Statins are inhibitors of 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), which is the rate-limiting enzyme in the mevalonate pathway; a pathway producing a range of cell signaling molecules with the potential to regulate oncogenesis. This is supported by strong laboratory evidence that statins induce anticarcinogenic effects on cell proliferation and survival in various cell lines (Sławińska-Brych et al., 2014; Crosbie et al., 2013; Ishikawa et al., 2014; Chang et al., 2013), and reduce tumor growth in a range of in vivo models (Narisawa et al., 1996a; Inano et al., 1997; Narisawa et al., 1996b; Clutterbuck et al., 1998; Kikuchi et al., 1997; Hawk et al., 1996). Furthermore, epidemiological studies of pre-diagnostic use of statins have been associated with reduced risk of specific cancer types (Khurana et al., 2007; Poynter et al., 2005; Pocobelli et al., 2008). However, meta-analyses of cardiovascular-focused randomized controlled trials have shown no effect of statins on cancer (Dale et al., 2006; Farooqi et al., 2018). Conclusions from these trials are limited as they lack adequate power and longitudinal follow-up necessary for assessing the impact on cancer risk. At present, no clinical trials have been designed to assess the role of statins in primary cancer prevention and their role in chemoprevention remains uncertain.

A putative protective effect of statins on cancer development could be through either cholesterol-dependent or independent effects (Goldstein and Brown, 1990; Mullen et al., 2016; Yeganeh et al., 2014; Denoyelle et al., 2001). Cholesterol is a key mediator produced by the mevalonate pathway and is essential to cell signaling and membrane structure, with evidence demonstrating the potential to drive oncogenic processes and tumor growth (Chen and Resh, 2002; Li et al., 2006). However, the epidemiological relationships between circulating cholesterol and cancer risk remain unclear. Individual observational studies have reported positive (Kitahara et al., 2011; Strohmaier et al., 2013), inverse (Kitahara et al., 2011; Strohmaier et al., 2013; Melvin et al., 2012; Katzke et al., 2017), and no association (Salonen, 1982; JPHC Study Group et al., 2009; Van Hemelrijck et al., 2012; Ma et al., 2016) between circulating levels of total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides with the risk of overall and site-specific cancers. Different cancer types have distinct underlying pathophysiology, and meta-analyses of observational studies highlight a likely complex relationship which varies according to both lipid fraction (Vílchez et al., 2014; Alsheikh-Ali et al., 2008) and cancer type (Ma et al., 2016; Lin et al., 2017; Passarelli and Newcomb, 2016; Yao and Tian, 2015). Furthermore, cancer can lower cholesterol levels for up to 20 months before diagnosis (Kritchevsky et al., 1991). Thus, the true relationship between lipids and cancer development remains equivocal.

Mendelian randomization is an epidemiological approach that assesses associations between genetically predicted levels of a risk factor and a disease outcome to predict the causal effect of the risk factor on an outcome (Davey Smith and Hemani, 2014). The use of genetic variants minimizes the influence of reverse causality and confounding factors on estimates. Mendelian randomization studies also have the potential to predict the outcomes of trials for specific therapeutic interventions. A limited number of Mendelian randomization studies have investigated the relationship between HMGCR inhibition and cancer (PRACTICAL consortium et al., 2016; Rodriguez-Broadbent et al., 2017; Orho-Melander et al., 2018; Nowak and Ärnlöv, 2018; Yarmolinsky et al., 2020), with protective associations observed for prostate cancer (PRACTICAL consortium et al., 2016), colorectal cancer (Rodriguez-Broadbent et al., 2017), breast cancer (Orho-Melander et al., 2018; Nowak and Ärnlöv, 2018), and ovarian cancer (Yarmolinsky et al., 2020). However, no comprehensive Mendelian randomization investigation has evaluated the predicted impact of HMGCR inhibition or the causal role of specific lipid fractions on the risk of many of the most common site-specific cancers.

Here we investigate the relationship between HMGCR inhibition and the risk of overall cancer and site-specific cancers using genetic variants in the HMGCR gene region. To understand whether statins may influence cancer risk through lipid-related mechanisms, we also assess the relationship between lipids and cancer risk by polygenic Mendelian randomization analyses using common lipid-associated genetic variants. Additionally, to mimic other lipid-lowering pharmaceutical interventions, gene-specific analyses were performed using variants in or near gene regions targeted by these therapies.

Our comprehensive Mendelian randomization investigation shows a positive association between overall cancer and variants in the HMGCR gene region which can be considered as proxies for statin therapy. However, gene regions which can be considered as proxies for alternative lipid-lowering therapies were not associated with cancer risk. Furthermore, there was little consistent evidence of an association between genetically-predicted lipid fractions and cancer outcomes in polygenic analyses either for overall cancer or for any site-specific cancer. Taken together, our findings predict that statins lower the risk of cancer, and provide important evidence that this occurs through mechanisms other than lipid lowering.

We found that genetic variants in the HMGCR region, serving as proxies for targets of statin therapy, were associated with a 26% decrease in risk of overall cancer per standard deviation (around 39 mg/dL or 1.0 mmol/L) reduction in genetically-predicted LDL-cholesterol. Our result replicates protective associations previously observed for prostate cancer (PRACTICAL consortium et al., 2016), colorectal cancer (Rodriguez-Broadbent et al., 2017), breast cancer (Orho-Melander et al., 2018; Nowak and Ärnlöv, 2018), and ovarian cancer (Yarmolinsky et al., 2020), although with a stronger weight of statistical evidence due to the additional number of cases analyzed here. For coronary artery disease, the short-term impact of statins in trials is around one-third of the genetic estimate, which represents the impact of lifelong reduced levels of LDL-cholesterol (Ference et al., 2012). Under the assumption that even if LDL-cholesterol may not be the relevant causal risk factor, it is a relevant prognostic factor for assessing the degree of HMGCR inhibition, this suggests that any reduction in cancer risk from statins in practice is likely to be modest. Mechanistically, cardiovascular risk reduction by statins is predominantly due to cholesterol lowering (Liao and Laufs, 2005), whereas we give evidence this is not the case for cancer.

While our results should be seen as tentative until trials have demonstrated benefit, associations of HMGCR variants show broad concordance with statin therapy for many continuous phenotypes (Würtz et al., 2016), and suggest that statins reduce the risk of coronary artery disease (Ference et al., 2016), increase risk of type 2 diabetes (Lotta et al., 2016), and increase risk of intracerebral hemorrhage (Sun et al., 2019; Allara et al., 2019a), as confirmed in clinical trials (ASCOT investigators et al., 2003; DIAGRAM Consortium et al., 2015; Collins et al., 2016). Genetic evidence pertaining to HMGCR has been proven to be a reliable guide for the performance of statins in trials. Clinical trials are required to confirm our promising findings for primary prevention of cancer risk.

The notion that statins could be used for chemoprevention is longstanding. Nobel Prize winners Goldstein and Brown proposed that this occurs through non-lipid lowering mechanisms (Goldstein and Brown, 1990). We provide evidence from human genetics to support this theory. Our results suggest that with respect to genetically predicted HMGCR inhibition and cancer risk, LDL-cholesterol is simply a biomarker of HMGCR inhibition that is accessible, but the true causal pathway is likely via another molecule whose levels are correlated with its LDL-cholesterol lowering effect. HMGCR catalyzes the rate-limiting step of the mevalonate pathway; a pathway with an arm leading to the endpoint of cholesterol synthesis and another arm leading to isoprenoid synthesis. Measuring levels of intra-cellular isoprenoids is challenging but these molecules are implicated in cancer via their role as major post-translational modifiers of key oncogenic proteins (Mullen et al., 2016). In particular, mevalonate and other isoprenoid metabolites are required for the prenylation and functioning of the Ras and Rho GTPases, which are oncoproteins, and involved in important cellular processes including apoptosis, phagocytosis, vascular trafficking, cell proliferation, transmigration, cytoskeleton organization, and recruitment of inflammatory cells. Statin inhibition of these metabolites has demonstrated anti-oncological effects in vivo and in vitro (Yeganeh et al., 2014) including the promotion of tumor cell death and apoptosis (Spampanato et al., 2012; Ghosh-Choudhury et al., 2010; Parikh et al., 2010; Misirkic et al., 2012), inhibition of angiogenesis (Park et al., 2008), and reduction of tumor cell invasion and metastasis (Wang et al., 2000; Yang et al., 2013). Other potential statin-mediated mechanisms of tumor suppression include the reduction of systemic inflammatory mediators like interleukin 1-beta and tumor necrosis factor (Park et al., 2008; Bruegel et al., 2006), and epigenetic regulation through inhibiting HMGCR-mediated deacetylation (Lin et al., 2008), which contributes to colorectal cancer in mouse models (Pisanti et al., 2014). Thus, our findings based on large-scale human genetic data are consistent with pre-clinical studies on statins in cancer which have repeatedly argued for a cholesterol independent mechanism for statin effects on cancer.

We have here presented the first wide-angled Mendelian randomization analysis of lipid subtypes for a range of site-specific cancers. We corroborate previous Mendelian randomization studies suggesting no causal role of lipids in the development of pancreatic cancer (Carreras-Torres et al., 2017a) and prostate cancer (PRACTICAL consortium et al., 2016). We also extend these findings to show little convincing evidence for total cholesterol levels or lipid fractions as a risk factor for any cancer type studied. However, it is plausible that lack of power or heterogeneity between cancer types could have contributed to these null results as previous large Mendelian randomization studies of the most common cancers (colorectal, lung, and breast) have shown significant associations with LDL-cholesterol, including results in both directions. Although our associations did not achieve a conventional level of statistical significance, we mirror the positive associations of genetically-predicted LDL-cholesterol and HDL-cholesterol with breast cancer (Nowak and Ärnlöv, 2018; Beeghly-Fadiel et al., 2019) observed in the Breast Cancer Association Consortium which studied over 60,000 cases of cancer. However, we did not observe the negative association observed between LDL-cholesterol and lung cancer found in a Mendelian randomization analysis of 29,266 cases, though in the same study rare LDL-cholesterol variants showed the opposite association (Carreras-Torres et al., 2017b). Of all the site-specific cancers studied, the present study only implicated dyslipidemia in driving bowel (i.e. colorectal) cancer with positive associations demonstrated for total and LDL-cholesterol levels. These findings corroborate a previous Mendelian randomization study of 26,397 colorectal cancer patients (Cornish et al., 2020). Furthermore, in a smaller Mendelian randomization study, genetically-predicted total cholesterol levels, but not LDL-cholesterol, were associated with colorectal cancer risk (Rodriguez-Broadbent et al., 2017), and several previous meta-analyses of observational studies have associated dyslipidemia with increased risk of colorectal adenoma (Passarelli and Newcomb, 2016) and cancer (Yao and Tian, 2015; Tian et al., 2015). However, the associations we found for colorectal cancer were attenuated in sensitivity analyses and must therefore be interpreted with caution. Overall, there was little consistent evidence for total cholesterol levels or lipid fractions as a risk factor for cancer, although this must be interpreted with caution.

Our headline finding relates to overall cancer, which is a combination of different malignancies. While they may have different underlying aetiologies, all cancers are known to share common underlying ‘hallmark’ molecular and cellular aberrations and there may thus be pathophysiologic relevance in combining outcomes (Hanahan and Weinberg, 2011). Furthermore, analyses for overall cancer are highly relevant from a public health perspective. In addition to the cardiovascular benefits of statins, any individual patient decision regarding whether to take them for primary cancer prevention is likely to reflect the risk of all cancer types, not one particular subtype. Overall, clinical trials are needed to confirm the protective effect of statins in the primary prevention of cancer and should characterize the adverse risks of statins before they are advocated in clinical guidance. In particular, any potential adverse effect of LDL-cholesterol lowering on lung cancer should be monitored, despite the lack of replication for this finding in the present analysis.

Our investigation has many strengths, but also limitations. The large sample size of over 360,000 participants and the broad set of outcomes analyzed render this the most comprehensive Mendelian randomization analysis of lipids and cancer outcomes conducted to date. However, the investigation has a number of limitations. For many site-specific cancers, there were not enough outcome events to obtain adequate power to rule out the possibility of moderate causal effects. This is particularly relevant to analyses of gene-specific target regions for LDL-cholesterol, which were not adequately powered to detect small effect sizes. Conversely, the study of a large number of outcomes across multiple risk factors as we have done is prone to type one errors, meaning that results are falsely found to be significant. However, correction for multiple testing may lead to type two errors, meaning that results are falsely judged to be inconclusive. We encourage readers to weigh the evidence presented carefully rather than to reduce findings to a binary designation of ‘significant’ or ‘not significant’. While there is evidence to support our assumption that genetic variants in relevant gene regions can be used as proxies for pharmacological interventions, our findings should be considered with caution until they have been replicated in clinical trials. Our investigation was able to compare subgroups of the population with different lifelong average levels of lipid fractions, but the impact of lowering a particular lipid fraction in practice is likely to differ from the genetic association, particularly quantitatively (Burgess et al., 2012). Combining cancer types to study overall cancer risk has the aforementioned benefits and also results in the largest number of cases and so the greatest power to detect a causal effect. However, this assumes a consistent effect between cancer types, so there is potential for directional heterogeneity and a consequent reduction in power. Furthermore, this combined endpoint is dependent on the characteristics of the analytic sample and the relative prevalence of different cancer types. In particular, cancers with greater survival chances will be overrepresented in the case sample. Finally, analyses were conducted in UK-based participants of European ancestries. While it is recommended to have a well-mixed study population for Mendelian randomization to ensure that genetic associations are not influenced by population stratification, it means that results may not be generalizable to other ethnicities or nationalities.

In conclusion, our findings suggest that HMGCR inhibition may have a chemopreventive role in cancer through non-lipid lowering properties and that this role may apply across cancer sites. The efficacy of statins for cancer prevention must be urgently evaluated.

All data generated or analysed during this study are publicly-available and/or provided in the supporting files. The UK Biobank can be accessed online at http://biobank.ctsu.ox.ac.uk/crystal/. The Global Lipids Genetics Consortium data can be accessed at http://csg.sph.umich.edu/willer/public/lipids2013/.

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Author details

1. Paul Carter

   Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Investigation, Writing - original draft

   Competing interests

   No competing interests declared

2. Mathew Vithayathil

   MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Writing - original draft

   Competing interests

   No competing interests declared

3. Siddhartha Kar

   1. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
   2. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

4. Rahul Potluri

   ACALM Study Unit, Aston Medical School, Birmingham, United Kingdom

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Amy M Mason

   Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Susanna C Larsson

   1. Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
   2. Department of Surgical Sciences, Uppsala University, Uppsala, Sweden

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Stephen Burgess

   1. Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
   2. MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Methodology, Writing - review and editing

   For correspondence

   sb452@medschl.cam.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5365-8760

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