Causal relationship between cholesterol-lowering therapy and Alzheimer Disease: evidence from genetic correlation and Mendelian randomization study

2 Methods

Ethical consent was not required as the data used in the study were obtained from publicly available databases. Figure 1 provides an overview of the study design.

2.1. Sources of GWAS data

The GWAS data for this study were obtained from two databases: UK Biobank (UKB), Global Lipids Genetics Consortium (GLGC), and GWAS ATLAS resource. The Neale laboratory in UKB provided genetic data for different cholesterol-lowering therapies, including atorvastatin, pravastatin, rosuvastatin, simvastatin, and ezetimibe. The sample sizes for each therapy group were as follows: 13,851 cases and 449,082 controls for atorvastatin, 2,208 cases and 460,725 controls for pravastatin, 2,870 cases and 460,063 controls for rosuvastatin, 52,427 cases and 410,506 controls for simvastatin, and 1,997 cases and 335,162 controls for ezetimibe. Genetic data for LDL-C were obtained from the GLGC, which included 173,082 samples providing laboratory indicators. Data on AD were acquired from a previous meta-analysis study, which included a total of 71,880 AD cases and 383,378 control subjects. All genetic datasets used in this research involved individuals of European ethnicity.

2.2. Screening conditions for instrumental variables

In order to investigate the relationship between statin therapy and AD, we conducted a univariable MR analysis using SNPs associated with statin therapy. We limited our analysis to SNPs within 1Mb with r^2 < 0.001 aggregation. SNPs were considered valid instruments if they had a P-value less than 5×10^-8, except for pravastatin-associated SNPs which required a P-value less than 1×10^-5 due to limited availability of IV SNPs, and ezetimibe-associated SNPs which required a P-value less than 5×10^-9 due to a shrinkage threshold. For the multivariable MR analysis, the IV SNPs for ezetimibe and pravastatin were also limited with a P-value of 1×10^-5. We ensured that the Hardy-Weinberg Law was met for all SNPs (32), and details of all instrument variables (IV) SNPs used in the analysis are listed in Supplementary Table 1. To eliminate the potential confounding effects of other variables on AD through alternative pathways, we removed SNPs associated with recognized dysfunction, age, and neurological disease using the phenoscanner V2.0 database (33). Details of all GWAS resource conducted in our study are listed in Table 1.

Table 1

**Details of the GWAS database resource included in the Mendelian randomization analysis.**
Consortium	Phenotype	Population	ncase	ncontrol	Sample size	Number of SNPs
Neale Lab Consortium	Atorvastatin therapy	European	13,851	449,082	462,933	9,851,867
Neale Lab Consortium	Provastatin therapy	European	2,208	460,725	462,933	9,851,867
Neale Lab Consortium	Rosuvastatin therapy	European	2,870	460,063	462,933	9,851,867
Neale Lab Consortium	Simvastatin therapy	European	52,427	410,506	462,933	9,851,867
Neale Lab Consortium	Ezetimibe therapy	European	1,997	335,162	337,159	10,894,596
Global Lipids Genetics Consortium	LDL cholesterol	Mixed	173,082	/	173,082	2,437,752
A meta-analysis of GWAS	Alzheimer Disease	European	71,880	383,378	455,258	13,367,301

Abbreviations: SNP, single nucleotide polymorphism; GWAS, genome-wide association studies.

2.3 Univariable MR analysis

For the univariable MR statistical analysis, we utilized various methods, including IVW (inverse-variance weighted), weighted median (WM), MR Egger, simple model, and weighted model, to investigate the causal relationship between GWAS effect alleles for statin therapy and AD. The IVW-MR method was primarily used to combine effect estimates by utilizing genetic variants associated with cholesterol lowering therapy as instruments. However, we also employed complementary methods such as MR Egger and WM to obtain more robust estimates in a wider range of scenarios, although these methods may have wider confidence intervals (CI). The "TwoSampleMR" package in R software version 4.2.2 was used for allele harmonization and analysis.

2.4 Multivariable MR analysis

For the multivariable MR analysis, we conducted an extended analysis of the univariate MR, known as multivariable MR, which allows for joint testing of the causal effects of multiple risk factors (34). This was done to investigate the comprehensive effects of various lipid-lowering drugs on AD. Multivariable MR takes into consideration that patients taking lipid-lowering drugs often use them in combination, and that ezetimibe-related SNPs used in MR analysis may be related to statin-type lipid-lowering drugs. The SNPs used for multivariable MR are combinations of instrumental variables for each exposure, while confounding variables related to outcomes, cognition, or age were excluded. Software packages such as "TwoSampleMR", "MendelianRandomization", and "MVMR" in R were used for the MR analysis.

2.5 Analysis of Drug Targets using MR

To identify potential genetic factors associated with LDL-C, we initially selected IV SNPs with a p-value less than 5×10^-8 and an r^2 value of less than 0.01. We then screened for candidate SNPs located within a ± 100 kb window of the targeted genes (HMGCR, NPC1L1, and PSCK9), ensuring that each SNP had a minor allele frequency greater than 1% (35). For single-SNP analyses, we used the Wald ratio method, while for multiple-SNP analyses, we employed instrumental variable weighted (IVW), MR-Egger, and WM methods to estimate effects using genetic variations linked to LDL-C levels as instruments. Heterogeneity and pleiotropy were assessed using established methods as described previously. Finally, allele coordination and analysis were performed using the "TwoSampleMR" package.

2.6 Sensitivity Analysis

To assess the validity and robustness of the results, we performed sensitivity analyses. We used the MR-Egger intercept test to assess horizontal pleiotropy, which is the potential for a single genetic variant to influence multiple traits or outcomes. Heterogeneity was estimated by Cochran's Q test to determine if any single instrument drove the results and to check consistency with the MR hypothesis. Additionally, we used MR-PRESSO to detect outliers or causal direction bias between two samples when enough instrumental variables existed (36–37). All analyses were conducted using the "TwoSampleMR" package and "MR-PRESSO" package in R software version 4.2.2.

3 Results

Overall, our results of MR estimates were not statistically significant, indicating that genetically predicted use of cholesterol-lowering therapy was not significantly associated with AD risk (Fig. 2).

3.1Univariable MR analyses

In our analysis, we utilized two-sample MR methods to investigate the causal relationship between simvastatin, atorvastatin, pravastatin, rosuvastatin, and ezetimibe, and AD. We identified 32, 15, 6, 4, and 5 SNPs, respectively, associated with each treatment, excluding SNPs related to the outcome or confounding factors. However, our IVW analysis did not reveal any significant causal effects for simvastatin (OR = 1.138, 95% CI = 0.976–1.328, p = 0.098). MR Egger (OR = 1.189, 95% CI = 0.834–1.695, p = 0.347) and WM analysis (OR = 1.078, 95% CI = 0.867–1.339, p = 0.501) showed similar risk estimates. Sensitivity analyses using simple and weighted models did not yield significant changes. Additionally, no outliers or heterogeneity were detected in our study (MR Egger p = 0.260; IVW p = 0.299 from Cochran Q test; MR-PRESSO p = 0.109), and no directional pleiotropy was observed (intercept=-0.0003; SE = 0.001; p = 0.792). Similar analyses were conducted for other cholesterol therapies and AD, but no significant effects were found (IVW results: Atorvastatin: OR = 0.943, 95% CI = 0.612–1.453, p = 0.789; Pravastatin: OR = 6.857, 95% CI = 0.514–90.864, p = 0.144; Rosuvastatin: OR = 2.466, 95% CI = 0.333–18.278, p = 0.377; Ezetimibe: OR = 1.292, 95% CI = 0.239-6,969, p = 0.766). Detailed data of IVW, MR Egger, and WM are presented in Table 2. Our results did not reveal any horizontal pleiotropy, outliers, or heterogeneity (Table 3).

Table 2

Mendelian randomization estimates of the causality associations from statin therapy to AD.
Exposure	Method	SNPs	OR	Low CI of OR	Up CI of OR	P_value
Atorvastatin therapy	MR Egger	15	0.758	0.217	2.654	0.672
Atorvastatin therapy	Weighted median	15	0.870	0.478	1.582	0.641
Atorvastatin therapy	Inverse variance weighted	15	0.943	0.612	1.453	0.789
Atorvastatin therapy	Simple mode	15	0.893	0.34	2.342	0.821
Atorvastatin therapy	Weighted mode	15	0.856	0.389	1.883	0.705
Provastatin therapy	MR Egger	6	230054.811	<0.001	8.91753E + 14	0.335
Provastatin therapy	Weighted median	6	6.188	0.234	163.821	0.276
Provastatin therapy	Inverse variance weighted	6	6.857	0.517	90.864	0.144
Provastatin therapy	Simple mode	6	4.964	0.035	704.471	0.554
Provastatin therapy	Weighted mode	6	4.272	0.033	554.371	0.584
Rosuvastatin therapy	MR Egger	4	4.794	<0.001	3045917252	0.893
Rosuvastatin therapy	Weighted median	4	2.582	0.252	26.413	0.424
Rosuvastatin therapy	Inverse variance weighted	4	2.466	0.333	18.278	0.377
Rosuvastatin therapy	Simple mode	4	3.128	0.126	77.628	0.537
Rosuvastatin therapy	Weighted mode	4	2.925	0.115	74.161	0.562
Simvastatin therapy	MR Egger	32	1.189	0.834	1.695	0.347
Simvastatin therapy	Weighted median	32	1.078	0.867	1.339	0.501
Simvastatin therapy	Inverse variance weighted	32	1.138	0.976	1.328	0.098
Simvastatin therapy	Simple mode	32	1.132	0.749	1.710	0.562
Simvastatin therapy	Weighted mode	32	1.120	0.846	1.481	0.435
Ezetimibe therapy	MR Egger	4	2.835	0.025	317.27	0.707
Ezetimibe therapy	Weighted median	4	1.055	0.152	7.336	0.957
Ezetimibe therapy	Inverse variance weighted	4	1.292	0.239	6.969	0.766
Ezetimibe therapy	Simple mode	4	0.857	0.061	12.052	0.916
Ezetimibe therapy	Weighted mode	4	0.810	0.064	10.257	0.881
Abbreviations: SNP, single nucleotide polymorphism; MR, mendelian randomization; OR, odds ratio; CI, confidence interval.

3.2 Multivariable MR analyses

We conducted a comprehensive multivariable MR study to investigate the effects of different statins and ezetimibe use on AD, taking into consideration that many patients are now using combination therapy. Firstly, we screened for significant exposure variables in multiple GWAS databases and controlled for confounding factors using the phenoscanner V2.0 database. Next, we analyzed the causal effect of statins and ezetimibe on AD. The results from inverse variance weighted (IVW) analysis showed no significant difference in the incidence of AD among different subtypes of statins when combined with ezetimibe (Simvastatin: OR = 0.758, 95% CI = 0.217–2.654, p = 0.461; Atorvastatin: OR = 0.758, 95% CI = 0.217–2.654, p = 0.782; Provastatin: OR = 0.758, 95% CI = 0.217–2.654, p = 0.689; Rosuvastatin: OR = 0.758, 95% CI = 0.217–2.654, p = 0.592). Furthermore, our multivariable MR analysis indicated that ezetimibe also did not affect the incidence of AD. Both Multivariable WM and Egger analyses revealed similar results. Additionally, MR-PRESSO and Cochran Q-test tests demonstrated no multicollinearity or heterogeneity, except in the Simvastatin & Ezetimibe group in our Multivariable MR analyses (Table 4).

Table 4

Causal relationships of Ezetimibe and statins therapy on AD estimated by multivariable MR.
Exposure	N.SNPs	MVMR-IVW OR (95% CI)	P_value	MVMR-Egger OR (95% CI)	P_value	MVMR-WM OR (95% CI)	P value	Egger (intercept) value	Cochran Q P_value
Simvastatin & Ezetimibe therapy	28							0.334	0.002
Simvastatin		1.196(0.742, 1.927)	0.461	1.513(0.734, 3.725)	0.225	1.704(0.975, 2.977)	0.061
Ezetimibe		0.318(0.007, 15.502)	0.564	8.551(0.002, 9.728)	0.368	0.034(0, 2.956)	0.138
Atorvastatin & Ezetimibe therapy	15							0.186	0.443
Atorvastatin		1.183(0.359, 3.904)	0.782	3.206(0.48, 21.413)	0.229	1.084(0.226, 5.202)	0.919
Ezetimibe		0.931(0.008, 105.425)	0.976	0.147(0.001, 34.674)	0.492	0.57(0.001, 268.54)	0.858
Provastatin & Ezetimibe therapy	22							0.508	0.966
Provastatin		1.298(0.361, 4.674)	0.689	2.678(0.22, 32.525)	0.440	2.337(0.446, 12.244)	0.315
Ezetimibe		8.534(0.785, 92.759)	0.078	10.444(0.892, 122.119)	0.062	5.618(0.23, 137.277)	0.290
Rosuvastatin & Ezetimibe therapy	4							1.000	0.334
Rosuvastatin		0.055(0, 2188.562)	0.592	0.055(0, 3158.966)	0.605	0.055(0, 2188.562)	0.885
Ezetimibe		36.162(0.002, 815861.59)	0.483	36.27(0, 1.56e + 11)	0.751	36.162(0.002, 815861.59)	0.793
Abbreviations: SNP, single nucleotide polymorphism; MVMR, multivariable mendelian randomization; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; N, number; WM, Weighted median.

3.3 Causal effect from targets of cholesterol-lowering drug to AD

After screening LDL-related cholesterol-lowering targets, our results did not provide suggestive evidence for a link between these targets (HMGCR, NPC1L1, and PCSK9) mediated LDL cholesterol (equivalent to a 1 mmol/L increase) and the incidence of AD. For HMGCR and NPC1L1, only one SNP remained after Wald ratio analysis was conducted (HMGCR: OR = 0.998, 95% CI = 0.942–1.059, p = 0.960; NPC1L1: OR = 1.019, 95% CI = 0.914–1.136, p = 0.736). For PCSK9, three SNPs were finally retained. The IVW-MR analysis also revealed no causal relationship (OR = 0.995, 95% CI = 0.965–1.026, p = 0.737) (Table 5). Additionally, WM and MR Egger analyses failed to provide any evidence suggesting that targeting lipid-lowering drugs could potentially impact AD. This finding further supports the idea that lipid-lowering therapy may not cause AD through its pharmacological targets.

Table 5

**Mendelian randomization estimates, heterogeneity and pleiotropy estimates of the causality associations from target exposure associated with LDL cholesterol level to AD.**
Exposure	Method	Causal Estimate (SD)	Intercept (SE)	P_value
Atorvastatin therapy	MR-PRESSO	0.059(0.172)		0.736
Atorvastatin therapy	MR Egger		0.001(0.002)	0.723
Atorvastatin therapy	Cochran Q MR Egger			0.819
Atorvastatin therapy	Cochran Q IVW			0.862
Provastatin therapy	MR-PRESSO	1.925(0.983)		0.107
Provastatin therapy	MR Egger		-0.008(0.009)	0.404
Provastatin therapy	Cochran Q MR Egger			0.752
Provastatin therapy	Cochran Q IVW			0.734
Rosuvastatin therapy	MR-PRESSO	0.903(0.684)		0.278
Rosuvastatin therapy	MR Egger		-0.001(0.013)	0.954
Rosuvastatin therapy	Cochran Q MR Egger			0.512
Rosuvastatin therapy	Cochran Q IVW			0.719
Simvastatin therapy	MR-PRESSO	0.130(0.078)		0.109
Simvastatin therapy	MR Egger		-0.0003(0.001)	0.792
Simvastatin therapy	Cochran Q MR Egger			0.260
Simvastatin therapy	Cochran Q IVW			0.299
Ezetimibe therapy	MR-PRESSO	0.256(0.598)		0.698
Ezetimibe therapy	MR Egger		-0.002(0.004)	0.760
Ezetimibe therapy	Cochran Q MR Egger			0.515
Ezetimibe therapy	Cochran Q IVW			0.694

Abbreviations: SNP, single nucleotide polymorphism; MR, mendelian randomization; OR, odds ratio; CI, confidence interval; IVW, inverse variance weighted; LDL-C, low-density lipoprotein cholesterol; HMGCR, 3-Hydroxy-3-Methylglutaryl Coenzyme A Reductase; NPC1L1, Niemann-Pick disease, type C1; PCSK9, Proprotein Convertase Subtilisin/Kexin Type 9.

4 Discussion

Cardiovascular diseases are becoming increasingly prevalent among the elderly, with 17.9 million deaths attributed to them in 2016, accounting for 31% of global deaths, as reported by the World Health Organization (WHO) (1). The incidence of cardiovascular disease is rising worldwide and poses a significant economic and social burden. Patients with cardiovascular and cerebrovascular diseases often require long-term medication therapy to lower their LDL-C cholesterol levels (38–39). Statins are recommended as first-line agents for adults with LDL-C levels greater than 190 mg/dl and diabetics with levels ranging from 70–190 mg/dl, as they have a low risk of adverse events and are effective in primary prevention of cardiovascular disease (40–42).

The standardized use of lipid-lowering drugs remains a topic of debate due to potential bias from study design in epidemiological studies, which can only establish correlation without elucidating temporal order or causality (27–28). In this study, the use of MR methods revealed causal relationships more accurately, without considering potential bias from study design. Our conclusion is consistent with previous high-quality meta-analyses. Using two sets of genetic instruments and excluding confounding factors, genetic evidence indicated no causal effect of statins on AD (AD) development (36–37). Furthermore, we found no significant causal relationship between statin or ezetimibe alone and the incidence of AD. Our results remained unchanged when considering the combination of cholesterol-lowering therapy. Carlos et al. demonstrated in a narrative review that most of the evidence for cognitive impairment caused by lipid-lowering drugs comes almost entirely from case reports (43). Although these case reports help identify potential clinical drug risks, they are limited by their observational nature, and it is important to interpret these reports accordingly (43). It is worth noting that the benefits and risks of statin use have been well established in several large-scale clinical cohort studies, showing a significant reduction in cardiovascular events, especially when used for secondary prevention (44–45). Currently, no large-sample clinical studies have found that statins can further impair existing cognitive impairment. Data from animal models and limited human data suggest that statin use may have cognitive benefits. The specific mechanism may be related to lowering plasma cholesterol levels, inhibiting cerebral vascular plaque formation, as well as non-cholesterol-related pathways such as alleviating endothelial dysfunction, increasing endothelial nitric oxide generation, anti-inflammatory effects, and antioxidant effects (23.25).

Regarding ezetimibe therapy, there are currently no reports of potential cognitive impairment effects. Existing research suggests that it may prevent further deterioration of cognition in rats with hyperlipidemia or AD rats, but more evidence is needed to determine its specific impact on cognition (18–19). PCSK9 inhibitors are the latest class of cholesterol-lowering drugs. A study conducted on a new monoclonal antibody against PCSK9 (LY3015014) showed that it did not have a significant impact on cognitive function when compared to placebo controls. However, the study was short-term in nature. Another ongoing prospective study included over 2,000 participants who were taking statins and PCSK9 inhibitor (evolocumab). However, no significant differences in cognition were observed during a series of cognitive tests, even when LDL-C levels reached the lowest point (below 0.65 mmol/L) for some participants (46–47). Olmastroni et al.'s systematic review also suggested that the benefit-harm balance of cholesterol-lowering drugs is generally favorable (42). In addition, lifestyle factors such as obesity, long-term smoking, alcohol abuse, and educational attainment may play more important roles in triggering AD compared to cholesterol-lowering therapy alone, but strong clinical evidence is still lacking (10–13).

However, there are still some key factors to consider when explaining the association between lipid-lowering drugs and AD. The first factor is the clinical significance of study results. For example, in one study, users of statins had a corrected Mini-Mental State Examination (MMSE) score of 93.7 compared to non-users who had a score of 92.7. Although these results were statistically significant, the difference was clinically irrelevant (48). The second factor to consider is the quality of studies involved. Multiple systematic reviews have shown that studies involving the effects of statins on cognitive impairment or AD are mostly observational or retrospective studies, and randomized controlled trials and stronger evidence are needed to determine their potential risks or benefits (41–42). Thirdly, statins or other lipid-lowering targeted drugs have not yet been explicitly approved by the FDA or Health Canada for the prevention or treatment of cognitive impairment.

In the past, LDL-C was considered a biomarker for cognitive impairment (49); however, current evidence suggests that normal doses of statins alone do not lead to very low LDL-C levels in the central nervous system due to the protection of the blood-brain barrier, which is maintained by cholesterol-like hormones and bile acids produced by the body (50–51). We also conducted an analysis on whether different lipid-lowering drugs that act on LDL-C causally induce AD. Interestingly, our results showed that the odds ratio (OR) values were very close to 1. Based on MR associations of both the drug itself and the drug targets with AD, neither of them were found to be significant, further confirming that the pharmacological effect of cholesterol-lowering therapy is unlikely to induce or aggravate the development of AD. It is worth noting that previous animal experiments have even found that users of PCSK9 inhibitors may have an increased risk of developing AD, suggesting that PCSK9 dysfunction may result in brain β-amyloid protein production and neuronal cell death, serving as a biomarker for AD (26). Therefore, in addition to reliable clinical evaluations of traditional statin drugs, we should not ignore the interaction between new lipid-lowering drugs and AD, as well as the potential cognitive impairment they may induce. Finally, it should be noted that although cognitive impairment is a rare side effect of lipid-lowering drugs, the clinical use by a large number of patients can amplify rare adverse reactions hundreds or thousands of times over. Therefore, standardized use of lipid-lowering drugs requires long-term follow-up investigations, observational trials, and randomized controlled trials with specific designs.

Our study has several limitations. Firstly, we were unable to directly analyze the causal relationship between the use of PCSK9 inhibitors and the incidence of AD due to the lack of related GWAS data. Secondly, MR analysis is limited by the population from which genetic data is obtained; therefore, our findings may not be representative of the entire British or European population, and there may be racial genetic differences between populations. Thirdly, some subgroups of SNPs for statin drugs have small sample sizes, which may not provide accurate correlations. Finally, since different types of statin drugs have varying pharmacological mechanisms and limited ability to cross the blood-brain barrier, our findings may not capture all tissue-specific relationships for all types of statin drugs.

References

Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O'Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS. Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation 2019; 139:e56-e528
Algotsson A, Winblad B. Patients with Alzheimer's disease may be particularly susceptible to adverse effects of statins. Dementia and geriatric cognitive disorders 2004; 17:109-116
Evans MA, Golomb BA. Statin-associated adverse cognitive effects: survey results from 171 patients. Pharmacotherapy 2009; 29:800-811
Williams DM, Finan C, Schmidt AF, Burgess S, Hingorani AD. Lipid lowering and Alzheimer disease risk: A mendelian randomization study. Annals of neurology 2020; 87:30-39
Ritchie K, Ropacki M, Albala B, Harrison J, Kaye J, Kramer J, Randolph C, Ritchie CW. Recommended cognitive outcomes in preclinical Alzheimer's disease: Consensus statement from the European Prevention of Alzheimer's Dementia project. Alzheimer's & dementia : the journal of the Alzheimer's Association 2017; 13:186-195
Scheltens P, De Strooper B, Kivipelto M, Holstege H, Chételat G, Teunissen CE, Cummings J, van der Flier WM. Alzheimer's disease. Lancet (London, England) 2021; 397:1577-1590
The Lancet N. Will Europe be ready for the treatment of Alzheimer's disease? The Lancet Neurology 2018; 17:1025
Bakhta K, Cecillon E, Lacombe E, Lamy M, Leboucher A, Philippe J. Alzheimer's disease and neurodegenerative diseases in France. Lancet (London, England) 2019; 394:466-467
Onyango IG, Jauregui GV, Čarná M, Bennett JP, Jr., Stokin GB. Neuroinflammation in Alzheimer's Disease. Biomedicines 2021; 9
Hampel H, Hardy J, Blennow K, Chen C, Perry G, Kim SH, Villemagne VL, Aisen P, Vendruscolo M, Iwatsubo T, Masters CL, Cho M, Lannfelt L, Cummings JL, Vergallo A. The Amyloid-β Pathway in Alzheimer's Disease. Molecular psychiatry 2021; 26:5481-5503
Padala KP, Padala PR, McNeilly DP, Geske JA, Sullivan DH, Potter JF. The effect of HMG-CoA reductase inhibitors on cognition in patients with Alzheimer's dementia: a prospective withdrawal and rechallenge pilot study. The American journal of geriatric pharmacotherapy 2012; 10:296-302
Muldoon MF, Barger SD, Ryan CM, Flory JD, Lehoczky JP, Matthews KA, Manuck SB. Effects of lovastatin on cognitive function and psychological well-being. The American journal of medicine 2000; 108:538-546
Barnes DE, Yaffe K. The projected effect of risk factor reduction on Alzheimer's disease prevalence. The Lancet Neurology 2011; 10:819-828
Ishii M, Iadecola C. Risk factor for Alzheimer's disease breaks the blood-brain barrier. Nature 2020; 581:31-32
Wagstaff LR, Mitton MW, Arvik BM, Doraiswamy PM. Statin-associated memory loss: analysis of 60 case reports and review of the literature. Pharmacotherapy 2003; 23:871-880
King DS, Wilburn AJ, Wofford MR, Harrell TK, Lindley BJ, Jones DW. Cognitive impairment associated with atorvastatin and simvastatin. Pharmacotherapy 2003; 23:1663-1667
Padala KP, Padala PR, Potter JF. Simvastatin-induced decline in cognition. The Annals of pharmacotherapy 2006; 40:1880-1883
Dalla Y, Singh N, Jaggi AS, Singh D, Ghulati P. Potential of ezetimibe in memory deficits associated with dementia of Alzheimer's type in mice. Indian journal of pharmacology 2009; 41:262-267
Boimel M, Grigoriadis N, Lourbopoulos A, Touloumi O, Rosenmann D, Abramsky O, Rosenmann H. Statins reduce the neurofibrillary tangle burden in a mouse model of tauopathy. Journal of neuropathology and experimental neurology 2009; 68:314-325
Papotti B, Adorni MP, Marchi C, Zimetti F, Ronda N, Panighel G, Lupo MG, Vilella A, Giuliani D, Ferri N, Bernini F. PCSK9 Affects Astrocyte Cholesterol Metabolism and Reduces Neuron Cholesterol Supplying In Vitro: Potential Implications in Alzheimer's Disease. International journal of molecular sciences 2022; 23
Jonas MC, Costantini C, Puglielli L. PCSK9 is required for the disposal of non-acetylated intermediates of the nascent membrane protein BACE1. EMBO reports 2008; 9:916-922
Nunley KA, Orchard TJ, Ryan CM, Miller R, Costacou T, Rosano C. Statin use and cognitive function in middle-aged adults with type 1 diabetes. World journal of diabetes 2017; 8:286-296
Swiger KJ, Martin SS, Tang F, Blaha MJ, Blumenthal RS, Alexander KP, Arnold SV, Spertus JA. Cognitive and Physical Function by Statin Exposure in Elderly Individuals Following Acute Myocardial Infarction. Clinical cardiology 2015; 38:455-461
Tan B, Rosenfeldt F, Ou R, Stough C. Evidence and mechanisms for statin-induced cognitive decline. Expert review of clinical pharmacology 2019:1-10
McGuinness B, Craig D, Bullock R, Passmore P. Statins for the prevention of dementia. The Cochrane database of systematic reviews 2016; 2016:Cd003160
Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. Journal of neuropathology and experimental neurology 2011; 70:960-969
Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Statistics in medicine 2008; 27:1133-1163
Swerdlow DI, Kuchenbaecker KB, Shah S, Sofat R, Holmes MV, White J, Mindell JS, Kivimaki M, Brunner EJ, Whittaker JC, Casas JP, Hingorani AD. Selecting instruments for Mendelian randomization in the wake of genome-wide association studies. International journal of epidemiology 2016; 45:1600-1616
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, Downey P, Elliott P, Green J, Landray M, Liu B, Matthews P, Ong G, Pell J, Silman A, Young A, Sprosen T, Peakman T, Collins R. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS medicine 2015; 12:e1001779
Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S, Beckmann JS, Bragg-Gresham JL, Chang HY, Demirkan A, Den Hertog HM, Do R, Donnelly LA, Ehret GB, Esko T, Feitosa MF, Ferreira T, Fischer K, Fontanillas P, Fraser RM, Freitag DF, Gurdasani D, Heikkilä K, Hyppönen E, Isaacs A, Jackson AU, Johansson Å, Johnson T, Kaakinen M, Kettunen J, Kleber ME, Li X, Luan J, Lyytikäinen LP, Magnusson PKE, Mangino M, Mihailov E, Montasser ME, Müller-Nurasyid M, Nolte IM, O'Connell JR, Palmer CD, Perola M, Petersen AK, Sanna S, Saxena R, Service SK, Shah S, Shungin D, Sidore C, Song C, Strawbridge RJ, Surakka I, Tanaka T, Teslovich TM, Thorleifsson G, Van den Herik EG, Voight BF, Volcik KA, Waite LL, Wong A, Wu Y, Zhang W, Absher D, Asiki G, Barroso I, Been LF, Bolton JL, Bonnycastle LL, Brambilla P, Burnett MS, Cesana G, Dimitriou M, Doney ASF, Döring A, Elliott P, Epstein SE, Ingi Eyjolfsson G, Gigante B, Goodarzi MO, Grallert H, Gravito ML, Groves CJ, Hallmans G, Hartikainen AL, Hayward C, Hernandez D, Hicks AA, Holm H, Hung YJ, Illig T, Jones MR, Kaleebu P, Kastelein JJP, Khaw KT, Kim E, Klopp N, Komulainen P, Kumari M, Langenberg C, Lehtimäki T, Lin SY, Lindström J, Loos RJF, Mach F, McArdle WL, Meisinger C, Mitchell BD, Müller G, Nagaraja R, Narisu N, Nieminen TVM, Nsubuga RN, Olafsson I, Ong KK, Palotie A, Papamarkou T, Pomilla C, Pouta A, Rader DJ, Reilly MP, Ridker PM, Rivadeneira F, Rudan I, Ruokonen A, Samani N, Scharnagl H, Seeley J, Silander K, Stančáková A, Stirrups K, Swift AJ, Tiret L, Uitterlinden AG, van Pelt LJ, Vedantam S, Wainwright N, Wijmenga C, Wild SH, Willemsen G, Wilsgaard T, Wilson JF, Young EH, Zhao JH, Adair LS, Arveiler D, Assimes TL, Bandinelli S, Bennett F, Bochud M, Boehm BO, Boomsma DI, Borecki IB, Bornstein SR, Bovet P, Burnier M, Campbell H, Chakravarti A, Chambers JC, Chen YI, Collins FS, Cooper RS, Danesh J, Dedoussis G, de Faire U, Feranil AB, Ferrières J, Ferrucci L, Freimer NB, Gieger C, Groop LC, Gudnason V, Gyllensten U, Hamsten A, Harris TB, Hingorani A, Hirschhorn JN, Hofman A, Hovingh GK, Hsiung CA, Humphries SE, Hunt SC, Hveem K, Iribarren C, Järvelin MR, Jula A, Kähönen M, Kaprio J, Kesäniemi A, Kivimaki M, Kooner JS, Koudstaal PJ, Krauss RM, Kuh D, Kuusisto J, Kyvik KO, Laakso M, Lakka TA, Lind L, Lindgren CM, Martin NG, März W, McCarthy MI, McKenzie CA, Meneton P, Metspalu A, Moilanen L, Morris AD, Munroe PB, Njølstad I, Pedersen NL, Power C, Pramstaller PP, Price JF, Psaty BM, Quertermous T, Rauramaa R, Saleheen D, Salomaa V, Sanghera DK, Saramies J, Schwarz PEH, Sheu WH, Shuldiner AR, Siegbahn A, Spector TD, Stefansson K, Strachan DP, Tayo BO, Tremoli E, Tuomilehto J, Uusitupa M, van Duijn CM, Vollenweider P, Wallentin L, Wareham NJ, Whitfield JB, Wolffenbuttel BHR, Ordovas JM, Boerwinkle E, Palmer CNA, Thorsteinsdottir U, Chasman DI, Rotter JI, Franks PW, Ripatti S, Cupples LA, Sandhu MS, Rich SS, Boehnke M, Deloukas P, Kathiresan S, Mohlke KL, Ingelsson E, Abecasis GR. Discovery and refinement of loci associated with lipid levels. Nature genetics 2013; 45:1274-1283
Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK, Hägg S, Athanasiu L, Voyle N, Proitsi P, Witoelar A, Stringer S, Aarsland D, Almdahl IS, Andersen F, Bergh S, Bettella F, Bjornsson S, Brækhus A, Bråthen G, de Leeuw C, Desikan RS, Djurovic S, Dumitrescu L, Fladby T, Hohman TJ, Jonsson PV, Kiddle SJ, Rongve A, Saltvedt I, Sando SB, Selbæk G, Shoai M, Skene NG, Snaedal J, Stordal E, Ulstein ID, Wang Y, White LR, Hardy J, Hjerling-Leffler J, Sullivan PF, van der Flier WM, Dobson R, Davis LK, Stefansson H, Stefansson K, Pedersen NL, Ripke S, Andreassen OA, Posthuma D. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk. Nature genetics 2019; 51:404-413
Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. International journal of epidemiology 2011; 40:740-752
Kamat MA, Blackshaw JA, Young R, Surendran P, Burgess S, Danesh J, Butterworth AS, Staley JR. PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics (Oxford, England) 2019; 35:4851-4853
Burgess S, Thompson SG. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. American journal of epidemiology 2015; 181:251-260
Huang W, Xiao J, Ji J, Chen L. Association of lipid-lowering drugs with COVID-19 outcomes from a Mendelian randomization study. eLife 2021; 10
Ong JS, MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective. Genetic epidemiology 2019; 43:609-616
Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. European journal of epidemiology 2017; 32:377-389
Spencer-Bonilla G, Chung S, Sarraju A, Heidenreich P, Palaniappan L, Rodriguez F. Statin Use in Older Adults with Stable Atherosclerotic Cardiovascular Disease. Journal of the American Geriatrics Society 2021; 69:979-985
Kim BK, Hong SJ, Lee YJ, Hong SJ, Yun KH, Hong BK, Heo JH, Rha SW, Cho YH, Lee SJ, Ahn CM, Kim JS, Ko YG, Choi D, Jang Y, Hong MK. Long-term efficacy and safety of moderate-intensity statin with ezetimibe combination therapy versus high-intensity statin monotherapy in patients with atherosclerotic cardiovascular disease (RACING): a randomised, open-label, non-inferiority trial. Lancet (London, England) 2022; 400:380-390
Psaty BM, Weiss NS. 2013 ACC/AHA guideline on the treatment of blood cholesterol: a fresh interpretation of old evidence. Jama 2014; 311:461-462
Cai T, Abel L, Langford O, Monaghan G, Aronson JK, Stevens RJ, Lay-Flurrie S, Koshiaris C, McManus RJ, Hobbs FDR, Sheppard JP. Associations between statins and adverse events in primary prevention of cardiovascular disease: systematic review with pairwise, network, and dose-response meta-analyses. BMJ (Clinical research ed) 2021; 374:n1537
Olmastroni E, Molari G, De Beni N, Colpani O, Galimberti F, Gazzotti M, Zambon A, Catapano AL, Casula M. Statin use and risk of dementia or Alzheimer's disease: a systematic review and meta-analysis of observational studies. European journal of preventive cardiology 2022; 29:804-814
Rojas-Fernandez CH, Cameron JC. Is statin-associated cognitive impairment clinically relevant? A narrative review and clinical recommendations. The Annals of pharmacotherapy 2012; 46:549-557
Genest J, McPherson R, Frohlich J, Anderson T, Campbell N, Carpentier A, Couture P, Dufour R, Fodor G, Francis GA, Grover S, Gupta M, Hegele RA, Lau DC, Leiter L, Lewis GF, Lonn E, Mancini GB, Ng D, Pearson GJ, Sniderman A, Stone JA, Ur E. 2009 Canadian Cardiovascular Society/Canadian guidelines for the diagnosis and treatment of dyslipidemia and prevention of cardiovascular disease in the adult - 2009 recommendations. The Canadian journal of cardiology 2009; 25:567-579
Collins R, Armitage J, Parish S, Sleight P, Peto R. Effects of cholesterol-lowering with simvastatin on stroke and other major vascular events in 20536 people with cerebrovascular disease or other high-risk conditions. Lancet (London, England) 2004; 363:757-767
Henderson VW, Guthrie JR, Dennerstein L. Serum lipids and memory in a population based cohort of middle age women. Journal of neurology, neurosurgery, and psychiatry 2003; 74:1530-1535
Olsson AG, Angelin B, Assmann G, Binder CJ, Björkhem I, Cedazo-Minguez A, Cohen J, von Eckardstein A, Farinaro E, Müller-Wieland D, Parhofer KG, Parini P, Rosenson RS, Starup-Linde J, Tikkanen MJ, Yvan-Charvet L. Can LDL cholesterol be too low? Possible risks of extremely low levels. Journal of internal medicine 2017; 281:534-553
Yaffe K, Barrett-Connor E, Lin F, Grady D. Serum lipoprotein levels, statin use, and cognitive function in older women. Archives of neurology 2002; 59:378-384
Robinson JG, Farnier M, Krempf M, Bergeron J, Luc G, Averna M, Stroes ES, Langslet G, Raal FJ, El Shahawy M, Koren MJ, Lepor NE, Lorenzato C, Pordy R, Chaudhari U, Kastelein JJ. Efficacy and safety of alirocumab in reducing lipids and cardiovascular events. The New England journal of medicine 2015; 372:1489-1499
Goldstein JL, Brown MS. A century of cholesterol and coronaries: from plaques to genes to statins. Cell 2015; 161:161-172
Sabatine MS, Giugliano RP, Wiviott SD, Raal FJ, Blom DJ, Robinson J, Ballantyne CM, Somaratne R, Legg J, Wasserman SM, Scott R, Koren MJ, Stein EA. Efficacy and safety of evolocumab in reducing lipids and cardiovascular events. The New England journal of medicine 2015; 372:1500-1509