{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:06:02Z","timestamp":1789031162681,"version":"build-2803163510"},"reference-count":32,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_almed-2026-0066_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the behaviour of insulin resistance indices and non-invasive liver fibrosis scores across the dysglycaemia spectrum using routine laboratory\u00a0data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_almed-2026-0066_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      This retrospective observational study included 1,949 outpatients with complete biochemical profiles. Patients were classified into five groups according to fasting plasma glucose and HbA\n                      <jats:sub>1c<\/jats:sub>\n                      : group A (normoglycaemia), group B (isolated impaired fasting glucose), group C (isolated elevated HbA\n                      <jats:sub>1c<\/jats:sub>\n                      ), group D (combined impairment), and group E (type 2 diabetes mellitus, T2DM). Insulin resistance indices (HOMA-IR, HOMA-\u03b2, QUICKI, TyG), liver fibrosis scores (FIB-4, APRI, m-APRI, Forns), and inflammatory markers (CRP, ferritin) were compared across groups. Discriminative performance was assessed using ROC analysis for the identification of T2DM (group E vs. groups A\u2013D) and early dysglycaemic impairment (group D vs. group A). Support vector machine (SVM) models integrating the best-performing indices were constructed with adjustment for age and\u00a0sex.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_almed-2026-0066_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Insulin resistance indices, fibrosis scores, and CRP showed progressive deterioration across worsening glycaemic stages (p&lt;0.001). TyG showed the highest individual discriminative performance for identifying T2DM (AUC=0.90), whereas QUICKI performed best for detecting combined early glycaemic impairment (AUC=0.78). Among fibrosis scores, the Forns index showed the strongest discriminative capacity (AUC=0.67\u20130.74) and the broadest correlations with metabolic and inflammatory parameters. SVM models integrating insulin resistance and fibrosis indices improved classification performance compared with individual markers alone (AUC=0.87\u20130.93).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_almed-2026-0066_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>TyG and the Forns index support laboratory-based hepatometabolic stratification across the dysglycaemia spectrum using routine analytical\u00a0data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/almed-2026-0066","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T03:56:01Z","timestamp":1783914961000},"page":"211-221","source":"Crossref","is-referenced-by-count":0,"title":["Routine biochemical indices for hepatometabolic stratification across the dysglycaemia spectrum: insulin resistance, fibrosis scores, and machine-learning integration"],"prefix":"10.1515","volume":"7","author":[{"given":"Pedro","family":"Villafruela-Rodr\u00edguez-Manzaneque","sequence":"first","affiliation":[{"name":"Laboratory Department , Consorci Hospital General Universitari de Val\u00e8ncia , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4622-2173","authenticated-orcid":false,"given":"Jordi","family":"Tortosa-Carreres","sequence":"additional","affiliation":[{"name":"Laboratory Department , Hospital Universitari i Polit\u00e8cnic la Fe , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7824-7239","authenticated-orcid":false,"given":"Antonio","family":"Sierra-Rivera","sequence":"additional","affiliation":[{"name":"Laboratory Department , Consorci Hospital General Universitari de Val\u00e8ncia , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuela","family":"Morales-Garc\u00e9s","sequence":"additional","affiliation":[{"name":"Laboratory Department , Consorci Hospital General Universitari de Val\u00e8ncia , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Sahuquillo-Frias","sequence":"additional","affiliation":[{"name":"Laboratory Department , Consorci Hospital General Universitari de Val\u00e8ncia , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goitzane","family":"Marcaida-Benito","sequence":"additional","affiliation":[{"name":"Laboratory Department , Consorci Hospital General Universitari de Val\u00e8ncia , Val\u00e8ncia , Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"2026091008070971870_j_almed-2026-0066_ref_001","unstructured":"American Diabetes Association Professional Practice Committee. 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