbA1c, TyG, TC\/HDL, and GGT\/PLT. Cluster analysis using these variables initially established four stable MASLD subtypes. To evaluate the generalizability of this classification, the cluster analysis was replicated in two large, independent cohorts: a health check-up cohort of 6,172 adults (MASLD prevalence: 43.9%; mean follow-up: 27.6 months) and the NHANES-III cohort comprising 7,406 participants (MASLD prevalence: 37.3%; mean follow-up: 280.2 months). The four-cluster structure remained consistent across both validation cohorts.<\/p>\n<p>Cluster 1 low CVD risk subgroup (41%): <br \/>\uf06chighest percentages of body fat<br \/>\uf06clowest levels of visceral fat<\/p>\n<p>Cluster 2 high fibrosis risk subgroup (26%): <br \/>\uf06csignificant lipid profile disorders<br \/>\uf06csubstantial liver damage<\/p>\n<p>Cluster 3 high cardiovascular\u2013kidney risk subgroup (19%):<br \/>\uf06clowest muscle mass<br \/>\uf06cobvious chronic systemic <a href=\"https:\/\/www.news-medical.net\/health\/What-Does-Inflammation-Do-to-the-Body.aspx\" class=\"linked-term\" rel=\"nofollow noopener\" target=\"_blank\">inflammation<\/a><\/p>\n<p>Cluster 4 high cardiovascular\u2013liver\u2013kidney risk subgroup (14%):<br \/>\uf06csevere insulin resistance<br \/>\uf06cpoor glucose control (&gt;98% with diabetes)<br \/>\uf06csubstantial liver damage<br \/>\uf06chigh visceral adiposity<br \/>\uf06chighest frequencies of PNPLA3 risk alleles (&gt;70%)<\/p>\n<p>To further explore the influence of genetic variants on fibrosis, we conducted an analysis examining the association between SNP genotypes and phenotypes in a subset of individuals. Cluster 4 (high cardiovascular\u2013liver\u2013kidney risk subgroup) exhibited the highest frequencies of risk alleles in PNPLA3, TM6SF2, and MBOAT7, followed by Cluster 2 (high fibrosis risk subgroup). PNPLA3 rs738409 C &gt; G variant carriers showed a 3.2-fold increase in significant fibrosis among those with the PNPLA3 CG genotype and a 2.7-fold increase among those with the PNPLA3 GG genotype.<\/p>\n<p>This classification facilitates the precise integration of MASLD risk stratification and management within the cardiovascular\u2013liver\u2013kidney\u2013metabolic framework. Professor Bi emphasized: Our subtyping enables targeted interventions, for example, prioritizing fibrosis screening for Cluster 2 while implementing aggressive cardiorenal protection for Cluster 3\u20134. The algorithm-based stratification system represents a paradigm shift toward precision hepatology.<\/p>\n<p>Source:<\/p>\n<p>Journal reference:<\/p>\n<p>Fang, D., et al. (2026). Data-driven classification of metabolic-associated steatotic liver disease subtypes predicting hepatic and extrahepatic progression.\u00a0Chinese Medical Journal.\u00a0DOI:\u00a010.1097\/CM9.0000000000003984.\u00a0<a href=\"https:\/\/journals.lww.com\/cmj\/fulltext\/9900\/data_driven_classification_of_metabolic_associated.1919.aspx\" rel=\"noopener nofollow\" target=\"_blank\">https:\/\/journals.lww.com\/cmj\/fulltext\/9900\/data_driven_classification_of_metabolic_associated.1919.aspx<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Metabolic-associated steatotic liver disease (MASLD) is a clinically heterogeneous condition with highly variable outcomes affecting more than 30%&hellip;\n","protected":false},"author":2,"featured_media":128137,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[3578,9563,4764,14790,1702,9384,27541,103,32688,3175,61,60,8499,4761,7939,16538,13079,89,60689,80,9398],"class_list":["post-367608","post","type-post","status-publish","format-standard","has-post-thumbnail","category-health","tag-cardiovascular-disease","tag-chronic","tag-chronic-kidney-disease","tag-cirrhosis","tag-diabetes","tag-diabetes-mellitus","tag-fibrosis","tag-health","tag-hepatology","tag-hospital","tag-ie","tag-ireland","tag-kidney","tag-kidney-disease","tag-liver","tag-liver-disease","tag-medical-school","tag-research","tag-steatosis","tag-technology","tag-type-2-diabetes"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/367608","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=367608"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/367608\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/128137"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=367608"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=367608"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=367608"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}