Atorvastatin, simvastatin, rosuvastatin and Amyotrophic lateral sclerosis establishing cause and effect

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Previous observational studies and experimental investigations have indicated that statin is associated with Amyotrophic Lateral Sclerosis (ALS). However, the causal relationship remains unclear.The present study employs a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between atorvastatin, simvastatin, rosuvastatin and ALS at the nenetic level. Methods: The study utilized genome-wide association studies (GWAS) based on single-nucleotide polymorphisms (SNPs) for three statins (atorvastatin, simvastatin, and rosuvastatin), and encompassing data of 462,933 participants obtained from the UK Biobank, 80,610 individuals of ALS in genetic level data from European. The investigation of causal effects implemented five methods: inverse variance weighting (IVW), MR-Egger regression, wighted median, simple mode, and weighted mode. To detect horizontal pleiotropy, the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test were employed. Instrument heterogeneity was evaluated by Cochran’s Q statistics. Sensitivity analysis was performed via the leave-one-out method. Results: The MR analysis suggest a potential causal relationship between atorvastatin, simvastatin, and rosuvastatin use and the risk of ALS, with the odds ratio (OR) and confidence interval (CI) providing further insight into the strength of this association. The results estimate for three statins use revealed a significantly elevated risk of ALS, atorvastatin (OR = 16.93, 95% CI: 5.42-52.89, p = 1.13E-06), simvastatin (OR = 5.05, 95% CI: 2.92-8.75, p = 7.49E-09), rosuvastatin (OR = 6.93E+5, 95% CI: 247.72-1.94E+9, p = 8.97-05). The sensitivity analysis highlighted the stability and reliability of the casual results. Conclusions: The present study provided genetic evidence that three statins (atorvastatin, simvastatin, rosuvastatin) were associated with the increased risk of ALS. Given the drug's effectiveness and potential side effects, individuals at higher risk of ALS should be cautious about th use of statins. Further investigations and robust reserch are needed to confirm the results, and the findings will provide valuable guidance for the drug use of ALS patients. Amyotrophic Lateral Sclerosis Statin Mendelian randomization Genome wide association study (GWAS) Causal relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Amyotrophic lateral sclerosis (ALS) is a cental neurodegenerative disease of central nervous system, also known as motor neuron disease[ 1 ]. ALS is characterized by the progressive degeneration of both upper motor neurons (UMN) and lower motor neurons (LMN), resulting in limb weakness, difficulty swallowing, speech impairments, and ultimately respiratory paralysis, which can lead to death[ 2 ]. Currently, ALS is recognized as one of the four major neurodegenerative diseases, alongside with Alzheimer’s disease (AD), Parkinson’s disease (PD), and Huntington’s disease (HD)[ 3 ]. While these diseases share common pathological features, including the accumulation of abnormal proteins and the disruption of cellular homeostasis, ALS stands out due to its rapid and relentless course, with most patients surviving only 3–5 years after diagnosis[ 4 , 5 ]. Despite significant advances in our understanding of ALS pathophysiology, the exact causes of the disease remain largely unknown, and there are currently no effective disease-modifying treatments. According to current studies, ALS is influenced by genetic or environmental factors, which can be classified into hereditary and sporadic forms[ 6 , 7 ]. Hereditary ALS is primarily associated with mutations in the gene encoding of Cu/Zn-dependent superoxide dismutase 1 (SOD1), which is located on chromosome 21, accounting for about 5–10% of cases[ 8 ]. The remaining 90–95% of ALS cases are classified as sporadic and are largely linked to environmental exposures[ 9 ]. Research has shown that factors such as plasma levels of organic pollutants, metals, pesticides, electromagnetic fields, smoking, alcohol consumption, fungi, and intense physical activity may increase the risk of developing ALS[ 10 – 17 ]. Additionally, recent studies suggest a potential link between certain medications and ALS, including antibiotics, antidiabetic drugs, antihypertensive medications, or anticancer agents[ 18 , 19 ]. Compared to other environmental risk factors, the role of medications in ALS has been relatively under researched, which makes medication use in ALS patients particularly challenging. Therefore, it is crucial to further investigate the effects of drug use in ALS patients to develop safer medication practices for this population. Statins are inhibitors of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase. HMG-CoA reductase is a key enzyme in the biosynthesis of cholesterol. By competitively inhibiting HMG-CoA reductase, statins effectively reduce the step of endogenous cholesterol synthesis pathway, preventing the conversion of HMG-CoA to mevalonate[ 20 ]. This inhibition leads to a significant reduction in the production of cholesterol within the liver, thereby decreasing overall cholesterol levels in the bloodstream[ 21 ]. As a result, statins have become widely prescribed for the management of hypercholesterolemia and hyperlipidemia, the main factors that contribute to the development of atherosclerosis and cardiovascular disease[ 22 , 23 ]. In addition to lipid lowering effects, statins also exert pleiotropic effects, including anti-inflammatory, antioxidant, and endothelial function-improving properties, which may further contribute to their cardiovascular protective effects[ 24 – 26 ]. Usually, statins can be classified based on their solubility into two groups: lipid-soluble (lipophilic) statins, such as simvastatin and atorvastatin, and water-soluble (hydrophilic) statins, such as rosuvastatin. Lipophilic statins are more readily absorbed by tissues, including the liver and muscle, due to their ability to pass through cell membranes[ 27 ]. In contrast, hydrophilic statins, such as rosuvastatin, are more soluble in water and are less likely to accumulate in non-hepatic tissues, potentially leading to a lower incidence of muscle-related side effects[ 27 , 28 ]. The choice between these statins may depend on various clinical factors, including the patient's individual risk profile and tolerance to side effects. Studies have reported a potential link between statin and ALS. For example, atorvastatin has been shown to protect motor neuron-like cells (NSC-34D) via scavenging free radicals[ 29 ]. Simvastatin has been reported to promote muscle regeneration in mice by activating the mTOR pathway[ 30 ], reducing astrocyte activation, and inhibiting the release of inflammatory cytokines[ 31 ]. These findings suggest that statin may have a protective effect against ALS. However, observational studies have raised concerns that statins use may increase the risk of ALS, with long-term statin therapy potentially having adverse effects on ALS patients. For instance, ALS patients on statins have an increased frequency of muscle cramps[ 32 ]. A cohort study found that statin use was associated with a higher risk of ALS in both men and women, with a notably higher risk in women. Additionally, neurotoxic effects of statins were observed to be more pronounced in the early stages of ALS in women compared to men[ 33 ]. To date, no definitive conclusion has been reached regarding the impact of statin use on ALS. Therefore, robust statistical methods are urgently needed to clarify the causal relationship between statin therapy and ALS risk. Mendelian Randomization (MR) is a method that leverages single nucleotide polymorphisms (SNPs) as instrumental variables to infer causal relationship[ 34 ]. In this study, we applied MR to robustly establish the relationship between statin use and ALS. Unlike traditional observational studies, MR capitalizes on the random assortment of genetic variants during meiosis, which allows more effectively exclude potential reverse causality and confounding factors[ 35 ]. This approach is similar to a genetically randomized controlled trial and avoids the high costs associated with clinical trials[ 36 , 37 ]. We conducted a two-sample MR analysis to investigate the causal relationship between statin use and ALS, utilizing genome wide association study (GWAS) data. This analysis aims to clarify the link between statins and ALS, providing a stronger evidence base for clinical decision-making. 2. Materials and Methods 2.1. study design The study flowchart is presented in Fig. 1 . A two-sample MR analysis was used to evaluate the relationship between statin and ALS. First, atorvastatin, simvastatin and rosuvastatin were selected as exposures, ALS as outcome. Next, we identified appropriate single nucleotide polymorphisms (SNPs) to serve as instrumental variables (IVs). The selected IVs must satisfy three key assumptions[ 38 ]: (1) the IVs are strongly associated with the three statin exposures (relevance assumption); (2) the IVs are not directly correlated with ALS through any confounders (independence assumption); (3) the IVs affect ALS only through their influence on the statin exposures, with no other indirect correlations (exclusivity assumption). In other words, IVs can only influence the outcome through the pathway: “IVs → exposure → outcome”. We applied five methods: inverse variance weighted (IVW), MR–Egger regression, weighted median, simple mode, and weighted mode. Additionally, we used multiple methods for sensitivity analysis to ensure the robustness of the results. The MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test were conducted to detect horizontal pleiotropy. Cochran’s Q statistics were used to evaluate instrument heterogeneity. Finally, we performed a sensitivity analysis using the leave-one-out method[ 39 – 41 ]. 2.2. Data source In this study, exposure data were extracted from the UK Biobank, which contains genotype data from approximately 500,000 individuals. We extracted exposure data related to statin use from three distinct datasets corresponding to the three most commonly prescribed statins: atorvastatin dataset (ukb-b-10008), comprising 13,851 cases and 449,082 controls; simvastatin dataset (ukb b-11268), comprising 52,427 cases and 410,506 controls; rosuvastatin dataset (ukb-b-13664), comprising 2,870 cases and 460,063 controls. ALS GWAS data were obtained from the International Oncology Unit (IEU) Open GWAS project ( https://gwas.mrcieu.ac.uk/datasets/ ), which is a publicly available resource that aggregates genetic data on various diseases and conditions. The ALS GWAS data encompass both ALS patients and controls, providing a comprehensive genetic profile of the disease. Detailed information about the four GWAS datasets is provided in Table 1 . The database is publicly available for download from the IEU Open GWAS platform, and as it is freely accessible, no ethical review approval or participant consent is required. 2.3. Selection of the instrumental variables Independence and exclusion of confounding are critical aspects of Mendelian randomization analysis[ 42 ]. Three core assumptions must be satisfied. Initially, SNPs should be strongly associated with exposures. SNPs with p -value less than 5×10 − 8 were selected as IVs, to ensure the genetic variants were robustly linked to the exposures under investigation[ 43 ]. This stringent criterion helped to ensure that the SNPs used as IVs would be suitable for estimating causal effects. Second, to minimize potential bias arising from confounding due to genetic correlation between SNPs, it is essential to exclude SNPs with strong linkage disequilibrium (LD), we applied an r² threshold of less than 0.001 within 10,000 kb physical window, ensuring the independence of the IVs. Third, the F-statistic was used to assess the strength of IVs, with F > 10 to avoid weak instruments. By this threshold, we ensured that the IVs included in our analysis had sufficient statistical power to provide reliable and precise estimates of the causal relationship. In addition to these criteria, we removed incompatible and palindromic SNPs, further refined the selection of IVs. Following these rigorous screening process, the remaining SNPs were deemed appropriate and selected as the final set of IVs for our Mendelian randomization analysis. 2.4. Statistical analysis In our research, we used several methods to analyze the causal relationship between atorvastatin, simvastatin, rosuvastatin and ALS, including IVW analysis, MR–Egger regression, weighted median, simple mode, and weighted mode. IVW is the primary method, in which the intercept term was excluded from the regression, and the inverse of outcome variance was used as the weight for fitting the model. The other four methods were served as supplementary analyses to IVW. By combining these multiple approaches, we aimed to ensure the validity and consistency of our conclusions, minimizing the impact of potential biases and model assumptions[ 44 , 45 ]. Cochran’s Q test was adopted to evaluate heterogeneity between each SNP estimate, with heterogeneity considered significant at p -value < 0.05. Horizontal pleiotropy was assessed using the MR-Egger intercept test and MR-PRESSO global test (pleiotropy was considered present if p < 0.05)[ 46 ]. Additionally, the MR-PRESSO was employed to identify the potential outliers. If an outlier was detected, it was excluded, and the MR analysis was rerun. Finally, the leave-one-out method was used to analyse sensitivity and reliability of the model: each SNP was gradually removed, and the remaining SNPs were analyzed to determine whether the results changed. All analyses were performed using the “Two Sample MR” and “MR-PRESSO” packages in R version 4.3.0. 3. Results 3.1. Instrumental variables of atorvastatin, simvastatin and rosuvastatin Through strict screening of SNPs, we identified 17 SNPs highly associated with atorvastatin use, 29 SNPs highly associated with simvastatin use, and 4 SNPs highly associated with rosuvastatin use. These SNPs were extracted from three distinct datasets within the UK Biobank: the ukb-b-10008 dataset for atorvastatin, the ukb-b-11268 dataset for simvastatin, and the ukb-b-13664 dataset for rosuvastatin. Each of these SNPs met the criteria for inclusion as IVs for the corresponding statin exposure, having passed the stringent selection process outlined above. Detailed information was provided in Table 2 . 3.2. Causal relationship of atorvastatin use and ALS According to Table 3 , the results of IVW indicated that atorvastatin was a risk factor for ALS (OR = 16.93, 95% CI: 5.42–52.89, p = 1.13E-06). The MR–Egger regression, weighted median, simple mode, and weighted mode showed the same trend. Results were visualized in scatter plot. Scatter plot illustrated a positive linear correlation for atorvastatin, suggesting that elevated atorvastatin use were associated with a higher probability of ALS development (Fig. 2 A). The results of Cochran’s Q heterogeneity test, MR-Egger intercept test and MR-PRESSO global test were shown in Table 4 . The p -value of Cochran’s Q was greater than 0.05, indicating no significant heterogeneity. Additionally, MR-Egger regression intercept analysis ( p = 0.17) suggested no significant pleotropy, and the MR-PRESSO global test ( p = 0.854) did not detect any abnormal SNPs in our funding. Funnel plots showed symmetric distribution of the SNPs, the plots exhibited a symmetrical distribution o IVs on both sides of the IVW, indicating the absence of significant outliers, and confirmation that the Mendelian randomization (MR) analysis adhered to the core principles of MR grouping (Fig. 2 B). Furthermore, the leave-one-out sensitivity analysis demonstrated that the removal of any single SNP did not significantly alter the causal relationship between atorvastatin use and ALS (Fig. 2 C). Finally, forest plots were constructed to evaluate the predictive efficacy of each SNP locus in relation to exposure factors and outcomes. In these plots, solid dots on the left indicated lower risk, while those on the right signified higher risk. The forest plot results consistently placed solid dots on the right, suggesting that increased atorvastatin use were associated with a higher risk of ALS, as consistent with the IVW approach (Fig. 2 D). These results suggest that the findings are robust and stable. 3.3. Causal relationship of simvastatin use and ALS The results of the MR analysis examining the effects of simvastatin use on ALS are shown in Table 3 . The IVW method revealed that simvastatin use was associated with a significantly increased risk of ALS (OR = 5.05, 95% CI: 2.92–8.75, p = 7.49E-09). This finding suggests that higher exposure to simvastatin may contribute to an elevated likelihood of ALS onset, aligning with the hypothesis of a potential causal relationship. This conclusion was supported by MR-Egger regression, the weighted median, simple mode, and weighted mode. Each of these methods supported the initial IVW findings, further enhancing the reliability and consistency of the observed effect. To visualize and further corroborate these results, the association between simvastatin use and ALS was illustrated through scatter plot. Scatter plot clearly depicted the relationship between simvastatin use and ALS risk, visually supporting the positive association observed in the statistical analyses (Fig. 3 A). The p -value of Cochran’s Q ( p = 0.0353) was less than 0.05, so we used a random effects IVW MR analysis[ 47 ]. MR-Egger regression intercept test ( p = 0.527) and MR-PRESSO global test (NA) indicated that there were no significant outliers, as comfirmed by the funnel plot (Fig. 3 B). The leave-one-out analysis showed that the results remained consistent after removing each SNP (Fig. 3 C). All sensitivity analysis proved MR analysis results were reliable. The forest plot, which shows the individual effect estimates for each SNP associated with simvastatin use, further confirmed the robustness of the findings by indicating that the majority of the SNPs were aligned with a higher risk of ALS (Fig. 3 D). Collectively, these visual representations underscored the consistency of the results and affirmed that simvastatin use may act as a risk factor for ALS. 3.4. Causal relationship of rosuvastatin use and ALS The results of MR analysis evaluated the relationship of rosuvastatin use and ALS were presented in Table 3 . Similar to the two statins, the IVW showed that rosuvastatin use was a risk factor of ALS (OR = 6.93E + 5, 95% CI: 247.72-1.94E + 9, p = 8.97-05). Other four methods (MR–Egger regression, weighted median, simple mode, weighted mode) sshowed the same trend. The results were visually represented in scatter plot, which supported the potential risk between rosuvastatin use and ALS (Fig. 4 A). The Cochran’s Q result of IVW ( p = 0.022) was less than 0.05, thus random effects IVW MR analysis was used. Additionally, MR-Egger regression intercept test ( p = 0.477), suggesting no significant evidence of directional pleiotropy. MR-PRESSO global test (NA) did not show significant pleiotropy, which further supported the validity of the instrumental variables. The absence of significant pleiotropy was visually confirmed by the funnel plot, which showed an even distribution of IVs (Fig. 4 B). Finally, the leave-one-out method demonstrated the results did not change after removing any SNP (Fig. 4 C). The forest plot was shown in Fig. 4 D. All these results provide strong evidence that rosuvastatin use may be adverse to ALS, with no significant confounding or pleiotropic effects detected, thereby supporting the validity of our causal inference. 4. Discussion In this study, we used GWAS data to perform a two-sample Mendelian randomization analysis to evaluate the impact of three statins (atorvastatin, simvastatin, and rosuvastatin) on ALS. The primary objective of this analysis was to overcome the limitations of traditional observational studies by leveraging genetic variants as IVs, thus providing a more reliable estimate of causality and reducing confounding factors. By using summary-level data from large-scale GWAS, we were able to investigate the genetic basis of statin use and its association with ALS in a large cohort, further enhancing the generalizability of our analysis. The results indicated that the use of three drugs is associated with an increased risk of ALS, with genetically determined higher use of these medications associated with an elevated risk of developing the disease. ALS is a rare, fatal neurodegenerative disease with limited therapeutic options[ 48 ]. Currently, the FDA has approved Riluzole and Edaravone for treatment[ 49 ]. Riluzole can reduce motor neuron damage through lowering glutamate levels[ 50 ], while Edaravone helps mitigate oxidative stress and eliminate free radicals[ 51 ]. Although these two drugs can protect the nervous system and slow disease progression and are commonly used for ALS treatment, they only delay disease progression rather than provide a cure. Emerging therapies, including gene therapy[ 52 ], immunotherapy[ 53 ], or stem cell therapy[ 54 ], have shown potential therapeutic effects but still require extensive clinical validation. To date, ALS remains incurable, with efforts focused on preventing risk factors and improving patients' quality of life[ 55 ]. The relationship between statin use and ALS remains controversial. Some studies suggest that statins may have a protective effect, as elevated cholesterol levels are commonly observed in ALS patients, with 65% showing lipid abnormalities and symptoms of hypercholesterolemia[ 56 ]. A survival analysis involving 336 ALS patients revealed a strong association between total cholesterol levels and patient mortality, with higher cholesterol levels linked to an increased risk of both ALS and overall mortality[ 57 ]. Statins may help address metabolic disorders and potentially reduce the incidence of ALS. Furthermore, the protective effects of statins could be attributed to their ability to inhibit neuroinflammation and reduce neural damage[ 58 ]. For instance, simvastatin has been shown to decrease neuroinflammation by reducing the activation of astrocytes and microglia, thus inhibiting the release of pro-inflammatory cytokines[ 31 ]. Other studies report no association between statin use and ALS[ 59 , 60 ], while some studies suggest that statins may increase the risk of developing ALS[ 61 ]. Early surveillance of the FDA adverse event reporting system found that ALS patients with statins use had a higher risk compared to those on placebo[ 62 ]. A population-based cohort analysis indicated that long-term statin use might accelerate the onset of ALS[ 63 ], with a stronger association observed in female patients[ 33 ]. From a genetic variation perspective, our study revealed that atorvastatin, simvastatin and rosuvastatin are linked to an increased risk of ALS, we employed GWAS and Mendelian randomization methods to identify this associations. These findings offer preliminary insights into the potential implications of statin use in ALS patients with hyperlipidemia, suggesting that avoiding atorvastatin, simvastatin, and rosuvastatin in ALS patients could help prevent the worsening of their condition. To date, only one Mendelian randomization study has repprted the relationship between statin use and ALS[ 64 ]. This study found that statins are associated with an increased risk of ALS, which aligns with our own findings. However, it focused on statins as a general class of drugs and did not differentiate between specific types of statins. Moreover, the study primarily examined the link between cholesterol levels and ALS. In contrast, our study provides a more detailed analysis by evaluating the risks associated with three specific statins: simvastatin, atorvastatin, and rosuvastatin. By investigating the relationship between these commonly prescribed lipid lowering medications and ALS, results provide more targeted risk assessments. These findings can serve as a more specific reference for managing medication use in ALS patients with hyperlipidemia, potentially aiding in the optimization of treatment strategies for this patient group. Our study has several limitations. Firstly, the population under investigation was predominantly of European descent. While our results provide valuable insights into the relationship between statins and ALS risk within this cohort, it remains uncertain whether these findings can be extrapolated to other ethnic groups or populations with different genetic backgrounds or environmental exposures. Therefore, future studies involving more diverse populations are essential to assess the generalizability of our conclusions. Second, we were unable to explore potential non-linear associations between the three statins (atorvastatin, simvastatin, and rosuvastatin) and ALS risk. Statin effects may not follow a simple linear pattern, and there may be threshold effects, dose-response relationships, or complex interactions with other genetic or environmental factors that were not captured in this study. Finally, our study focused exclusively on the impact of statins on ALS risk, without considering other lipid-lowering medications, such as fibrates, which may also influence ALS pathophysiology. Given the growing interest in the role of lipid metabolism in neurodegenerative diseases, following research should expand the scope to include a broader range of lipid-lowering agents. Meanwhile, The larger and more diverse datasets are required in future studies, which would enable more robust analyses, and further clarifying the underlying mechanisms driving the relationship between statin use and ALS. 5. Conclusions This study provides significant foundational insights for future investigations into the relationship between statins and the progression of ALS, particularly from a genetic standpoint. By leveraging Mendelian Randomization methods, we have provided a robust analysis of atorvastatin, simvastatin, and rosuvastatin use and ALS, and highlighted the complex interplay between pharmacological interventions in the context of ALS diseases. The results not only enhance the understanding of ALS pathophysiology, but also offer novel avenues for therapeutic development. The implications of these findings are particularly relevant for the design of future clinical trials and genetic studies, which could help refine therapeutic strategies targeting ALS. Ultimately, this research lays the groundwork for more targeted and personalized treatment approaches, with the potential to improve patient outcomes and inform clinical practice in the management of ALS. Declarations Author contributions Min Li and Yaping Li contributed equally to organize and write the manuscript. Xuping Yang and Yang Sun contributed to revise the grammar of the manuscript. Yilan Huang and Longyang Jiang were responsible for the idea, fund, and paper revision. All authors have read and approved to the published version of the manuscript. Data availability statement All data generated or analyzed during this study are included in this published article and its supplementary information files. Codes generated or used during the study are available from the corresponding author by request. Acknowledgement Data from a publicly available GWAS were used in this study, and the authors would like to thank all those who contributed to data collection. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This study was supported by the Scientific research fund of The Affiliated Hospital, Southwest Medical University (24089); Scientific research fund of Southwest Medical University (2024ZKY026). Ethics approval and consent to participate Since all analyses were performed using publicly available genome-wide association study (GWAS) summary data that had already obtained ethical review board approvals, no additional ethical permission was required from our institutional research ethics committees. Consent for publication Not applicable. References Kiernan MC, Vucic S, Talbot K, McDermott CJ, Hardiman O, Shefner JM, et al. Improving clinical trial outcomes in amyotrophic lateral sclerosis. Nat Rev Neurol. 2021 ;17:104-18. Feldman EL, Goutman SA, Petri S, Mazzini L, Savelieff MG, Shaw PJ, et al. Amyotrophic lateral sclerosis. Lancet. 2022 ;400:1363-80. Ciurea AV, Mohan AG, Covache-Busuioc RA, Costin HP, Glavan LA, Corlatescu AD, et al. Unraveling Molecular and Genetic Insights into Neurodegenerative Diseases: Advances in Understanding Alzheimer's, Parkinson's, and Huntington's Diseases and Amyotrophic Lateral Sclerosis. International Journal of Molecular Sciences. 2023 ;24. 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Tables Table 1 Detailed information about atorvastatin, simvastatin, rosuvastatin use and ALS aggregated GWAS results. GWAS ID Trait Consortium Sample size Number of SNPs Population ukb-b-10008 Atorvastatin MRC-IEU 462,933 9,851,867 European ukb-b-11268 Simvastatin MRC-IEU 462,933 9,851,867 European ukb-b-13664 Rrosuvastatin MRC-IEU 462,933 9,851,867 European ebi-a-GCST005647 ALS NA 80,610 39,630,630 European Table 2 Results of IVWs about the aggregated GWAS results. Exposure Outcome SNP Effect allele Other allele β se p -value r 2 F Atorvastatin ALS rs10738606 T A 0.0024954 0.000353 1.60E-12 3.11E-06 49.90486 rs117113213 A G 0.0060889 0.001006 1.40E-09 2.34E-06 36.66172 rs12260037 T C 0.0021746 0.000392 2.90E-08 1.92E-06 30.75595 rs12748266 C T -0.002739 0.000436 3.40E-10 2.48E-06 39.42095 rs12916 C T 0.0029141 0.000361 6.50E-16 4.08E-06 65.26511 rs13022873 C A 0.0027708 0.000405 7.70E-12 2.93E-06 46.83273 rs17411168 C T -0.002475 0.0004 6.20E-10 2.39E-06 38.2556 rs2519093 T C 0.0025431 0.000455 2.30E-08 1.95E-06 31.20817 rs2569550 C T 0.0025504 0.000361 1.50E-12 3.14E-06 50.04378 rs2927472 C T 0.0034287 0.000481 1.00E-12 3.21E-06 50.79658 rs34468875 T C 0.0031815 0.000357 4.60E-19 4.98E-06 79.60145 rs4299376 T G -0.00318 0.000378 4.00E-17 4.42E-06 70.78485 rs4915853 A C 0.0021461 0.00036 2.50E-09 2.22E-06 35.54796 rs56130071 C G 0.0024233 0.00043 1.80E-08 2E-06 31.74613 rs6093446 A G 0.0022428 0.00039 9.20E-09 2.06E-06 33.00353 rs61194703 T A -0.006673 0.000546 2.40E-34 9.34E-06 149.317 rs629301 T G 0.0057628 0.000425 6.70E-42 1.15E-05 183.9217 rs964184 C G -0.006059 0.000519 1.90E-31 8.5E-06 136.1452 Simvastatin ALS rs10410835 C T 0.0044587 0.000664 1.80E-11 9.92E-06 45.09021 rs1081105 C A 0.0134163 0.001998 1.90E-11 9.69E-06 45.08941 rs10857147 T A 0.0044724 0.000723 6.30E-10 8.24E-06 38.26577 rs112552009 G T -0.015159 0.001013 1.20E-50 4.8E-05 223.9381 rs11570891 T C -0.006482 0.00106 9.60E-10 8.01E-06 37.39911 rs11601507 A C 0.007686 0.001271 1.50E-09 7.64E-06 36.56871 rs117733303 G A 0.0160565 0.002431 4.00E-11 9.32E-06 43.62464 rs12916 C T 0.0050082 0.000668 6.30E-14 1.2E-05 56.20891 rs13167071 C G 0.0038975 0.000683 1.10E-08 7.01E-06 32.56281 rs1367117 A G 0.0081071 0.000692 1.10E-31 2.93E-05 137.2507 rs2207132 A G 0.0131506 0.00182 5.00E-13 1.12E-05 52.20936 rs2618567 T G -0.004072 0.00069 3.60E-09 7.46E-06 34.82385 rs2673134 G A 0.0042521 0.00068 3.90E-10 8.37E-06 39.10056 rs2737231 G A -0.003942 0.000714 3.30E-08 6.57E-06 30.48416 rs2738447 C A 0.0054309 0.000667 3.70E-16 1.42E-05 66.29744 rs2980888 C T -0.007827 0.000712 4.50E-28 2.58E-05 120.8332 rs3104415 C A 0.0041126 0.000692 2.80E-09 7.57E-06 35.32069 rs3127580 T C 0.0053049 0.000903 4.20E-09 7.38E-06 34.51322 rs34042070 G C 0.0054163 0.000841 1.20E-10 8.96E-06 41.47819 rs4299376 T G -0.006798 0.000699 2.50E-22 2.02E-05 94.583 rs4360309 T C 0.003777 0.000666 1.40E-08 7.13E-06 32.16218 rs57180587 T A 0.0069055 0.000928 9.80E-14 1.18E-05 55.37252 rs645040 T G 0.0046508 0.00078 2.50E-09 7.59E-06 35.55171 rs693668 A G 0.0041271 0.000686 1.80E-09 7.76E-06 36.19426 rs74617384 T A 0.0111719 0.001214 3.50E-20 1.81E-05 84.68699 rs7513688 A G 0.0038205 0.000684 2.30E-08 6.73E-06 31.19794 rs7534572 G C 0.0055511 0.000685 5.10E-16 1.41E-05 65.6715 rs8126001 T C -0.003626 0.000657 3.40E-08 6.57E-06 30.45444 rs964184 C G -0.011051 0.000961 1.40E-30 2.83E-05 132.2309 Rosuvastatin ALS rs17248783 A G -0.001553 0.000247 3.50E-10 5.3E-07 39.37458 rs2954038 A C -0.001173 0.000178 3.90E-11 5.81E-07 43.64267 rs4665492 C A 0.0012043 0.000172 2.50E-12 6.58E-07 49.08025 rs964184 C G -0.001762 0.00024 2.00E-13 7.19E-07 54.04354 Table 3 Two-sample MR analysis causal effects of atorvastatin, simvastatin, rosuvastatin use and ALS. Exposure Outcome Method IVs β se p -value OR (95% CI) Atorvastatin ALS MR Egger 17 4.869 1.583 0.0030 130.19 (5.84-2901.54) Weighted median 17 2.506 0.839 0.0028 12.26 (2.36–63.57) IVW 17 2.829 0.581 1.137E-06 16.93 (5.42–52.89) Simple mode 17 2.014 1.689 0.2374 7.49 (0.27-205.58) Simvastatin ALS MR Egger 29 2.032 0.709 0.0049 7.64 (1.9-30.68) Weighted median 29 1.117 0.385 0.0037 3.06 (1.44–6.51) IVW 29 1.710 0.280 7.494E-09 5.05 (2.92–8.75) Simple mode 29 1.400 0.935 0.1373 4.05 (0.65–25.37) Weighted mode 29 1.274 0.792 0.1103 3.58 (0.76–16.88) Rosuvastatin ALS MR Egger 4 31.042 24.482 0.2241 3.03E + 13 (0-2.09E + 34) Weighted median 4 20.823 4.6827 8.711E-06 1.1E + 9 (114181.47-1.07E + 13) IVW 4 13.500 4.0497 0.0008 6.93E + 5 (247.72-1.94E + 9) Simple mode 4 26.216 9.2677 0.0121 2.43E + 11 (3139.1-1.88E + 19) Weighted mode 4 25.204 8.0649 0.0065 8.83E + 10 (12054.82-6.47E + 17) Table 4 Reliability test of MR analysis results. Exposure Outcome Method Cochran’s Q test MR-Egger intercept test p -value MR-PRESSO global test p -value Q Q_df p -value Atorvastatin ALS MR Egger 52.608 67 0.9009 0.1706 0.854 IVW 54.526 68 0.8817 Simvastatin MR Egger 148.976 119 0.0326 0.5270 NA IVW 149.479 120 0.0352 Rosuvastatin MR Egger 28.291 15 0.0198 0.4772 NA IVW 29.294 16 0.0220 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6250968","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435355133,"identity":"932bbc23-d5ba-4bc9-8148-9146d08aeace","order_by":0,"name":"Min Li","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":435355134,"identity":"09f5d484-5050-43d8-a196-f78b3a478d1a","order_by":1,"name":"Yaping Li","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Li","suffix":""},{"id":435355135,"identity":"db52e370-ee98-4c4c-9f71-2833cf82cef4","order_by":2,"name":"Xuping Yang","email":"","orcid":"","institution":"Affiliated Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuping","middleName":"","lastName":"Yang","suffix":""},{"id":435355136,"identity":"f786954d-4c74-418c-8498-545486bea1d0","order_by":3,"name":"Yang Sun","email":"","orcid":"","institution":"Pharmacy of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Sun","suffix":""},{"id":435355137,"identity":"bad19496-8f27-4bca-bb78-3f930238b0a3","order_by":4,"name":"Yilan Huang","email":"","orcid":"","institution":"Pharmacy of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yilan","middleName":"","lastName":"Huang","suffix":""},{"id":435355138,"identity":"5112ecb8-d318-424f-ba5e-e8680acf5fab","order_by":5,"name":"Longyang Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBACxmYQWQDjGtjI2R9vbHzwgaAWAxi3IM2Y4czhZsMZBO2Ca/lwOLHhRnqbNAcexcztzM8efjGwyZOPSAYxDhszznzYIM3AYCen24DLYWzmxjIGacWGN9JAjHQ5ZunEBuMChmRjswM4/WImLWFwOHHjjAQQw9qYDagleQbDgcRtOLWwf4NqSQcxmBN7JA82HObBq4XHTPIDUMt8iRwQwzlxhgRjYzMBLWXSDAZpiRt43oAZxgY8ic2MMwxw+8Ww//g2yR8VNonz29OBjD82cgbsx5//+FBhJ4dTSwMwoHmADIMLCRAGBBhgVw4C8iDH/QAx+g9AGKNgFIyCUTAK0AEAWYpfkC/B75UAAAAASUVORK5CYII=","orcid":"","institution":"Pharmacy of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Longyang","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-03-18 08:28:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6250968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6250968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79750317,"identity":"aef34bb6-f090-4a63-9e3c-97bdbc0d4270","added_by":"auto","created_at":"2025-04-02 09:18:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1901751,"visible":true,"origin":"","legend":"\u003cp\u003eThe design of this study. Three assumptions of the MR analysis. SNPs, single nucleotide polymorphisms.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6250968/v1/8703e65f759555f6d1386564.png"},{"id":79749608,"identity":"d1f75eda-a4b5-4ec2-9d05-6efbc328ba4b","added_by":"auto","created_at":"2025-04-02 09:10:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3662240,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of atorvastatin use and ALS. \u003cstrong\u003e(A)\u003c/strong\u003eScatter plot of the causal effect of atorvastatin use and ALS. \u003cstrong\u003e(B)\u003c/strong\u003eFunnel plot of the causal effect of atorvastatin use and ALS. \u003cstrong\u003e(C)\u003c/strong\u003e Forest plot of the causal effect of atorvastatin use and ALS. \u003cstrong\u003e(D)\u003c/strong\u003e Forest plot of the leave-one-out analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6250968/v1/b2039634126a970be3863ef8.png"},{"id":79749609,"identity":"4f754553-397c-4122-94b9-6251e54dc325","added_by":"auto","created_at":"2025-04-02 09:10:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3708651,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of simvastatin use and ALS. \u003cstrong\u003e(A)\u003c/strong\u003e Scatter plot of the causal effect of simvastatin use and ALS. \u003cstrong\u003e(B)\u003c/strong\u003e Funnel plot of the causal effect of simvastatin use and ALS.\u003cstrong\u003e (C) \u003c/strong\u003eForest plot of the causal effect of simvastatin use and ALS. \u003cstrong\u003e(D)\u003c/strong\u003e Forest plot of the leave-one-out analysis.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6250968/v1/7c8bd14071f716be90ed091b.png"},{"id":79750319,"identity":"86f03f68-1379-4260-af72-139586c78c6d","added_by":"auto","created_at":"2025-04-02 09:19:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3337155,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of rosuvastatin use and ALS. \u003cstrong\u003e(A)\u003c/strong\u003e Scatter plot of the causal effect of rosuvastatin use and ALS. \u003cstrong\u003e(B)\u003c/strong\u003e Funnel plot of the causal effect of rosuvastatin use and ALS. \u003cstrong\u003e(C)\u003c/strong\u003e Forest plot of the causal effect of rosuvastatin use and ALS. \u003cstrong\u003e(D)\u003c/strong\u003e Forest plot of the leave-one-out analysis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6250968/v1/9dc6d9b76e70d75f7853565d.png"},{"id":91865959,"identity":"e806fbcc-2a37-4b61-9b6c-47866de6e01c","added_by":"auto","created_at":"2025-09-22 13:18:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12224539,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6250968/v1/846352e3-70b9-428b-a9b5-ee1b0a57e201.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atorvastatin, simvastatin, rosuvastatin and Amyotrophic lateral sclerosis establishing cause and effect","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAmyotrophic lateral sclerosis (ALS) is a cental neurodegenerative disease of central nervous system, also known as motor neuron disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. ALS is characterized by the progressive degeneration of both upper motor neurons (UMN) and lower motor neurons (LMN), resulting in limb weakness, difficulty swallowing, speech impairments, and ultimately respiratory paralysis, which can lead to death[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, ALS is recognized as one of the four major neurodegenerative diseases, alongside with Alzheimer\u0026rsquo;s disease (AD), Parkinson\u0026rsquo;s disease (PD), and Huntington\u0026rsquo;s disease (HD)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While these diseases share common pathological features, including the accumulation of abnormal proteins and the disruption of cellular homeostasis, ALS stands out due to its rapid and relentless course, with most patients surviving only 3\u0026ndash;5 years after diagnosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite significant advances in our understanding of ALS pathophysiology, the exact causes of the disease remain largely unknown, and there are currently no effective disease-modifying treatments.\u003c/p\u003e \u003cp\u003eAccording to current studies, ALS is influenced by genetic or environmental factors, which can be classified into hereditary and sporadic forms[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Hereditary ALS is primarily associated with mutations in the gene encoding of Cu/Zn-dependent superoxide dismutase 1 (SOD1), which is located on chromosome 21, accounting for about 5\u0026ndash;10% of cases[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The remaining 90\u0026ndash;95% of ALS cases are classified as sporadic and are largely linked to environmental exposures[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research has shown that factors such as plasma levels of organic pollutants, metals, pesticides, electromagnetic fields, smoking, alcohol consumption, fungi, and intense physical activity may increase the risk of developing ALS[\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, recent studies suggest a potential link between certain medications and ALS, including antibiotics, antidiabetic drugs, antihypertensive medications, or anticancer agents[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Compared to other environmental risk factors, the role of medications in ALS has been relatively under researched, which makes medication use in ALS patients particularly challenging. Therefore, it is crucial to further investigate the effects of drug use in ALS patients to develop safer medication practices for this population.\u003c/p\u003e \u003cp\u003eStatins are inhibitors of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase. HMG-CoA reductase is a key enzyme in the biosynthesis of cholesterol. By competitively inhibiting HMG-CoA reductase, statins effectively reduce the step of endogenous cholesterol synthesis pathway, preventing the conversion of HMG-CoA to mevalonate[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This inhibition leads to a significant reduction in the production of cholesterol within the liver, thereby decreasing overall cholesterol levels in the bloodstream[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. As a result, statins have become widely prescribed for the management of hypercholesterolemia and hyperlipidemia, the main factors that contribute to the development of atherosclerosis and cardiovascular disease[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition to lipid lowering effects, statins also exert pleiotropic effects, including anti-inflammatory, antioxidant, and endothelial function-improving properties, which may further contribute to their cardiovascular protective effects[\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Usually, statins can be classified based on their solubility into two groups: lipid-soluble (lipophilic) statins, such as simvastatin and atorvastatin, and water-soluble (hydrophilic) statins, such as rosuvastatin. Lipophilic statins are more readily absorbed by tissues, including the liver and muscle, due to their ability to pass through cell membranes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast, hydrophilic statins, such as rosuvastatin, are more soluble in water and are less likely to accumulate in non-hepatic tissues, potentially leading to a lower incidence of muscle-related side effects[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The choice between these statins may depend on various clinical factors, including the patient's individual risk profile and tolerance to side effects.\u003c/p\u003e \u003cp\u003eStudies have reported a potential link between statin and ALS. For example, atorvastatin has been shown to protect motor neuron-like cells (NSC-34D) via scavenging free radicals[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Simvastatin has been reported to promote muscle regeneration in mice by activating the mTOR pathway[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], reducing astrocyte activation, and inhibiting the release of inflammatory cytokines[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These findings suggest that statin may have a protective effect against ALS. However, observational studies have raised concerns that statins use may increase the risk of ALS, with long-term statin therapy potentially having adverse effects on ALS patients. For instance, ALS patients on statins have an increased frequency of muscle cramps[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A cohort study found that statin use was associated with a higher risk of ALS in both men and women, with a notably higher risk in women. Additionally, neurotoxic effects of statins were observed to be more pronounced in the early stages of ALS in women compared to men[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To date, no definitive conclusion has been reached regarding the impact of statin use on ALS. Therefore, robust statistical methods are urgently needed to clarify the causal relationship between statin therapy and ALS risk.\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR) is a method that leverages single nucleotide polymorphisms (SNPs) as instrumental variables to infer causal relationship[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, we applied MR to robustly establish the relationship between statin use and ALS. Unlike traditional observational studies, MR capitalizes on the random assortment of genetic variants during meiosis, which allows more effectively exclude potential reverse causality and confounding factors[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This approach is similar to a genetically randomized controlled trial and avoids the high costs associated with clinical trials[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. We conducted a two-sample MR analysis to investigate the causal relationship between statin use and ALS, utilizing genome wide association study (GWAS) data. This analysis aims to clarify the link between statins and ALS, providing a stronger evidence base for clinical decision-making.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. study design\u003c/h2\u003e \u003cp\u003eThe study flowchart is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A two-sample MR analysis was used to evaluate the relationship between statin and ALS. First, atorvastatin, simvastatin and rosuvastatin were selected as exposures, ALS as outcome. Next, we identified appropriate single nucleotide polymorphisms (SNPs) to serve as instrumental variables (IVs). The selected IVs must satisfy three key assumptions[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]: (1) the IVs are strongly associated with the three statin exposures (relevance assumption); (2) the IVs are not directly correlated with ALS through any confounders (independence assumption); (3) the IVs affect ALS only through their influence on the statin exposures, with no other indirect correlations (exclusivity assumption). In other words, IVs can only influence the outcome through the pathway: \u0026ldquo;IVs \u0026rarr; exposure \u0026rarr; outcome\u0026rdquo;. We applied five methods: inverse variance weighted (IVW), MR\u0026ndash;Egger regression, weighted median, simple mode, and weighted mode. Additionally, we used multiple methods for sensitivity analysis to ensure the robustness of the results. The MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test were conducted to detect horizontal pleiotropy. Cochran\u0026rsquo;s Q statistics were used to evaluate instrument heterogeneity. Finally, we performed a sensitivity analysis using the leave-one-out method[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data source\u003c/h2\u003e \u003cp\u003eIn this study, exposure data were extracted from the UK Biobank, which contains genotype data from approximately 500,000 individuals. We extracted exposure data related to statin use from three distinct datasets corresponding to the three most commonly prescribed statins: atorvastatin dataset (ukb-b-10008), comprising 13,851 cases and 449,082 controls; simvastatin dataset (ukb b-11268), comprising 52,427 cases and 410,506 controls; rosuvastatin dataset (ukb-b-13664), comprising 2,870 cases and 460,063 controls. ALS GWAS data were obtained from the International Oncology Unit (IEU) Open GWAS project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is a publicly available resource that aggregates genetic data on various diseases and conditions. The ALS GWAS data encompass both ALS patients and controls, providing a comprehensive genetic profile of the disease. Detailed information about the four GWAS datasets is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The database is publicly available for download from the IEU Open GWAS platform, and as it is freely accessible, no ethical review approval or participant consent is required.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Selection of the instrumental variables\u003c/h2\u003e \u003cp\u003eIndependence and exclusion of confounding are critical aspects of Mendelian randomization analysis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Three core assumptions must be satisfied. Initially, SNPs should be strongly associated with exposures. SNPs with \u003cem\u003ep\u003c/em\u003e-value less than 5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e were selected as IVs, to ensure the genetic variants were robustly linked to the exposures under investigation[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This stringent criterion helped to ensure that the SNPs used as IVs would be suitable for estimating causal effects. Second, to minimize potential bias arising from confounding due to genetic correlation between SNPs, it is essential to exclude SNPs with strong linkage disequilibrium (LD), we applied an r\u0026sup2; threshold of less than 0.001 within 10,000 kb physical window, ensuring the independence of the IVs. Third, the F-statistic was used to assess the strength of IVs, with F\u0026thinsp;\u0026gt;\u0026thinsp;10 to avoid weak instruments. By this threshold, we ensured that the IVs included in our analysis had sufficient statistical power to provide reliable and precise estimates of the causal relationship. In addition to these criteria, we removed incompatible and palindromic SNPs, further refined the selection of IVs. Following these rigorous screening process, the remaining SNPs were deemed appropriate and selected as the final set of IVs for our Mendelian randomization analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eIn our research, we used several methods to analyze the causal relationship between atorvastatin, simvastatin, rosuvastatin and ALS, including IVW analysis, MR\u0026ndash;Egger regression, weighted median, simple mode, and weighted mode. IVW is the primary method, in which the intercept term was excluded from the regression, and the inverse of outcome variance was used as the weight for fitting the model. The other four methods were served as supplementary analyses to IVW. By combining these multiple approaches, we aimed to ensure the validity and consistency of our conclusions, minimizing the impact of potential biases and model assumptions[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCochran\u0026rsquo;s Q test was adopted to evaluate heterogeneity between each SNP estimate, with heterogeneity considered significant at \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Horizontal pleiotropy was assessed using the MR-Egger intercept test and MR-PRESSO global test (pleiotropy was considered present if \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Additionally, the MR-PRESSO was employed to identify the potential outliers. If an outlier was detected, it was excluded, and the MR analysis was rerun. Finally, the leave-one-out method was used to analyse sensitivity and reliability of the model: each SNP was gradually removed, and the remaining SNPs were analyzed to determine whether the results changed. All analyses were performed using the \u0026ldquo;Two Sample MR\u0026rdquo; and \u0026ldquo;MR-PRESSO\u0026rdquo; packages in R version 4.3.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Instrumental variables of atorvastatin, simvastatin and rosuvastatin\u003c/h2\u003e \u003cp\u003eThrough strict screening of SNPs, we identified 17 SNPs highly associated with atorvastatin use, 29 SNPs highly associated with simvastatin use, and 4 SNPs highly associated with rosuvastatin use. These SNPs were extracted from three distinct datasets within the UK Biobank: the ukb-b-10008 dataset for atorvastatin, the ukb-b-11268 dataset for simvastatin, and the ukb-b-13664 dataset for rosuvastatin. Each of these SNPs met the criteria for inclusion as IVs for the corresponding statin exposure, having passed the stringent selection process outlined above. Detailed information was provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Causal relationship of atorvastatin use and ALS\u003c/h2\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the results of IVW indicated that atorvastatin was a risk factor for ALS (OR\u0026thinsp;=\u0026thinsp;16.93, 95% CI: 5.42\u0026ndash;52.89, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.13E-06). The MR\u0026ndash;Egger regression, weighted median, simple mode, and weighted mode showed the same trend. Results were visualized in scatter plot. Scatter plot illustrated a positive linear correlation for atorvastatin, suggesting that elevated atorvastatin use were associated with a higher probability of ALS development (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The results of Cochran\u0026rsquo;s Q heterogeneity test, MR-Egger intercept test and MR-PRESSO global test were shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ep\u003c/em\u003e-value of Cochran\u0026rsquo;s Q was greater than 0.05, indicating no significant heterogeneity. Additionally, MR-Egger regression intercept analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17) suggested no significant pleotropy, and the MR-PRESSO global test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.854) did not detect any abnormal SNPs in our funding. Funnel plots showed symmetric distribution of the SNPs, the plots exhibited a symmetrical distribution o IVs on both sides of the IVW, indicating the absence of significant outliers, and confirmation that the Mendelian randomization (MR) analysis adhered to the core principles of MR grouping (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, the leave-one-out sensitivity analysis demonstrated that the removal of any single SNP did not significantly alter the causal relationship between atorvastatin use and ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Finally, forest plots were constructed to evaluate the predictive efficacy of each SNP locus in relation to exposure factors and outcomes. In these plots, solid dots on the left indicated lower risk, while those on the right signified higher risk. The forest plot results consistently placed solid dots on the right, suggesting that increased atorvastatin use were associated with a higher risk of ALS, as consistent with the IVW approach (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). These results suggest that the findings are robust and stable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Causal relationship of simvastatin use and ALS\u003c/h2\u003e \u003cp\u003eThe results of the MR analysis examining the effects of simvastatin use on ALS are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The IVW method revealed that simvastatin use was associated with a significantly increased risk of ALS (OR\u0026thinsp;=\u0026thinsp;5.05, 95% CI: 2.92\u0026ndash;8.75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.49E-09). This finding suggests that higher exposure to simvastatin may contribute to an elevated likelihood of ALS onset, aligning with the hypothesis of a potential causal relationship. This conclusion was supported by MR-Egger regression, the weighted median, simple mode, and weighted mode. Each of these methods supported the initial IVW findings, further enhancing the reliability and consistency of the observed effect. To visualize and further corroborate these results, the association between simvastatin use and ALS was illustrated through scatter plot. Scatter plot clearly depicted the relationship between simvastatin use and ALS risk, visually supporting the positive association observed in the statistical analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The \u003cem\u003ep\u003c/em\u003e-value of Cochran\u0026rsquo;s Q (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0353) was less than 0.05, so we used a random effects IVW MR analysis[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. MR-Egger regression intercept test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.527) and MR-PRESSO global test (NA) indicated that there were no significant outliers, as comfirmed by the funnel plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The leave-one-out analysis showed that the results remained consistent after removing each SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). All sensitivity analysis proved MR analysis results were reliable. The forest plot, which shows the individual effect estimates for each SNP associated with simvastatin use, further confirmed the robustness of the findings by indicating that the majority of the SNPs were aligned with a higher risk of ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Collectively, these visual representations underscored the consistency of the results and affirmed that simvastatin use may act as a risk factor for ALS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Causal relationship of rosuvastatin use and ALS\u003c/h2\u003e \u003cp\u003eThe results of MR analysis evaluated the relationship of rosuvastatin use and ALS were presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Similar to the two statins, the IVW showed that rosuvastatin use was a risk factor of ALS (OR\u0026thinsp;=\u0026thinsp;6.93E\u0026thinsp;+\u0026thinsp;5, 95% CI: 247.72-1.94E\u0026thinsp;+\u0026thinsp;9, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.97-05). Other four methods (MR\u0026ndash;Egger regression, weighted median, simple mode, weighted mode) sshowed the same trend. The results were visually represented in scatter plot, which supported the potential risk between rosuvastatin use and ALS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eThe Cochran\u0026rsquo;s Q result of IVW (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022) was less than 0.05, thus random effects IVW MR analysis was used. Additionally, MR-Egger regression intercept test (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.477), suggesting no significant evidence of directional pleiotropy. MR-PRESSO global test (NA) did not show significant pleiotropy, which further supported the validity of the instrumental variables. The absence of significant pleiotropy was visually confirmed by the funnel plot, which showed an even distribution of IVs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Finally, the leave-one-out method demonstrated the results did not change after removing any SNP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The forest plot was shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD. All these results provide strong evidence that rosuvastatin use may be adverse to ALS, with no significant confounding or pleiotropic effects detected, thereby supporting the validity of our causal inference.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we used GWAS data to perform a two-sample Mendelian randomization analysis to evaluate the impact of three statins (atorvastatin, simvastatin, and rosuvastatin) on ALS. The primary objective of this analysis was to overcome the limitations of traditional observational studies by leveraging genetic variants as IVs, thus providing a more reliable estimate of causality and reducing confounding factors. By using summary-level data from large-scale GWAS, we were able to investigate the genetic basis of statin use and its association with ALS in a large cohort, further enhancing the generalizability of our analysis. The results indicated that the use of three drugs is associated with an increased risk of ALS, with genetically determined higher use of these medications associated with an elevated risk of developing the disease.\u003c/p\u003e \u003cp\u003eALS is a rare, fatal neurodegenerative disease with limited therapeutic options[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Currently, the FDA has approved Riluzole and Edaravone for treatment[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Riluzole can reduce motor neuron damage through lowering glutamate levels[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], while Edaravone helps mitigate oxidative stress and eliminate free radicals[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Although these two drugs can protect the nervous system and slow disease progression and are commonly used for ALS treatment, they only delay disease progression rather than provide a cure. Emerging therapies, including gene therapy[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], immunotherapy[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], or stem cell therapy[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], have shown potential therapeutic effects but still require extensive clinical validation.\u003c/p\u003e \u003cp\u003eTo date, ALS remains incurable, with efforts focused on preventing risk factors and improving patients' quality of life[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The relationship between statin use and ALS remains controversial. Some studies suggest that statins may have a protective effect, as elevated cholesterol levels are commonly observed in ALS patients, with 65% showing lipid abnormalities and symptoms of hypercholesterolemia[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. A survival analysis involving 336 ALS patients revealed a strong association between total cholesterol levels and patient mortality, with higher cholesterol levels linked to an increased risk of both ALS and overall mortality[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Statins may help address metabolic disorders and potentially reduce the incidence of ALS. Furthermore, the protective effects of statins could be attributed to their ability to inhibit neuroinflammation and reduce neural damage[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For instance, simvastatin has been shown to decrease neuroinflammation by reducing the activation of astrocytes and microglia, thus inhibiting the release of pro-inflammatory cytokines[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Other studies report no association between statin use and ALS[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], while some studies suggest that statins may increase the risk of developing ALS[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Early surveillance of the FDA adverse event reporting system found that ALS patients with statins use had a higher risk compared to those on placebo[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. A population-based cohort analysis indicated that long-term statin use might accelerate the onset of ALS[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], with a stronger association observed in female patients[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a genetic variation perspective, our study revealed that atorvastatin, simvastatin and rosuvastatin are linked to an increased risk of ALS, we employed GWAS and Mendelian randomization methods to identify this associations. These findings offer preliminary insights into the potential implications of statin use in ALS patients with hyperlipidemia, suggesting that avoiding atorvastatin, simvastatin, and rosuvastatin in ALS patients could help prevent the worsening of their condition. To date, only one Mendelian randomization study has repprted the relationship between statin use and ALS[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. This study found that statins are associated with an increased risk of ALS, which aligns with our own findings. However, it focused on statins as a general class of drugs and did not differentiate between specific types of statins. Moreover, the study primarily examined the link between cholesterol levels and ALS. In contrast, our study provides a more detailed analysis by evaluating the risks associated with three specific statins: simvastatin, atorvastatin, and rosuvastatin. By investigating the relationship between these commonly prescribed lipid lowering medications and ALS, results provide more targeted risk assessments. These findings can serve as a more specific reference for managing medication use in ALS patients with hyperlipidemia, potentially aiding in the optimization of treatment strategies for this patient group.\u003c/p\u003e \u003cp\u003eOur study has several limitations. Firstly, the population under investigation was predominantly of European descent. While our results provide valuable insights into the relationship between statins and ALS risk within this cohort, it remains uncertain whether these findings can be extrapolated to other ethnic groups or populations with different genetic backgrounds or environmental exposures. Therefore, future studies involving more diverse populations are essential to assess the generalizability of our conclusions. Second, we were unable to explore potential non-linear associations between the three statins (atorvastatin, simvastatin, and rosuvastatin) and ALS risk. Statin effects may not follow a simple linear pattern, and there may be threshold effects, dose-response relationships, or complex interactions with other genetic or environmental factors that were not captured in this study. Finally, our study focused exclusively on the impact of statins on ALS risk, without considering other lipid-lowering medications, such as fibrates, which may also influence ALS pathophysiology. Given the growing interest in the role of lipid metabolism in neurodegenerative diseases, following research should expand the scope to include a broader range of lipid-lowering agents. Meanwhile, The larger and more diverse datasets are required in future studies, which would enable more robust analyses, and further clarifying the underlying mechanisms driving the relationship between statin use and ALS.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study provides significant foundational insights for future investigations into the relationship between statins and the progression of ALS, particularly from a genetic standpoint. By leveraging Mendelian Randomization methods, we have provided a robust analysis of atorvastatin, simvastatin, and rosuvastatin use and ALS, and highlighted the complex interplay between pharmacological interventions in the context of ALS diseases. The results not only enhance the understanding of ALS pathophysiology, but also offer novel avenues for therapeutic development. The implications of these findings are particularly relevant for the design of future clinical trials and genetic studies, which could help refine therapeutic strategies targeting ALS. Ultimately, this research lays the groundwork for more targeted and personalized treatment approaches, with the potential to improve patient outcomes and inform clinical practice in the management of ALS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMin Li and Yaping Li contributed equally to organize and write the manuscript. Xuping Yang and Yang Sun contributed to revise the grammar of the manuscript. Yilan Huang and Longyang Jiang were responsible for the idea, fund, and paper revision. All authors have read and approved to the published version of the manuscript.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files. Codes generated or used during the study are available from the corresponding author by request.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eData from a publicly available GWAS were used in this study, and the authors would like to thank all those who contributed to data collection.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis study was supported by the Scientific research fund of The Affiliated Hospital, Southwest Medical University (24089); Scientific research fund of Southwest Medical University (2024ZKY026).\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSince all analyses were performed using publicly available genome-wide association study (GWAS) summary data that had already obtained ethical review board approvals, no additional ethical permission was required from our institutional research ethics committees.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKiernan MC, Vucic S, Talbot K, McDermott CJ, Hardiman O, Shefner JM, et al. 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An evaluation of a data mining signal for amyotrophic lateral sclerosis and statins detected in FDA\u0026apos;s spontaneous adverse event reporting system. Pharmacoepidem Dr S. \u003cstrong\u003e2008\u003c/strong\u003e;17:1068-76.\u003c/li\u003e\n \u003cli\u003eMariosa D, Kamel F, Bellocco R, Ronnevi LO, Almqvist C, Larsson H, et al. Antidiabetics, statins and the risk of amyotrophic lateral sclerosis. European Journal of Neurology. \u003cstrong\u003e2020\u003c/strong\u003e;27:1010-6.\u003c/li\u003e\n \u003cli\u003eWang WJ, Zhang LJ, Xia KL, Huang T, Fan DS. Mendelian Randomization Analysis Reveals Statins Potentially Increase Amyotrophic Lateral Sclerosis Risk Independent of Peripheral Cholesterol-Lowering Effects. Biomedicines. \u003cstrong\u003e2023\u003c/strong\u003e;11.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetailed information about atorvastatin, simvastatin, rosuvastatin use and ALS aggregated GWAS results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGWAS ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrait\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConsortium\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of SNPs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b-10008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtorvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e462,933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b-11268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e462,933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eukb-b-13664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRrosuvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMRC-IEU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e462,933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e9,851,867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eebi-a-GCST005647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80,610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e39,630,630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of IVWs about the aggregated GWAS results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEffect\u003c/p\u003e\n \u003cp\u003eallele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003cp\u003eallele\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"18\"\u003e\n \u003cp\u003eAtorvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"18\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10738606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.90486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers117113213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0060889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.66172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12260037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0021746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.75595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12748266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.42095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0029141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.50E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.26511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers13022873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0027708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.70E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.83273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers17411168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.2556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2519093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.20817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2569550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0025504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.04378\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2927472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0034287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.79658\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers34468875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0031815\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.60E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.98E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.60145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4299376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.42E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.78485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4915853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0021461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.54796\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers56130071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0024233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.74613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers6093446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0022428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.00353\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers61194703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.34E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.317\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers629301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0057628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.70E-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.9217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers964184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.1452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"29\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"29\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10410835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0044587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.92E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.09021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1081105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0134163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.69E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.08941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers10857147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0044724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.24E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.26577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers112552009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20E-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.8E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.9381\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11570891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.60E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.01E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.39911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers11601507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.50E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.56871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers117733303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0160565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.00E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.32E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.62464\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers12916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0050082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.30E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.20891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers13167071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0038975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.01E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.56281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers1367117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0081071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.2507\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2207132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0131506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.00E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.20936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2618567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.46E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.82385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2673134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0042521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.37E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.10056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2737231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.48416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2738447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0054309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.70E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.29744\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2980888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.50E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.8332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3104415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0041126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.57E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.32069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers3127580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0053049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.38E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.51322\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers34042070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0054163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.96E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.47819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4299376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4360309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.13E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.16218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers57180587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0069055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.80E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.37252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers645040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0046508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.59E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.55171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers693668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0041271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.76E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.19426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers74617384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0111719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50E-20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.68699\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7513688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0038205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.30E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.73E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.19794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers7534572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0055511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.41E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.6715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers8126001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.40E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.45444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers964184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40E-30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132.2309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eRosuvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"4\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers17248783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.50E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.37458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers2954038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.90E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.81E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.64267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers4665492\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0012043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.58E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.08025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ers964184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.00E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.19E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.04354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTwo-sample MR analysis causal effects of atorvastatin, simvastatin, rosuvastatin use and ALS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ese\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAtorvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.19 (5.84-2901.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.26 (2.36\u0026ndash;63.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.137E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.93 (5.42\u0026ndash;52.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.49 (0.27-205.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.64 (1.9-30.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06 (1.44\u0026ndash;6.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.494E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.05 (2.92\u0026ndash;8.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05 (0.65\u0026ndash;25.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.58 (0.76\u0026ndash;16.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eRosuvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.03E\u0026thinsp;+\u0026thinsp;13 (0-2.09E\u0026thinsp;+\u0026thinsp;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.711E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1E\u0026thinsp;+\u0026thinsp;9 (114181.47-1.07E\u0026thinsp;+\u0026thinsp;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.93E\u0026thinsp;+\u0026thinsp;5 (247.72-1.94E\u0026thinsp;+\u0026thinsp;9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43E\u0026thinsp;+\u0026thinsp;11 (3139.1-1.88E\u0026thinsp;+\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.83E\u0026thinsp;+\u0026thinsp;10 (12054.82-6.47E\u0026thinsp;+\u0026thinsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReliability test of MR analysis results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOutcome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eCochran\u0026rsquo;s \u003cem\u003eQ\u003c/em\u003e test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMR-Egger\u003c/p\u003e\n \u003cp\u003eintercept test\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003cp\u003eglobal test\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ_df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAtorvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.1706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8817\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSimvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148.976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.5270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eRosuvastatin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e0.4772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Amyotrophic Lateral Sclerosis, Statin, Mendelian randomization, Genome wide association study (GWAS), Causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-6250968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6250968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground/Objectives:\u003c/strong\u003e Statins are drugs that lower lipids levels, and widely used to reduce the risk of cardiovascular disease. Previous observational studies and experimental investigations have indicated that statin is associated with Amyotrophic Lateral Sclerosis (ALS). However, the causal relationship remains unclear.The present study employs a two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between atorvastatin, simvastatin, rosuvastatin and ALS at the nenetic level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The study utilized genome-wide association studies (GWAS) based on single-nucleotide polymorphisms (SNPs) for three statins (atorvastatin, simvastatin, and rosuvastatin), and encompassing data of 462,933 participants obtained from the UK Biobank, 80,610 individuals of ALS in genetic level data from European. The investigation of causal effects implemented five methods: inverse variance weighting (IVW), MR-Egger regression, wighted median, simple mode, and weighted mode. To detect horizontal pleiotropy, the MR-Egger intercept test and MR pleiotropy residual sum and outlier (MR-PRESSO) global test were employed. Instrument heterogeneity was evaluated by Cochran’s Q statistics. Sensitivity analysis was performed via the leave-one-out method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The MR analysis suggest a potential causal relationship between atorvastatin, simvastatin, and rosuvastatin use and the risk of ALS, with the odds ratio (OR) and confidence interval (CI) providing further insight into the strength of this association. The results estimate for three statins use revealed a significantly elevated risk of ALS, atorvastatin (OR = 16.93, 95% CI: 5.42-52.89, \u003cem\u003ep\u003c/em\u003e = 1.13E-06), simvastatin (OR = 5.05, 95% CI: 2.92-8.75, \u003cem\u003ep\u003c/em\u003e = 7.49E-09), rosuvastatin (OR = 6.93E+5, 95% CI: 247.72-1.94E+9, \u003cem\u003ep\u003c/em\u003e = 8.97-05). The sensitivity analysis highlighted the stability and reliability of the casual results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe present study provided genetic evidence that three statins (atorvastatin, simvastatin, rosuvastatin) were associated with the increased risk of ALS. Given the drug's effectiveness and potential side effects, individuals at higher risk of ALS should be cautious about th use of statins. Further investigations and robust reserch are needed to confirm the results, and the findings will provide valuable guidance for the drug use of ALS patients.\u003c/p\u003e","manuscriptTitle":"Atorvastatin, simvastatin, rosuvastatin and Amyotrophic lateral sclerosis establishing cause and effect","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 09:10:54","doi":"10.21203/rs.3.rs-6250968/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e57d077-f487-49d9-89e4-b9a3757bf189","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T13:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 09:10:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6250968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6250968","identity":"rs-6250968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}