Causal associations between modifiable risk factors and the Alzheimer's phenome
Open accessPublished version of a preprint
Abstract
Objective: The purpose of this study was to infer causal relationships between 22 previously reported risk factors for Alzheimer's disease (AD) and the “AD phenome”: AD, AD age of onset (AAOS), hippocampal volume, cortical surface area and thickness, cerebrospinal fluid (CSF) levels of amyloid‐β (Aβ 42 ), tau, and ptau 181 , and the neuropathological burden of neuritic plaques, neurofibrillary tangles (NFTs), and vascular brain injury (VBI). Methods: Polygenic risk scores (PRS) for the 22 risk factors were computed in 26,431 AD cases/controls and the association with AD was evaluated using logistic regression. Two‐sample Mendelian randomization (MR) was used to infer the causal effect of risk factors on the AD phenome. Results: PRS for increased education and diastolic blood pressure were associated with reduced risk for AD. MR indicated that only education was causally associated with reduced risk of AD, delayed AAOS, and increased cortical surface area and thickness. Total‐ and LDL‐cholesterol levels were causally associated with increased neuritic plaque burden, although the effects were driven by single nucleotide polymorphisms (SNPs) within the APOE locus. Diastolic blood pressure and pulse pressure are causally associated with increased risk of VBI. Furthermore, total cholesterol was associated with decreased hippocampal volume; smoking initiation with decreased cortical thickness; type 2 diabetes with an earlier AAOS; and sleep duration with increased cortical thickness. Interpretation: Our comprehensive examination of the genetic evidence for the causal relationships between previously reported risk factors in AD using PRS and MR supports a causal role for education, blood pressure, cholesterol levels, smoking, and diabetes with the AD phenome. ANN NEUROL 2021;89:54–65
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.
Integrity signals
Registry
Cites
Statistics
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Dementia prevention, intervention, and care: 2020 report of the Lancet Commission
Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30367-6 · Cited by 10,430 · cited 2× · see citing works
“preventable causes, such as low educational attainment, hearing loss, traumatic brain injury, hypertension, alcohol consumption, obesity, smoking, depression, physical inactivity, social isolation, air pollution, and diabetes.(3,4) However, the quality of evidence for interventions targeting these risk factors to redu…”
BackgroundResultRetractedPLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089 · see citing works
The Human Genome Browser at UCSC
Kent, W. J., Sugnet, C. W., Furey, T. S., Roskin, K. M., Pringle, T. H., Zahler, A. M., & Haussler, A. D. (2002) · Genome Research · doi:10.1101/gr.229102 · Cited by 10,400 · see citing works
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591 · see citing works
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
Dementia prevention, intervention, and care
Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., Ballard, C., Banerjee, S., Burns, A., Cohen-Mansfield, J., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31363-6 · Cited by 6,072 · see citing works
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882 · see citing works
A Direct Approach to False Discovery Rates
Storey, J. D. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00346 · Cited by 5,603 · see citing works
2016 Alzheimer's disease facts and figures
Alzheimer's Association (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.03.001 · Cited by 5,069 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works
Self-citationAvoiding bias from weak instruments in Mendelian randomization studies
Burgess, S., Thompson, S. G., & CRP CHD Genetics Collaboration (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr036 · Cited by 3,952 · see citing works
Cognitive reserve in ageing and Alzheimer's disease
Stern, Y. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70191-6 · Cited by 3,557 · cited 1× · see citing works
“There was no evidence that education was causally associated with AD neuropathology or CSF biomarkers, supporting the hypothesis that education mitigates dementia risk via cognitive reserve rather than affecting AD pathogenesis.(47) Bidirectional MR analysis also indicated a bidirectional effect of cortical surface ar…”
BackgroundRobust relationship inference in genome-wide association studies
Manichaikul, A., Mychaleckyj, J. C., Rich, S. S., Daly, K., Sale, M., & Chen, W.-M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq559 · Cited by 3,541 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
Self-citationA reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836 · see citing works
Snakemake—a scalable bioinformatics workflow engine
Köster, J., & Rahmann, S. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts480 · Cited by 2,780 · see citing works
Howard, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., Coleman, J. R. I., Hagenaars, S. P., Ward, J., Wigmore, E. M., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0326-7 · Cited by 2,565 · see citing works
23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470 · see citing works
2020 Alzheimer's disease facts and figures
(2020) · Alzheimer's & Dementia · doi:10.1002/alz.12068 · Cited by 2,353 · see citing works
Yengo, L., Sidorenko, J., Kemper, K. E., Zheng, Z., Wood, A. R., Weedon, M. N., Frayling, T. M., Hirschhorn, J. N., Yang, J., & Visscher, P. M. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy271 · Cited by 2,305 · see citing works
Naj, A. C., Jun, G., Beecham, G. W., Wang, L.-S., Vardarajan, B. N., Buros, J., Gallins, P. J., Buxbaum, J. D., Jarvik, G. P., Crane, P. K., et al. (2011) · Nature Genetics · doi:10.1038/ng.801 · Cited by 1,901 · see citing works
Self-citation23andMe Research Team, Liu, M., Psychiatry, H. A.-I., Jiang, Y., Wedow, R., Li, Y., Brazel, D. M., Chen, F., Datta, G., Davila-Velderrain, J., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0307-5 · Cited by 1,896 · see citing works
Calculating statistical power in Mendelian randomization studies.
Brion, M.-J. A., Shakhbazov, K., & Visscher, P. M. (2013) · International Journal of Epidemiology · doi:10.1093/ije/dyt179 · Cited by 1,751 · see citing works
Baumgart, M., Snyder, H. M., Carrillo, M. C., Fazio, S., Kim, H., & Johns, H. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.05.016 · Cited by 1,660 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
PRSice-2: Polygenic Risk Score software for biobank-scale data
Choi, S. W., & O’Reilly, P. F. (2019) · GigaScience · doi:10.1093/gigascience/giz082 · Cited by 1,481 · see citing works
Self-citationPierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389 · see citing works
Program, T. M. V., Evangelou, E., Warren, H. R., Mosen-Ansorena, D., Mifsud, B., Pazoki, R., Gao, H., Ntritsos, G., Dimou, N., Cabrera, C. P., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0205-x · Cited by 1,381 · see citing works
Self-citationTriangulation in aetiological epidemiology
Lawlor, D. A., Tilling, K., & Davey Smith, G. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyw314 · Cited by 1,129 · see citing works
Xue, A., Wu, Y., Zhu, Z., Zhang, F., Kemper, K. E., Zheng, Z., Yengo, L., Lloyd‐Jones, L. R., Sidorenko, J., Wu, Y., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04951-w · Cited by 884 · see citing works
The genetic architecture of the human cerebral cortex
Grasby, K. L., Jahanshad, N., Painter, J. N., Colodro-Conde, L., Bralten, J., Hibar, D. P., Lind, P. A., Pizzagalli, F., Ching, C. R. K., McMahon, M. A. B., et al. (2020) · Science · doi:10.1126/science.aay6690 · Cited by 876 · see citing works
Self-citationCommon genetic variants influence human subcortical brain structures
Initiative, T. A. D. N., Hibar, D. P., The CHARGE Consortium, EPIGEN, IMAGEN, SYS, Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., et al. (2015) · Nature · doi:10.1038/nature14101 · Cited by 858 · see citing works
Toledo, J. B., Arnold, S. E., Raible, K., Brettschneider, J., Xie, S. X., Grossman, M., Monsell, S. E., Kukull, W. A., & Trojanowski, J. Q. (2013) · Brain · doi:10.1093/brain/awt188 · Cited by 849 · see citing works
Jansen, P. R., Watanabe, K., Stringer, S., Skene, N., Bryois, J., Hammerschlag, A. R., de Leeuw, C., Benjamins, J. S., Muñoz‐Manchado, A. B., Nagel, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0333-3 · Cited by 839 · see citing works
23andMe Research Team, Walters, R. K., Polimanti, R., Johnson, E. C., McClintick, J. N., Adams, M. J., Adkins, A. E., Aliev, F., Bacanu, S.-A., Batzler, A., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0275-1 · Cited by 632 · see citing works
Self-citationDashti, H. S., Jones, S. E., Wood, A. R., Lane, J. M., van Hees, V. T., Wang, H., Rhodes, J. A., Song, Y., Patel, K., Anderson, S. G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08917-4 · Cited by 595 · see citing works
Shim, H., Chasman, D. I., Smith, J. D., Mora, S., Ridker, P. M., Nickerson, D. A., Krauss, R. M., & Stephens, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0120758 · Cited by 562 · see citing works
Alcohol use and dementia: a systematic scoping review
Rehm, J., Hasan, O. S. M., Black, S. E., Shield, K. D., & Schwarzinger, M. (2019) · Alzheimer's Research & Therapy · doi:10.1186/s13195-018-0453-0 · Cited by 450 · see citing works
Conomos, M. P., Miller, M. B., & Thornton, T. A. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21896 · Cited by 440 · see citing works
Sanchez-Roige, S., Palmer, A. A., Fontanillas, P., Elson, S. L., the 23andMe Research Team, T. S. U. D. W. G. O. T. P. G. C., Adams, M. J., Howard, D. M., Edenberg, H. J., Davies, G., Crist, R. C., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18040369 · Cited by 430 · see citing works
A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
The International Genomics of Alzheimer's Project, Huang, K.-L., Initiative, T. A. D. N., Marcora, E., Pimenova, A. A., Di Narzo, A. F., Kapoor, M., Jin, S. C., Harari, O., Bertelsen, S., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4587 · Cited by 423 · see citing works
Self-citationKlimentidis, Y. C., Raichlen, D. A., Bea, J., Garcia, D. O., Wineinger, N. E., Mandarino, L. J., Alexander, G. E., Chen, Z., & Going, S. B. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0120-3 · Cited by 403 · see citing works
Modifiable pathways in Alzheimer’s disease: Mendelian randomisation analysis
Larsson, S. C., Traylor, M., Malik, R., Dichgans, M., Burgess, S., & Markus, H. S. (2017) · BMJ · doi:10.1136/bmj.j5375 · Cited by 382 · see citing works
Beecham, G. W., Hamilton, K., Naj, A. C., Martin, E. R., Huentelman, M., Myers, A. J., Corneveaux, J. J., Hardy, J., Vonsattel, J.-P., Younkin, S. G., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004606 · Cited by 343 · see citing works
Kivimäki, M., Singh-Manoux, A., Pentti, J., Sabia, S., Nyberg, S. T., Alfredsson, L., Goldberg, M., Knutsson, A., Koskenvuo, M., Koskinen, A., et al. (2019) · BMJ · doi:10.1136/bmj.l1495 · Cited by 313 · see citing works
Novel genetic loci associated with hippocampal volume
Hibar, D. P., Adams, H. H. H., Jahanshad, N., Chauhan, G., Stein, J. L., Hofer, E., Renteria, M. E., Bis, J. C., Arias-Vasquez, A., Ikram, M. K., et al. (2017) · Nature Communications · doi:10.1038/ncomms13624 · Cited by 299 · see citing works
Education and dementia: a meta-analytic study
Caamaño-Isorna, F., Corral, M., Montes-Martínez, A., & Takkouche, B. (2006) · Neuroepidemiology · doi:10.1159/000093378 · Cited by 288 · see citing works
The variant call format provides efficient and robust storage of GWAS summary statistics
Lyon, M., Andrews, S. J., Elsworth, B., Gaunt, T. R., Hemani, G., & Marcora, E. M. (2021) · Genome biology · doi:10.1186/s13059-020-02248-0 · Cited by 260 · see citing works
Self-citation(ADNI), A. D. N. I., Deming, Y., The Alzheimer Disease Genetic Consortium (ADGC), Li, Z., Kapoor, M., Harari, O., Del-Aguila, J. L., Black, K., Carrell, D., Cai, Y., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1685-y · Cited by 231 · see citing works
Self-citationØstergaard, S. D., Mukherjee, S., Sharp, S. J., Proitsi, P., Lotta, L. A., Day, F., Perry, J. R. B., Boehme, K. L., Walter, S., Kauwe, J. S., et al. (2015) · PLOS Medicine · doi:10.1371/journal.pmed.1001841 · Cited by 203 · see citing works
Elucidating the genetic basis of social interaction and isolation
Day, F. R., Ong, K. K., & Perry, J. R. B. (2018) · Nature Communications · doi:10.1038/s41467-018-04930-1 · Cited by 203 · see citing works
Richardson, T. G., Harrison, S., Hemani, G., & Davey Smith, G. (2019) · eLife · doi:10.7554/elife.43657 · Cited by 198 · see citing works
Anstey, K. J., Ee, N., Eramudugolla, R., Jagger, C., & Peters, R. (2019) · Journal of Alzheimer’s Disease · doi:10.3233/jad-190181 · Cited by 187 · see citing works
Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?
Gkatzionis, A., & Burgess, S. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyy202 · Cited by 182 · see citing works
Wells, H. R. R., Freidin, M. B., Abidin, F. N. Z., Payton, A., Dawes, P., Munro, K. J., Morton, C. C., Moore, D. R., Dawson, S. J., & Williams, F. M. K. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.09.008 · Cited by 173 · see citing works
A principal component approach to improve association testing with polygenic risk scores
Coombes, B. J., Ploner, A., Bergen, S. E., & Biernacka, J. M. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22339 · Cited by 93 · see citing works
Korologou-Linden, R., Bhatta, L., Brumpton, B. M., Howe, L. D., Millard, L. A. C., Kolaric, K., Ben-Shlomo, Y., Williams, D. M., Smith, G. D., Anderson, E. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32183-6 · Cited by 71 · see citing works
Niarchou, M., Byrne, E. M., Trzaskowski, M., Sidorenko, J., Kemper, K. E., McGrath, J. J., O’Donovan, M. C., Owen, M. J., & Wray, N. R. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0688-y · Cited by 45 · see citing works
The variant call format provides efficient and robust storage of GWAS summary statisticsPreprint
Lyon, M., Andrews, S. J., Elsworth, B. L., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.29.115824 · Cited by 36 · see citing works
Self-citationKorologou-Linden, R., Howe, L. D., Millard, L. A. C., et al. (2019) · medRxiv · doi:10.1101/2019.12.18.19013847 · Cited by 15 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [5]World Health Organization, Risk Reduction of Cognitive Decline and Dementia: WHO Guidelines 2019. 31219687
Cited by
Sorted by citations
Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission
Livingston, G., Huntley, J., Liu, K. Y., Costafreda, S. G., Selbæk, G., Alladi, S., Ames, D., Banerjee, S., Burns, A., Brayne, C., et al. (2024) · The Lancet · doi:10.1016/s0140-6736(24)01296-0 · Cited by 3,860 · see citing works
Thompson, D. J., Wells, D., Selzam, S., Peneva, I., Moore, R., Sharp, K., Tarran, W. A., Beard, E. J., Riveros-Mckay, F., Giner-Delgado, C., et al. (2022) · doi:10.1101/2022.06.16.22276246 · Cited by 280 · see citing works
Phenomic studies on diseases: potential and challenges
Ying, W. (2023) · Phenomics · doi:10.1007/s43657-022-00089-4 · Cited by 96 · see citing works
Alzheimer’s Disease and Inflammaging
Kosyreva, A., Sentyabreva, A., Tsvetkov, I., & Makarova, O. (2022) · Brain Sciences · doi:10.3390/brainsci12091237 · Cited by 98 · see citing works
Korologou-Linden, R., Bhatta, L., Brumpton, B. M., Howe, L. D., Millard, L. A. C., Kolaric, K., Ben-Shlomo, Y., Williams, D. M., Smith, G. D., Anderson, E. L., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32183-6 · Cited by 71 · see citing works
Petermann-Rocha, F., Lyall, D. M., Gray, S. R., Gill, J. M. R., Sattar, N., Welsh, P., Quinn, T. J., Stewart, W., Pell, J. P., Ho, F. K., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02172-5 · Cited by 60 · see citing works
Bloomberg, M., Dugravot, A., Sommerlad, A., Kivimäki, M., Singh-Manoux, A., & Sabia, S. (2023) · Age and Ageing · doi:10.1093/ageing/afad019 · Cited by 34 · see citing works
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
Chen, C., Zhang, M., Wang, Z., Deng, J., Bao, Y., Shi, J., Lu, L., & Shi, L. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70096 · Cited by 24 · see citing works
- Authors
- Andrews, S. J., Fulton‐Howard, B., O'Reilly, P., Marcora, E., Goate, A. M., & collaborators of the Alzheimer's Disease Genetics Consortium
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York NY USA
- Corresponding author
- Andrews, Shea J.
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2020-10-15
- Volume
- 89
- Issue
- 1
- Pages
- 54-65
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- PMC
- PMC8088901
- OpenAlex
- W3089475858
- Semantic Scholar
- c575469f45f4b6ac61a2c90906e549f29f112c7e
- Cited by
- 156
- References
- 61
- Citations by year
- 2026: 16 · 2025: 28 · 2024: 28 · 2023: 31 · 2022: 30 · 2021: 20 · 2020: 3
- FWCI
- 15.32
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Annals of Neurology (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- JPB Foundation
- National Institute on Aging
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease, modifiable risk factors, causal inference, polygenic risk score, Mendelian randomization, diastolic blood pressure, pulse pressure, cholesterol, LDL cholesterol, apolipoprotein E, smoking, type 2 diabetes, sleep duration, age of onset, hippocampal volume, cortical thickness, cortical surface area, amyloid-beta, tau protein, p-tau181, neuritic plaques, neurofibrillary tangles, vascular brain injury, cerebrospinal fluid biomarkers
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Brain, Cholesterol, Cognition, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, Peptide Fragments, Risk Factors, Sleep, Amyloid beta-Peptides, Neurofibrillary Tangles
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
