{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T04:02:34Z","timestamp":1793332954644,"version":"4.5.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation programme","award":["No 874739, LongITools"],"award-info":[{"award-number":["No 874739, LongITools"]}]}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Recent evidence suggests that psycho-cardio-metabolic (PCM) multimorbidity finds its origins in exposure to early-life factors (ELFs), making the exploration of this association crucial for understanding and effective management of these complex health issues. Moreover, risk prediction models for cardiovascular diseases (CVD) and diabetes, as recommended by current clinical guidelines, typically demonstrate sub-optimal performance in clinically relevant sub-populations where these ELFs may play a substantial role. Our methodological approach investigates the contribution of ELFs to machine-learning-based risk prediction models for comorbid populations, incorporating a wide set of early-life and proximal variables, with a special focus on prenatal and postnatal ELFs. To address the complexity of integrating diverse early-life and proximal factors, we leverage models capable of handling high-dimensional, heterogeneous data sources to enhance prediction accuracy in complex clinical populations. The long-term predictive ability of ELFs, along with their influence on model decisions, is assessed with the learned models, and global and local model-agnostic interpretative techniques allow us to elucidate some interactions leading to multimorbidity. The data for this study is derived from the UK Biobank, showcasing both the strengths and limitations inherent in utilizing a single, large-scale database for such research. Our results show enhanced predictive performance for CVD (AUC-ROC: +7.9%, Acc: +14.7%, Cohen\u2019s d: 1.5) among individuals with concurrent mental health issues (depression or anxiety) and diabetes. Similarly, we demonstrate improved diabetes risk prediction (AUC-ROC: +12.3%, Acc: +13.5%, Cohen\u2019s d: 2.5) in those with concurrent mental health conditions and CVD. The inspection of these models, which integrate a large set of ELFs and other predictors (including the 7-core Framingham and UKDiabetes variables), provides key information that could lead to a more profound understanding of psycho-cardio-metabolic multimorbidity. Our findings highlight the utility of incorporating life-course factors into risk models. Integrating a diverse range of physiological, psychological, and ELFs becomes particularly pertinent in the context of multimorbidity.<\/jats:p>","DOI":"10.1371\/journal.pdig.0000982","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T17:48:23Z","timestamp":1755539303000},"page":"e0000982","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction"],"prefix":"10.1371","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6271-818X","authenticated-orcid":true,"given":"Vien Ngoc","family":"Dang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charlotte","family":"Cecil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmine M.","family":"Pariante","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jer\u00f3nimo","family":"Hern\u00e1ndez-Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karim","family":"Lekadir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"pdig.0000982.ref001","unstructured":"World Health Organization. Noncommunicable diseases. 2024. [cited 2024 May 2]. 2024. https:\/\/www.who.int\/health-topics\/noncommunicable-diseases"},{"key":"pdig.0000982.ref002","unstructured":"World Health Organization. Cardiovascular diseases. 2024. [cited 2024 May 2]. https:\/\/www.who.int\/health-topics\/cardiovascular-diseases"},{"issue":"10397","key":"pdig.0000982.ref003","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/S0140-6736(23)01301-6","article-title":"Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"402","year":"2023","journal-title":"Lancet."},{"key":"pdig.0000982.ref004","article-title":"Integrating mental health with other non-communicable diseases","volume":"364","author":"DJ Stein","year":"2019","journal-title":"BMJ."},{"issue":"1","key":"pdig.0000982.ref005","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1186\/s12933-018-0728-6","article-title":"Prevalence of cardiovascular disease in type 2 diabetes: a systematic literature review of scientific evidence from across the world in 2007 -2017","volume":"17","author":"TR Einarson","year":"2018","journal-title":"Cardiovasc Diabetol."},{"issue":"1","key":"pdig.0000982.ref006","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0245475","article-title":"Identifying causative mechanisms linking early-life stress to psycho-cardio-metabolic multi-morbidity: the earlycause project","volume":"16","author":"N Mariani","year":"2021","journal-title":"PLoS One."},{"issue":"12","key":"pdig.0000982.ref007","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1097\/NMD.0000000000000049","article-title":"The role of early life stress in adult psychiatric disorders: a systematic review according to childhood trauma subtypes","volume":"201","author":"CP Carr","year":"2013","journal-title":"J Nerv Ment Dis."},{"key":"pdig.0000982.ref008","unstructured":"Leeb RT. Child maltreatment surveillance: uniform definitions for public health and recommended data elements. Centers for Disease Control and Prevention, National Center for Injury Prevention and Control; 2008. https:\/\/stacks.cdc.gov\/view\/cdc\/11493"},{"issue":"1","key":"pdig.0000982.ref009","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.expneurol.2011.10.032","article-title":"Current research trends in early life stress and depression: review of human studies on sensitive periods, gene-environment interactions, and epigenetics","volume":"233","author":"C Heim","year":"2012","journal-title":"Exp Neurol."},{"issue":"1","key":"pdig.0000982.ref010","doi-asserted-by":"crossref","first-page":"4395","DOI":"10.1038\/s41598-019-40698-0","article-title":"The impact of early life stress on anxiety symptoms in late adulthood","volume":"9","author":"A L\u00e4hdepuro","year":"2019","journal-title":"Sci Rep."},{"key":"pdig.0000982.ref011","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.neubiorev.2016.07.018","article-title":"Developmental origins of cardiovascular disease: Impact of early life stress in humans and rodents","volume":"74","author":"MO Murphy","year":"2017","journal-title":"Neurosci Biobehav Rev."},{"key":"pdig.0000982.ref012","doi-asserted-by":"crossref","first-page":"485082","DOI":"10.1155\/2013\/485082","article-title":"Early life factors and type 2 diabetes mellitus","volume":"2013","author":"X Jiang","year":"2013","journal-title":"J Diabetes Res."},{"issue":"5","key":"pdig.0000982.ref013","article-title":"Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants","volume":"14","author":"AM Alaa","year":"2019","journal-title":"PLoS One."},{"issue":"25","key":"pdig.0000982.ref014","doi-asserted-by":"crossref","DOI":"10.1016\/j.jacc.2010.09.002","article-title":"2010 ACCF\/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation\/American Heart Association Task Force on Practice Guidelines","volume":"56","author":"P Greenland","year":"2010","journal-title":"J Am Coll Cardiol."},{"key":"pdig.0000982.ref015","article-title":"Comparisons of established risk prediction models for cardiovascular disease: systematic review","volume":"344","author":"GCM Siontis","year":"2012","journal-title":"BMJ."},{"issue":"2","key":"pdig.0000982.ref016","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1001\/jamapsychiatry.2020.2549","article-title":"Prediction, not association, paves the road to precision medicine","volume":"78","author":"D Bzdok","year":"2021","journal-title":"JAMA Psychiatry."},{"issue":"3","key":"pdig.0000982.ref017","doi-asserted-by":"crossref","DOI":"10.1214\/10-STS330","article-title":"To explain or to predict?","volume":"25","author":"G Shmueli","year":"2010","journal-title":"Statist Sci."},{"issue":"6","key":"pdig.0000982.ref018","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000276","article-title":"AutoPrognosis 2.0: democratizing diagnostic and prognostic modeling in healthcare with automated machine learning","volume":"2","author":"F Imrie","year":"2023","journal-title":"PLOS Digit Health."},{"key":"pdig.0000982.ref019","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016. p. 785\u201394. https:\/\/doi.org\/10.1145\/2939672.2939785","DOI":"10.1145\/2939672.2939785"},{"issue":"1","key":"pdig.0000982.ref020","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1186\/s13098-021-00767-9","article-title":"Machine learning and deep learning predictive models for type 2 diabetes: a systematic review","volume":"13","author":"L Fregoso-Aparicio","year":"2021","journal-title":"Diabetol Metab Syndr."},{"issue":"3","key":"pdig.0000982.ref021","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"C Sudlow","year":"2015","journal-title":"PLoS Med."},{"key":"pdig.0000982.ref022","unstructured":"American Diabetes Association. A1C and diagnosis. 2024. [cited 2024 Dec 8]. https:\/\/diabetes.org\/diabetes\/a1c\/diagnosis"},{"issue":"1","key":"pdig.0000982.ref023","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-52023-5","article-title":"Identifying top ten predictors of type 2 diabetes through machine learning analysis of UK Biobank data","volume":"14","author":"M Lugner","year":"2024","journal-title":"Sci Rep."},{"key":"pdig.0000982.ref024","article-title":"Development and validation of QDiabetes-2018 risk prediction algorithm to estimate future risk of type 2 diabetes: cohort study","volume":"359","author":"J Hippisley-Cox","year":"2017","journal-title":"BMJ."},{"key":"pdig.0000982.ref025","unstructured":"National Heart, Lung, and Blood Institute. Framingham Heart Study. 2024. [cited 2024 May 2]. https:\/\/www.framinghamheartstudy.org"},{"key":"pdig.0000982.ref026","unstructured":"Diabetes UK. Know your risk of type 2 diabetes. 2024. [cited 2024 May 2]. https:\/\/riskscore.diabetes.org.uk\/start"},{"key":"pdig.0000982.ref027","first-page":"2825","article-title":"Scikit-learn: machine learning in python","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research."},{"issue":"4","key":"pdig.0000982.ref028","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1175\/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2","article-title":"A new vector partition of the probability score","volume":"12","author":"AH Murphy","year":"1973","journal-title":"J Appl Meteor."},{"issue":"6","key":"pdig.0000982.ref029","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"AJ Vickers","year":"2006","journal-title":"Med Decis Making."},{"key":"pdig.0000982.ref030","unstructured":"Chen IY, Johansson FD, Sontag D. Why is my classifier discriminatory?. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. Curran Associates Inc.; 2018. p. 3543\u201354. https:\/\/dl.acm.org\/doi\/10.5555\/3327144.3327272"},{"key":"pdig.0000982.ref031","first-page":"1","article-title":"On the generalized distance in statistics","volume":"80","author":"PC Mahalanobis","year":"2018","journal-title":"Sankhya A."},{"key":"pdig.0000982.ref032","first-page":"99","article-title":"On a measure of divergence between two statistical populations defined by their probability distributions","volume":"35","author":"A Bhattacharyya","year":"1943","journal-title":"Bull Calcutta Math Soc."},{"issue":"1","key":"pdig.0000982.ref033","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"T Cover","year":"1967","journal-title":"IEEE Trans Inform Theory."},{"issue":"1","key":"pdig.0000982.ref034","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Machine Learning."},{"key":"pdig.0000982.ref035","doi-asserted-by":"crossref","unstructured":"Zar JH. Spearman Rank Correlation. Encyclopedia of Biostatistics. Wiley; 2005. https:\/\/doi.org\/10.1002\/0470011815.b2a15150","DOI":"10.1002\/0470011815.b2a15150"},{"key":"pdig.0000982.ref036","unstructured":"Lundberg S, Lee SI. A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems. Curran Associates; 2017 . p. 4765\u201374. https:\/\/dl.acm.org\/doi\/10.5555\/3295222.3295230"},{"key":"pdig.0000982.ref037","doi-asserted-by":"crossref","unstructured":"Cohen J. Statistical power analysis for the behavioral sciences. Routledge; 2013. https:\/\/doi.org\/10.4324\/9780203771587","DOI":"10.4324\/9780203771587"},{"issue":"9","key":"pdig.0000982.ref038","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/S2213-8587(24)00179-7","article-title":"Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1\u00b797 million adults with 100 000 incident cases from 31 cohorts in 20 countries","volume":"12","author":"C Li","year":"2024","journal-title":"Lancet Diabetes Endocrinol."},{"issue":"29","key":"pdig.0000982.ref039","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000029588","article-title":"Early prediction of diabetes by applying data mining techniques: a retrospective cohort study","volume":"101","author":"MZ Al Yousef","year":"2022","journal-title":"Medicine (Baltimore)."},{"issue":"1","key":"pdig.0000982.ref040","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-65044-x","article-title":"Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers","volume":"14","author":"H Yousef","year":"2024","journal-title":"Sci Rep."},{"issue":"14","key":"pdig.0000982.ref041","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.3390\/diagnostics13142383","article-title":"Application of machine learning models for early detection and accurate classification of type 2 diabetes","volume":"13","author":"O Iparraguirre-Villanueva","year":"2023","journal-title":"Diagnostics (Basel)."},{"issue":"3","key":"pdig.0000982.ref042","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1001\/archpedi.159.3.255","article-title":"Immigration, race\/ethnicity, and social and economic factors as predictors of breastfeeding initiation","volume":"159","author":"AC Celi","year":"2005","journal-title":"Arch Pediatr Adolesc Med."},{"issue":"3","key":"pdig.0000982.ref043","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1002\/ab.20016","article-title":"Social disadvantage and neglectful parenting as precursors to the development of antisocial and aggressive child behavior: Testing a theoretical model","volume":"30","author":"JF Knutson","year":"2004","journal-title":"Aggressive Behavior."},{"key":"pdig.0000982.ref044","doi-asserted-by":"crossref","unstructured":"Noll JG, Shalev I. The Biology of Early Life Stress. Springer; 2018. https:\/\/doi.org\/10.1007\/978-3-319-72589-5","DOI":"10.1007\/978-3-319-72589-5"}],"container-title":["PLOS Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000982","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T17:48:33Z","timestamp":1755539313000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pdig.0000982"}},"subtitle":[],"editor":[{"given":"Gloria Hyunjung","family":"Kwak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pdig.0000982","relation":{},"ISSN":["2767-3170"],"issn-type":[{"value":"2767-3170","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]}}}