{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T11:30:19Z","timestamp":1789471819368,"version":"build-2803163510"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100003161","name":"National Alopecia Areata Foundation","doi-asserted-by":"publisher","award":["2023"],"award-info":[{"award-number":["2023"]}],"id":[{"id":"10.13039\/100003161","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shemesh","award":["2023"],"award-info":[{"award-number":["2023"]}]},{"DOI":"10.13039\/501100003483","name":"Hebrew University of Jerusalem","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003483","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["GeroScience"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Dementia, particularly Alzheimer\u2019s disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating health, genetic, and lifestyle factors. The cohort included 2878 AD cases and 72,366 controls. Among several algorithms, CatBoost performed best (ROC-AUC\u2009=\u20090.773), especially in females. Inputs included ICD-10 codes from 5\u00a0years pre-diagnosis, ApoE-\u03b54 genotype, and large collection of modifiable risk factors. Despite fewer cases, the risk predictive models for vascular dementia (VaD) outperformed the unique AD models. ApoE-\u03b54 was the most predictive genetic marker, while other common variants had limited utility. Key non-genetic predictors included comorbidities (e.g., diabetes, hypertension), education, physical activity, and diet. These findings highlight the value of integrating diverse data sources for dementia risk prediction and emphasize the role of sex-specific modeling and modifiable factors in early, personalized intervention strategies.<\/jats:p>\n                  <jats:p>\n                    <jats:bold>Graphical Abstract<\/jats:bold>\n                  <\/jats:p>","DOI":"10.1007\/s11357-025-01828-x","type":"journal-article","created":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T07:04:18Z","timestamp":1756278258000},"page":"3007-3023","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Integrative machine learning approach to risk prediction for dementia and Alzheimer\u2019s disease"],"prefix":"10.1007","volume":"48","author":[{"given":"Amos","family":"Stern","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9357-4526","authenticated-orcid":false,"given":"Michal","family":"Linial","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"1828_CR1","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(20)32205-4","volume":"397","author":"P Scheltens","year":"2021","unstructured":"Scheltens P, De Strooper B, Kivipelto M, Holstege H, Ch\u00e9telat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer\u2019s disease. Lancet. 2021;397:1577\u201390.","journal-title":"Lancet"},{"key":"1828_CR2","doi-asserted-by":"crossref","first-page":"377","DOI":"10.33588\/rn.6611.2017519","volume":"66","author":"J Garre-Olmo","year":"2018","unstructured":"Garre-Olmo J. Epidemiology of Alzheimer\u2019s disease and other dementias. Rev Neurol. 2018;66:377\u201386.","journal-title":"Rev Neurol"},{"key":"1828_CR3","doi-asserted-by":"publisher","first-page":"313","DOI":"10.14283\/jpad.2021.15","volume":"8","author":"X-X Zhang","year":"2021","unstructured":"Zhang X-X, Tian Y, Wang Z-T, Ma Y-H, Tan L, Yu J-T. The epidemiology of Alzheimer\u2019s disease modifiable risk factors and prevention. J Prevent Alzheim Dis. 2021;8:313\u201321.","journal-title":"J Prevent Alzheim Dis"},{"key":"1828_CR4","doi-asserted-by":"publisher","first-page":"937486","DOI":"10.3389\/fnagi.2022.937486","volume":"14","author":"X Li","year":"2022","unstructured":"Li X, Feng X, Sun X, Hou N, Han F, Liu Y. Global, regional, and national burden of Alzheimer\u2019s disease and other dementias, 1990\u20132019. Front Ag Neurosci. 2022;14:937486.","journal-title":"Front Ag Neurosci"},{"key":"1828_CR5","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.pharep.2014.09.004","volume":"67","author":"A Kumar","year":"2015","unstructured":"Kumar A, Singh A. A review on Alzheimer\u2019s disease pathophysiology and its management: an update. Pharmacol Rep. 2015;67:195\u2013203.","journal-title":"Pharmacol Rep"},{"key":"1828_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.14283\/jpad.2021.15","volume":"8","author":"X-X Zhang","year":"2021","unstructured":"Zhang X-X, Tian Y, Wang Z-T, Ma Y-H, Tan L, Yu J-T. The epidemiology of Alzheimer\u2019s disease modifiable risk factors and prevention. J Prev Alzheimers Dis. 2021;8:313\u201321.","journal-title":"J Prev Alzheimers Dis"},{"key":"1828_CR7","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1007\/s40120-022-00338-8","volume":"11","author":"AA Tahami Monfared","year":"2022","unstructured":"Tahami Monfared AA, Byrnes MJ, White LA, Zhang Q. Alzheimer\u2019s disease: epidemiology and clinical progression. Neurol Ther. 2022;11:553\u201369.","journal-title":"Neurol Ther"},{"key":"1828_CR8","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/S0140-6736(20)30367-6","volume":"396","author":"G Livingston","year":"2020","unstructured":"Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, Brayne C, Burns A, Cohen-Mansfield J, Cooper C, Costafreda SG, Dias A, Fox N, Gitlin LN, Howard R, Kales HC, Kivimaki M, Larson EB, Ogunniyi A, Orgeta V, Ritchie K, Rockwood K, Sampson EL, Samus Q, Schneider LS, Selbaek G, Teri L, Mukadam N. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396:413\u201346.","journal-title":"Lancet"},{"key":"1828_CR9","doi-asserted-by":"publisher","first-page":"87","DOI":"10.5114\/fn.2019.85929","volume":"57","author":"RA Armstrong","year":"2019","unstructured":"Armstrong RA. Risk factors for Alzheimer\u2019s disease. Folia Neuropathol. 2019;57:87\u2013105.","journal-title":"Folia Neuropathol"},{"key":"1828_CR10","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/B978-0-12-804766-8.00009-1","volume":"167","author":"OL Lopez","year":"2019","unstructured":"Lopez OL, Kuller LH. Epidemiology of aging and associated cognitive disorders: prevalence and incidence of Alzheimer\u2019s disease and other dementias. Handb Clin Neurol. 2019;167:139\u201348.","journal-title":"Handb Clin Neurol"},{"key":"1828_CR11","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/S0140-6736(20)30367-6","volume":"396","author":"G Livingston","year":"2020","unstructured":"Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, Brayne C, Burns A, Cohen-Mansfield J, Cooper C. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396:413\u201346.","journal-title":"Lancet"},{"key":"1828_CR12","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1016\/S0140-6736(24)01296-0","volume":"404","author":"G Livingston","year":"2024","unstructured":"Livingston G, Huntley J, Liu KY, Costafreda SG, Selbaek G, Alladi S, Ames D, Banerjee S, Burns A, Brayne C, Fox NC, Ferri CP, Gitlin LN, Howard R, Kales HC, Kivimaki M, Larson EB, Nakasujja N, Rockwood K, Samus Q, Shirai K, Singh-Manoux A, Schneider LS, Walsh S, Yao Y, Sommerlad A, Mukadam N. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024;404:572\u2013628.","journal-title":"Lancet"},{"key":"1828_CR13","doi-asserted-by":"publisher","DOI":"10.12688\/f1000research.50786.1","volume":"10","author":"SF Javaid","year":"2021","unstructured":"Javaid SF, Giebel C, Khan MA, Hashim MJ. Epidemiology of Alzheimer\u2019s disease and other dementias: rising global burden and forecasted trends. F1000Res. 2021;10: 425.","journal-title":"F1000Res"},{"key":"1828_CR14","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/S1474-4422(15)70016-5","volume":"14","author":"MT Heneka","year":"2015","unstructured":"Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, Jacobs AH, Wyss-Coray T, Vitorica J, Ransohoff RM, Herrup K, Frautschy SA, Finsen B, Brown GC, Verkhratsky A, Yamanaka K, Koistinaho J, Latz E, Halle A, Petzold GC, Town T, Morgan D, Shinohara ML, Perry VH, Holmes C, Bazan NG, Brooks DJ, Hunot S, Joseph B, Deigendesch N, Garaschuk O, Boddeke E, Dinarello CA, Breitner JC, Cole GM, Golenbock DT, Kummer MP. Neuroinflammation in Alzheimer\u2019s disease. Lancet Neurol. 2015;14:388\u2013405.","journal-title":"Lancet Neurol"},{"key":"1828_CR15","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1038\/s41593-020-0599-5","volume":"23","author":"R Sims","year":"2020","unstructured":"Sims R, Hill M, Williams J. The multiplex model of the genetics of Alzheimer\u2019s disease. Nat Neurosci. 2020;23:311\u201322.","journal-title":"Nat Neurosci"},{"key":"1828_CR16","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.cell.2015.12.056","volume":"164","author":"B De Strooper","year":"2016","unstructured":"De Strooper B, Karran E. The cellular phase of Alzheimer\u2019s disease. Cell. 2016;164:603\u201315.","journal-title":"Cell"},{"key":"1828_CR17","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1093\/brain\/awv236","volume":"138","author":"H Braak","year":"2015","unstructured":"Braak H, Del Tredici K. The preclinical phase of the pathological process underlying sporadic Alzheimer\u2019s disease. Brain. 2015;138:2814\u201333.","journal-title":"Brain"},{"key":"1828_CR18","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s13311-021-01146-y","volume":"19","author":"JA Trejo-Lopez","year":"2023","unstructured":"Trejo-Lopez JA, Yachnis AT, Prokop S. Neuropathology of Alzheimer\u2019s disease. Neurotherapeutics. 2023;19:173\u201385.","journal-title":"Neurotherapeutics"},{"key":"1828_CR19","doi-asserted-by":"publisher","first-page":"535","DOI":"10.2147\/CIA.S51571","volume":"1","author":"E Bagyinszky","year":"2014","unstructured":"Bagyinszky E, Youn YC, An SS, Kim S. The genetics of Alzheimer\u2019s disease. Clin Interv Aging. 2014;1:535\u201351.","journal-title":"Clin Interv Aging"},{"key":"1828_CR20","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1016\/j.jalz.2016.01.012","volume":"12","author":"R Cacace","year":"2016","unstructured":"Cacace R, Sleegers K, Van Broeckhoven C. Molecular genetics of early-onset Alzheimer\u2019s disease revisited. Alzheimers Dement. 2016;12:733\u201348.","journal-title":"Alzheimers Dement"},{"key":"1828_CR21","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.1016\/j.neuron.2022.03.004","volume":"110","author":"YA Martens","year":"2022","unstructured":"Martens YA, Zhao N, Liu C-C, Kanekiyo T, Yang AJ, Goate AM, Holtzman DM, Bu G. ApoE cascade hypothesis in the pathogenesis of Alzheimer\u2019s disease and related dementias. Neuron. 2022;110:1304\u201317.","journal-title":"Neuron"},{"issue":"3","key":"1828_CR22","doi-asserted-by":"publisher","first-page":"921","DOI":"10.3233\/JAD-150749","volume":"53","author":"V Chouraki","year":"2016","unstructured":"Chouraki V, Reitz C, Maury F, Bis JC, Bellenguez C, Yu L, Jakobsdottir J, Mukherjee S, Adams HH, Choi SH, Larson EB. Evaluation of a genetic risk score to improve risk prediction for Alzheimer\u2019s disease. J Alzheim Dis. 2016;53(3):921\u201332.","journal-title":"J Alzheim Dis"},{"key":"1828_CR23","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1590\/1980-57642016dn11-040005","volume":"11","author":"N Custodio","year":"2017","unstructured":"Custodio N, Montesinos R, Lira D, Herrera-P\u00e9rez E, Bardales Y, Valeriano-Lorenzo L. Mixed dementia: a review of the evidence. Dement Neuropsychol. 2017;11:364\u201370.","journal-title":"Dement Neuropsychol"},{"key":"1828_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.105864","volume":"30","author":"SC Bir","year":"2021","unstructured":"Bir SC, Khan MW, Javalkar V, Toledo EG, Kelley RE. Emerging concepts in vascular dementia: a review. J Stroke Cerebrovasc Dis. 2021;30: 105864.","journal-title":"J Stroke Cerebrovasc Dis"},{"issue":"4","key":"1828_CR25","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1093\/braincomms\/fcab246","volume":"3","author":"TW Rowe","year":"2021","unstructured":"Rowe TW, Katzourou IK, Stevenson-Hoare JO, Bracher-Smith MR, Ivanov DK, Escott-Price V. Machine learning for the life-time risk prediction of Alzheimer\u2019s disease: a systematic review. Brain Commun. 2021;3(4):246.","journal-title":"Brain Commun"},{"key":"1828_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.tjpad.2025.100169","author":"S Akter","year":"2025","unstructured":"Akter S, Liu Z, Simoes EJ, Rao P. Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer\u2019s disease and related dementias. J Prev Alzheimers Dis. 2025. https:\/\/doi.org\/10.1016\/j.tjpad.2025.100169.","journal-title":"J Prev Alzheimers Dis"},{"key":"1828_CR27","doi-asserted-by":"publisher","first-page":"3506","DOI":"10.1002\/alz.12967","volume":"19","author":"Q Li","year":"2023","unstructured":"Li Q, Yang X, Xu J, Guo Y, He X, Hu H, Lyu T, Marra D, Miller A, Smith G, DeKosky S, Boyce RD, Schliep K, Shenkman E, Maraganore D, Wu Y, Bian J. Early prediction of Alzheimer\u2019s disease and related dementias using real-world electronic health records. Alzheimers Dement. 2023;19:3506\u201318.","journal-title":"Alzheimers Dement"},{"key":"1828_CR28","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1038\/s41598-023-27551-1","volume":"13","author":"XR Gao","year":"2023","unstructured":"Gao XR, Chiariglione M, Qin K, Nuytemans K, Scharre DW, Li YJ, Martin ER. Explainable machine learning aggregates polygenic risk scores and electronic health records for Alzheimer\u2019s disease prediction. Sci Rep. 2023;13:450.","journal-title":"Sci Rep"},{"key":"1828_CR29","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaf156","volume":"41","author":"AM Kashyap","year":"2025","unstructured":"Kashyap AM, Rao D, Boland MR, Shen L, Callison-Burch C. Predicting explainable dementia types with LLM-aided feature engineering. Bioinformatics. 2025;41: 156.","journal-title":"Bioinformatics"},{"key":"1828_CR30","doi-asserted-by":"publisher","DOI":"10.2196\/65178","volume":"8","author":"M West","year":"2025","unstructured":"West M, Cheng Y, He Y, Leng Y, Magdamo C, Hyman BT, Dickson JR, Serrano-Pozo A, Blacker D, Das S. Unsupervised deep learning of electronic health records to characterize heterogeneity across Alzheimer disease and related dementias: cross-sectional study. JMIR Aging. 2025;8: e65178.","journal-title":"JMIR Aging"},{"key":"1828_CR31","doi-asserted-by":"publisher","DOI":"10.1002\/alz.70069","volume":"21","author":"RJ Vermeulen","year":"2025","unstructured":"Vermeulen RJ, Andersson V, Banken J, Hannink G, Govers TM, Rovers MM, Rikkert M. Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: a systematic review. Alzheimers Dement. 2025;21: e70069.","journal-title":"Alzheimers Dement"},{"key":"1828_CR32","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1136\/jnnp-2018-318212","volume":"90","author":"XH Hou","year":"2019","unstructured":"Hou XH, Feng L, Zhang C, Cao XP, Tan L, Yu JT. Models for predicting risk of dementia: a systematic review. J Neurol Neurosurg Psychiatry. 2019;90:373\u20139.","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"1828_CR33","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","volume":"562","author":"C Bycroft","year":"2018","unstructured":"Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, Motyer A, Vukcevic D, Delaneau O, O\u2019Connell J. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203\u20139.","journal-title":"Nature"},{"key":"1828_CR34","unstructured":"Dorogush AV, Ershov V, Gulin A. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. 2018."},{"key":"1828_CR35","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock JT, Khoshgoftaar TM. Catboost for big data: an interdisciplinary review. J Big Data. 2020;7:94.","journal-title":"J Big Data"},{"key":"1828_CR36","doi-asserted-by":"crossref","unstructured":"Hancock J, Khoshgoftaar TM. Performance of catboost and xgboost in medicare fraud detection. In 2020 19th IEEE international conference on machine learning and applications (ICMLA) 2020 Dec 14 (pp. 572-579). IEEE.","DOI":"10.1109\/ICMLA51294.2020.00095"},{"key":"1828_CR37","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1038\/s41588-018-0248-z","volume":"50","author":"O Canela-Xandri","year":"2018","unstructured":"Canela-Xandri O, Rawlik K, Tenesa A. An atlas of genetic associations in UK Biobank. Nat Genet. 2018;50:1593\u20139.","journal-title":"Nat Genet"},{"key":"1828_CR38","doi-asserted-by":"publisher","first-page":"D1311","DOI":"10.1093\/nar\/gkaa840","volume":"49","author":"M Ghoussaini","year":"2021","unstructured":"Ghoussaini M, Mountjoy E, Carmona M, Peat G, Schmidt EM, Hercules A, Fumis L, Miranda A, Carvalho-Silva D, Buniello A. Open targets genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res. 2021;49:D1311-20.","journal-title":"Nucleic Acids Res"},{"key":"1828_CR39","doi-asserted-by":"publisher","first-page":"D896","DOI":"10.1093\/nar\/gkw1133","volume":"45","author":"J MacArthur","year":"2017","unstructured":"MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, Junkins H, McMahon A, Milano A, Morales J. The new NHGRI-EBI catalog of published genome-wide association studies (GWAS catalog). Nucleic Acids Res. 2017;45:D896\u2013901.","journal-title":"Nucleic Acids Res"},{"key":"1828_CR40","doi-asserted-by":"publisher","first-page":"6642","DOI":"10.1093\/nar\/gkz546","volume":"47","author":"N Brandes","year":"2019","unstructured":"Brandes N, Linial N, Linial M. Quantifying gene selection in cancer through protein functional alteration bias. Nucleic Acids Res. 2019;47:6642\u201355.","journal-title":"Nucleic Acids Res"},{"key":"1828_CR41","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1186\/s13059-020-02089-x","volume":"21","author":"N Brandes","year":"2020","unstructured":"Brandes N, Linial N, Linial M. PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins. Genome Biol. 2020;21:173.","journal-title":"Genome Biol"},{"key":"1828_CR42","doi-asserted-by":"publisher","first-page":"D1132","DOI":"10.1093\/nar\/gkae1125","volume":"53","author":"R Zucker","year":"2025","unstructured":"Zucker R, Kelman G, Linial M. PWAS Hub: exploring gene-based associations of complex diseases with sex dependency. Nucleic Acids Res. 2025;53:D1132\u201343.","journal-title":"Nucleic Acids Res"},{"key":"1828_CR43","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-12391-2","volume":"12","author":"C Gouveia","year":"2022","unstructured":"Gouveia C, Gibbons E, Dehghani N, Eapen J, Guerreiro R, Bras J. Genome-wide association of polygenic risk extremes for Alzheimer\u2019s disease in the UK Biobank. Sci Rep. 2022;12: 8404.","journal-title":"Sci Rep"},{"issue":"9","key":"1828_CR44","doi-asserted-by":"publisher","first-page":"e0307270","DOI":"10.1371\/journal.pone.0307270","volume":"19","author":"DJ Thompson","year":"2024","unstructured":"Thompson DJ, Wells D, Selzam S, Peneva I, Moore R, Sharp K, Tarran WA, Beard EJ, Riveros-Mckay F, Giner-Delgado C, Palmer D. A systematic evaluation of the performance and properties of the UK Biobank polygenic risk score (PRS) release. Plos one. 2024;19(9):e0307270.","journal-title":"Plos one."},{"key":"1828_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-90277-9","volume":"15","author":"B Wang","year":"2025","unstructured":"Wang B, Chibnik LB, Choi SH, Blacker D, DeStefano AL, Lin H. Association of genetic risk of Alzheimer\u2019s disease and cognitive function in two European populations. Sci Rep. 2025;15: 6410.","journal-title":"Sci Rep"},{"key":"1828_CR46","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-24082-z","volume":"12","author":"G Leonenko","year":"2021","unstructured":"Leonenko G, Baker E, Stevenson-Hoare J, Sierksma A, Fiers M, Williams J, de Strooper B, Escott-Price V. Identifying individuals with high risk of Alzheimer\u2019s disease using polygenic risk scores. Nat Commun. 2021;12: 4506.","journal-title":"Nat Commun"},{"key":"1828_CR47","doi-asserted-by":"crossref","unstructured":"Marc\u00edlio WE, Eler DM. From explanations to feature selection: assessing SHAP values as feature selection mechanism. In 2020 33rd SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI) 2020 Nov 7 (pp. 340-347). Ieee.","DOI":"10.1109\/SIBGRAPI51738.2020.00053"},{"key":"1828_CR48","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1186\/s13059-020-02089-x","volume":"21","author":"N Brandes","year":"2020","unstructured":"Brandes N, Linial N, Linial M. PWAS: proteome-wide association study\u2014linking genes and phenotypes by functional variation in proteins. Genome Biol. 2020;21:173.","journal-title":"Genome Biol"},{"key":"1828_CR49","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230173","volume":"308","author":"S Haller","year":"2023","unstructured":"Haller S, Jager HR, Vernooij MW, Barkhof F. Neuroimaging in dementia: more than typical Alzheimer disease. Radiology. 2023;308: e230173.","journal-title":"Radiology"},{"issue":"Suppl 1","key":"1828_CR50","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1590\/S1807-59322011001300003","volume":"66","author":"LK Ferreira","year":"2011","unstructured":"Ferreira LK, Busatto GF. Neuroimaging in Alzheimer\u2019s disease: current role in clinical practice and potential future applications. Clinics (Sao Paulo). 2011;66(Suppl 1):19\u201324.","journal-title":"Clinics (Sao Paulo)"},{"issue":"3","key":"1828_CR51","doi-asserted-by":"publisher","first-page":"1978","DOI":"10.1002\/alz.13610","volume":"20","author":"PT Buto","year":"2024","unstructured":"Buto PT, Wang J, La Joie R, Zimmerman SC, Glymour MM, Ackley SF, Hoffmann TJ, Yaffe K, Zeki Al Hazzouri A, Brenowitz WD. Genetic risk score for Alzheimer\u2019s disease predicts brain volume differences in mid and late life in UK biobank participants. Alzheimers Dement. 2024;20(3):1978\u201387.","journal-title":"Alzheimers Dement"},{"issue":"3","key":"1828_CR52","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1001\/jamaneurol.2017.4198","volume":"75","author":"LG Apostolova","year":"2018","unstructured":"Apostolova LG, Risacher SL, Duran T, Stage EC, Goukasian N, West JD, Do TM, Grotts J, Wilhalme H, Nho K, Phillips M. Associations of the top 20 Alzheimer disease risk variants with brain amyloidosis. JAMA Neurol. 2018;75(3):328\u201341.","journal-title":"JAMA Neurol"},{"key":"1828_CR53","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-023-01256-z","volume":"15","author":"XY He","year":"2023","unstructured":"He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer\u2019s disease and brain structure in children and adults. Alzheimers Res Ther. 2023;15: 109.","journal-title":"Alzheimers Res Ther"},{"key":"1828_CR54","doi-asserted-by":"publisher","first-page":"2507","DOI":"10.1007\/s11357-024-01413-8","volume":"47","author":"MA Beydoun","year":"2025","unstructured":"Beydoun MA, Beydoun HA, Li Z, Hu YH, Noren Hooten N, Ding J, Hossain S, Maino Vieytes CA, Launer LJ, Evans MK, Zonderman AB. Alzheimer\u2019s disease polygenic risk, the plasma proteome, and dementia incidence among UK older adults. Geroscience. 2025;47:2507\u201323.","journal-title":"Geroscience"},{"key":"1828_CR55","doi-asserted-by":"publisher","first-page":"101665","DOI":"10.1016\/j.eclinm.2022.101665","volume":"53","author":"J You","year":"2022","unstructured":"You J, Zhang YR, Wang HF, Yang M, Feng JF, Yu JT, Cheng W. Development of a novel dementia risk prediction model in the general population: a large, longitudinal, population-based machine-learning study. EClinicalMedicine. 2022;53:101665.","journal-title":"EClinicalMedicine"},{"key":"1828_CR56","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.91360","volume":"12","author":"D Yan","year":"2024","unstructured":"Yan D, Hu B, Darst BF, Mukherjee S, Kunkle BW, Deming Y, Dumitrescu L, Wang Y, Naj A, Kuzma A, Zhao Y. Biobank-wide association scan identifies risk factors for late-onset Alzheimer\u2019s disease and endophenotypes. Elife. 2024;12: 1360.","journal-title":"Elife"},{"issue":"1","key":"1828_CR57","doi-asserted-by":"publisher","DOI":"10.1186\/s13195-024-01664-9","volume":"17","author":"E Bellou","year":"2025","unstructured":"Bellou E, Kim W, Leonenko G, Tao F, Simmonds E, Wu Y, Mattsson-Carlgren N, Hansson O, Nagle MW, Escott-Price V. Alzheimer\u2019s disease neuroimaging initiative benchmarking Alzheimer\u2019s disease prediction: personalised risk assessment using polygenic risk scores across various methodologies and genome-wide studies. Alzheim Res Ther. 2025;17(1): 6.","journal-title":"Alzheim Res Ther"},{"key":"1828_CR58","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-025-03998-z","volume":"23","author":"C Li","year":"2025","unstructured":"Li C, Dite GS, Nguyen TL, Hopper JL, Li S. Cancer incidence inconsistency between UK Biobank participants and the population: a prospective cohort study. BMC Med. 2025;23: 181.","journal-title":"BMC Med"},{"key":"1828_CR59","doi-asserted-by":"crossref","unstructured":"Andrews SJ, Fulton\u2010Howard B, O'Reilly P, Marcora E, Goate AM, Collaborators of the Alzheimer's Disease Genetics Consortium, Farrer LA, Haines JL, Mayeux R, Naj AC, Pericak\u2010Vance MA. Causal associations between modifiable risk factors and the Alzheimer's phenome. Annals of neurology. 2021 89(1):54-65.","DOI":"10.1002\/ana.25918"}],"container-title":["GeroScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11357-025-01828-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11357-025-01828-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11357-025-01828-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:03:59Z","timestamp":1773050639000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11357-025-01828-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,27]]},"references-count":59,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["1828"],"URL":"https:\/\/doi.org\/10.1007\/s11357-025-01828-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2025.06.02.25328770","asserted-by":"object"}]},"ISSN":["2509-2723"],"issn-type":[{"value":"2509-2723","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,27]]},"assertion":[{"value":"7 June 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2025","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 study was approved by the University Committee for the Use of Human Subjects in Research Approval number 12072022 (July 2025). This study uses the UK-Biobank (UKB) application ID 26664 (Linial lab).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}