{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T21:17:28Z","timestamp":1788902248892,"version":"build-2803163510"},"posted":{"date-parts":[[2026,8,5]]},"group-title":"Neurology","reference-count":38,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,8,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>Identifying individuals with mild cognitive impairment (MCI) likely to progress to Alzheimer\u2019s disease (AD) is important for patient management and clinical trial enrollment. Although cognitive assessments, genetics, neuroimaging, and fluid biomarkers are each associated with disease progression, their predictive value has not been systematically compared using an identical cohort and evaluation framework. This study compared the predictive discrimination of clinical, cognitive, genetic, imaging, and cerebrospinal fluid (CSF) biomarkers, individually and combined, for 24-month progression from MCI to AD.<\/jats:p>\n                <jats:p>A retrospective analysis used data from 2,430 participants with MCI enrolled in ADNI, including 547 who progressed to AD within 24 months and 1,883 who remained stable. Seven models were evaluated using identical preprocessing and modeling procedures: a clinical baseline (age and sex), the baseline plus a single modality \u2014 cognitive assessment, APOE \u03b54 genotype, structural MRI, CSF biomarkers, or PET biomarkers \u2014 and a multimodal model combining all five. Performance was assessed using repeated 5\u00d710 stratified cross-validation. Out-of-fold predictions from a separate 5-fold split were used to estimate confidence intervals and compare AUCs via DeLong\u2019s test with Holm\u2013Bonferroni correction.<\/jats:p>\n                <jats:p>Discrimination increased progressively across modalities. The clinical baseline achieved an AUC of 0.556; adding APOE \u03b54 genotype increased performance to 0.692, CSF biomarkers to 0.729, PET biomarkers to 0.783, structural MRI to 0.836, and cognitive assessment to 0.918. Cognitive assessment significantly outperformed all other individual modalities, including MRI (\u0394AUC = 0.079, P &lt; 0.001). The multimodal model achieved the highest overall discrimination (AUC = 0.933), significantly outperforming cognitive assessment alone (\u0394AUC = 0.016, P &lt; 0.001), though it required complete data from only 20.5% of participants, versus 99.3% for cognitive assessment.<\/jats:p>\n                <jats:p>Within a common evaluation framework, cognitive assessment demonstrated the greatest predictive discrimination among individual modalities for 24-month progression from MCI to AD, followed by structural MRI and PET. A multimodal model achieved the highest overall discrimination but required complete data from only one-fifth of the cohort. These findings suggest that routinely collected cognitive assessments capture substantial prognostic information, while full multimodal integration offers only modest incremental value relative to its reduced applicability.<\/jats:p>","DOI":"10.64898\/2026.08.03.26359630","type":"posted-content","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:30:23Z","timestamp":1785954623000},"source":"Crossref","is-referenced-by-count":2,"title":["Standardized Comparison of Clinical, Cognitive, Genetic, Neuroimaging, and Fluid Biomarkers for Predicting 24-Month Progression from Mild Cognitive Impairment to Alzheimer\u2019s Disease"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3288-8708","authenticated-orcid":false,"given":"Sophie","family":"Choe","sequence":"first","affiliation":[{"name":"Clinovia.ai"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026080718103440000_2026.08.03.26359630v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(09)70299-6"},{"key":"2026080718103440000_2026.08.03.26359630v1.2","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(12)70291-0"},{"key":"2026080718103440000_2026.08.03.26359630v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2011.03.003"},{"key":"2026080718103440000_2026.08.03.26359630v1.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2011.03.008"},{"key":"2026080718103440000_2026.08.03.26359630v1.5","first-page":"164","article-title":"Integration and relative value of biomarkers for prediction of MCI to AD progression: spatial patterns of brain atrophy, cognitive scores, APOE genotype and CSF biomarkers","volume":"4","year":"2013","journal-title":"Neuroimage Clin"},{"key":"2026080718103440000_2026.08.03.26359630v1.6","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.1038\/s41598-019-38793-3","article-title":"Predicting short-term MCI-to-AD progression using imaging, CSF, genetic factors, cognitive resilience, and demographics","volume":"9","year":"2019","journal-title":"Sci Rep"},{"key":"2026080718103440000_2026.08.03.26359630v1.7","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s13195-021-00900-w","article-title":"Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer\u2019s disease dementia: a systematic review","volume":"13","year":"2021","journal-title":"Alzheimers Res Ther"},{"key":"2026080718103440000_2026.08.03.26359630v1.8","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1186\/s13195-023-01304-8","article-title":"Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer\u2019s disease","volume":"15","year":"2023","journal-title":"Alzheimers Res Ther"},{"key":"2026080718103440000_2026.08.03.26359630v1.9","doi-asserted-by":"publisher","DOI":"10.1007\/s11065-017-9361-5"},{"issue":"12","key":"2026080718103440000_2026.08.03.26359630v1.10","doi-asserted-by":"crossref","first-page":"3442","DOI":"10.3390\/jcm13123442","article-title":"Neuropsychological assessment for early detection and diagnosis of dementia: current knowledge and new insights","volume":"13","year":"2024","journal-title":"J Clin Med"},{"issue":"1","key":"2026080718103440000_2026.08.03.26359630v1.11","doi-asserted-by":"crossref","first-page":"e14363","DOI":"10.1002\/alz.14363","article-title":"The role of neuropsychological assessment in the evaluation of patients with cognitive-behavioral change due to suspected Alzheimer\u2019s disease and other causes of cognitive impairment and dementia","volume":"21","year":"2025","journal-title":"Alzheimers Dement"},{"key":"2026080718103440000_2026.08.03.26359630v1.12","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2012.263"},{"key":"2026080718103440000_2026.08.03.26359630v1.13","first-page":"34","article-title":"Predictability of polygenic risk score for progression to dementia and its interaction with APOE \u03b54 in mild cognitive impairment","volume":"10","author":"Alzheimer\u2019s Disease Neuroimaging Initiative","year":"2021","journal-title":"Transl Neurodegener"},{"key":"2026080718103440000_2026.08.03.26359630v1.14","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2009.215"},{"key":"2026080718103440000_2026.08.03.26359630v1.15","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2018.9"},{"key":"2026080718103440000_2026.08.03.26359630v1.16","doi-asserted-by":"publisher","DOI":"10.1111\/joim.12816"},{"key":"2026080718103440000_2026.08.03.26359630v1.17","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3181cb3e25"},{"key":"2026080718103440000_2026.08.03.26359630v1.18","doi-asserted-by":"publisher","DOI":"10.1016\/0022-3956(75)90026-6"},{"key":"2026080718103440000_2026.08.03.26359630v1.19","unstructured":"Wechsler D. Wechsler Memory Scale-Revised. San Antonio (TX): The Psychological Corporation; 1987."},{"key":"2026080718103440000_2026.08.03.26359630v1.20","unstructured":"Schmidt M. Rey Auditory Verbal Learning Test: A Handbook. Los Angeles (CA): Western Psychological Services; 1996."},{"key":"2026080718103440000_2026.08.03.26359630v1.21","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.21049"},{"key":"2026080718103440000_2026.08.03.26359630v1.22","doi-asserted-by":"publisher","DOI":"10.1002\/ana.21610"},{"key":"2026080718103440000_2026.08.03.26359630v1.23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2010.03.003"},{"key":"2026080718103440000_2026.08.03.26359630v1.24","doi-asserted-by":"crossref","unstructured":"Chen T , Guestrin C. XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13-17; San Francisco, CA. New York: ACM; 2016. p. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"issue":"11","key":"2026080718103440000_2026.08.03.26359630v1.25","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1016\/j.csda.2009.04.009","article-title":"Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap","volume":"53","year":"2009","journal-title":"Comput Stat Data Anal"},{"key":"2026080718103440000_2026.08.03.26359630v1.26","doi-asserted-by":"publisher","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3"},{"key":"2026080718103440000_2026.08.03.26359630v1.27","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.143.1.7063747"},{"key":"2026080718103440000_2026.08.03.26359630v1.28","doi-asserted-by":"crossref","unstructured":"Brodersen KH , Ong CS , Stephan KE , Buhmann JM . The balanced accuracy and its posterior distribution. In: Proceedings of the 20th International Conference on Pattern Recognition; 2010 Aug 23-26; Istanbul, Turkey. Piscataway (NJ): IEEE; 2010. p. 3121\u20134.","DOI":"10.1109\/ICPR.2010.764"},{"key":"2026080718103440000_2026.08.03.26359630v1.29","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0493(1950)078&lt;0001:VOFEIT&gt;2.0.CO;2"},{"key":"2026080718103440000_2026.08.03.26359630v1.30","doi-asserted-by":"crossref","unstructured":"Efron B , Tibshirani RJ . An Introduction to the Bootstrap. New York: Chapman & Hall; 1993.","DOI":"10.1007\/978-1-4899-4541-9"},{"key":"2026080718103440000_2026.08.03.26359630v1.31","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.b2393"},{"key":"2026080718103440000_2026.08.03.26359630v1.32","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"issue":"1-2","key":"2026080718103440000_2026.08.03.26359630v1.33","first-page":"28","volume":"34","year":"1947","journal-title":"The generalization of \u201cStudent\u2019s\u201d problem when several different population variances are involved. Biometrika"},{"key":"2026080718103440000_2026.08.03.26359630v1.34","doi-asserted-by":"publisher","DOI":"10.1080\/14786440009463897"},{"key":"2026080718103440000_2026.08.03.26359630v1.35","unstructured":"Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale (NJ): Lawrence Erlbaum Associates; 1988."},{"issue":"2","key":"2026080718103440000_2026.08.03.26359630v1.36","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","year":"1979","journal-title":"Scand J Stat"},{"key":"2026080718103440000_2026.08.03.26359630v1.37","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2018.02.018"},{"issue":"2","key":"2026080718103440000_2026.08.03.26359630v1.38","doi-asserted-by":"crossref","first-page":"e12417","DOI":"10.1002\/dad2.12417","article-title":"Generalizability of cognitive results from clinical trial participants to an older adult population: Addressing external validity","volume":"15","author":"Alzheimer\u2019s Disease Neuroimaging Initiative","year":"2023","journal-title":"Alzheimers Dement (Amst)"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.08.03.26359630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T01:10:45Z","timestamp":1786151445000},"score":1,"resource":{"primary":{"URL":"http:\/\/medrxiv.org\/lookup\/doi\/10.64898\/2026.08.03.26359630"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":38,"URL":"https:\/\/doi.org\/10.64898\/2026.08.03.26359630","relation":{},"subject":[],"published":{"date-parts":[[2026,8,5]]},"subtype":"preprint"}}