{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:49:39Z","timestamp":1792511379534,"version":"4.5.0"},"reference-count":51,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Cardiometabolic diseases, encompassing obesity, insulin resistance, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), hypertension, and atherosclerotic cardiovascular disease (ASCVD), represent a vast continuum driven by multi-organ network dysregulation. Clinical risk assessment remains dominated by late-stage measures (e.g., fasting glucose, HbA1c, standard lipids). While these assessments predominate the literature and clinical trial endpoints, each incompletely capture early mechanistic risk, inter-individual heterogeneity, and differential response to interventions. Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle\/exosome cargo profiling) expands the biomarker landscape but introduces translational barriers: high dimensionality, cohort heterogeneity, limited causal inference, and insufficient validation pipelines. AI-driven systems biology platforms can support cardiometabolic biomarker discovery and therapeutic translation by enabling systems-level biological inference across heterogeneous datasets, prioritizing mechanism and traceability over purely correlation-based models. GATC Health\u2019s Operon\u2122 platform is described as a proprietary, AI-driven internal scientific computing platform designed to support therapeutic discovery and development decision-making across the pharmaceutical lifecycle, including evaluation of drug efficacy, safety, off-target effects, pharmacokinetics (PK), pharmacodynamics (PD), and overall development risk. Operon evolved from earlier generations of GATC Health\u2019s internal multiomic modeling systems (formerly referred to as the Multiomics Advanced Technology, MAT) and incorporates expanded data types, orchestration layers, validation workflows, and productization frameworks. Operon is operated by GATC scientists and generates structured, productized outputs (e.g., formal assessments, analyses, and decision frameworks) that are reviewed by experts. Operon methodologies have undergone internal validation and independent academic evaluation under blinded conditions, with reported classification performance (true positive rate 86% and true negative rate 91%) in controlled evaluation settings; these performance metrics should not be interpreted as guarantees of clinical success. This review provides a T2D-centered cardiometabolic biomarker landscape with cardiovascular extension and outlines how Operon-enabled multiomic integration and scenario-based simulation can support early screening, endotype stratification, mechanistic interpretation, and precision intervention design, including AI-guided polypharmacology strategies.<\/jats:p>","DOI":"10.3390\/ijms27093969","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T13:32:22Z","timestamp":1777469542000},"page":"3969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Operon\u2122 Platform-Enabled for Cardiometabolic Biomarker Screening and Precision Treatment Strategies: A Type 2 Diabetes-Centered Review with Cardiovascular Extension"],"prefix":"10.3390","volume":"27","author":[{"given":"Ian","family":"Jenkins","sequence":"first","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2018-2476","authenticated-orcid":false,"given":"Krista","family":"Casazza","sequence":"additional","affiliation":[{"name":"Departments of Surgery and Biomedical Engineering, University of California Irvine, Irvine, CA 92868, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vaishnavi","family":"Narayan","sequence":"additional","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waldemar","family":"Lernhardt","sequence":"additional","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Savich","sequence":"additional","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jayson","family":"Uffens","sequence":"additional","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro","family":"Gutierrez-Castrellon","sequence":"additional","affiliation":[{"name":"Elemental Translational Research SAPI, Mexico City 14357, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan R. T.","family":"Lakey","sequence":"additional","affiliation":[{"name":"GATC Health Corp., Irvine, CA 92614, USA"},{"name":"Departments of Surgery and Biomedical Engineering, University of California Irvine, Irvine, CA 92868, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee (2025). 10. Cardiovascular Disease and Risk Management: Standards of Care in Diabetes-2025. Diabetes Care, 48, S207\u2013S238.","DOI":"10.2337\/dc25-S010"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/S2213-8587(23)00128-6","article-title":"Predicting and preventing heart failure in type 2 diabetes","volume":"11","author":"Pandey","year":"2023","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2086","DOI":"10.2337\/dbi20-0001","article-title":"Subtypes of Type 2 Diabetes Determined From Clinical Parameters","volume":"69","author":"Ahlqvist","year":"2020","journal-title":"Diabetes"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2040","DOI":"10.1007\/s00125-020-05211-7","article-title":"The clinical consequences of heterogeneity within and between different diabetes types","volume":"63","author":"Redondo","year":"2020","journal-title":"Diabetologia"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/s41586-024-07019-6","article-title":"Genetic drivers of heterogeneity in type 2 diabetes pathophysiology","volume":"627","author":"Suzuki","year":"2024","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.2337\/db25-0354","article-title":"Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes","volume":"74","author":"Li","year":"2025","journal-title":"Diabetes"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s11892-022-01453-4","article-title":"Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus","volume":"22","author":"Hlavinka","year":"2022","journal-title":"Curr. Diabetes Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1093\/ejendo\/lvae123","article-title":"Dissociation between liver fat content and fasting metabolic markers of selective hepatic insulin resistance in humans","volume":"191","author":"Westcott","year":"2024","journal-title":"Eur. J. Endocrinol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s13059-017-1215-1","article-title":"Multi-omics approaches to disease","volume":"18","author":"Hasin","year":"2017","journal-title":"Genome Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1186\/s13059-024-03401-9","article-title":"Assessing and mitigating batch effects in large-scale omics studies","volume":"25","author":"Yu","year":"2024","journal-title":"Genome Biol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"GTEx Consortium (2020). The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science, 369, 1318\u20131330.","DOI":"10.1126\/science.aaz1776"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.7326\/M14-0697","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann. Intern. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s13059-020-02015-1","article-title":"MOFA+: A statistical framework for comprehensive integration of multi-modal single-cell data","volume":"21","author":"Argelaguet","year":"2020","journal-title":"Genome Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1186\/s12967-025-06425-2","article-title":"Machine learning and multi-omics integration: Advancing cardiovascular translational research and clinical practice","volume":"23","author":"Lin","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"21","DOI":"10.34297\/AJBSR.2024.23.003034","article-title":"AI-Based Predictions of Molecular Target Activity from Blind Chemical Structures","volume":"23","author":"Jenkins","year":"2024","journal-title":"Am. J. Biomed. Sci. Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e13469","DOI":"10.1111\/eci.13469","article-title":"Limitations of the fasting proinsulin to insulin ratio as a measure of \u03b2-cell health in people with and without impaired glucose tolerance","volume":"51","author":"Egan","year":"2021","journal-title":"Eur. J. Clin. Investig."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bragg, F., Trichia, E., Aguilar-Ramirez, D., Be\u0161evi\u0107, J., Lewington, S., and Emberson, J. (2022). Predictive value of circulating NMR metabolic biomarkers for type 2 diabetes risk in the UK Biobank study. BMC Med., 20.","DOI":"10.1186\/s12916-022-02354-9"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e3252","DOI":"10.1002\/dmrr.3252","article-title":"Prediction of progression from pre-diabetes to diabetes: Development and validation of a machine learning model","volume":"36","author":"Cahn","year":"2020","journal-title":"Diabetes Metab. Res. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s40121-019-00267-4","article-title":"Use of Antifungals and Outcomes Among Inpatients at Risk of Invasive Aspergillosis or Mucormycosis in the USA: A Retrospective Cohort Study","volume":"8","author":"Stull","year":"2019","journal-title":"Infect. Dis. Ther."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.beem.2011.09.002","article-title":"Is genetic testing useful to predict type 2 diabetes?","volume":"26","author":"Vassy","year":"2012","journal-title":"Best Pract. Res. Clin. Endocrinol. Metab."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1038\/s41588-020-0637-y","article-title":"Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis","volume":"52","author":"Vujkovic","year":"2020","journal-title":"Nat. Genet."},{"key":"ref_22","first-page":"322","article-title":"Personalized risk prediction for type 2 diabetes: The potential of genetic risk scores","volume":"19","author":"Morris","year":"2017","journal-title":"Genet. Med. Off. J. Am. Coll. Med. Genet."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2091","DOI":"10.1161\/CIRCULATIONAHA.116.024436","article-title":"Polygenic Risk Score Identifies Subgroup With Higher Burden of Atherosclerosis and Greater Relative Benefit From Statin Therapy in the Primary Prevention Setting","volume":"135","author":"Natarajan","year":"2017","journal-title":"Circulation"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"337","DOI":"10.2337\/dc15-1111","article-title":"A Type 1 Diabetes Genetic Risk Score Can Aid Discrimination Between Type 1 and Type 2 Diabetes in Young Adults","volume":"39","author":"Oram","year":"2016","journal-title":"Diabetes Care"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1038\/nrcardio.2017.78","article-title":"Mendelian randomization in cardiometabolic disease: Challenges in evaluating causality","volume":"14","author":"Holmes","year":"2017","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1001\/jamacardio.2018.1717","article-title":"Associations of Combined Genetic and Lifestyle Risks With Incident Cardiovascular Disease and Diabetes in the UK Biobank Study","volume":"3","author":"Said","year":"2018","journal-title":"JAMA Cardiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1038\/s41576-018-0018-x","article-title":"The personal and clinical utility of polygenic risk scores","volume":"19","author":"Torkamani","year":"2018","journal-title":"Nat. Rev. Genet."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"F327","DOI":"10.1152\/ajprenal.00115.2024","article-title":"Update: The role of epigenetics in the metabolic memory of diabetic complications","volume":"327","author":"Chen","year":"2024","journal-title":"Am. J. Physiol. Ren. Physiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s00125-022-05652-2","article-title":"Epigenome-wide association study of incident type 2 diabetes: A meta-analysis of five prospective European cohorts","volume":"65","author":"Fraszczyk","year":"2022","journal-title":"Diabetologia"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.2337\/db18-0290","article-title":"Epigenome-Wide Association Study of Incident Type 2 Diabetes in a British Population: EPIC-Norfolk Study","volume":"68","author":"Cardona","year":"2019","journal-title":"Diabetes"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"450","DOI":"10.1038\/s43587-023-00391-4","article-title":"Development and validation of DNA methylation scores in two European cohorts augment 10-year risk prediction of type 2 diabetes","volume":"3","author":"Cheng","year":"2023","journal-title":"Nat. Aging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1161\/CIRCRESAHA.123.322135","article-title":"Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine","volume":"132","author":"Baccarelli","year":"2023","journal-title":"Circ. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bonaroti, J., Billiar, I., Moheimani, H., Wu, J., Namas, R., Li, S., Kar, U.K., Vodovotz, Y., Neal, M.D., and Sperry, J.L. (2022). Plasma proteomics reveals early, broad release of chemokine, cytokine, TNF, and interferon mediators following trauma with delayed increases in a subset of chemokines and cytokines in patients that remain critically ill. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1038086"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104821","DOI":"10.1016\/j.ebiom.2023.104821","article-title":"Gut microbiota in the pathogenesis and therapeutic approaches of diabetes","volume":"97","author":"Crudele","year":"2023","journal-title":"eBioMedicine"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"bvac184","DOI":"10.1210\/jendso\/bvac184","article-title":"Type 2 Diabetes and the Microbiome","volume":"7","author":"Barlow","year":"2022","journal-title":"J. Endocr. Soc."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, J., Yang, K., Fan, H., Wei, M., and Xiong, Q. (2023). Targeting the gut microbiota and its metabolites for type 2 diabetes mellitus. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1114424"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","article-title":"Personalized Nutrition by Prediction of Glycemic Responses","volume":"163","author":"Zeevi","year":"2015","journal-title":"Cell"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.7150\/thno.65778","article-title":"Integrative biology of extracellular vesicles in diabetes mellitus and diabetic complications","volume":"12","author":"Liu","year":"2022","journal-title":"Theranostics"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e14865","DOI":"10.1111\/dme.14865","article-title":"Research progress of extracellular vesicles in type 2 diabetes and its complications","volume":"39","author":"Zhang","year":"2022","journal-title":"Diabet. Med. J. Br. Diabet. Assoc."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111837","DOI":"10.1016\/j.diabres.2024.111837","article-title":"The interplay of extracellular vesicles in the pathogenesis of metabolic impairment and type 2 diabetes","volume":"216","author":"Carciero","year":"2024","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e180900","DOI":"10.1172\/JCI180900","article-title":"Erythrocyte-derived extracellular vesicles induce endothelial dysfunction through arginase-1 and oxidative stress in type 2 diabetes","volume":"135","author":"Collado","year":"2025","journal-title":"J. Clin. Investig."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zygmunciak, P., Str\u00f3\u017cna, K., B\u0142a\u017cowska, O., and Mrozikiewicz-Rakowska, B. (2024). Extracellular Vesicles in Diabetic Cardiomyopathy-State of the Art and Future Perspectives. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25116117"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1396823","DOI":"10.1080\/20013078.2017.1396823","article-title":"Updating the MISEV minimal requirements for extracellular vesicle studies: Building bridges to reproducibility","volume":"6","author":"Witwer","year":"2017","journal-title":"J. Extracell. Vesicles"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1186\/s12967-024-05760-0","article-title":"Endothelial extracellular vesicles: Their possible function and clinical significance in diabetic vascular complications","volume":"22","author":"Fang","year":"2024","journal-title":"J. Transl. Med."},{"key":"ref_45","unstructured":"FDA-NIH Biomarker Working Group (2026, January 29). BEST (Biomarkers, EndpointS, and Other Tools) Resource, Available online: http:\/\/www.ncbi.nlm.nih.gov\/books\/NBK326791\/."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e010224226610","DOI":"10.2174\/0115733998276990240117113408","article-title":"Machine Learning and Augmented Intelligence Enables Prognosis of Type 2 Diabetes Prior to Clinical Manifestation","volume":"21","author":"Lakey","year":"2025","journal-title":"Curr. Diabetes Rev."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Gajjar, A., Raju, A.K., Gajjar, A., Menon, M., Shah, S.A.Y., Dani, S., and Weinberg, A. (2025). SGLT2 Inhibitors and GLP-1 Receptor Agonists in Cardiovascular-Kidney-Metabolic Syndrome. Biomedicines, 13.","DOI":"10.3390\/biomedicines13081924"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.2337\/dci22-0034","article-title":"Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)","volume":"45","author":"Davies","year":"2022","journal-title":"Diabetes Care"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"The EMPA-KIDNEY Collaborative Group, Herrington, W.G., Staplin, N., Wanner, C., Green, J.B., Hauske, S.J., Emberson, J.R., Preiss, D., Judge, P., and Mayne, K.J. (2023). Empagliflozin in Patients with Chronic Kidney Disease. N. Engl. J. Med., 388, 117\u2013127.","DOI":"10.1056\/NEJMoa2204233"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1056\/NEJMoa2206286","article-title":"Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction","volume":"387","author":"Solomon","year":"2022","journal-title":"N. Engl. J. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1056\/NEJMoa2307563","article-title":"Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes","volume":"389","author":"Lincoff","year":"2023","journal-title":"N. Engl. J. Med."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/9\/3969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T04:21:59Z","timestamp":1778214119000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/27\/9\/3969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":51,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["ijms27093969"],"URL":"https:\/\/doi.org\/10.3390\/ijms27093969","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]}}}