The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Lipid lowering and Alzheimer disease risk: A mendelian randomization study
Open accessAbstract
Objective: To examine whether genetic variation affecting the expression or function of lipid‐lowering drug targets is associated with Alzheimer disease (AD) risk, to evaluate the potential impact of long‐term exposure to corresponding therapeutics. Methods: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets of several approved lipid‐lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target for PCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encoding the target of mipomersen). Variants were weighted by associations with low‐density lipoprotein cholesterol (LDL‐C) using data from lipid genetics consortia (n up to 295,826). We meta‐analyzed Mendelian randomization estimates for regional variants weighted by LDL‐C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls). Results: Models for HMGCR, APOB , and NPC1L1 did not suggest that the use of related lipid‐lowering drug classes would affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase AD risk in both of the AD samples (combined odds ratio per standard deviation lower LDL‐C inducible by the drug target = 1.45, 95% confidence interval = 1.23–1.69). This risk increase was opposite to, although more modest than, the degree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition. Interpretation: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for AD prevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9 inhibitors may be warranted. ANN NEUROL 2020;87:30–39
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“Only one of the reviewed studies reported evidence of increased AD risk associated with drug use, specifically for the PCSK9 inhibitors evolocumab and alirocumab (Williams et al., 2020).”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Williams et al., 2020
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
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“As an extension of MR, drug target MR has emerged as efficient way to estimate therapeutic potential by using genetic variants in genes encoding drug targets (referred to as cis variants) to mimic the actions of drugs.”
From Genetic insights into causal effects of lipids and lipid-modifying targets on calcific aortic valve stenosis: a Mendelian randomized study (2025) · cites this paper as [14]
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“Indeed, the occurrence of undesirable side effects has been questioned in terms of cognitive function [196,197] and negative impact on the immune system regulation that could lead to different inflammatory diseases [104].”
From The Emerging Role of PCSK9 in the Pathogenesis of Alzheimer’s Disease: A Possible Target for the Disease Treatment (2024) · cites this paper as [197]
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“These drugs included lipid-lowering agents (ezetimibe, mipomersen, and statins) (Williams et al., 2020), antihypertensives (Walker et al., 2020), dimethyl fumarate (approved for the treatment of multiple sclerosis) (Möhle et al., 2021), and drugs commonly used in treatment of rheumatoid arthritis (tofacitinib, tocilizumab, and TNF inhibitors) (Desai et al., 2022).”
From Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review (2023) · cites this paper as Williams et al., 2020
In the citing paper
“Genetic studies show that many of the genes which have risk alleles for AD involve cholesterol/lipid systems16,17.”
From GRAMD1B is a regulator of lipid homeostasis, autophagic flux and phosphorylated tau (2025) · cites this paper as [16]
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Cites
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247 · cited 1×
“This method relies on the use of reference data to estimate the correlations between variants in summary GWAS datasets, for which we used correlation matrices derived from 503 participants of European ancestry in the 1000 Genomes project, phase 3.(27) We tested the robustness of using these reference data by inputting…”
MethodAmyloid oligomers exacerbate tau pathology in a mouse model of tauopathy
Selenica, M.-L. B., Brownlow, M., Jimenez, J. P., Lee, D. C., Pena, G., Dickey, C. A., Gordon, M. N., & Morgan, D. (2013) · Neurodegenerative Diseases · doi:10.1159/000337230 · Cited by 10,359
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · cited 1×
“We repeated analyses using alternative methods—weighted median and MR Egger estimators—which provide different degrees of robustness to bias from genetic pleiotropy.(24,25) For the primary gene (drug target)-specific models, data on all successfully harmonized SNPs within the gene regions ( 1 kilobase flank) were elig…”
MethodSelf-citationBowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · cited 1×
“We repeated analyses using alternative methods—weighted median and MR Egger estimators—which provide different degrees of robustness to bias from genetic pleiotropy.(24,25) For the primary gene (drug target)-specific models, data on all successfully harmonized SNPs within the gene regions ( 1 kilobase flank) were elig…”
MethodSelf-citationThe 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
Self-citationMendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · cited 6×
“32 Volume 87, No. 1 ANNALS of Neurology To assess a potential effect of LDL-C lowering on AD, regardless of drug target, we used the fixed-effects inverse variance weighted (IVW) meta-analysis method, which combines ratios of coefficients (Wald estimators) for the association estimates of each eligible SNP with LDL-C…”
MethodInfluentialSelf-citationMendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618 · cited 1×
“Because genotypes are inherited randomly at conception in an analogous manner to treatment allocation in clinical trials, associations of variants with biomarkers and disease outcomes are not expected to be subject to biases from confounding and reverse causation seen in other types of observational epidemiology—a pri…”
BackgroundCognitive 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
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983
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 · cited 2×
“Full information on genetic quality control procedures and association modeling are provided in the study references.(19,20)”
Method
Flagged first, then by citations
Research works printed but not linked: 2
- McGuinness B, 2016, Statins for the prevention of dementia, Cochrane Database Syst Rev, 1, CD003160
- 10.1136/bmj.j1648 · DOI · Paper page
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Zhao, T., Li, H., Zhang, M., Xu, Y. Y., Zhang, M., & Chen, L. (2024) · eLife · doi:10.7554/elife.96224.1
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“As a test for directional horizontal pleiotropy, a non-significant MR-Egger intercept test ( p > 0.05) suggested the absence of such pleiotropic bias among the IVs [41].”
InfluentialOu, Y.-N., Yang, Y.-X., Shen, X.-N., Ma, Y.-H., Chen, S.-D., Dong, Q., Tan, L., & Yu, J.-T. (2021) · Figshare · doi:10.6084/m9.figshare.13843888
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Pharmacological Precision Medicine to Treat Dementia
Lee, K., Lin, P., Wu, M.-J., & Sung, P. (2026) · Springer eBooks · doi:10.1007/978-981-95-5222-1_9
A narrative review of PCSK9 inhibitors: from evolocumab to novel discoveries
Liu, H., Li, S. N., & Li, J. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1800502
“Mendelian randomization analyses and meta-analyses have indicated that long-term exposure to low PCSK9 levels may increase the risk of AD (Williams et al., 2020), creating a paradox that the inhibition beneficial for cardiovascular protection could potentially be detrimental for neurocognitive…”
Wang, J. D. J., Teo, A. Y. T., Xiao, B., Chao, Y., Zhou, Z. D., Tan, B. J.-W., Chan, L.-L., Rosalie, E., & Tan, E.-K. (2026) · Translational Neurodegeneration · doi:10.1186/s40035-026-00568-y
Tian, X., & 薛晓琳 (2026) · Genes · doi:10.3390/genes17101218
“MR estimates of drug-target effects also vary according to the target gene and the variants used as instruments [20,21].”
Messina, M. J., Nagra, M., Goldman, D., & Nechuta, S. (2026) · Current Nutrition Reports · doi:10.1007/s13668-026-00806-9
Xu, W., Liu, M., Yu, X., Hao, Y., Fu, Z., Jin, L., Liang, H., Liao, K., Wang, X., & Xu, J. (2026) · Neurotherapeutics · doi:10.1016/j.neurot.2026.e01092
Gau, S.-Y., Lo, S., HSU, C., Tu, Y.-F., Su, Y.-J., Chen, C.-C., Wu, M.-C., Mitsukura, Y., & Chang, H.-C. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1942691
Metadata
- Authors
- Williams, D. M., Finan, C., Schmidt, A. F., Burgess, S., & Hingorani, A. D.
- Affiliations
- University of London (GB)
- British Heart Foundation (GB)
- University Medical Center Utrecht (NL)
- University of Cambridge (GB)
- Corresponding author
- Williams, Dylan M.
- Venue
- Annals of Neurology
- Publisher
- Wiley
- Published
- 2019-12-13
- Volume
- 87
- Issue
- 1
- Pages
- 30-39
- ISSN
- 0364-5134 (ISSN-L)
- 1531-8249
- PMC
- PMC6944510
- OpenAlex
- W2987364071
- Cited by
- 157
- References
- 49
- Citations by year
- 2026: 7 · 2025: 18 · 2024: 51 · 2023: 40 · 2022: 9 · 2021: 27 · 2020: 4
- FWCI
- 2.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annals of Neurology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- British Heart Foundation (PG/18/5033837)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Alzheimer's disease risk, lipid-lowering drugs, PCSK9 inhibitors, statins, ezetimibe, mipomersen, HMGCR, NPC1L1, apolipoprotein B, LDL cholesterol, drug repurposing, pharmacovigilance, genetic variants
- MeSH terms
- Proprotein Convertase 9, Aged, Alzheimer Disease, Female, Humans, Hydroxymethylglutaryl CoA Reductases, Male, Middle Aged, Models, Genetic, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Membrane Transport Proteins, Apolipoprotein B-100, Mendelian Randomization Analysis
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