Prediction of Alzheimer’s Disease Progression from Mild Cognitive Impairment Using Polygenic Risk Scores

Prediction of Alzheimer’s Disease Progression from Mild Cognitive Impairment Using Polygenic Risk Scores | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prediction of Alzheimer’s Disease Progression from Mild Cognitive Impairment Using Polygenic Risk Scores Yalu Wen, Shuyao Wang, Yu Chen, Hongmei Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7812084/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Mild cognitive impairment (MCI) represents the prodromal stage of Alzheimer’s disease (AD), and not all MCI patients progress to AD. Accurate stratification of MCI clinical outcomes is therefore crucial. Although polygenic risk scores (PRS) can distinguish AD patients from cognitively normal individuals, their utility in predicting heterogeneous MCI outcomes remains unclear. This study evaluated the predictive ability of PRS for both MCI progression and reversion. PRS were constructed using four algorithms and showed strong inter-method correlations. When divided into quartiles, MCI patients in the highest PRS quartile had a significantly greater risk of progression to AD, while lower PRS were asso-ciated with increased likelihood of reversion to normal cognition. We developed stepwise prediction models incorporating PRS, demographic variables, clinical assessments, and cerebrospinal fluid (CSF) biomarkers. Prediction performance did not differ significantly across PRS algorithms. The best-performing model combined PRS, demographic variables, and clinical assessments, while the addition of CSF biomarkers provided no further im-provement. These findings highlight the potential of PRS, integrated with routine clinical information, to enhance individualized risk prediction for MCI outcomes. Biological sciences/Genetics/Genomics/Personalized medicine Health sciences/Biomarkers/Predictive markers Alzheimer’s disease Mild cognitive impairment Polygenic risk scores Progression Reversion Figures Figure 1 Figure 1 Figure 2 Figure 2 Figure 3 Figure 3 Figure 4 Figure 4 Figure 5 Figure 5 Full Text Additional Declarations The authors have declared there is NO conflict of interest to disclose Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7812084","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528156630,"identity":"7ac10ff0-3adc-4347-995c-08e5eaf57949","order_by":0,"name":"Yalu 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13:56:52","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26730204,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/f94e1c80277202ea4b5fb66d.tif"},{"id":94397902,"identity":"b1959cb5-5bb1-4e16-b6b0-805d6065b9a6","added_by":"auto","created_at":"2025-10-27 13:56:53","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":26730204,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.5.tif","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/ecf0b270ab106c2b2edac393.tif"},{"id":94398989,"identity":"566b40b8-f463-421f-85a8-0994e2cb12cc","added_by":"auto","created_at":"2025-10-27 13:57:18","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5526,"visible":true,"origin":"","legend":"","description":"","filename":"2025TP002470.json","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/1091482229ad30577a231a14.json"},{"id":94463903,"identity":"50206691-ed42-454f-a81c-d7752e1777c2","added_by":"auto","created_at":"2025-10-27 15:09:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3678346,"visible":true,"origin":"","legend":"\u003cp\u003eConcordance among polygenic risk scores (PRS) derived from four algorithms. Scatter plots below the diagonal show pairwise PRS comparisons, density plots along the diagonal depict PRS distributions, and Spearman’s rank correlations are shown above the diagonal ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.11.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/8661bd3405261545c57ca8dd.png"},{"id":94399472,"identity":"06d874ce-49af-47b0-9fef-2d971b22cbae","added_by":"auto","created_at":"2025-10-27 13:57:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3678346,"visible":true,"origin":"","legend":"\u003cp\u003eConcordance among polygenic risk scores (PRS) derived from four algorithms. Scatter plots below the diagonal show pairwise PRS comparisons, density plots along the diagonal depict PRS distributions, and Spearman’s rank correlations are shown above the diagonal ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/3526ac390a537504bafa47e5.png"},{"id":94463702,"identity":"02819670-5864-46c9-a959-f8b611b3d8b4","added_by":"auto","created_at":"2025-10-27 15:09:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":952550,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of polygenic risk scores (PRS) across MCI progression groups. PRS derived from LDpred, lassosum, PRS-CS, and DBSLMM are compared among the “MCI-to-CN,” “stable MCI,” and “MCI-to-AD” groups. Dunn’s test assessed pairwise dif- ferences, and the Jonckheere-Terpstra test evaluated ordered trends. ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/bd4618a2efc49deecb7de479.png"},{"id":94396411,"identity":"2ddcb731-0924-4524-a24a-e56674c3d1e3","added_by":"auto","created_at":"2025-10-27 13:55:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":952550,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of polygenic risk scores (PRS) across MCI progression groups. PRS derived from LDpred, lassosum, PRS-CS, and DBSLMM are compared among the “MCI-to-CN,” “stable MCI,” and “MCI-to-AD” groups. Dunn’s test assessed pairwise dif- ferences, and the Jonckheere-Terpstra test evaluated ordered trends. ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/846b2a4cac19e667e77b69de.png"},{"id":94463899,"identity":"c0313737-d026-46da-84a4-ccfa7d1143d4","added_by":"auto","created_at":"2025-10-27 15:09:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1829567,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative event occurrence probabilities of MCI patients progressing to AD or reverting to CN in different PRS groups. “Low PRS” group (lower than median, Yellow line) and “High PRS” group (higher than median, Red line). MCI = mild cognitive impairment; AD = Alzheimer’s disease; CN = cognitive normal; PRS = polygenic risk score. ***P \u0026nbsp;\u0026lt; 0.001; **P \u0026nbsp;\u0026lt; 0.01; *P \u0026nbsp;\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig.31.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/2168dd2e6bd4568f949649c7.png"},{"id":94399325,"identity":"47e3dddc-6d43-47f3-bc92-2cb60d5711bc","added_by":"auto","created_at":"2025-10-27 13:57:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1829567,"visible":true,"origin":"","legend":"\u003cp\u003eThe cumulative event occurrence probabilities of MCI patients progressing to AD or reverting to CN in different PRS groups. “Low PRS” group (lower than median, Yellow line) and “High PRS” group (higher than median, Red line). MCI = mild cognitive impairment; AD = Alzheimer’s disease; CN = cognitive normal; PRS = polygenic risk score. ***P \u0026nbsp;\u0026lt; 0.001; **P \u0026nbsp;\u0026lt; 0.01; *P \u0026nbsp;\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/ba4f6fa9e027f8018c818c8b.png"},{"id":94463568,"identity":"ff6ab0f6-62f8-41bb-b6bf-e4a30b17fd9a","added_by":"auto","created_at":"2025-10-27 15:08:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3354712,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves of the prediction models for the progression of MCI patients to AD with different PRS algorithms. Model 0: an unad- justed model, where only PRS is considered. Model 1: Model 0 + demographic variables. Model 2: Model 1 + clinical assessments. Model 3: Model 2 + cerebrospinal fluid bi- omarkers. Demographic variables include age and APOE ??4. Clinical assessments include Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), and Alzheimer’s Disease Assessment Scale (ADAS). Cerebrospinal fluid bi- omarkers include amyloid ??-protein (A??), tau, and phosphorylated tau (p-tau). MCI = mild cognitive impairment; AD = Alzheimer’s disease; PRS = polygenic risk score.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/40b5fc57de0f7964c52efd7f.png"},{"id":94397968,"identity":"8d8e8f13-e2ad-4110-871d-91f2c63c5b57","added_by":"auto","created_at":"2025-10-27 13:56:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3354712,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves of the prediction models for the progression of MCI patients to AD with different PRS algorithms. Model 0: an unad- justed model, where only PRS is considered. Model 1: Model 0 + demographic variables. Model 2: Model 1 + clinical assessments. Model 3: Model 2 + cerebrospinal fluid bi- omarkers. Demographic variables include age and APOE ??4. Clinical assessments include Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), and Alzheimer’s Disease Assessment Scale (ADAS). Cerebrospinal fluid bi- omarkers include amyloid ??-protein (A??), tau, and phosphorylated tau (p-tau). MCI = mild cognitive impairment; AD = Alzheimer’s disease; PRS = polygenic risk score.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/6dbf30103e02ee26b228b329.png"},{"id":94463898,"identity":"589cadc8-6aa7-4071-9e07-372b6c575bf1","added_by":"auto","created_at":"2025-10-27 15:09:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3431608,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves of the prediction models for the regression of MCI patients to CN with different PRS algorithms. Model 0: an unad- justed model, where only PRS is considered. Model 1: Model 0 + demographic variables. Model 2: Model 1 + clinical assessments. Model 3: Model 2 + cerebrospinal fluid bi- omarkers. Demographic variables include age and APOE ??4. Clinical assessments include Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), and Alzheimer’s Disease Assessment Scale (ADAS). Cerebrospinal fluid bi- omarkers include amyloid ??-protein (A??), tau, and phosphorylated tau (p-tau). MCI = mild cognitive impairment; CN = cognitive normal; PRS = polygenic risk score.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/2a3f6d13b8f0b0edae625851.png"},{"id":94398212,"identity":"470a03dc-ebad-4754-9fd6-811e1b747c5f","added_by":"auto","created_at":"2025-10-27 13:57:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3431608,"visible":true,"origin":"","legend":"\u003cp\u003eThe receiver operating characteristic curves of the prediction models for the regression of MCI patients to CN with different PRS algorithms. Model 0: an unad- justed model, where only PRS is considered. Model 1: Model 0 + demographic variables. Model 2: Model 1 + clinical assessments. Model 3: Model 2 + cerebrospinal fluid bi- omarkers. Demographic variables include age and APOE ??4. Clinical assessments include Mini-Mental State Examination (MMSE), Functional Activities Questionnaire (FAQ), Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB), and Alzheimer’s Disease Assessment Scale (ADAS). Cerebrospinal fluid bi- omarkers include amyloid ??-protein (A??), tau, and phosphorylated tau (p-tau). MCI = mild cognitive impairment; CN = cognitive normal; PRS = polygenic risk score.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1/e2aad0f5240019881ba9b928.png"},{"id":107488825,"identity":"3c157d44-3402-49aa-9f6e-467680461326","added_by":"auto","created_at":"2026-04-22 02:45:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5274276,"visible":true,"origin":"","legend":"Article File","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7812084/v1_covered_4dedeb06-fd80-4e10-be6f-becb6f9dc1ab.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Prediction of Alzheimer’s Disease Progression from Mild Cognitive Impairment Using Polygenic Risk Scores","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer’s disease, Mild cognitive impairment, Polygenic risk scores, Progression, Reversion","lastPublishedDoi":"10.21203/rs.3.rs-7812084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7812084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mild cognitive impairment (MCI) represents the prodromal stage of Alzheimer’s disease (AD), and not all MCI patients progress to AD. Accurate stratification of MCI clinical outcomes is therefore crucial. Although polygenic risk scores (PRS) can distinguish AD patients from cognitively normal individuals, their utility in predicting heterogeneous MCI outcomes remains unclear. This study evaluated the predictive ability of PRS for both MCI progression and reversion. PRS were constructed using four algorithms and showed strong inter-method correlations. When divided into quartiles, MCI patients in the highest PRS quartile had a significantly greater risk of progression to AD, while lower PRS were asso-ciated with increased likelihood of reversion to normal cognition. We developed stepwise prediction models incorporating PRS, demographic variables, clinical assessments, and cerebrospinal fluid (CSF) biomarkers. Prediction performance did not differ significantly across PRS algorithms. The best-performing model combined PRS, demographic variables, and clinical assessments, while the addition of CSF biomarkers provided no further im-provement. These findings highlight the potential of PRS, integrated with routine clinical information, to enhance individualized risk prediction for MCI outcomes.","manuscriptTitle":"Prediction of Alzheimer’s Disease Progression from Mild Cognitive Impairment Using Polygenic Risk Scores","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 00:53:27","doi":"10.21203/rs.3.rs-7812084/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fc186204-4080-4454-b9aa-3841e0406a55","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56141640,"name":"Biological sciences/Genetics/Genomics/Personalized medicine"},{"id":56141641,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2026-04-21T11:45:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 00:53:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7812084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7812084","identity":"rs-7812084","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}