Coronary heart disease (CHD) continues to be a leading cause of morbidity and mortality among adults worldwide. Deregulated lipid metabolism (dyslipidemia) that manifests as hypercholesterolemia, hypertriglyceridemia, low high-density-lipoprotein (HDL) cholesterol levels or a combination of those, is an established risk factor for CHD among other established risk factors. Linoleic acid (LA, C18:2n-6) and alpha-linolenic acid (ALA, C18:3n-3) are polyunsaturated fatty acids (PUFAs) that cannot be synthesized de novo by human or animal cells, and therefore must be obtained from the diet. From these two PUFAs, two series of long-chain PUFAs are formed; the omega-6 series that are synthesized from LA, and the omega-3 series that are from ALA. Formation of these long-chain PUFAs involves a series of alternate desaturation and elongation processes. These PUFAs, especially, omega-3 PUFAs, have long been observed to reduce CHD risk. In contrast to the consistently observed cardiovascular protective effects of omega-3 PUFAs, accumulating evidence suggests a potential pro-atherogenic effects of omega-6 PUFAs, which is now still under debate.
It has been estimated that genetic factors account for 26%-69% of inter-individual variation in CHD risk. These genetic factors are thought to influence CHD risk both directly and through effects on known CHD risk factors such as plasma lipid levels. The heritability of plasma lipid levels (total cholesterol, LDL cholesterol, HDL cholesterol, and triglycerides (TG)) is estimated to be about 50% (ranging from 28%-78%). With the success of recent genome-wide association studies (GWAS), many genetic variants underlying intermediate risk factors of CHD (including plasma lipid levels) and CHD itself have been identified. Whether this new genetic information could be used to improve CHD risk prediction is still marginally explored, and for some variants, the underlying mechanisms for their mediated effects on CHD risk are still unknown. The aim of this research is to investigate common genetic determinants of plasma lipid levels (cholesterol and polyunsaturated fatty acid levels) using a pathway-driven approach, and to explore whether such common genetic variants could be used to improve CHD prediction using a population based genetic approach. An additional aim was to explore the underlying mechanisms of cardiovascular protective effects of PUFAs using a genomic approach.
In order to explore whether common genetic variants are involved in determining plasma cholesterol levels, we used data from 3575 men and women from the Doetinchem cohort, which was examined thrice over 11 years. They were genotyped on 384 single nucleotide polymorphisms (SNPs) across 251 genes in regulatory pathways that control fatty acid, glucose, cholesterol and bile salt homeostasis.
In order to explore whether common genetic variants could be used to predict future CHD risk,we used the data from CAREMA cohort that involved 15,236 middle-aged subjects and was followed up for a median of 12.1 years. 179 SNPs associated with CHD or its risk factors in GWAS published up to May 2, 2011 were genotyped in the 2221 subcohort members and 742 incident CHD cases. In addition, fatty acids from plasma cholesteryl esters were quantified in 1323 subcohort members and 537 CHD cases. They were used to explore whether δ-5 and δ-6 desaturase activities were associated with CHD risk.
In order to perform a comparative analysis of the effects of fenofibrate and fish oil at transcriptome and metabolome level, 34 mice were randomized by weight-matching into three groups (n = 10 in control group, and n = 12 in fenofibrate or fish oil intervention group), and fed a research diet supplemented with sunflower oil (containing 81.3% oleic acid, 7% energy intake) in control group, sunflower oil (7% energy intake) and fenofibrate (0.03% w/w) in fenofibrate group, and fish oil (Marinol C-38 fish oil: 23.1% EPA and 21.1% DHA, 7% energy intake) in fish oil group for 2 weeks. At the end of treatment, mice were fasted with drinking water available, and were subsequently sacrificed by cervical dislocation under isoflurane anesthesia. Blood was collected via orbital puncture. Livers were dissected, directly frozen in liquid nitrogen and stored at −80°C until further analysis. Microarray analysis was performed on individual mouse livers. The LC-MS method was used for measuring plasma lipids and non-esterified free fatty acids, and the GC-MS method was used for measuring a broad range of metabolites.
In chapter 2, 3, and 4, common genetic variants in the genes along known cholesterol metabolic pathways, such as bile acid and bile metabolic pathways, the HDL cholesterol metabolic pathway, and the plasma total cholesterol metabolic pathway, are involved in determining plasma cholesterol levels. The modest effect associated with each individual variant, however, caused the amount of heritability explained by them in aggregate to be relatively small: 13 single nucleotide polymorphisms (SNPs) explained 4% of inter-individual variation in HDL cholesterol levels (Chapter 3), whereas 12 SNPs explained 6.9% of inter-individual variation in total cholesterol levels (Chapter 4).
In chapter 5, we found that genetic variants in the FADS1 gene potentially interact with dietary PUFA intake to affect plasma cholesterol levels. A high intake of omega-3 PUFAs was associated with increased plasma non-HDL cholesterol levels, consistent with increased plasma LDL cholesterol levels observed in fish oil intervention studies. Increased LDL cholesterol levels could be due to hepatic downregulation of the LDL receptor gene (LDLR) in subjects with high omega-3 PUFA intakes. This is further confirmed by the findings described in Chapter 6 that the hepatic LDLR gene was significantly downregulated in fish oil treated mice. This study also confirmed PUFAs to be weak PPAR ligands. The increased plasma HDL cholesterol levels in the subjects with high PUFA intakes in Chapter 5 could be due to PPARs-mediated genes that are directly involved in HDL lipoprotein metabolism. All these may explain the changes in blood cholesterol levels upon PUFA intake observed in human studies.
In Chapter 6, we found that not only downregulation in the hepatic lipogenic pathway but also upregulation in hepatic fatty acid oxidation pathways are involved in lowering plasma TG levels upon fish oil treatment. The striking parallel between fenofibrate and fish oil in hepatic downregulation of blood coagulation and fibrinolysis pathways suggest that hepatic activation of PPARα is potentially one of the mechanisms responsible for anticoagulation effects of fish oil treatment observed in humans.
In Chapter 7, with confirmed effects of rs174547 in FADS1 on PUFA levels and δ-5 desaturase activities and also protective effects of DHA on CHD, we observed a reduced CHD risk of increased δ-5 desaturase activity. Increased δ-5 desaturase activity could contribute to the intracellular increase of EPA and especially arachidonic acid (C20:4n-6) levels. Despite the potential pro-coagulant and pro-inflammatory effects of increased exposures of arachidonic acid and its derived eicosanoid metabolites, there is no evidence of increased CHD risk with increased habitual arachidonic acid intake so far. Some of the oxygenated metabolites of arachidonic acid were found to have anti-inflammatory and pro-resolving actions. High dietary n-6 PUFA intakes or high plasma n-6 PUFA levels are associated with increased blood HDL cholesterol levels and reduced TG (or VLDL particle) levels. All these point to a potential cardiovascular protective effect of n-6 PUFAs. The fact that increased EPA and/or DHA levels associated with increased δ-5 desaturase activity protect against CHD is consistent with the current established cardiovascular protective effect of increased n-3 PUFA exposure, especially EPA and DHA.
In Chapter 8, the current known common genetic variants associated with CHD risk factors (blood pressure, obesity, blood lipid levels, and type 2 diabetes) and CHD itself from published GWAS are examined to see whether they provide additional value in CHD risk prediction beyond established traditional CHD risk factors. We constructed several gene risk scores (GRS) for CHD that consisted of SNPs directly associated with CHD or intermediate CHD risk factors in GWAS, and tested their relationship to incident CHD and their potential to improve risk prediction. The weighted GRS based on 29 CHD SNPs predicted future CHD independently from established traditional risk factors. However, the GRS based on 153 SNPs associated with intermediate risk factors and the GRS based on the total 179 SNPs did not. None of them improved risk discrimination. Risk classification of CHD, measured by the net reclassification index, improved only when the GRS based on the 29 CHD SNPs was used. These results are generally consistent with the results from other recent studies that took a similar approach as ours. However, the final conclusions on GRS application could not be drawn at this early stage. With a great understanding of the genetic architecture of CHD in the future, more research should be done on this topic.
Our studies in this thesis demonstrated that common genetic variants along the known candidate cholesterol metabolic pathways are involved in determining the plasma cholesterol levels. PUFAs are not only weak PPARα ligands, but also inhibit SREBPs’ activities. All these could explain part of the cardiovascular protective effects (increased HDL cholesterol levels and reduced TG levels) of PUFAs, increased LDL cholesterol levels upon fish oil treatment in humans, and potentially reduced CHD risk of high δ-5 desaturase activities. At present, many questions remain about the feasibility of genetic risk prediction of CHD. Clinicians should continue to inquire about family history of CHD for risk prediction, because this represents a simple, cheap, and useful risk factor for CHD that likely represents the net integrated effects from hundreds of genetic risk variants.
|Qualification||Doctor of Philosophy|
|Award date||8 Nov 2011|
|Place of Publication||[S.l.]|
|Publication status||Published - 2011|
- lipid metabolism
- polyenoic fatty acids
- heart diseases
- risk assessment
- genetic factors