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GWAS meta-analysis of CSF Alzheimer's disease biomarkers 18,948 individuals reveal novel loci and genes regulating lipid metabolism, brain volume and autophagy
Carlos Cruchaga, Jigyasha Timsina, Chenyang Jiang, Daniel L. McCartney, Feifei Tao, Carolina Dalmasso, Jenna Najar, Federica Anastasi, Olena Ohlei, Raquel Puerta, Joseph Bradley, Daniel Western, Muhammad Ali, Ciyang Wang, Chengran Yang, Ying Wu, Menghan Liu, John Budde, Julie Williams, Rebecca Mahoney, Atahualpa Castillo-Morales, Timothy J. Hohman, Logan Dumitrescu, Ting‐Chen Wang, Niccoló Tesi, Silke Kern, Margda Wærn, Argonde van Harten, Wiesje M. van der Flier, Pascual Sánchez‐Juan, Eloy Rodríguez‐Rodríguez, Luca Kleineidam, Oliver Peters, Anja Schneider, Fahri Küçükali, Céline Bellenguez, Benjamin Grenier‐Boley, Sami Heikkinen, Itziar de Rojas, Dan Rujescu, Norbert Scherbaum, Lucrezia Hausner, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Rik Vandenberghe, Sebastiaan Engelborghs, Stefanie Heilmann‐Heimbach, Matthias Schmid, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols‐Icardo, Fermín Moreno, Jordi Pérez‐Tur, María J. Bullido, Raquel Sánchez‐Valle, Victoria Álvarez, Pablo García‐González, Pablo Mir, Luís Miguel Real, Gerard Piñol-Ripoll, José María García‐Alberca, Harro Seelaar, Inez Ramakers, Janne M. Papma, Marc Hulsman, Christoph Laske, Stefan Teipel, Josef Priller, Robert Perneczky, Katharina Büerger, Markus M. Nöthen, Piotr Lewczuk, Johannes Kornhuber, Wolfgang Maier, Harald Hampel, Ina Giegling, Oliver Goldhardt, Janine Diehl‐Schmid, Víctor Andrade, Michael T. Heneka, Lutz Froelich, Jonathan Vogelgsang, Caroline Graff, Håkan Thonberg, Abbe Ullgren, Goran Papenberg, Anne Boland, Jean‐François Deleuze, Michael Wagner, Frank Jessen, Henne Holstege, Cornelia M. van Duijn, Thibaud Lebouvier, Olivier Hanon, Ville Leinonen, Hilkka Soininen, Sanna‐Kaisa Herukka, Vilmantas Giedraitis, Malin Löwenmark, Lena Kilander, Patricia Genius, Blanca Rodríguez, Emma S. Luckett, Arcadi Navarro, Amanda Cano, Marta Marquié, Kaj Blennow, Henrik Zetterberg, Alberto Lleó, Merçé Boada, Agustı́n Ruiz, Virginia M.‐Y. Lee, Vivianna Van Deerlin, Yuetiva Deming, Sterling C. Johnson, Corinne D. Engelman, Pau Pástor, Ignacio Álvarez, Elaine R. Peskind, Amanda Heslegrave, Andrew J. Saykin, Kwangsik Nho, Suzanne E. Schindler, John C. Morris, David M. Holtzman, Eric McDade, Alan E. Renton, Alison Goate, Laura Ibáñez, Marilyn Albert, Simon M. Laws, Tenielle Porter, Eleanor K. O’Brien, Leslie M. Shaw, Betty M. Tijms, Martin Ingelsson, Pieter Jelle Visser, Mikko Hiltunen, Kristel Sleegers, Craig Ritchie, Rebecca Sims, Jean‐Charles Lambert, Natàlia Vilor‐Tejedor, Marı́a Fernández, Qingqin S. Li, Michael W. Nagle, Riccardo E. Marioni, Alfredo Ramı́rez, Lars Bertram, Sven J. van der Lee
Bibliographic record
VenueResearch Square · 2025
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsnot available
FundersNational Institute on AgingInstituto de Salud Carlos IIICharles F. and Joanne Knight Alzheimer Disease Research Center, Washington University in St. LouisCanadian Institutes of Health ResearchNational Institutes of HealthFleniIXICOServierH. Lundbeck A/SDeutsches Zentrum für Neurodegenerative ErkrankungenEisaiKorea Health Industry Development InstituteNorthern California Institute for Research and EducationJapan Agency for Medical Research and DevelopmentNovartis Pharmaceuticals CorporationMinistry of Science and ICT, South KoreaGenentechNational Alzheimer's Coordinating CenterBiogenBioClinicaEli Lilly and CompanyU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativeUniversity of Southern CaliforniaHope Center for Neurological DisordersAlzheimer's AssociationBristol-Myers SquibbU.S. Department of Veterans AffairsFoundation for the National Institutes of Health
KeywordsGenome-wide association studyAutophagyGeneLipid metabolismBiologyDiseaseGeneticsAlzheimer's diseaseBrain sizeBioinformaticsMedicineSingle-nucleotide polymorphismPathologyBiochemistryGenotype
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.106
GPT teacher head0.402
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations1
Published2025
Admission routes1
Has abstractno