CoChat
Sign Up
Cited by 6Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Data-driven CSF biomarker profiling: imaging and clinical outcomes in a cohort at risk of Alzheimer’s disease

Open access
Argiris, Georgette, Akinci, Muge, Peña-Gómez, Cleofé, Palpatzis, Eleni, Garcia-Prat, Marina, Shekari, Mahnaz, Blennow, Kaj, Zetterberg, Henrik, Kollmorgen, Gwendlyn, Quijano-Rubio, Clara, Ashton, Nicholas J., Karikari, Thomas K., Brinkmalm-Westman, Ann, Lantero-Rodriguez, Juan, Fauria, Karine, Sánchez-Benavides, Gonzalo, Grau-Rivera, Oriol, Suárez-Calvet, Marc, Arenaza-Urquijo, Eider M., study, for the ALFA·2024·Alzheimer's Research & Therapy
DOI 10.1186/s13195-024-01629-yPMC PMC11667858
Open this paper in CoChat — free accountOpen PDF

Abstract

Cerebrospinal fluid (CSF) biomarkers of synaptic dysfunction, neuroinflammation, and glial response, complementing Alzheimer’s disease (AD) core biomarkers, have improved the pathophysiological characterization of the disease. Here, we tested the hypothesis that the co-expression of multiple CSF biomarkers will help the identification of AD-like phenotypes when biomarker positivity thresholds are not met yet. Two hundred and seventy cognitively unimpaired adults with family history (FH) of sporadic AD (mean age = 60.6 ± 4.85 years, 64.8% women) underwent lumbar puncture, magnetic resonance imaging ( n = 266) and positron emission tomography imaging ( n = 239) protocols, and clinical evaluations. CSF Aβ 42 , Aβ 40 , p-tau 181 , p-tau 217, p-tau 231, NfL, neurogranin, sTREM2, YKL40, GFAP, S100, α-Synuclein, SYT1, and SNAP25 were measured. Participants were clustered based on CSF biomarker co-expression with an agglomerative algorithm. The predictive value of the classification against brain and cognitive outcomes was evaluated. Three clusters (C) were identified. Higher Aβ burden and CSF p-tau was the hallmark of C1. The other two clusters showed lower Aβ burden but higher expression of glial (C2) or synaptic markers (C3). Participants in C1 showed an AD-like clinical phenotype, comprising participants with the overall highest percentage of two parent FH and APOE -ε4 carriers, in addition to comprising more females compared to C2. C3 displayed better vascular health compared to C1. C2 were older and comprised a lower percentage of females compared to C3. C1 showed an AD-like gray matter reduction in medial temporal (notably hippocampus) and frontal regions that were not observed in Aβ 42/40 + compared with Aβ 42/40 − . Furthermore, Aβ 42/40 − participants in C1 showed GM reduction in inferior temporal areas compared with Aβ 42/40 + participants overall. C1 membership also predicted cognitive decline in executive function, but not memory, beyond Aβ + status, overall suggesting a better prognosis in Aβ 42/40 + participants without C1 membership. Additionally, C1 displayed a higher rate of conversion to Aβ + (25%) over time. Our results suggest that examining multiple CSF biomarkers reflecting diverse pathological pathways may complement and/or outperform AD core biomarkers and thresholding approaches to identify individuals showing a clinical and cognitive AD-like phenotype, including higher conversion to Aβ + , GM reductions and cognitive decline. The clinical utility of this approach warrants further investigation and replication in other cohorts.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13195-024-01629-y

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Advanced AI techniques such as machine learning classifiers and dimensionality reduction algorithms have been applied to integrate multi-analyte CSF profiles with clinical metadata to enhance diagnostic accuracy.

    “To enhance diagnostic accuracy, advanced AI techniques, such as machine learning classifiers and dimensionality reduction algorithms, have been applied to integrate multi-analyte CSF profiles with clinical metadata76, 77, 78, 79.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [78]

    The Methods (Statistical analysis; page unavailable) describe unsupervised hierarchical clustering of multiple CSF biomarkers, followed by comparisons of clinical variables between clusters. The Conclusions (page unavailable) say this approach may help identify AD-like phenotypes, but the paper does not report using machine-learning classifiers or dimensionality-reduction algorithms, incorporating clinical metadata into the clustering, or demonstrating improved diagnostic accuracy. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 45

  1. Milà-Alomà M, Suárez-Calvet M, Molinuevo JL. Latest advances in cerebrospinal fluid and blood biomarkers of Alzheimer’s disease. Ther Adv Neurol Disord. 2019;18(12):1756286419888819. · DOI
  2. Jansen WJ, Ossenkoppele R, Knol DL, Tijms BM, Scheltens P, Verhey FRJ, et al. Prevalence of Cerebral Amyloid Pathology in Persons Without Dementia. JAMA. 2015;313(19):1924–38. · DOI · Paper page
  3. Arenaza-Urquijo EM, Salvadó G, Operto G, Minguillón C, Sánchez-Benavides G, Crous-Bou M, et al. Association of years to parent’s sporadic onset and risk factors with neural integrity and Alzheimer biomarkers. Neurology. 2020;95(15):e2065–74. · DOI
  4. Villeneuve S, Vogel JW, Gonneaud J, Pichet Binette A, Rosa-Neto P, Gauthier S, et al. Proximity to Parental Symptom Onset and Amyloid-β Burden in Sporadic Alzheimer Disease. JAMA Neurol. 2018;75(5):608–19. · DOI
  5. Bischof GN, Jacobs HIL. Subthreshold amyloid and its biological and clinical meaning: Long way ahead. Neurology. 2019;93(2):72–9. · DOI
  6. Ferrari-Souza JP, Ferreira PCL, Bellaver B, Tissot C, Wang YT, Leffa DT, et al. Astrocyte biomarker signatures of amyloid-β and tau pathologies in Alzheimer’s disease. Mol Psychiatry. 2022;27(11):4781–9. · DOI
  7. Pelkmans W, Shekari M, Brugulat-Serrat A, Sánchez-Benavides G, Minguillón C, Fauria K, et al. Astrocyte biomarkers GFAP and YKL-40 mediate early Alzheimer’s disease progression. Alzheimers Dement. 2023. · DOI · Paper page
  8. Van Hulle C, Jonaitis EM, Betthauser TJ, Batrla R, Wild N, Kollmorgen G, et al. An examination of a novel multipanel of CSF biomarkers in the Alzheimer’s disease clinical and pathological continuum. Alzheimers Dement. 2021;17(3):431–45. · DOI
  9. Johnson SC, Suárez-Calvet M, Suridjan I, Minguillón C, Gispert JD, Jonaitis E, et al. Identifying clinically useful biomarkers in neurodegenerative disease through a collaborative approach: the NeuroToolKit. Alzheimers Res Ther. 2023;15(1):25. · DOI
  10. Jack CR. PART and SNAP. Acta Neuropathol. 2014;128(6):773–6. · DOI
  11. Molinuevo JL, Gramunt N, Gispert JD, Fauria K, Esteller M, Minguillon C, et al. The ALFA project: A research platform to identify early pathophysiological features of Alzheimer’s disease. Alzheimers Dement (N Y). 2016;2(2):82–92. · DOI
  12. Suárez-Calvet M, Karikari TK, Ashton NJ, Lantero Rodríguez J, Milà-Alomà M, Gispert JD, et al. Novel tau biomarkers phosphorylated at T181, T217 or T231 rise in the initial stages of the preclinical Alzheimer’s continuum when only subtle changes in Aβ pathology are detected. EMBO Mol Med. 2020;12(12):e12921. · DOI · Paper page
  13. Milà-Alomà M, Brinkmalm A, Ashton NJ, Kvartsberg H, Shekari M, Operto G, et al. CSF Synaptic Biomarkers in the Preclinical Stage of Alzheimer Disease and Their Association With MRI and PET: A Cross-sectional Study. Neurology. 2021;97(21):e2065–78. · DOI
  14. Milà-Alomà M, Salvadó G, Shekari M, Grau-Rivera O, Sala-Vila A, Sánchez-Benavides G, et al. Comparative Analysis of Different Definitions of Amyloid-β Positivity to Detect Early Downstream Pathophysiological Alterations in Preclinical Alzheimer. J Prev Alzheimers Dis. 2021;8(1):68–77.
  15. Salvadó G, Shekari M, Falcon C, Operto G, Milà-Alomà M, Sánchez-Benavides G, et al. Brain alterations in the early Alzheimer’s continuum with amyloid-β, tau, glial and neurodegeneration CSF markers. Brain Communications. 2022;4(3):fcac134. · DOI
  16. Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr, Jagust WJ, et al. The Centiloid Project: Standardizing quantitative amyloid plaque estimation by PET. Alzheimer’s & Dementia. 2015;11(1):1-15.e4. · DOI · Paper page
  17. Salvadó G, Molinuevo JL, Brugulat-Serrat A, Falcon C, Grau-Rivera O, Suárez-Calvet M, et al. Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers. Alzheimers Res Ther. 2019;11(1):27. · DOI · Paper page
  18. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95–113. · DOI · Paper page
  19. Reitan R, Reitan R. Trail Making Test: Manual for Administration and Scoring. In 1992 [cited 2023 Oct 16]. Available from: https://www.semanticscholar.org/paper/Trail-Making-Test%3A-Manual-for-Administration-and-Reitan-Reitan/fb3612f133568883caa7c9c61e12d8cfe27807d3.
  20. Gramunt N, Buschke H, Sánchez-Benavides G, Lipton RB, Peña-Casanova J, Diéguez-Vide F, et al. Reference Data of the Spanish Memory Binding Test in a Midlife Population from the ALFA STUDY (Alzheimer’s and Family). J Alzheimers Dis. 2015;48(3):613–25. · DOI
  21. Buschke H, Mowrey WB, Ramratan WS, Zimmerman ME, Loewenstein DA, Katz MJ, et al. Memory Binding Test Distinguishes Amnestic Mild Cognitive Impairment and Dementia from Cognitively Normal Elderly. Arch Clin Neuropsychol. 2017;32(1):29–39. · DOI
  22. Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, et al. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA. 1997;278(16):1349–56. · DOI · Paper page
  23. Mosconi L, Rinne JO, Tsui WH, Berti V, Li Y, Wang H, et al. Increased fibrillar amyloid-{beta} burden in normal individuals with a family history of late-onset Alzheimer’s. Proc Natl Acad Sci U S A. 2010;107(13):5949–54. · DOI · Paper page
  24. Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539–47. · DOI · Paper page
  25. Kärkkäinen M, Prakash M, Zare M, Tohka J, For The Alzheimer’s Disease Neuroimaging Initiative null. Structural Brain Imaging Phenotypes of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) Found by Hierarchical Clustering. Int J Alzheimers Dis. 2020;2020:2142854. · DOI
  26. Hernández-Lorenzo L, Gil-Moreno MJ, Ortega-Madueño I, Cárdenas MC, Diez-Cirarda M, Delgado-Álvarez A, et al. A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers. CNS Neurosci Ther. 2023. · DOI
  27. Dong A, Toledo JB, Honnorat N, Doshi J, Varol E, Sotiras A, et al. Heterogeneity of neuroanatomical patterns in prodromal Alzheimer’s disease: links to cognition, progression and biomarkers. Brain. 2017;140(3):735–47.
  28. Gamberger D, Lavrač N, Srivatsa S, Tanzi RE, Doraiswamy PM. Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer’s disease. Sci Rep. 2017;7(1):6763. · DOI
  29. Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2017;3(3):CD010803. · DOI
  30. Heneka MT, Carson MJ, El Khoury J, Landreth GE, Brosseron F, Feinstein DL, et al. Neuroinflammation in Alzheimer’s Disease. Lancet Neurol. 2015;14(4):388–405. · DOI · Paper page
  31. Dicks E, Vermunt L, van der Flier WM, Visser PJ, Barkhof F, Scheltens P, et al. Modeling grey matter atrophy as a function of time, aging or cognitive decline show different anatomical patterns in Alzheimer’s disease. Neuroimage Clin. 2019;22: 101786. · DOI
  32. Jack CR, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS, et al. Update on hypothetical model of Alzheimer’s disease biomarkers. Lancet Neurol. 2013;12(2):207–16. · DOI · Paper page
  33. Convit A, de Asis J, de Leon MJ, Tarshish CY, De Santi S, Rusinek H. Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiol Aging. 2000;21(1):19–26. · DOI · Paper page
  34. Scott MR, Hampton OL, Buckley RF, Chhatwal JP, Hanseeuw BJ, Jacobs HI, et al. Inferior temporal tau is associated with accelerated prospective cortical thinning in clinically normal older adults. Neuroimage. 2020;15(220): 116991. · DOI
  35. Halawa OA, Gatchel JR, Amariglio RE, Rentz DM, Sperling RA, Johnson KA, et al. Inferior and medial temporal tau and cortical amyloid are associated with daily functional impairment in Alzheimer’s disease. Alzheimers Res Ther. 2019;11(1):14. · DOI
  36. Jack CR, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS, et al. Defining imaging biomarker cut-points for brain aging and Alzheimer’s disease. Alzheimers Dement. 2017;13(3):205–16. · DOI · Paper page
  37. Fjell AM, Westlye LT, Amlien I, Espeseth T, Reinvang I, Raz N, et al. High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex. 2009;19(9):2001–12. · DOI
  38. Bakkour A, Morris JC, Wolk DA, Dickerson BC. The effects of aging and Alzheimer’s disease on cerebral cortical anatomy: Specificity and differential relationships with cognition. Neuroimage. 2013;1(76):332–44. · DOI
  39. Chetelat G, Baron JC. Early diagnosis of Alzheimer’s disease: contribution of structural neuroimaging. Neuroimage. 2003;18(2):525–41. · DOI
  40. Da Cunha E, Plonka A, Arslan S, Mouton A, Meyer T, Robert P, et al. Logogenic Primary Progressive Aphasia or Alzheimer Disease: Contribution of Acoustic Markers in Early Differential Diagnosis. Life (Basel). 2022;12(7):933.
  41. Jobson DD, Hase Y, Clarkson AN, Kalaria RN. The role of the medial prefrontal cortex in cognition, ageing and dementia. Brain Commun. 2021;3(3):fcab125. · DOI · Paper page
  42. Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 2011;7(3):270–9. · DOI · Paper page
  43. Sánchez-Cubillo I, Periáñez JA, Adrover-Roig D, Rodríguez-Sánchez JM, Ríos-Lago M, Tirapu J, et al. Construct validity of the Trail Making Test: role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. J Int Neuropsychol Soc. 2009;15(3):438–50. · DOI
  44. Tideman P, Stomrud E, Leuzy A, Mattsson-Carlgren N, Palmqvist S, Hansson O, et al. Association of β-Amyloid Accumulation With Executive Function in Adults With Unimpaired Cognition. Neurology. 2022;98(15):e1525–33. · DOI
  45. Idland AV, Sala-Llonch R, Watne LO, Brækhus A, Hansson O, Blennow K, et al. Biomarker profiling beyond amyloid and tau: cerebrospinal fluid markers, hippocampal atrophy, and memory change in cognitively unimpaired older adults. Neurobiol Aging. 2020;93:1–15. · DOI

Cited by

Sorted by citations

Metadata

Authors
Argiris, G., Akinci, M., Peña-Gómez, C., Palpatzis, E., Garcia-Prat, M., Shekari, M., Blennow, K., Zetterberg, H., Kollmorgen, G., Quijano-Rubio, C., Ashton, N. J., Karikari, T. K., Brinkmalm-Westman, A., Lantero-Rodriguez, J., Fauria, K., Sánchez-Benavides, G., Grau-Rivera, O., Suárez-Calvet, M., Arenaza-Urquijo, E. M., & study, F. T. A.
Affiliations
  • Columbia University (US)
  • Pasqual Maragall Foundation (ES)
  • Universitat Pompeu Fabra (ES)
  • Sahlgrenska University Hospital (SE)
  • University of Wisconsin–Madison (US)
  • King's College London (GB)
  • University of Pittsburgh (US)
Corresponding author
Fauria, Karine
ORCID
Venue
Alzheimer's Research & Therapy
Publisher
Springer Science and Business Media LLC
Published
2024-12-23
Volume
16
Issue
1
Pages
274-274
ISSN
1758-9193 (ISSN-L)
DOI
10.1186/s13195-024-01629-y
PMC
PMC11667858
OpenAlex
W4405700756
Cited by
6
References
5
Citations by year
2025: 6
FWCI
1.76
Type
journal article
Language
en
Open access
Licence
Locations
  • Alzheimer s Research & Therapy (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion, cc-by-nc-nd)
  • PUBLISSO (German National Library of Medicine) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Columbia Academic Commons (Columbia University)
Funders
  • Alzheimer's Association (AARG2019-AARG-644641, 100010434, AARG 2019-AARG-644641-RAPID, 23AARF-1028873, TriBEKa-17-519007, 2019-AARG-644641)
  • Alzheimer's Drug Discovery Foundation (2016862, UKDRI-1003, 860197, JPND2021-00694)
  • Familjen Erling-Perssons Stiftelse (860197)
  • Stiftelsen för Gamla Tjänarinnor (FO2022‐0270, 860197)
  • EU Joint Programme – Neurodegenerative Disease Research (JPND 2021-00694)
  • National Institute for Health and Care Research
  • University College London (UKDRI‐1003)
  • European Commission (100010434, 847648, 948677, 101,053,962, JPND2021‐00694, HORIZON2020, ALFGBG-71320, 860197, ID 100010434)
  • Hjärnfonden (#FO2022-0270)
  • Vetenskapsrådet (#101053962, 2022-01018, ALFGBG‐71320, 2019-02397)
  • Ministerio de Ciencia e Innovación (ID100010434, 847648, 100010434)
  • UK Dementia Research Institute (UKDRI‐ 1003)
  • Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona (ID 100010434, 100010434)
  • HORIZON EUROPE Framework Programme (847648, 101053962, 860197)
  • Instituto de Salud Carlos III (PI19/00155)
  • National Institute on Aging (P30AG072980, R01AG083874)
  • European Social Fund
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
Keywords
Alzheimer's disease, cerebrospinal fluid biomarkers, amyloid-beta, phosphorylated tau, neuroinflammation, glial activation, synaptic dysfunction, family history of Alzheimer's disease, APOE ε4, neurogranin, YKL-40, sTREM2, glial fibrillary acidic protein, S-100 protein, alpha-synuclein, synaptotagmin-1, SNAP-25, neurofibrillary tangles, gray matter reduction, hippocampus, inferior temporal cortex, executive function decline, cognitive decline prediction, neurodegeneration
MeSH terms
Aged, Alzheimer Disease, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Peptide Fragments, Cohort Studies, Biomarkers, Amyloid beta-Peptides, tau Proteins, Positron-Emission Tomography

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review