{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T10:07:36Z","timestamp":1783937256217,"version":"3.55.0"},"reference-count":36,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:00:00Z","timestamp":1783814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Metabolites"],"abstract":"<jats:p>Background: Metabolic dysfunction is a complex process arising from coordinated interactions among insulin resistance, dyslipidemia, hepatic dysfunction, obesity, low-grade inflammation, and endocrine alterations. The triglyceride\u2013glucose (TyG) index is a simple, reproducible surrogate of insulin resistance, but most studies have evaluated it through isolated association-based analyses. We characterized the endocrine\u2013metabolic architecture associated with TyG in patients with thyroid dysfunction using a systems-phenotyping approach. Methods: In this retrospective, single-center, cross-sectional study of 387 adults evaluated for thyroid dysfunction, unsupervised phenotypes were derived by principal component analysis (PCA), K-means, Gaussian mixture models (GMM), and hierarchical (Ward) clustering using fully measured primary metabolic variables (fasting glucose, triglycerides, HDL- and LDL-cholesterol, body mass index, alanine, and aspartate aminotransferase). TyG was then examined as a descriptor of the identified architecture. Model-based latent profile analysis (BIC, entropy) and a censoring-aware, rank-based Gaussian graphical model (GGM) of partial correlations were additionally estimated. Results: Three overlapping, clinically interpretable phenotypic partitions of an underlying metabolic continuum were identified: a metabolically preserved phenotype (MP), an intermediate hepatic-dominant phenotype (IHD), and a severe insulin-resistance-dominant phenotype (SIRD), characterized by obesity, atherogenic dyslipidemia, and hyperuricemia. TyG values increased monotonically across the three partitions (medians 8.24, 8.74, and 8.87 in MP, IHD, and SIRD, respectively; p &lt; 0.001), providing an accessible single-number summary of the metabolic gradient. Cluster separation was modest (silhouette = 0.17), consistent with a continuum, but the three-profile solution was supported by latent profile analysis (\u0394BIC = \u221296.7; relative entropy = 0.75) and was reproduced across frameworks (adjusted Rand index of 0.75 with GMM and 0.46 with Ward). In the censoring-aware GGM, metabolic and thyroid variables formed two near-orthogonal blocks (maximum absolute metabolic\u2013thyroid partial correlation: 0.16; median: 0.05). Conclusions: The systems phenotyping approach identified three clinically interpretable phenotypic partitions (MP, IHD, SIRD) along a metabolic continuum in patients evaluated for thyroid dysfunction. TyG values increased monotonically across partitions (medians 8.24, 8.74, 8.87) and provided a pragmatic single-number summary of the principal metabolic axis. The IHD phenotype indicates that hepatic dysregulation can emerge as a distinct early stage rather than solely a late consequence of insulin resistance. Metabolic and thyroid axes were near-orthogonal, positioning thyroid autoimmunity as a parallel modulatory layer. These findings support TyG as an accessible descriptor for multidimensional endocrine\u2013metabolic risk stratification.<\/jats:p>","DOI":"10.3390\/metabo16070490","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:53:58Z","timestamp":1783932838000},"page":"490","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TyG-Centered Endocrine\u2013Metabolic Architecture in Patients with Thyroid Dysfunction: A Systems Phenotyping Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5249-6859","authenticated-orcid":false,"given":"Mirela","family":"Frandes","sequence":"first","affiliation":[{"name":"Department of Functional Sciences\u2014Medical Informatics and Biostatistics, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"},{"name":"Center for Modeling Biological Systems and Data Analysis, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adriana","family":"Gherbon","sequence":"additional","affiliation":[{"name":"Department VII Internal Medicine\u2014Diabetes, Nutrition, Metabolic Diseases and Systemic Rheumatology, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"},{"name":"Diabetes, Nutrition, and Metabolic Diseases, \u201cPius Br\u00eenzeu\u201d Emergency Hospital, 300723 Timisoara, Romania"},{"name":"Center of Molecular Research in Nephrology and Vascular Disease, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9630-4727","authenticated-orcid":false,"given":"Anca","family":"Tudor","sequence":"additional","affiliation":[{"name":"Department of Functional Sciences\u2014Medical Informatics and Biostatistics, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2387-5104","authenticated-orcid":false,"given":"Oana","family":"Albai","sequence":"additional","affiliation":[{"name":"Department VII Internal Medicine\u2014Diabetes, Nutrition, Metabolic Diseases and Systemic Rheumatology, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"},{"name":"Diabetes, Nutrition, and Metabolic Diseases, \u201cPius Br\u00eenzeu\u201d Emergency Hospital, 300723 Timisoara, Romania"},{"name":"Center of Molecular Research in Nephrology and Vascular Disease, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0276-1650","authenticated-orcid":false,"given":"Mihaela Maria","family":"Vlad","sequence":"additional","affiliation":[{"name":"Center of Molecular Research in Nephrology and Vascular Disease, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"},{"name":"Department of Internal Medicine II, University Clinic of Endocrinology, \u201cVictor Babe\u0219\u201d University of Medicine and Pharmacy Timi\u0219oara, 300041 Timisoara, Romania"},{"name":"Department of Endocrinology, \u201cPius Br\u00eenzeu\u201d County Emergency Clinic Hospital Timi\u0219oara, 300723 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0497-0405","authenticated-orcid":false,"given":"C\u0103lin","family":"Muntean","sequence":"additional","affiliation":[{"name":"Department of Functional Sciences\u2014Medical Informatics and Biostatistics, \u201cVictor Babes\u201d University of Medicine and Pharmacy, 300041 Timisoara, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.jacc.2015.06.1328","article-title":"The CardioMetabolic Health Alliance: Working toward a new care model for the metabolic syndrome","volume":"66","author":"Sperling","year":"2015","journal-title":"J. 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