{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T13:19:36Z","timestamp":1793711976236,"version":"4.6.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":53,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-47362-4","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T04:06:55Z","timestamp":1776312415000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A leakage-controlled machine learning framework for postprandial triglyceride phenotyping using synthetic clinical data"],"prefix":"10.1038","volume":"16","author":[{"given":"Nattakitti","family":"Piyavechvirat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Jheng","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qazi Mazhar Ul","family":"Haq","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"47362_CR1","doi-asserted-by":"publisher","first-page":"258","DOI":"10.2174\/157016111795495549","volume":"9","author":"G Kolovou","year":"2011","unstructured":"Kolovou, G. et al. Assessment and clinical relevance of non-fasting and postprandial triglycerides: An expert panel statement. Current Vascular Pharmacol. 9, 258\u2013270. https:\/\/doi.org\/10.2174\/157016111795495549 (2011).","journal-title":"Current Vascular Pharmacology"},{"key":"47362_CR2","doi-asserted-by":"publisher","DOI":"10.1017\/jns.2021.73","volume":"10","author":"BH Keirns","year":"2021","unstructured":"Keirns, B. H., Sciarrillo, C. M., Koemel, N. A. & Emerson, S. R. Fasting, non-fasting and postprandial triglycerides for screening cardiometabolic risk. J. Nutrit. Sci. 10, e75. https:\/\/doi.org\/10.1017\/jns.2021.73 (2021).","journal-title":"Journal of Nutritional Science"},{"key":"47362_CR3","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1161\/01.CIR.60.3.473","volume":"60","author":"D Zilversmit","year":"1979","unstructured":"Zilversmit, D. B. Atherogenesis: A postprandial phenomenon. Circulation 60, 473\u2013485. https:\/\/doi.org\/10.1161\/01.CIR.60.3.473 (1979).","journal-title":"Circulation"},{"key":"47362_CR4","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1001\/jama.298.3.299","volume":"298","author":"BG Nordestgaard","year":"2007","unstructured":"Nordestgaard, B. G., Benn, M., Schnohr, P. & Tybj\u00e6rg-Hansen, A. Nonfasting triglycerides and risk of myocardial infarction, ischemic heart disease, and death in men and women. JAMA 298, 299\u2013308. https:\/\/doi.org\/10.1001\/jama.298.3.299 (2007).","journal-title":"JAMA"},{"key":"47362_CR5","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/0021-9150(94)90085-X","volume":"106","author":"F Karpe","year":"1994","unstructured":"Karpe, F., Steiner, G., Uffelman, K., Olivecrona, T. & Hamsten, A. Postprandial lipoproteins and progression of coronary atherosclerosis. Atherosclerosis 106, 83\u201397. https:\/\/doi.org\/10.1016\/0021-9150(94)90085-X (1994).","journal-title":"Atherosclerosis"},{"key":"47362_CR6","first-page":"512","volume":"10","author":"GAM Pop","year":"2002","unstructured":"Pop, G. A. M. et al. The clinical significance of whole blood viscosity in (cardio)vascular medicine. Netherlands Heart J. 10, 512\u2013516 (2002).","journal-title":"Netherlands Heart Journal"},{"key":"47362_CR7","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1046\/j.1365-2141.1997.8532481.x","volume":"96","author":"GDO Lowe","year":"1997","unstructured":"Lowe, G. D. O., Lee, A. J., Rumley, A., Price, J. F. & Fowkes, F. G. R. Blood viscosity and risk of cardiovascular events: The Edinburgh artery study. British J. Haematol. 96, 168\u2013173. https:\/\/doi.org\/10.1046\/j.1365-2141.1997.8532481.x (1997).","journal-title":"British Journal of Haematology"},{"key":"47362_CR8","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1093\/aje\/kwn243","volume":"168","author":"LJ Tamariz","year":"2008","unstructured":"Tamariz, L. J. et al. Blood viscosity and hematocrit as risk factors for type 2 diabetes mellitus: The Atherosclerosis Risk in Communities (ARIC) study. Am. J. Epidemiol. 168, 1153\u20131160. https:\/\/doi.org\/10.1093\/aje\/kwn243 (2008).","journal-title":"American Journal of Epidemiology"},{"key":"47362_CR9","doi-asserted-by":"publisher","first-page":"287","DOI":"10.4070\/kcj.2011.41.6.287","volume":"41","author":"Y-I Cho","year":"2011","unstructured":"Cho, Y.-I. & Cho, D. J. Hemorheology and microvascular disorders. Korean Circul. J. 41, 287\u2013295. https:\/\/doi.org\/10.4070\/kcj.2011.41.6.287 (2011).","journal-title":"Korean Circulation Journal"},{"key":"47362_CR10","doi-asserted-by":"publisher","DOI":"10.1002\/jcla.22688","volume":"33","author":"H-C Wu","year":"2019","unstructured":"Wu, H.-C., Lee, L.-C. & Wang, W.-J. Plasmapheresis for hypertriglyceridemia: The association between blood viscosity and triglyceride clearance rate. J. Clin. Lab. Anal. 33, e22688. https:\/\/doi.org\/10.1002\/jcla.22688 (2019).","journal-title":"Journal of Clinical Laboratory Analysis"},{"key":"47362_CR11","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/RBME.2020.3007816","volume":"14","author":"F Shamout","year":"2021","unstructured":"Shamout, F., Zhu, T. & Clifton, D. A. Machine learning for clinical outcome prediction. IEEE Rev. Biomed. Eng. 14, 116\u2013126. https:\/\/doi.org\/10.1109\/RBME.2020.3007816 (2021).","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"47362_CR12","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar, A., Dean, J. & Kohane, I. Machine learning in medicine. New England J. Med. 380, 1347\u20131359. https:\/\/doi.org\/10.1056\/NEJMra1814259 (2019).","journal-title":"New England Journal of Medicine"},{"key":"47362_CR13","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.1038\/s41467-024-46150-w","volume":"15","author":"M Rosenblatt","year":"2024","unstructured":"Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S. & Scheinost, D. Data leakage inflates prediction performance in connectome-based machine learning models. Nature Commun. 15, 1829. https:\/\/doi.org\/10.1038\/s41467-024-46150-w (2024).","journal-title":"Nature Communications"},{"key":"47362_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804","volume":"4","author":"S Kapoor","year":"2023","unstructured":"Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804. https:\/\/doi.org\/10.1016\/j.patter.2023.100804 (2023).","journal-title":"Patterns"},{"key":"47362_CR15","doi-asserted-by":"publisher","unstructured":"Varoquaux, G. & Cheplygina, V. Machine learning for medical imaging: methodological failures and recommendations for the future. npj Digital Med.  5, 48, https:\/\/doi.org\/10.1038\/s41746-022-00592-y (2022).","DOI":"10.1038\/s41746-022-00592-y"},{"key":"47362_CR16","doi-asserted-by":"publisher","unstructured":"Tucker, A., Wang, Z., Rotalinti, Y. & Myles, P. Generating high-fidelity synthetic patient data for assessing machine learning healthcare software. npj Digital Med. 3, 147, https:\/\/doi.org\/10.1038\/s41746-020-00353-9 (2020).","DOI":"10.1038\/s41746-020-00353-9"},{"key":"47362_CR17","doi-asserted-by":"publisher","first-page":"2892","DOI":"10.1016\/j.csbj.2024.07.005","volume":"23","author":"VC Pezoulas","year":"2024","unstructured":"Pezoulas, V. C. et al. Synthetic data generation methods in healthcare: a review on open-source tools and methods. Comput. Struct. Biotech. J. 23, 2892\u20132910. https:\/\/doi.org\/10.1016\/j.csbj.2024.07.005 (2024).","journal-title":"Computational and Structural Biotechnology Journal"},{"key":"47362_CR18","first-page":"4765","volume":"30","author":"SM Lundberg","year":"2017","unstructured":"Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Adv. Neural Inf. Proc. Syst. (NIPS) 30, 4765\u20134774 (2017).","journal-title":"In Advances in Neural Information Processing Systems (NIPS)"},{"key":"47362_CR19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1161\/01.CIR.81.1.107","volume":"81","author":"G de Simone","year":"1990","unstructured":"de Simone, G. et al. Relation of blood viscosity to demographic and physiologic variables and to cardiovascular risk factors in apparently normal adults. Circulation 81, 107\u2013117. https:\/\/doi.org\/10.1161\/01.CIR.81.1.107 (1990).","journal-title":"Circulation"},{"key":"47362_CR20","doi-asserted-by":"publisher","unstructured":"Niculescu-Mizil, A. & Caruana, R. Predicting good probabilities with supervised learning. In Proceedings of the 22nd International Conference on Machine Learning (ICML \u201905), 625\u2013632, https:\/\/doi.org\/10.1145\/1102351.1102430 (2005).","DOI":"10.1145\/1102351.1102430"},{"key":"47362_CR21","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1002\/sim.5544","volume":"32","author":"JE Dalton","year":"2013","unstructured":"Dalton, J. E. Flexible recalibration of binary clinical prediction models. Statist. Med. 32, 282\u2013289. https:\/\/doi.org\/10.1002\/sim.5544 (2013).","journal-title":"Statistics in Medicine"},{"key":"47362_CR22","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.i6","volume":"352","author":"AJ Vickers","year":"2016","unstructured":"Vickers, A. J., Calster, B. V. & Steyerberg, E. W. Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests. BMJ 352, i6. https:\/\/doi.org\/10.1136\/bmj.i6 (2016).","journal-title":"BMJ"},{"key":"47362_CR23","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1186\/s12874-022-01577-x","volume":"22","author":"P Dhiman","year":"2022","unstructured":"Dhiman, P. et al. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med. Res. Method. 22, 101. https:\/\/doi.org\/10.1186\/s12874-022-01577-x (2022).","journal-title":"BMC Medical Research Methodology"},{"key":"47362_CR24","doi-asserted-by":"publisher","DOI":"10.2196\/47645","volume":"26","author":"Y-Q Cai","year":"2024","unstructured":"Cai, Y.-Q. et al. Pitfalls in developing machine learning models for predicting cardiovascular diseases: challenge and solutions. J. Med. Internet Res.  26, e47645. https:\/\/doi.org\/10.2196\/47645 (2024).","journal-title":"Journal of Medical Internet Research"},{"key":"47362_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0260311","volume":"16","author":"JW Han","year":"2021","unstructured":"Han, J. W., Sung, P. S., Jang, J. W., Choi, J. Y. & Yoon, S. K. Whole blood viscosity is associated with extrahepatic metastases and survival in patients with hepatocellular carcinoma. PLoS ONE 16, e0260311. https:\/\/doi.org\/10.1371\/journal.pone.0260311 (2021).","journal-title":"PLoS ONE"},{"key":"47362_CR26","doi-asserted-by":"publisher","first-page":"964","DOI":"10.3390\/biomedicines11030964","volume":"11","author":"M Carlisi","year":"2023","unstructured":"Carlisi, M., Presti, R. L., Mancuso, S., Siragusa, S. & Caimi, G. Calculated whole blood viscosity and albumin\/fibrinogen ratio in patients with a new diagnosis of multiple myeloma: Relationships with some prognostic predictors. Biomedicines 11, 964. https:\/\/doi.org\/10.3390\/biomedicines11030964 (2023).","journal-title":"Biomedicines"},{"key":"47362_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2023.105209","volume":"179","author":"A Atehort\u00faa","year":"2023","unstructured":"Atehort\u00faa, A. et al. Cardiometabolic risk estimation using exposome data and machine learning. Int. J. Med. Inf. 179, 105209. https:\/\/doi.org\/10.1016\/j.ijmedinf.2023.105209 (2023).","journal-title":"International Journal of Medical Informatics"},{"key":"47362_CR28","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1186\/s12920-024-01998-1","volume":"17","author":"D Shin","year":"2024","unstructured":"Shin, D. Prediction of metabolic syndrome using machine learning approaches based on genetic and nutritional factors: A 14-year prospective-based cohort study. BMC Med.Genom. 17, 224. https:\/\/doi.org\/10.1186\/s12920-024-01998-1 (2024).","journal-title":"BMC Medical Genomics"},{"key":"47362_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Scient. Data 3, 160018. https:\/\/doi.org\/10.1038\/sdata.2016.18 (2016).","journal-title":"Scientific Data"},{"key":"47362_CR30","doi-asserted-by":"publisher","first-page":"2584","DOI":"10.1073\/pnas.1708290115","volume":"115","author":"V Stodden","year":"2018","unstructured":"Stodden, V., Seiler, J. & Ma, Z. An empirical analysis of journal policy effectiveness for computational reproducibility. Proce. National Academy Sci. 115, 2584\u20132589. https:\/\/doi.org\/10.1073\/pnas.1708290115 (2018).","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"47362_CR31","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1186\/s40537-025-01193-8","volume":"12","author":"L Sasse","year":"2025","unstructured":"Sasse, L. et al. Overview of leakage scenarios in supervised machine learning. J. Big Data 12, 135. https:\/\/doi.org\/10.1186\/s40537-025-01193-8 (2025).","journal-title":"Journal of Big Data"},{"key":"47362_CR32","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1097\/EDE.0b013e3181c30fb2","volume":"21","author":"EW Steyerberg","year":"2010","unstructured":"Steyerberg, E. W. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128\u2013138. https:\/\/doi.org\/10.1097\/EDE.0b013e3181c30fb2 (2010).","journal-title":"Epidemiology"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47362-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47362-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47362-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T22:55:52Z","timestamp":1780872952000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47362-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,16]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["47362"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-47362-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,16]]},"assertion":[{"value":"3 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study relied exclusively on statistically reconstructed synthetic data generated from non-identifiable aggregate statistics. Because no identifiable human data were accessed, stored, or analyzed, the study does not constitute human-subject research under prevailing ethical or regulatory definitions. Institutional review board (IRB) approval was therefore not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"No human or animal subjects were involved in this research. All analyses were conducted on fully synthetic datasets containing no identifiable clinical information.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and Animal Rights"}},{"value":"Not applicable. This manuscript contains no identifiable personal data.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}}],"article-number":"17585"}}