A Mendelian Randomization Approach Using 3-HMG-Coenzyme-A Reductase Gene Variation to Evaluate the Association of Statin-Induced Low-Density Lipoprotein Cholesterol Lowering With Noncardiovascular Disease Phenotypes

Statin use and fall risk in adults: a cross-sectional survey and mendelian randomization analysis

Background and Objective

The issue of falls poses a significant threat to the health of the elderly population. Although statins can cause myopathy, which implies that they may cause balance problems and increase the risk of falling, this has not been tested. Our objective was to assess whether the use of statins is linked to a higher risk of falls.

Methods

A cross-sectional survey study and Mendelian randomization (MR) study were conducted to examine whether the use of statins was associated with an increased risk of falling and balance problems. The cross-sectional study included 2,656 participants from the US population (NHANES) who reported information on balance and falling problems in the past year and their use of statins. Univariate and multivariate logistic regression models were used to investigate the association between statin use and the likelihood of falling or experiencing balance problems. The MR study identified five Single Nucleotide Polymorphisms (SNPs) that predict statin use across five ancestry groups: Admixed African or African, East Asian, European, Hispanic, and South Asian. Additionally, SNPs predicting the risk of falls were acquired from the UK Biobank population. A two-sample MR analysis was performed to examine whether genetically predicted statin use increased the risk of falls.

Results

The use of statins was found to be associated with an increased likelihood of balance and falling problems (balance problem, OR 1.25, 95%CI 1.02 to 1.55; falling problem, OR 1.27, 95%CI 1.03-1.27). Subgroup analysis revealed that patients under the age of 65 were more susceptible to these issues when taking statins (balance problem, OR 3.42, 95%CI 1.40 to 9.30; falling problem, OR 5.58, 95%CI 2.04-15.40). The MR analysis indicated that the use of statins, as genetically proxied, resulted in an increased risk of falling problems (OR 1.21, 95% CI 1.1-1.33).

Conclusion

Our study found an association between the use of statins and an increased risk of balance problems and falls in adults over 40 years old, and the MR study result suggested statin use increased risk of falls. The risk was higher in participants under 65 years old compared to those over 65 years old.

Lipid-lowering drugs affect lung cancer risk via sphingolipid metabolism: a drug-target Mendelian randomization study

Background: The causal relationship between lipid-lowering drug (LLD) use and lung cancer risk is controversial, and the role of sphingolipid metabolism in this effect remains unclear. Methods: Genome-wide association study data on low-density lipoprotein (LDL), apolipoprotein B (ApoB), and triglycerides (TG) were used to develop genetic instrumental variables (IVs) for LLDs. Two-step Mendelian randomization analyses were performed to examine the causal relationship between LLDs and lung cancer risk. The effects of ceramide, sphingosine-1-phosphate (S1P), and ceramidases on lung cancer risk were explored, and the proportions of the effects of LLDs on lung cancer risk mediated by sphingolipid metabolism were calculated. Results: APOB inhibition decreased the lung cancer risk in ever-smokers via ApoB (odds ratio [OR] 0.81, 95% confidence interval [CI] 0.70-0.92, p = 0.010), LDL (OR 0.82, 95% CI 0.71-0.96, p = 0.040), and TG (OR 0.63, 95% CI 0.46-0.83, p = 0.015) reduction by 1 standard deviation (SD), decreased small-cell lung cancer (SCLC) risk via LDL reduction by 1 SD (OR 0.71, 95% CI 0.56-0.90, p = 0.016), and decreased the plasma ceramide level and increased the neutral ceramidase level. APOC3 inhibition decreased the lung adenocarcinoma (LUAD) risk (OR 0.60, 95% CI 0.43-0.84, p = 0.039) but increased SCLC risk (OR 2.18, 95% CI 1.17-4.09, p = 0.029) via ApoB reduction by 1 SD. HMGCR inhibition increased SCLC risk via ApoB reduction by 1 SD (OR 3.04, 95% CI 1.38-6.70, p = 0.014). The LPL agonist decreased SCLC risk via ApoB (OR 0.20, 95% CI 0.07-0.58, p = 0.012) and TG reduction (OR 0.58, 95% CI 0.43-0.77, p = 0.003) while increased the plasma S1P level. PCSK9 inhibition decreased the ceramide level. Neutral ceramidase mediated 8.1% and 9.5% of the reduced lung cancer risk in ever-smokers via ApoB and TG reduction by APOB inhibition, respectively, and mediated 8.7% of the reduced LUAD risk via ApoB reduction by APOC3 inhibition. Conclusion: We elucidated the intricate interplay between LLDs, sphingolipid metabolites, and lung cancer risk. Associations of APOB, APOC3, and HMGCR inhibition and LPL agonist with distinct lung cancer risks underscore the multifaceted nature of these relationships. The observed mediation effects highlight the considerable influence of neutral ceramidase on the lung cancer risk reduction achieved by APOB and APOC3 inhibition.

Genetic variations in HMGCR and PCSK9 and kidney function: a Mendelian randomization study

BACKGROUND

The genetically predicted lipid-lowering effect of HMGCR or PCSK9 variant can be used to assess drug proxy effects on kidney function.

METHODS

Mendelian randomization (MR) analysis-identified HMGCR and PCSK9 genetic variants were used to predict the low-density lipoprotein (LDL) cholesterol-lowering effects of medications targeting related molecules. Primary summary-level outcome data for log-estimated glomerular filtration rate (eGFR; creatinine) were provided by the CKDGen Consortium (n = 1,004,040 European) from a meta-analysis of CKDGen and UK Biobank data. We also conducted a separate investigation of summary-level data from CKDGen (n = 567,460, log-eGFR [creatinine]) and UK Biobank (n = 436,581, log-eGFR [cystatin C]) samples. Summary-level MRs using an inverse variance weighted method and pleiotropy-robust methods were performed.

RESULTS

Summary-level MR analysis indicated that the LDL-lowering effect predicted genetically by HMGCR variants (50-mg/dL decrease) was significantly associated with a decrease in eGFR (-1.67%; 95% confidence interval [CI], -2.20% to -1.13%). Similar significance was found in results from the pleiotropy-robust MR methods when the CKDGen and UK Biobank data were analyzed separately. However, the LDL-lowering effect predicted genetically by PCSK9 variants was significantly associated with an increase in eGFR (+1.17%; 95% CI, 0.10%-2.25%). The results were similarly supported by the weighted median method and in each CKDGen and UK Biobank dataset, but the significance obtained by MR-Egger regression was attenuated.

CONCLUSION

Genetically predicted HMG-CoA reductase inhibition was associated with low eGFR, while genetically predicted PCSK9 inhibition was associated with high eGFR. Clinicians should consider that the direct effect of different types of lipid-lowering medication on kidney function can vary.

Cardiovascular prevention: sometimes dreams can come true

Cardiovascular disease (CVD) is a chronic condition driven by the complex interaction of different risk factors including genetics, lifestyle, environment, etc. which, differently from other pathologies, can be prevented. Treatment of CVD has been inconceivably successful but now it seems that it has reached a plateau suggesting that prevention is the way forward. However, the COVID-19 pandemic has spotted all the limits of the actual health system regarding territorial and, particularly, of preventive medicine. To this end, recently, the SCORE2 risk prediction algorithms, a contemporary model to estimate 10 years risk of CVD in Europe and the new guidelines on prevention have been released. The present review article describes a dream: how prevention of CVD should be addressed in the future. New concepts and paradigms like early genetically personalized and imaging driven risk factors, cardiac risk cartography, measurements of the exposome, estimation of costs of a delayed outcome vs. healthy lifespan, are all addressed. We highlight the importance of technologies and the concept of being engaged in a 'healthy' and not just 'sick' system as it is today. The concept of 'clearing house' with a 'care health team' instead of a 'heart team' is described. Finally, we articulate the four points necessary for the dream to come true.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.

Cardiovascular prevention: sometimes dreams can come true

Cardiovascular disease (CVD) is a chronic condition driven by the complex interaction of different risk factors including genetics, lifestyle, environment, etc. which, differently from other pathologies, can be prevented. Treatment of CVD has been inconceivably successful but now it seems that it has reached a plateau suggesting that prevention is the way forward. However, the COVID-19 pandemic has spotted all the limits of the actual health system regarding territorial and, particularly, of preventive medicine. To this end, recently, the SCORE2 risk prediction algorithms, a contemporary model to estimate 10-years risk of CVD in Europe and the new guidelines on prevention have been released. The present review article describes a dream: how prevention of CVD should be addressed in the future. New concepts and paradigms like early genetically personalized and imaging driven risk factors, cardiac risk cartography, measurements of the exposome, estimation of costs of a delayed outcome vs. healthy lifespan, are all addressed. We highlight the importance of technologies and the concept of being engaged in a ‘healthy’ and not just ‘sick’ system as it is today. The concept of ‘clearing house’ with a ‘healthcare team’ instead of a ‘heart team’ is described. Finally, we articulate the four points necessary for the dream to come true.

Metabolite Signature of Simvastatin Treatment Involves Multiple Metabolic Pathways

Statins inhibit the 3-hydroxy-3-methylglutaryl-CoA reductase enzyme and are the most widely used medication for hypercholesterolemia. Previous studies on the metabolite signature of simvastatin treatment have included only a small number of metabolites. We performed a high-throughput liquid chromatography–tandem mass spectroscopy profiling on the effects of simvastatin treatment on 1098 metabolite concentrations in the participants of the METSIM (Metabolic Syndrome In Men) study including 1332 participants with simvastatin treatment and 6200 participants without statin treatment. We found that simvastatin exerts profound pleiotropic effects on different metabolite pathways, affecting not only lipids, but also amino acids, peptides, nucleotides, carbohydrates, co-factors, vitamins, and xenobiotics. We identified 321 metabolites significantly associated with simvastatin treatment, and 313 of these metabolites were novel. Our study is the first comprehensive evaluation of the metabolic signature of simvastatin treatment in a large population-based study.

Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods

Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.

Updated Understanding of the Crosstalk Between Glucose/Insulin and Cholesterol Metabolism

Glucose and cholesterol engage in almost all human physiological activities. As the primary energy substance, glucose can be assimilated and converted into diverse essential substances, including cholesterol. Cholesterol is mainly derived from de novo biosynthesis and the intestinal absorption of diets. It is evidenced that glucose/insulin promotes cholesterol biosynthesis and uptake, which have been targeted by several drugs for lipid-lowering, e.g., bempedoic acid, statins, ezetimibe, and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors. Inversely, these lipid-lowering drugs may also interfere with glucose metabolism. This review would briefly summarize the mechanisms of glucose/insulin-stimulated cholesterol biosynthesis and uptake, and discuss the effect and mechanisms of lipid-lowering drugs and genetic mutations on glucose homeostasis, aiming to help better understand the intricate relationship between glucose and cholesterol metabolism.

Cardiac Calcifications: Phenotypes, Mechanisms, Clinical and Prognostic Implications

There is a growing interest in arterial and heart valve calcifications, as these contribute to cardiovascular outcome, and are leading predictors of cardiovascular and kidney diseases. Cardiovascular calcifications are often considered as one disease, but, in effect, they represent multifaced disorders, occurring in different milieus and biological phenotypes, following different pathways. Herein, we explore each different molecular process, its relative link with the specific clinical condition, and the current therapeutic approaches to counteract calcifications. Thus, first, we explore the peculiarities between vascular and valvular calcium deposition, as this occurs in different tissues, responds differently to shear stress, has specific etiology and time courses to calcification. Then, we differentiate the mechanisms and pathways leading to hyperphosphatemic calcification, typical of the media layer of the vessel and mainly related to chronic kidney diseases, to those of inflammation, typical of the intima vascular calcification, which predominantly occur in atherosclerotic vascular diseases. Finally, we examine calcifications secondary to rheumatic valve disease or other bacterial lesions and those occurring in autoimmune diseases. The underlying clinical conditions of each of the biological calcification phenotypes and the specific opportunities of therapeutic intervention are also considered and discussed.

Lipid-lowering drug targets and Parkinson's disease: A sex-specific Mendelian randomization study

Parkinson's disease (PD) affects millions of individuals worldwide, and it is the second most common late-onset neurodegenerative disorder. There is no cure and current treatments only alleviate symptoms. Modifiable risk factors have been explored as possible options for decreasing risk or developing drug targets to treat PD, including low-density lipoprotein cholesterol (LDL-C). There is evidence of sex differences for cholesterol levels as well as for PD risk. Genetic datasets of increasing size are permitting association analyses with increased power, including sex-stratified analyses. These association results empower Mendelian randomization (MR) studies, which, given certain assumptions, test whether there is a causal relationship between the risk factor and the outcome using genetic instruments. Sex-specific causal inference approaches could highlight sex-specific effects that may otherwise be masked by sex-agnostic approaches. We conducted a sex-specific two-sample cis-MR analysis based on genetic variants in LDL-C target encoding genes to assess the impact of lipid-lowering drug targets on PD risk. To complement the cis-MR analysis, we also conducted a sex-specific standard MR analysis (using genome-wide independent variants). We did not find evidence of a causal relationship between LDL-C levels and PD risk in females [OR (95% CI) = 1.01 (0.60, 1.69), IVW random-effects] or males [OR (95% CI) = 0.93 (0.55, 1.56)]. The sex-specific standard MR analysis also supported this conclusion. We encourage future work assessing sex-specific effects using causal inference techniques to better understand factors that may contribute to complex disease risk differently between the sexes.

Effect of blood lipids and lipid-lowering therapies on osteoarthritis risk: A Mendelian randomization study

Background

We aimed to investigate the effects of blood lipids and lipid-lowering agents on osteoarthritis (OA) risk.

Materials and methods

We performed Mendelian randomization (MR) analyses to estimate the causal effect of blood low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels on knee and hip OA. Single nucleotide polymorphisms (SNPs) were selected from large genome-wide association studies (GWASs) of individuals of European ancestry as genetic instruments for blood lipid levels. The associations of selected genetic instruments with knee and hip OA were estimated in a recent GWAS of the UK Biobank and arcOGEN datasets. Univariate and multivariate MR analyses were performed to detect and adjust for potential pleiotropy. Furthermore, genetic instruments in HMGCR, NPC1L1, and PCSK9 regions were used to mimic LDL-C-lowering effects of statin, ezetimibe, and evolocumab, respectively.

Results

Genetically determined LDL-C increments led to reduced risks of both knee OA (OR = 0.91 per 1-SD increment, 95% CI: 0.86-0.95, P = 6.3 × 10^-5) and hip OA (OR = 0.92, 95% CI: 0.85-0.99, P = 0.027). Multivariate MR analysis proved that the effect was independent of HDL-C, TG, and body mass index. TG increment was associated with reduced risks of hip OA in the univariate MR analysis; however, this was not supported by the multivariate MR analysis. Genetically proxied LDL-C-lowering effects of statins are related to increased risks of knee OA but not hip OA.

Conclusions

The findings suggested that LDL-C increments have independent protective effects on both knee and hip OA. LDL-C-lowering effects of statins may increase the risk of knee OA.

A Possible Role for HMG-CoA Reductase Inhibitors and Its Association with HMGCR Genetic Variation in Parkinson's Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease characterised by both motor- and non-motor symptoms, including cognitive impairment. The aetiopathogenesis of PD, as well as its protective and susceptibility factors, are still elusive. Neuroprotective effects of 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitors—statins—via both cholesterol-dependent and independent mechanisms have been shown in animal and cell culture models. However, the available data provide conflicting results on the role of statin treatment in PD patients. Moreover, cholesterol is a vital component for brain functions and may be considered as protective against PD. We present possible statin effects on PD under the hypothesis that they may depend on the HMG-CoA reductase gene (HMGCR) variability, such as haplotype 7, which was shown to affect cholesterol synthesis and statin treatment outcome, diminishing possible neuroprotection associated with HMG-CoA reductase inhibitors administration. Statins are among the most prescribed groups of drugs. Thus, it seems important to review the available data in the context of their possible neuroprotective effects in PD, and the HMG-CoA reductase gene’s genetic variability.