Genetic and lifestyle risks of cardiovascular disease
Said, M. Abdullah
DOI:
10.33612/diss.157192207
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Publication date: 2021
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Said, M. A. (2021). Genetic and lifestyle risks of cardiovascular disease. University of Groningen. https://doi.org/10.33612/diss.157192207
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Coronary artery disease is one of the leading causes of death worldwide1. Further
enhancing our understanding of the pathophysiological mechanisms leading to this and other cardiovascular diseases will aid us in the development of better preventive and therapeutic strategies, as well as improved risk predictions.
Variety in human phenotypes, including complex diseases such as coronary artery disease, is the product of both variations in our DNA and environmental influences. In the past two decades, the discovery of thousands of associations between genetic variants with diseases and traits through genome-wide association studies (GWAS) has vastly increased our understanding of the biology underlying these phenotypes2. Now,
in the post-GWAS era, it is necessary to translate the wealth of information harvested through GWAS into information that can be used by clinicians and in the investigation of pharmaceutical therapies.
This dissertation aims to increase our understanding of the risk factors underlying cardiovascular disease, and in particular coronary artery disease. To this end, I describe the association between genetic variants with known or suspected cardiovascular risk factors, but also the evidence for causal links between cardiovascular risk factors and the development of cardiovascular disease through Mendelian randomization analyses. Mendelian randomization analyses use the random allocation of the genetic variants associated with a risk factor to assess potential causal links with a disease. The incidence of the disease of interest can then be compared between individuals who have been exposed to a genetically determined higher level of the risk factor due to their genetic make-up and individuals with a genetically determined lower exposure to the risk factor.
PART I - GENETICS OF CARDIOVASCULAR RISK FACTORS
In the first part of this dissertation (Chapter 2), I set out by studying the causal link between genetically determined telomere length (TL) with cardiovascular disease (CVD) and cancer. Previous studies reported associations between shorter TL with various diseases including coronary artery disease, atherosclerosis, and heart failure3,4. For
many of these associations, however, there was no evidence for a causal relationship. In 2013, a study in 37,684 individuals reported seven genetic variants associated with TL as well as an association between genetically determined telomere length and coronary artery disease5. I used these genetic variants and performed Mendelian
randomization analyses in 134,773 individuals of the UK Biobank to test the association between specific and overall CVD, as well as cancer. I report evidence for an association between genetically determined shorter telomeres and a lower risk of developing CVD,
hypertension, and cancer. The protective association between genetically determined shorter telomeres and cancer was later also reported for specific cancers6. Telomeres
have important roles in chromosomal stability, but shorten with each cell division in most somatic cells7. When a critical length is reached, the loss of the telomeric protection
triggers a DNA damage response ending in apoptosis7. Because of the limiting effect on
cellular proliferation, telomere shortening act as a tumor suppressor, as individuals with longer telomeres would be more likely to gain somatic mutations due to the higher proliferative potential of the cells8. The protective effect of shorter telomeres leading to
earlier tissue degeneration might be the result of an evolutionary trade-off for a lower susceptibility to cancers, while settling for a higher risk of degenerative diseases such as coronary artery disease9,10. My results of a lower overall cardiovascular disease and
hypertension risk, however, do not seem to be in line with this hypothesis or previous reports for coronary artery disease5. The associations between genetically determined
TL and specific cardiovascular disease may however be a spectrum with protective and harmful relationships, similar to the associations between TL and specific cancers6, rather
than all having a similar direction of effect. Future studies warrant the investigation of the association between genetically determined TL and a wider range of cardiovascular diseases to determine their direction of effect.
In Chapter 3, I aimed to better characterize the genetic architecture of lipoprotein(a) [Lp(a)], and its link with coronary artery disease. Lipids have a critical role in the risk and development of coronary artery disease11 and are already markers of pharmacological
interventions12-14. Of all lipids, Lp(a) is under the strictest genetic control, with over 90%
of its variance being determined by genetics15. Increased Lp(a) concentrations have
been previously associated to the development of cardiovascular diseases, including coronary artery disease, calcific aortic valve disease, and ischemic stroke16. However,
whether the association between Lp(a) on coronary artery disease is independent from LDL-C remains incompletely understood. I studied the genetic architecture of Lp(a) and found 37 novel loci associated with Lp(a) concentrations in 371,212 unrelated individuals of the UK Biobank. I continued to investigate the causal link between Lp(a) and coronary artery disease using Mendelian randomization analyses. Mendelian randomization analyses allow the disentanglement of the effects of correlated phenotypes, such as Lp(a) and LDL-C. With this approach, my study contributes new evidence for an LDL-C independent causal link between Lp(a) and the development of coronary artery disease in an independent cohort of 184,305 individuals17. These findings, despite Lp(a) being
mostly made up out of an LDL particle18, indicate Lp(a) is an independent risk factor
that should be dealt with accordingly. Both in the univariable (without taking LDL-C into account) and multivariable (taking LDL-C into account) Mendelian randomization analyses, I allowed for potential pleiotropic effects of the genetic variants and provided
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a framework for these analyses. One previous study also reported an LDL-C independent association of Lp(a) with coronary artery disease by using a genetic variant that mimics the effect of statins19. The multivariable Mendelian randomization approach I employed
allows us to estimate the direct effect of Lp(a) on coronary artery disease, i.e., the effect that is not driven by LDL-C, thereby allowing a more robust investigation of the effects of each factor. This investigation also moved beyond the traditional GWAS by performing statistical fine-mapping of the LPA gene locus, and report, amongst 15 candidate causal variants, a protective missense variant that leads to very low Lp(a) concentrations. Possibly, this is due to an impaired linkage between the apo(a) tail and the apolipoprotein-B(100) of the LDL particle20. Further experimental validation of this
variant is needed, as this may be a potential drug target. This is important, as existing therapies are unable to effectively lower Lp(a) concentrations21 and thereby the risk of
coronary artery disease, aortic valve stenosis and ischemic stroke.
In Chapter 4 I explored the causal link between genetically determined iron parameters and the development of coronary artery disease. Iron is an essential trace element with unique properties22, which is acquired from our diet through absorption in the gut
in case of depletion. The majority of the iron content in our bodies is incorporated in hemoglobin in the blood22. In certain cell types, iron is bound to ferritin and forms an
iron reserve22. Iron is involved in many organ systems and has roles in oxygen transport
and storage, mitochondrial function, myocardial and skeletal muscle metabolism, functioning of the immune system, and more22. Beneficial effects of iron replenishment
have been shown in patients with heart failure and iron deficiency, irrespective of whether they had anemia23. However, in 1981, Sullivan posed the hypothesis that
higher levels of stored iron were associated with increased risks of heart diseases24.
The epidemiological studies that followed reported conflicting results22. Because these
studies may have suffered from residual confounding or reverse causation, the cause-or-consequence of the association remains a topic of ongoing debate. I investigated the evidence for causality by applying a Mendelian randomization approach using genetic variants associated with ferritin, iron, transferrin, and transferrin saturation to estimate the causal effect on coronary artery disease. Using data of >400.000 UK Biobank participants, my study provides evidence of a protective effect of genetically determined higher iron and ferritin levels on coronary artery disease development. Higher transferrin saturation and transferrin had a protective direction of effect, but did not meet statistical significance. Sensitivity analyses showed no evidence for pleiotropic effects of the SNPs, and in meta-analyses including the CARDIoGRAMplusC4D cohort, the results remained similar. These results are additionally in line with a previous report that used three genetic variants25, and oppose Sullivan’s hypothesis. My findings are
also clinically relevant for the prevention of coronary artery disease. Future research should investigate whether iron supplementation, which can be easily achieved both orally or intravenously, can help decrease the risk of coronary artery disease.
PART II – INTERPLAY OF GENETICS AND LIFESTYLE WITH
CARDIOVASCULAR DISEASE
In the second part of this dissertation, I first review the current literature on the contribution of genetic and lifestyle factors in the development of coronary artery disease in Chapter 5. I discuss the different approaches used to unravel the genetic architecture underlying coronary artery disease, which include candidate gene approaches and GWAS. I describe the search of the genetic underpinnings of lifestyle factors that have been associated with coronary artery disease by the INTERHEART study26. Finally, I zoom in on studies investigating the role of both genetic and lifestyle
factors, their independent risks and possible interactions, in the context of coronary artery disease. I discuss several aspects put forward in Chapter 5 in the following chapters of this thesis.
One of the studies that investigated the risks of both genetic and lifestyle factors in their relation to new-onset cardiovascular disease is presented in Chapter 6. Modifiable lifestyle factors and genetics are both associated with the risk of developing cardiovascular diseases27-30 and type 2 diabetes31. The first report of the joint effects of
lifestyle and genetic risk factors by Khera et al. indicated a nearly two-fold higher risk of coronary artery disease amongst individuals with poor health behaviors compared to individuals with ideal health behaviors, while having similar genetic risks of the disease32.
I explored the extent to which this notion could be applied to other cardiometabolic diseases. Using data of 339,003 UK Biobank participants, I investigated whether poor combined health behaviors were associated with similar increases in the risk of coronary artery disease, atrial fibrillation, hypertension, stroke, and type 2 diabetes among subjects with low, intermediate, and high genetic risks of the respective diseases. Using polygenic risk scores, it is possible to estimate the overall genetic risk of a disease based on the summation of the number of risk increasing alleles of genetic variants associated with the disease, after multiplication with the effect size between the genetic variant and the disease33. I provide evidence that genetic and lifestyle factors are independent
risk factors of a range of cardio-metabolic diseases. Additionally, I show that regardless of the genetic risk, adherence to an ideal lifestyle is associated with much lower risks of developing the disease than when adhering to a poor lifestyle. In individuals with a higher genetic risk of a disease, this is perhaps even of more importance, as their genetic
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makeup puts them at a higher set-off point even if they have an ideal lifestyle. The results for coronary artery disease are in line with the 2016 study by Khera et al.32. I was
however the first to report the associations for atrial fibrillation, stroke, hypertension and type 2 diabetes. My findings for these diseases are in line with previous research that investigated genetic variants and lifestyle factors individually and reported their associations with the risk of developing these diseases27-29,34-39. It remains a topic of
ongoing discussion whether disclosure of the genetic risk can aid in reducing the risk of disease. Several studies with small to moderate sample sizes in which the genetic risk was communicated to the participants have shown inconclusive results on its effect on behavioral changes40. A positive effect on lifestyle behaviors was recently shown
in an observational study41. This study however lacked a control group in which the
genetic risk was not disclosed, making it difficult to compare these results to the current situation. It is worth further study to investigate whether knowledge of the genetic risk can aid in motivating positive behavioral changes. A deterministic interpretation of the polygenic risk score should however be avoided, as even in people with a high polygenic risk score of a disease, this is not a guarantee the disease will develop. In my study, an ideal lifestyle was defined as not smoking, not being under- or overweight, having sufficient physical activity, and adhering to an ideal diet for cardiovascular health42 in accordance with the American Heart Association 2020 impact
goal guidelines43. Other lifestyle factors have however also been indicated as risk factors
for cardiovascular diseases as well as type 2 diabetes, and are worth further study, also from a genetic point of view. One of these lifestyle factors is sedentary behavior, which I studied in Chapter 7. Sedentary lifestyles are an increasing problem across the globe. Current estimates indicate adults in the United Kingdom spend an average 5 hours a day sedentary44, while adults in the United States spend an average 7.7 hours a day
sedentary45. Prolonged time spent on sedentary behaviors has been associated with an
increased risk of cardiovascular diseases46 and mortality47 in observational studies. The
link with coronary artery disease is however less clear. I explored the genetic variants underlying leisure sedentary behaviors, namely television watching, computer use and driving. My study uncovered 145, 36 and 4 genetic loci underlying these respective leisure sedentary behaviors in 422,218 UK Biobank participants. I then set out to investigate the causal link with coronary artery disease using Mendelian randomization analyses. I report evidence for a causal link between increased leisure television watching and driving with coronary artery disease. Also in a multivariable Mendelian randomization setting adjusting for education, leisure television watching remained associated with an increased risk of coronary artery disease. I found no evidence for a causal link between leisure driving and coronary artery disease, possibly due to pleiotropic effects of the genetic variants. Nonetheless, my findings are clinically relevant as they
provide evidence for a causal role in the development of coronary artery disease for two domain-specific leisure sedentary behaviors. Reducing the amount of time spent television watching or driving may help reduce the risk of coronary artery disease. Current guidelines in the United Kingdom already not only recommend sufficient physical activity, but also minimizing the time spent in sedentary behavior48. My study
supports these recommendations for adults between 40 and 69 years of age, as well as traditional epidemiological data reporting associations between sedentary behaviors and risk of coronary artery disease49. A recent study using the genetic variants identified
in our study also found evidence for a causal link between genetically determined excessive television watching and an increased risk of type 2 diabetes50. It would be
interesting if future studies would investigate the link between domain-specific leisure sedentary behaviors with other cardiovascular diseases.
In Chapter 8 I explored the genetics of caffeine intake and the causal link between caffeine intake with the development of coronary artery disease. Caffeine, a natural insecticide, is the most widely consumed psychostimulant worldwide51. Previous epidemiological
studies have generally reported beneficial associations between moderate intakes of coffee, the main dietary source of caffeine, and the risk of cardiovascular diseases52 and
type 2 diabetes53. However, contrasting reports for the association with coronary artery
disease have also been reported52,54-56. Whether an association exists and whether it may
be causal, remains unknown. Altering the caffeine intake may however be an interesting method to influence the risk of developing coronary artery disease and type 2 diabetes on a large scale. In my study, I observed U-type shaped associations between caffeine from coffee and caffeine from tea with both coronary artery disease and type 2 diabetes, with the lowest risks of disease at moderate intakes. These findings are in line with previous reports, including a meta-analysis in over 1.2 million individuals investigating the association between coffee intake and coronary artery disease52. My results for type
2 diabetes are also in line with a previous meta-analysis, although they did not report a U-type association57. I also did not find U-type associations between caffeine intake
from both sources combined with either coronary artery disease or type 2 diabetes, arguing for potential residual confounding in the previous analyses and reports. This indicates the associations observed for caffeine from coffee and caffeine from tea with both diseases may not be driven by caffeine, but other constituents of coffee and tea. Genome-wide association analyses on the caffeine intakes from coffee, tea or both sources in a total of 407,072 UK Biobank participants identified 56 genetic loci, of which 51 novel, associated with caffeine intake. Besides replicating previously reported genes such as AHR, CYP1A1, and POR, which have clear roles in the metabolism of caffeine58, I
report two novel genes, GOLPH3L and HORMAD1, that were associated with all caffeine traits. These genes respectively have regulatory roles in Golgi trafficking and meiotic
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progression. Whether these genes are only statistically associated with caffeine intake or whether there is a biological link remains unclear and warrants further study. I then performed Mendelian randomization analyses using the caffeine trait specific variants as instrumental variants in independent cohorts of coronary artery disease and type 2 diabetes, and found no evidence for a causal link between caffeine intake from any source with either disease, while taking into account potential pleiotropic effects of the variants. My findings are relevant for the prevention of coronary artery disease and type 2 diabetes, as they do not support recommending caffeine intake as a protective measure against the development of either disease. The genetic causal inference analyses were however limited to linear associations59. Novel methods that are able to investigate
non-linear associations could not be applied, as these require individual level data on the exposure (i.e., caffeine intake) in the outcome cohort59. Given its widespread usage, this
may be a question worth further study.
FUTURE PERSPECTIVES
Since the completion of the Human Genome Project, GWAS have yielded a vast amount of data on the potential biological underpinnings of human phenotypes2. This knowledge
can assist us to find novel and better therapeutic strategies tailored to the individual. The emergence of huge biobanks and collaborations with hundreds of thousands of participants60-62, or in some cases even over a million63, has aided in these efforts and
helped elucidate some of the biology of various phenotypes. In this thesis, I performed several large-scale GWAS on known or suspected cardiovascular risk factors in the UK Biobank population. This thesis thereby contributes hundreds of genetic variants that have been newly associated to Lp(a) or lifestyle factors, namely leisure sedentary behaviors and caffeine intake. This thesis furthermore contributed to our knowledge of potential causal links between risk factors and the development of coronary artery disease or type 2 diabetes. The number of genetic variants associated with cardiovascular traits and risk factors will expand rapidly in the future as developments in genome-wide sequencing allow it to become both faster and cost-efficient64. In this thesis, I mainly
focused on genetic variants that are common, i.e., that are found in more than 5% of the population. However, much of the variance of human traits is estimated to be explained by less common, or rare, variants that are not necessarily captured or tagged by genotyping chips. The reliable identification of these rare variants using a genome-wide association analysis approach may require even larger biobanks than currently available, and with a higher resolution of the sequence variations. Ideally, genome-wide sequencing data of all participants of these mega-biobanks will become available for researchers around the globe.
The analyses required to confidently associate rare variants to phenotypes may prove to be complex. One of the challenges in current genome-wide association analyses is that the genetic associations are detected by linkage disequilibrium between genetic variants. Variants in high linkage disequilibrium are inherited together, and it is sufficient for Mendelian randomization analyses to employ genetic variants that are in linkage disequilibrium with the causal variant. Distinguishing between the tagging variants and the actual causal variant or differentiating between highly correlated genes is however a road-block that has hindered us from gaining biological insights from GWAS or translating the effects into clinically relevant information. Future studies should incorporate structured analyses aimed at finding the causal variants, including steps for functional validation of potential causal variants, genes and pathways via which the genetic variants are involved in the pathophysiological mechanisms that eventually lead to disease. For this, future research could investigate epigenetic mechanisms, including DNA methylation, and their interaction with the genetic variants. Also, multiple resources already exist with gene-expression data in various tissues65-67. These multiple facets of
data can subsequently be used to triangulate the evidence on candidate causal variants and genes. These candidate genes and loci can then also be followed-up using CRISPR68
technologies in model organisms or human cell lines, which will enable us to better understand the GWAS results and to leverage this knowledge in developing preventive, diagnostic and therapeutic strategies. These therapeutic strategies include specifically targeted drugs, which can be personalized based on the genetic variants.
The developments in the field of human genetics have also made it possible to use enormous sample sizes of human subjects in a myriad of experiments by exploiting the association between genetic variations and phenotypes33. Due to the lifelong
effect of variants which are randomly allocated, Mendelian randomization analyses are analogous to randomized controlled trials33. The advantage over clinical randomized
controlled trials is that this field of research also allows experiments which would otherwise be unpractical or even unethical, such as a 40-year long trial on sedentary behavior or caffeine intake. It however also allows the investigation of potential drug side effects or the likely efficacy of a drug. These analyses may help guide clinical trials. For example, the pursuit of cholesterol ester transfer protein inhibitors (CETPi), aimed at increasing HDL cholesterol69, was discontinued70 following, and in part owing to,
Mendelian randomization analyses indicating there was no protective causal effect for coronary artery disease71.
Finally, future studies should investigate whether genetic risk scores based on genetic variants should be communicated to the general population as a measure to lower the cardiovascular risk72. So far, studies have been unable to provide convincing evidence
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for such beneficial effects40, although a recent study that incorporated the polygenic
risk score into an existing risk score for atherosclerotic cardiovascular disease indicated positive changes in health behaviors41. Improved risk prediction through enhancement
of existing risk scores with the genetic risk would be yet another step towards personalized medical healthcare.
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