{ "abstract": "Background and Objectives: Blood-based biomarkers are strongly correlated with amyloid positivity in research settings, but their long-term performance in heterogeneous populations remains unclear. Our aim was to determine their long-term prediction accuracy for incident all-cause dementia on their own and when used alongside known risk factors in community-dwelling older adults.\n\nMethods: This was a retrospective study based on the UK Whitehall II longitudinal, population-based cohort study established in 1985 among civil servants that includes a clinical examination every 4–5 years. Blood samples stored at the 2013-2013 wave were assayed in 2024 for measures of amyloid-beta (Aβ) 42 and 40, glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and phosphorylated tau protein 217 (p-tau217). Incident all-cause dementia, from 2012 to 2013 to 2025, was ascertained from linked medical records, and Cox regression was used to examine associations of biomarkers with dementia, followed by estimation of prediction statistics.\n\nResults: Of the 5,225 (27.2% female) participants (mean age in 2012–2013, 69.4 ± 5.7 years), 516 (30.0% female) had a dementia diagnosis over a 12.2-year median follow-up. Participants with values in the worst quartile of amyloid-beta 42 (Aβ42)/40 ratio (hazard ratio [HR] 1.88; 95% CI 1.57–2.24), p-tau217 (HR 3.74; 95% CI 3.12–4.50), NfL (HR 1.88; 95% CI 1.56–2.28), and GFAP (HR 2.25; 95% CI 1.87–2.72) had higher risk of dementia. The C-index for predicting dementia was highest for p-tau217 (0.693; 95% CI 0.672–0.714), lowest for Aβ42/40 ratio (0.587; 95% CI 0.566–0.608), and was 0.760 (0.738, 0.781) for all biomarkers together. Sensitivity (between 41.7% and 59.5%) and positive predictive value (between 16.4% and 23.5%) of all biomarkers were modest, but specificity (between 76.8% and 78.8%) and negative predictive value (92.3%–94.7%) were good. A second approach to prediction modeling showed a model containing sociodemographic, behavioral, and health-related risk factors to have a C-index of 0.765 (95% CI 0.746–0.785); adding all biomarkers to this model led to a C-index of 0.813 (95% CI 0.795–0.830).\n\nDiscussion: The long-term predictive ability of blood-based biomarkers for incident dementia in community-dwelling older adults was modest due to poor sensitivity and similar to that of a model containing known risk factors.", "abstract_shown": true, "affiliations": [ "Université Paris Cité, INSERM U1153, Epidemiology of Ageing and Neurodegenerative Diseases (EpiAgeing), France", "Faculty of Brain Sciences, University College London, United Kingdom", "Department of Neurodegenerative Disease, University College London, Institute of Neurology, Queen Square, United Kingdom", "Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden", "Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden", "UK Dementia Research Institute at University College London, United Kingdom", "Hong Kong Centre for Neurodegenerative Diseases, InnoHK, China", "Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, WI; and", "Centre for Brain Research, Indian Institute of Science, Bangalore, India ." ], "article_type": "research article", "arxiv": null, "author_total": 9, "authors": [ { "affs": [ 0, 1 ], "name": "Séverine Sabia", "orcid": "0000-0003-3109-9720" }, { "affs": [ 0 ], "name": "Julien Dumurgier", "orcid": "0000-0003-2070-9706" }, { "affs": [ 0 ], "name": "Clémence Cavaillès", "orcid": "0000-0002-8443-2307" }, { "affs": [ 0 ], "name": "Ian Meneghel Danilevicz", "orcid": "0000-0003-4541-0524" }, { "affs": [ 0 ], "name": "Aurore Fayosse", "orcid": "0000-0003-2646-9408" }, { "affs": [ 2 ], "name": "Amanda J. Heslegrave", "orcid": "0000-0002-7290-6405" }, { "affs": [ 3, 4, 5, 6, 7, 8 ], "name": "Henrik Zetterberg", "orcid": "0000-0003-3930-4354" }, { "affs": [ 1 ], "name": "Mika Kivimaki", "orcid": "0000-0002-4699-5627" }, { "affs": [ 0, 1 ], "name": "Archana Singh-Manoux", "orcid": "0000-0002-1244-5037" } ], "bibtex": "https://research.draconitic.com/paper/aypqhb5d4cdkx.bib", "cited_by": 0, "doi": "10.1212/wnl.0000000000218681", "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13714890/", "id": "aypqhb5d4cdkx", "issns": [ "0028-3878", "1526-632X" ], "landing_url": "https://doi.org/10.1212/wnl.0000000000218681", "language": "en", "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "heading", "25" ], [ "heading", "54" ], [ "heading", "59" ], [ "heading", "Research Article" ], [ "openalex", "2738|Dementia and Cognitive Impairment Research" ] ], "openalex": "W7223276843", "pdf_url": "https://www.neurology.org/doi/pdf/10.1212/WNL.0000000000218681", "pmcid": "PMC13714890", "pmid": "42913444", "published": "2026-10-23", "publisher": null, "ris": "https://research.draconitic.com/paper/aypqhb5d4cdkx.ris", "sources": [ { "first_seen": "2026-11-01T12:22:18Z", "native_id": "PMC13714890", "source": "pmc", "url": "https://doi.org/10.1212/wnl.0000000000218681" } ], "subjects": [ { "field": "medicine", "origin": "openalex:2738|Dementia and Cognitive Impairment Research", "subfield": "psychiatry" } ], "title": "Blood-Based Biomarkers as Predictors of All-Cause Dementia", "title_text": "Blood-Based Biomarkers as Predictors of All-Cause Dementia", "type": "peer-reviewed", "updated_at": "2026-11-01T12:22:18Z", "url": "https://research.draconitic.com/paper/aypqhb5d4cdkx", "venue": "Neurology", "versions": [] }