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
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.
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?
Only one of the reviewed studies reported evidence of increased AD risk associated with drug use, specifically for the PCSK9 inhibitors evolocumab and alirocumab.
“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
The Results report a genetically predicted increase in Alzheimer disease risk for inhibition of PCSK9, and the Methods identify evolocumab and alirocumab as examples of PCSK9 inhibitors (pages not provided). But the Discussion cautions that this genetic prediction may not apply to use of those drugs in practice; the study did not establish increased risk among their users. The claim that this was the only reviewed study is the citing authors’ review framing, not a finding assessed here. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDrug target MR uses genetic variants in genes encoding drug targets to mimic the actions of drugs and estimate therapeutic potential.
“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]
Undesirable side effects of PCSK9-targeting therapy have been questioned in terms of cognitive function.
“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]
The drugs with evidence of little to no effect on AD risk included lipid-lowering agents (ezetimibe, mipomersen, and statins), antihypertensives, dimethyl fumarate, and drugs commonly used in treatment of rheumatoid arthritis (tofacitinib, tocilizumab, and TNF inhibitors).
“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
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
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 · see citing works
Amyloid 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 · see citing works
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816 · see citing works
Self-citationBowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922 · see citing works
Self-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 · see citing works
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 · see citing works
Self-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 · 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 · 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
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277 · see citing works
Self-citationStages of the Pathologic Process in Alzheimer Disease: Age Categories From 1 to 100 Years
Braak, H., Thal, D. R., Ghebremedhin, E., & Del Tredici, K. (2011) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0b013e318232a379 · Cited by 2,194 · see citing works
Burgess, S., Bowden, J., Fall, T., Ingelsson, E., & Thompson, S. G. (2017) · Epidemiology · doi:10.1097/ede.0000000000000559 · Cited by 2,017 · see citing works
Self-citationYavorska, O. O., & Burgess, S. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx034 · Cited by 2,013 · see citing works
Self-citationDrug Transport across the Blood–Brain Barrier
Pardridge, W. M. (2012) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/jcbfm.2012.126 · Cited by 1,994 · see citing works
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912 · see citing works
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease
Cardiogenics, Schunkert, H., the CARDIoGRAM Consortium, König, I. R., Kathiresan, S., Reilly, M. P., Assimes, T. L., Holm, H., Preuss, M., Stewart, A. F. R., et al. (2011) · Nature Genetics · doi:10.1038/ng.784 · Cited by 1,866 · see citing works
EPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0011-z · Cited by 1,803 · see citing works
Self-citationDetection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Collins, R., Armitage, J., Parish, S., Sleight, P., & Peto, R. (2004) · The Lancet · doi:10.1016/s0140-6736(04)15690-0 · Cited by 1,055 · 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
Kettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11122 · Cited by 738 · see citing works
Exome-wide association study of plasma lipids in >300,000 individuals
Charge Diabetes Working Group, Liu, D. J., Program, V. M. V., Peloso, G. M., Yu, H., Butterworth, A. S., Wang, X. Q., Mahajan, A., Saleheen, D., Emdin, C. A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3977 · Cited by 538 · see citing works
Statins for the prevention of dementia
McGuinness, B., Craig, D., Bullock, R., & Passmore, P. (2016) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd003160.pub3 · Cited by 490 · see citing works
Lotta, L. A., Sharp, S. J., Burgess, S., Perry, J. R. B., Stewart, I. D., Willems, S. M., Luan, J., Ardanaz, E., Arriola, L., Balkau, B., et al. (2016) · JAMA · doi:10.1001/jama.2016.14568 · Cited by 394 · see citing works
Self-citationSelecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387 · see citing works
Self-citationModifiable 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
Self-citationPCSK9 genetic variants and risk of type 2 diabetes: a mendelian randomisation study
Schmidt, A. F., Swerdlow, D. I., Holmes, M. V., Patel, R. S., Fairhurst-Hunter, Z., Lyall, D. M., Hartwig, F. P., Horta, B. L., Hyppönen, E., Power, C., et al. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(16)30396-5 · Cited by 380 · see citing works
Self-citationJack, C. R., Wiste, H. J., Weigand, S. D., Therneau, T. M., Knopman, D. S., Lowe, V., Vemuri, P., Mielke, M. M., Roberts, R. O., Machulda, M. M., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30077-7 · Cited by 333 · see citing works
Drug repositioning for Alzheimer's disease
Corbett, A., Pickett, J., Burns, A. S., Corcoran, J. P. T., Dunnett, S. B., Edison, P., Hagan, J. J., Holmes, C., Jones, E., Katona, C., et al. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3869 · Cited by 302 · see citing works
Hingorani, A. D., & Humphries, S. E. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67767-7 · Cited by 277 · see citing works
Self-citationAssociations Between Serum Cholesterol Levels and Cerebral Amyloidosis
Reed, B., Villeneuve, S., Mack, W., DeCarli, C., Chui, H. C., & Jagust, W. (2014) · JAMA Neurology · doi:10.1001/jamaneurol.2013.5390 · Cited by 267 · see citing works
Lipinski, M. J., Benedetto, U., Escarcega, R. O., Biondi-Zoccai, G., Lhermusier, T., Baker, N. C., Torguson, R., Brewer, H. B., & Waksman, R. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehv563 · Cited by 256 · see citing works
Improving the odds of drug development success through human genomics: modelling study
Hingorani, A. D., Kuan, V., Finan, C., Krüger, F. A., Gaulton, A., Chopade, S., Sofat, R., MacAllister, R. J., Overington, J. P., Hemingway, H., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-54849-w · Cited by 242 · see citing works
Self-citationDa Dalt, L., Ruscica, M., Bonacina, F., Balzarotti, G., Dhyani, A., Di Cairano, E., Baragetti, A., Arnaboldi, L., De Metrio, S., Pellegatta, F., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehy357 · Cited by 209 · see citing works
Burgess, S., Zuber, V., Valdés‐Márquez, E., Sun, B. B., & Hopewell, J. C. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22077 · Cited by 204 · 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
Benn, M., Nordestgaard, B. G., Frikke-Schmidt, R., & Tybjærg-Hansen, A. (2017) · BMJ · doi:10.1136/bmj.j1648 · Cited by 178 · see citing works
Meng, X.-F., Yu, J.-T., Wang, H.-F., Tan, M.-S., Wang, C., Tan, C.-C., & Tan, L. (2014) · Journal of Alzheimer s Disease · doi:10.3233/jad-140954 · Cited by 150 · see citing works
Detection of Dementia Cases in Two Swedish Health Registers: A Validation Study
Rizzuto, D., Feldman, A. L., Karlsson, I., Aslan, A. K. D., Gatz, M., & Pedersen, N. L. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-170572 · Cited by 132 · see citing works
Implication of the proprotein convertase NARC-1/PCSK9 in the development of the nervous system
Poirier, S., Prat, A., Marcinkiewicz, E., Paquin, J., Chitramuthu, B. P., Baranowski, D., Cadieux, B., Bennett, H. P. J., & Seidah, N. G. (2006) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2006.03928.x · Cited by 121 · see citing works
de Carvalho, L. S. F., Campos, A. M., & Sposito, A. C. (2018) · Diabetes Care · doi:10.2337/dc17-1464 · Cited by 121 · see citing works
Jonas, M. C., Costantini, C., & Puglielli, L. (2008) · The EMBO Reports · doi:10.1038/embor.2008.132 · Cited by 119 · see citing works
Association of Alzheimer disease pathology with abnormal lipid metabolism
Matsuzaki, T., Sasaki, K., Hata, J., Hirakawa, Y., Fujimi, K., Ninomiya, T., Suzuki, S. O., Kanba, S., Kiyohara, Y., & Iwaki, T. (2011) · Neurology · doi:10.1212/wnl.0b013e31822e145d · Cited by 108 · see citing works
Larsson, S. C., & Markus, H. S. (2018) · Journal of Alzheimer s Disease · doi:10.3233/jad-180288 · Cited by 94 · see citing works
Rao, A. S., Lindholm, D., Rivas, M. A., Knowles, J. W., Montgomery, S. B., & Ingelsson, E. (2018) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.118.002162 · Cited by 60 · see citing works
Pott, J., Schlegel, V., Teren, A., Horn, K., Kirsten, H., Blücher, C., Kratzsch, J., Loeffler, M., Thiery, J., Burkhardt, R., et al. (2018) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.117.001992 · Cited by 48 · see citing works
Milionis, H. J., Barkas, F., Ntaios, G., Papavasileiou, V., Vemmos, K. N., Michel, P., & Elisaf, M. (2016) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2016.06.023 · Cited by 26 · see citing works
Lyall, D. M., Ward, J., Banach, M., Smith, G. D., Gill, J. M. R., Pell, J. P., Holmes, M. V., & Sattar, N. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/335877 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [36]McGuinness B, Craig D, Bullock R, Passmore P. Statins for the prevention of dementia. Cochrane Database Syst Rev 2016;(1): CD003160. PMC9346344 26727124
- [38]Benn M, Nordestgaard BG, Frikke‐Schmidt R, Tybjærg‐Hansen A. Re: Low LDL cholesterol, PCSK9 and HMGCR genetic variation, and risk of Alzheimer's disease and Parkinson's disease: Mendelian randomisation study. BMJ 2017; 357: j1648. 28438747 PMC5421439
Cited by
Sorted by citations
Zuber, V., Grinberg, N. F., Gill, D., Manipur, I., Slob, E. A. W., Patel, A., Wallace, C., & Burgess, S. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.04.001 · Cited by 532 · see citing works
Self-citationOlmastroni, E., Molari, G., De Beni, N., Colpani, O., Galimberti, F., Gazzotti, M., Zambon, A., Catapano, A. L., & Casula, M. (2022) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwab208 · Cited by 197 · see citing works
Fang, J., Zhang, P., Zhou, Y., Chiang, C.-W., Tan, J., Hou, Y., Stauffer, S., Li, L., Pieper, A. A., Cummings, J., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00138-z · Cited by 206 · see citing works
Association of lipid-lowering drugs with COVID-19 outcomes from a Mendelian randomization study
Huang, W., Xiao, J., Ji, J., & Chen, L.-W. (2021) · eLife · doi:10.7554/elife.73873 · Cited by 164 · see citing works
Cholesteryl ester transfer protein (CETP) as a drug target for cardiovascular disease
Schmidt, A. F., Hunt, N. B., Gordillo‐Marañón, M., Charoen, P., Drenos, F., Casas, J. P., Mika Kivimaki, B., Lawlor, D. A., Giambartolomei, C., Papacosta, O., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25703-3 · Cited by 145 · see citing works
Self-citationLi, Z., Zhang, B., Liu, Q., Tao, Z., Ding, L., Guo, B., Zhang, E., Zhang, H., Meng, Z., Guo, S., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104543 · Cited by 137 · see citing works
Mendelian Randomization Study of PCSK9 and HMG-CoA Reductase Inhibition and Cognitive Function
Rosoff, D. B., Bell, A. S., Jung, J., Wagner, J., Mavromatis, L. A., & Lohoff, F. W. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.05.041 · Cited by 126 · see citing works
Proprotein Convertase Subtilisin/Kexin Type 9
Macchi, C., Ferri, N., Sirtori, C. R., Corsini, A., Banach, M., & Ruscica, M. (2021) · American Journal Of Pathology · doi:10.1016/j.ajpath.2021.04.016 · Cited by 101 · see citing works
Cholesterol as a key player in amyloid β-mediated toxicity in Alzheimer’s disease
Rudajev, V., & Novotný, J. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.937056 · Cited by 94 · see citing works
Goldstein, L. B., Toth, P. P., Dearborn, J. L., Giugliano, R. P., Hirsh, B. J., Peña, J. M., Selim, M. H., & Woo, D. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atv.0000000000000164 · Cited by 95 · see citing works
Using human genetics to improve safety assessment of therapeutics
Carss, K., Deaton, A. M., Río-Espínola, A. D., Diogo, D., Fielden, M. R., Kulkarni, D. A., Moggs, J. G., Newham, P., Nelson, M. R., Sistare, F. D., et al. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00561-w · Cited by 88 · see citing works
Statin therapy and risk of Alzheimer's and age‐related neurodegenerative diseases
Torrandell‐Haro, G., Branigan, G. L., Vitali, F., Geifman, N., Zissimopoulos, J., & Brinton, R. D. (2020) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12108 · Cited by 78 · see citing works
Cholesterol Metabolism in Aging and Age-Related Disorders
Saher, G. (2023) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-091922-034237 · Cited by 81 · see citing works
Lipids and peripheral neuropathy
Iqbal, Z., Bashir, B., Ferdousi, M., Kalteniece, A., Alam, U., Malik, R. A., & Soran, H. (2021) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000770 · Cited by 73 · see citing works
Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis
Chen, L., Qiu, W., Sun, X., Gao, M., Zhao, Y., Li, M., Fan, Z., & Lv, G. (2023) · Gut · doi:10.1136/gutjnl-2023-330784 · Cited by 65 · see citing works
Statins and cognition: Modifying factors and possible underlying mechanisms
Jamshidnejad-Tosaramandani, T., Kashanian, S., Al-Sabri, M. H., Kročianová, D., Clemensson, L. E., Gentreau, M., & Schiöth, H. B. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.968039 · Cited by 59 · see citing works
Lipids, Apolipoproteins, Statins, and Intracerebral Hemorrhage: A Mendelian Randomization Study
Zhou, Y., Zhang, L., Zhang, G., Xia, K., Yang, Q., Huang, T., & Fan, D. (2022) · Annals of Neurology · doi:10.1002/ana.26426 · Cited by 55 · see citing works
Drug repurposing for Alzheimer’s disease from 2012–2022—a 10-year literature review
Grabowska, M. E., Huang, A., Wen, Z., Li, B., & Wei, W.-Q. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1257700 · Cited by 55 · see citing works
“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…”
BackgroundSu, C., Hou, Y., Xu, J., Xu, Z., Zhou, M., Ke, A., Li, H., Xu, J., Brendel, M., Maasch, J. R. M. A., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01175-9 · Cited by 55 · see citing works
The translational potential of cholesterol-based therapies for neurological disease
Valenza, M., Birolini, G., & Cattaneo, E. F. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00864-5 · Cited by 48 · see citing works
Lipids, lipid-modifying drug target genes and migraine: a Mendelian randomization study
Bi, Y., Zhu, Y., Tang, S., & Huang, Y. (2023) · The Journal of Headache and Pain · doi:10.1186/s10194-023-01633-x · Cited by 41 · see citing works
Insight into the Evolving Role of PCSK9
Maligłówka, M., Kosowski, M., Hachuła, M., Cyrnek, M., Bułdak, Ł., Basiak, M., Bołdys, A., Machnik, G., Bułdak, R. J., & Okopień, B. (2022) · Metabolites · doi:10.3390/metabo12030256 · Cited by 41 · see citing works
Human Genomics and Drug DevelopmentPreprint
Schmidt, A. F., Hingorani, A. D., & Finan, C. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039230 · Cited by 36 · see citing works
Self-citationProprotein convertase subtilisin/kexin type 9 (PCSK9) in the central nervous system
Bell, A. S., Wagner, J., Rosoff, D. B., & Lohoff, F. W. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105155 · Cited by 36 · see citing works
Zhang, X., Geng, T., Li, N., Wu, L., Wang, Y., Zheng, D., Guo, B., & Wang, B. (2022) · Nutrients · doi:10.3390/nu15010069 · Cited by 35 · see citing works
Shared signaling pathways in Alzheimer’s and metabolic disease may point to new treatment approaches
Suresh, J., Khor, I. W., Kaur, P., Heng, H. L., Torta, F. T., Dawe, G. S., Tai, E. S., & Tolwinski, N. S. (2020) · FEBS Journal · doi:10.1111/febs.15540 · Cited by 34 · see citing works
Coming Back to Physiology: Extra Hepatic Functions of Proprotein Convertase Subtilisin/Kexin Type 9
Schlüter, K., Wolf, A., & Schreckenberg, R. (2020) · Frontiers in Physiology · doi:10.3389/fphys.2020.598649 · Cited by 32 · see citing works
Ou, Y.-N., Yang, Y.-X., Shen, X.-N., Ma, Y.-H., Chen, S.-D., Dong, Q., Tan, L., & Yu, J.-T. (2020) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00782-y · Cited by 27 · see citing works
The landscape of the methodology in drug repurposing using human genomic data: a systematic review
Wang, L., Lü, Y., Li, D., Zhou, Y., Yu, L., Mesa‐Eguiagaray, I., Campbell, H. J., Li, X., & Τheodoratou, E. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbad527 · Cited by 29 · see citing works
Liu, H., Li, J., Liu, F., Huang, K., Cao, J., Chen, S., Li, H., Shen, C., Hu, D., Huang, J., et al. (2023) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwad111 · Cited by 27 · see citing works
McCarty, M. F., DiNicolantonio, J. J., & Lerner, A. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22042140 · Cited by 26 · see citing works
Martin, L., Boutwell, B. B., Messerlian, C., & Adams, C. D. (2024) · Communications Biology · doi:10.1038/s42003-024-05887-2 · Cited by 25 · see citing works
Hypercholesterolemia and the Increased Risk of Vascular Dementia: a Cholesterol Perspective
Shang, G., Shao, Q., Lv, K., Xu, W., Ji, J., Fan, S., Kang, X., Cheng, F., Wang, X., & Wang, Q. (2024) · Current Atherosclerosis Reports · doi:10.1007/s11883-024-01217-3 · Cited by 24 · see citing works
The Interface of Therapeutics and Genomics in Cardiovascular Medicine
Magavern, E. F., Kaski, J. C., Turner, R. M., Janmohamed, A., Borry, P., & Pirmohamed, M. (2021) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-021-07149-3 · Cited by 21 · see citing works
Bell, A. S., Rosoff, D. B., Mavromatis, L. A., Jung, J., Wagner, J., & Lohoff, F. W. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026122 · Cited by 18 · see citing works
Exploring the Role of Plasma Lipids and Statin Interventions on Multiple Sclerosis Risk and Severity
Almramhi, M. M., Finan, C., Storm, C. S., Schmidt, A. F., Kia, D. A., Coneys, R., Chopade, S., Hingorani, A. D., & Wood, N. W. (2023) · Neurology · doi:10.1212/wnl.0000000000207777 · Cited by 17 · see citing works
Self-citationRosoff, D. B., Bell, A. S., Wagner, J., Mavromatis, L. A., Hamandi, A. M., Park, L. Y., Jung, J., & Lohoff, F. W. (2023) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2023.09.001 · Cited by 15 · see citing works
Testa, G., Giannelli, S., Staurenghi, E., Cecci, R., Floro, L., Gamba, P., Sottero, B., & Leonarduzzi, G. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413637 · Cited by 14 · see citing works
“…some meta-analyses reported that, though these inhibitors significantly decreased LDL levels, they increased the incidence of neurocognitive impairments, thus suggesting that any impact of PCSK9 activity on the brain was independent from its regulation of peripheral cholesterol [196,197].”
Gouverneur, A., Sanchez-Peña, P., Veyrac, G., Salem, J., Bégaud, B., & Bezin, J. (2021) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-021-07242-7 · Cited by 13 · see citing works
Kang, T., Zhou, Y., Fan, C., Zhang, Y., Yu, Y., & Jiang, J. C. (2024) · The EPMA Journal · doi:10.1007/s13167-024-00373-5 · Cited by 11 · see citing works
Lee, M., Lee, K. J., Kim, J., Lee, D. Y., Park, R. W., Rhee, S. Y., Cha, J. M., Yang, H., Jang, J., Jung, S., et al. (2025) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp-2024-334708 · Cited by 11 · see citing works
GRAMD1B is a regulator of lipid homeostasis, autophagic flux and phosphorylated tau
Ingram, D. A., Turkes, E., Kim, T. Y., Vo, S., Sweeney, N., Bonte, M.-A., Rutherford, R., Julian, D. L., Pan, M., Marsh, J. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58585-w · Cited by 12 · see citing works
Feng, X., Zhang, L., Hou, Y., Ma, W., Ma, J., Chang, X., & Yang, L. (2024) · The journal of prevention of Alzheimer's disease · doi:10.14283/jpad.2024.30 · Cited by 9 · see citing works
Unraveling the role of proteins in dementia: insights from two UK cohorts with causal evidence
Gong, J., Williams, D. M., Scholes, S., Assaad, S., Bu, F., Hayat, S. A., Zaninotto, P., & Steptoe, A. (2025) · Brain Communications · doi:10.1093/braincomms/fcaf097 · Cited by 17 · see citing works
Self-citationThe safety and efficacy of Ezetimibe Plus Statins on ASVD and Related Diseases
Wan, S., Ding, Y., Ji, X., & Meng, R. (2021) · Aging and Disease · doi:10.14336/ad.2021.0412 · Cited by 9 · see citing works
Genetically Determined Low‐Density Lipoprotein Cholesterol and Risk of Subarachnoid Hemorrhage
Acosta, J., Both, C. P., Szejko, N., Leasure, A. C., Abdelhakim, S., Torres‐Lopez, V., Brown, S. C., Matouk, C. C., Günel, M., Sheth, K. N., et al. (2021) · Annals of Neurology · doi:10.1002/ana.26250 · Cited by 9 · see citing works
Pham, K., Mulugeta, A., Lumsden, A. L., & Hyppönen, E. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15793 · Cited by 8 · see citing works
Vascular and Alzheimer Disease in Dementia
Strandberg, T. E., Tienari, P. J., & Mika Kivimaki, B. (2020) · Annals of Neurology · doi:10.1002/ana.25715 · Cited by 9 · see citing works
Gong, J., Williams, D. M., Scholes, S., Assaad, S., Bu, F., Hayat, S. A., Zaninotto, P., & Steptoe, A. (2024) · medRxiv · doi:10.1101/2024.06.04.24308415 · Cited by 8 · see citing works
Self-citationA review on the efficacy and safety of lipid-lowering drugs in neurodegenerative disease
Rajabian, A., McCloskey, A. P., Jamialahmadi, T., Moallem, S. A., & Sahebkar, A. (2023) · Reviews in the Neurosciences · doi:10.1515/revneuro-2023-0005 · Cited by 7 · see citing works
Ding, M., Wennberg, A. M. V., Engström, G., & Modig, K. (2024) · Alzheimer s & Dementia · doi:10.1002/alz.14389 · Cited by 7 · see citing works
Zhao, J., Chen, R., Luo, M., Gong, H., Li, K., & Zhao, Q. (2024) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01252-1 · Cited by 7 · see citing works
Zhang, Y., Jiang, Z., Chen, L., Lei, T., & Zheng, X. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05359-5 · Cited by 7 · see citing works
Cai, G., Liu, J., Cai, M., & Shao, L. (2024) · Lipids in Health and Disease · doi:10.1186/s12944-024-02218-6 · Cited by 7 · see citing works
Zhao, J., Wang, R., Song, L., Han, H., Wang, P., Zhao, Y., Zhang, Y., & Zhang, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1295412 · Cited by 7 · see citing works
Huang, J., Lin, Z., Lin, J., Xie, S., Xia, S., Chen, G., Zheng, Z., Xu, Z., Liu, F., Wu, H., et al. (2023) · Thorax · doi:10.1136/thorax-2023-220789 · Cited by 6 · see citing works
Sparse Variable Selection on High Dimensional Heterogeneous Data With Tree Structured Responses
Liu, H., Liu, X., Diao, J., Ye, W., Liu, X., & Wei, D. (2024) · IEEE Access · doi:10.1109/access.2024.3384309 · Cited by 6 · see citing works
Unlocking the Medicinal Mysteries: Preventing Lacunar Stroke with Drug Repurposing
Zhang, L., Fan, W., Xia, K., Zhou, Y., Fu, Y., Huang, T., & Fan, D. (2023) · Biomedicines · doi:10.3390/biomedicines12010017 · Cited by 6 · see citing works
Life course plasma metabolomic signatures of genetic liability to Alzheimer’s disease
Compton, H., Smith, M. L., Bull, C. J., Korologou‐Linden, R., Ben‐Shlomo, Y., Bell, J. A., Williams, D. M., & Anderson, E. L. (2024) · Scientific Reports · doi:10.1038/s41598-024-54569-w · Cited by 5 · see citing works
Self-citationNiu, Q., Zhang, T., Mao, R., Zhao, N., & Deng, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-69180-2 · Cited by 5 · see citing works
Baik, K., Kang, M., Park, Y. J., Chung, S. J., Oh, K., Koh, S., & Kang, S. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-16620-2 · Cited by 5 · see citing works
Meng, X., Li, X., Cao, M., Dong, J., Wang, H., Cao, W., Liu, D., & Wang, Y. (2025) · Systematic Reviews · doi:10.1186/s13643-025-02792-5 · Cited by 5 · see citing works
Li, W., Li, H., Zha, C., Che, B., Yu, Y. Q., Yang, J., & Li, T. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1392533 · Cited by 5 · see citing works
Precision health in Alzheimer disease: Risk assessment‐based strategies
Azar, J., Salama, M., Chidambaram, S. B., Al‐Balushi, B., Essa, M. M., & Qoronfleh, M. W. (2021) · Precision Medical Sciences · doi:10.1002/prm2.12036 · Cited by 7 · see citing works
Zhu, X., Xu, Y., Li, L., Sun, J., Wang, Y., Chen, J., Wang, C., Zhang, S., & Jin, L. (2024) · Drug Development Research · doi:10.1002/ddr.22131 · Cited by 5 · see citing works
Are lipid-lowering drugs harmful to cognitive performance?: A Mendelian randomization study
Han, L., Su, C., Lian, R., Han, J., Fan, X., & Gao, L. (2025) · Medicine · doi:10.1097/md.0000000000044260 · Cited by 4 · see citing works
Selecting invalid instruments to improve Mendelian randomization with two-sample summary data
Patel, A., DiTraglia, F. J., Zuber, V., & Burgess, S. (2024) · The Annals of Applied Statistics · doi:10.1214/23-aoas1856 · Cited by 4 · see citing works
Self-citationIs the Relationship Between Cardiovascular Disease and Alzheimer’s Disease Genetic? A Scoping Review
Moore, A. K., & Ritchie, M. D. (2024) · Genes · doi:10.3390/genes15121509 · Cited by 4 · see citing works
Fu, L., Liu, Q., Cheng, H., Zhao, X., Xiong, J., & Mi, J. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.038857 · Cited by 4 · see citing works
Zhao, Y., Du, D., Wei, L., & Chen, Z. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e20230 · Cited by 4 · see citing works
Huang, S., Liu, Y., Zhang, Y., Wang, Y., Gao, Y., Li, R., Yu, L., Hu, X., & Fang, Q. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1331537 · Cited by 4 · see citing works
Su, C., Hou, Y., Xu, J., Xu, Z., Xu, J., Brendel, M., Maasch, J. R. M. A., Bai, Z., Zhang, H., Zhu, Y., et al. (2021) · medRxiv · doi:10.1101/2021.07.18.21260731 · Cited by 4 · see citing works
Wu, C., Pu, Q., Zou, Y., Jin, J., Lei, Y., Hu, Y., Chen, Z., & Yu, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-15525-4 · Cited by 3 · see citing works
“This approach has also been widely applied to predict likely beneficial effects of drug targets on cardiovascular diseases 13 , neurological diseases 14 and cancers 15 .”
Petek, B., Mo, M., Xu, H., Norgren, J., Hoang, M. T., Villa‐Lopez, M., Häbel, H., Kele, J., Naia, L., Maioli, S., et al. (2025) · Journal of Alzheimer s Disease Reports · doi:10.1177/25424823251385903 · Cited by 3 · see citing works
Correia, J. P. S., Campos, A. L. B., Correia, S. S., & Nascimento, C. S. (2021) · Research Society and Development · doi:10.33448/rsd-v10i4.14391 · Cited by 3 · see citing works
Zhou, K., Yang, H., Xie, Z., Wang, W., & Qu, Z. (2024) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-023-00718-2 · Cited by 3 · see citing works
Lu, J., Liu, Y., Wang, Z., Zhou, K., Pan, Y., Zhong, S., & Jiang, G. (2024) · JACC Asia · doi:10.1016/j.jacasi.2024.07.011 · Cited by 3 · see citing works
Ma, K., Wang, H., Du, Y., Chen, T., Yang, D., Li, Y., & Li, D. (2025) · Food Science & Nutrition · doi:10.1002/fsn3.70293 · Cited by 2 · see citing works
Palmer, J. C., Hart, E. C., Anderson, E. L., Love, S., & Lawlor, D. A. (2025) · BMC Medicine · doi:10.1186/s12916-025-04295-5 · Cited by 3 · see citing works
Zhang, L., Zhang, L., Zhao, J., Zhang, W. Y., & Zhang, H. (2025) · Molecular Neurobiology · doi:10.1007/s12035-025-05424-0 · Cited by 3 · see citing works
Ma, Y., Xu, L., Lin, Y., Zhu, J., Liu, J., Wang, L., & Chen, L. (2025) · Kidney Diseases · doi:10.1159/000548430 · Cited by 2 · see citing works
Liu, X., Liu, X., Diao, J., Zheng, M., Li, J., Xie, Y., Lai, K., Geng, X., Song, Y., & Jiang, L. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822861 · Cited by 2 · see citing works
Liu, M., Yin, N., Zhu, Y., Du, A., Cai, C.-Y., & Leng, P. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1398023 · Cited by 2 · see citing works
MosGraphFlow: a novel integrative graph AI model mining signaling targets from multi-omic data
Zhang, H., Cao, D., Xu, T., Chen, E., Li, G., Chen, Y., Payne, P., Province, M. A., & Li, F. (2025) · BMC Methods · doi:10.1186/s44330-025-00041-8 · Cited by 2 · see citing works
Skutki hamowania funkcji PCSK9 w obrębie wybranych tkanek *
Maligłówka, M., Bułdak, Ł., Okopień, B., & Bołdys, A. (2021) · Postępy Higieny i Medycyny Doświadczalnej · doi:10.5604/01.3001.0014.9127 · Cited by 2 · see citing works
Zhao, T., Li, H., Zhang, M., Xu, Y. Y., Zhang, M., & Chen, L. (2024) · eLife · doi:10.7554/elife.96224.3 · Cited by 2 · see citing works
PCSK9 in extrahepatic tissues: What can we expect from its inhibition?
Pirillo, A., Tokgözoğlu, L. S., & Catapano, A. L. (2023) · European Atherosclerosis Journal · doi:10.56095/eaj.v2i2.47 · Cited by 1 · see citing works
Wen, S., Li, Z., Qiu, B., Zhang, Q., Zeng, Y., Zheng, S., Chen, N., Zou, J., Huang, G., & Zeng, Q. (2025) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.104198 · Cited by 1 · see citing works
Diawara, A., Traore, M., Diabaté, O., Kintu, C., Saeed, A. A., Abiola, J. O., Cissé, C., Traore, K., Wélé, M., Nash, O., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-3956597/v1 · Cited by 1 · see citing works
Zhao, T., Li, H., Zhang, M., Xu, Y. Y., Zhang, M., & Chen, L. (2024) · eLife · doi:10.7554/elife.96224.2 · Cited by 1 · see citing works
McGuinness, L. A., Higgins, J. P. T., Walker, V. M., Davies, N. M., Martin, R. M., Coulthard, E. J., Smith, G. D., Kehoe, P. G., & Ben‐Shlomo, Y. (2021) · medRxiv · doi:10.1101/2021.10.21.21265131
Ek espacio de trabajo como factor estratégico: Nuevas tendencias
Saldaña, J. L. L. (2000) · Forum calidad
Zhou, K., Wu, S., Yang, H., Xie, Z., Wang, W., & Qu, Z. (2023) · Research Square · doi:10.21203/rs.3.rs-2456472/v1
Huan, S., Gao, J., & Guoping, Y. I. N. (2023) · Research Square · doi:10.21203/rs.3.rs-2822676/v1
Qiao, L., Lv, S., Meng, K., & Yang, J. (2024) · Clinical Rheumatology · doi:10.1007/s10067-023-06837-9
Lipid-lowering drug targets and lung related diseases: a Mendelian randomization study
Yu, H., Zhang, X., Wen, B., Hu, S., & Ni, B. (2024) · All Life · doi:10.1080/26895293.2024.2329440
Bi, Y., Zhu, Y., Tang, S., & Huang, Y. (2023) · Figshare · doi:10.6084/m9.figshare.23993407
Wang, H., Liu, Z., Yang, Z., Lu, Y., & Zou, C. (2024) · Research Square · doi:10.21203/rs.3.rs-4537908/v1
- 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 › Surgery › Lipoproteins and Cardiovascular Health
- 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 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
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