Lipidomics of cardiovascular diseases
Research Article
Lipidomics analysis of blood plasma helps to predict and differentiate various cardiovascular diseases.
Summary
• Different CVDs have distinct lipid blood plasma profiles
• Lipidomic risk score allows for CVD prediction
• Multiomics analysis is beneficial for CVD prediction and differentiation
Resources
Shotgun mass spectrometry-based lipid profiling…
Matthiesen et al. | eBioMedicine (2021)
Lauber et al. | PLOS Biology (2022)
Adipose tissue ATGL modifies the cardiac lipidome…
Salatzki et al. | PGen (2018)
Genetic architecture of human plasma…
Tabassum et al. | Nat Commun (2019)
An automated shotgun lipidomics platform…
Surma et al. | EJLT (2015)
Systematic screening for novel lipids by…
Papan et al. | Anal. Chem. (2014)
ACCORDING to the World Health Organization, cardiovascular diseases (CVD) are the leading cause of death worldwide, being responsible for over 30% of deaths globally. Cardiovascular disease is an overall term describing diseases of the heart and blood vessels. There are several risk factors for cardiovascular diseases, and the main of them are unhealthy diet, lack of physical activity, and tobacco and alcohol use.
Despite CVD being one of the biggest threats to the global population, there are still a lot of things to learn about this disease group. Researchers and medical doctors worldwide work hard on identifying additional risk factors for cardiovascular diseases, on better prediction and stratification of various CVDs, and better treatment of these diseases.
In this article, we would like to underline the main achievement in the field of lipidomics and multiomics analyses applied to cardiovascular diseases research.
Lipid profiles of CVD
Matthiesen and Vieira conducted a study investigating the unique characteristics of lipidome profiles in the blood plasma of individuals with cardiovascular diseases and ischemic stroke, among other conditions. The researchers utilized shotgun lipidomics technology to examine the blood plasma of 427 participants, which included both a control group and experimental groups. This analytical approach enabled them to identify, detect, and quantify a total of 596 lipids. The control groups were adjusted based on age to match the experimental groups. The primary objective of the study was to determine whether there are differences in the lipidome profiles between cardiovascular diseases (CVD), ischemic stroke (IS), and their various manifestations, and to assess the extent of these distinct alterations.
The study design overview: Total of 427 patients participated in the study, with 85 belonging to the control group, 217 to the cardiovascular disease (CVD) group, 21 to the ischemic stroke (IS) group, and 104 to the systemic lupus erythematosus (SLE) group. Sample acquisition was followed by shotgun mass spectrometry lipidomics analysis of obtained samples, lipid identification and quantification, and data analysis.
Matthiesen et al., EBioMedicine 70 (2021) 103504, 10.1016/j.ebiom.2021.103504
The analysis of the acquired dataset included several machine learning techniques, such as supervised and unsupervised analyses, and the principal component analysis (PCA) was used. A reasonable separation between age-matched controls and IS, CVD1, CVD2, CVD3, CVD4, and CVD5 was achieved. While CVD3 showed the poorest separation from controls, CVD4 and CVD5 showed almost complete separation, suggesting that lipid abundances correlate with the cardiovascular disease presence.
Principal component analysis (PCA) using all 596 selected lipids as input: A IS vs control (n=46), B CVD1 vs control_50 (n=101), C CVD2 vs control_50 (n=110), D CVD3 vs control_50 (n=48), E CVD4 vs control_50 (n=73), and F CVD5 vs control_50 (n=55).
Matthiesen et al., EBioMedicine 70 (2021) 103504, 10.1016/j.ebiom.2021.103504
The findings of this study demonstrate the potential of mass spectrometry analysis of plasma lipids as a valuable tool for distinguishing between different vascular diseases associated with atherosclerosis. Specifically, it shows promising results in the differential diagnosis of conditions such as cardiovascular diseases and ischemic stroke.
Lipidomics risk scores in CVD
A comprehensive approach involving multiple disciplines is necessary to accurately detect, measure, and evaluate CVD risk. This approach takes into account diverse risk factors and considers the unique circumstances and requirements of each individual. Lauber and Simons suggest that a combination of genetic background, lipidomic profile alterations, and clinical risk factors provides a more accurate assessment of future CVD risk, compared to relying on classical risk factors, such as body mass index (BMI), age, sex, and other clinical parameters. To validate their hypothesis, the researchers examined the lipidomes of more than 4000 patients and assessed the categorization of these individuals based on their lipidome profiles, genetic and clinical parameters.
Study design. Genomic and lipidomic analyses of blood plasma samples from 4067 participants were performed. The risk of type 2 diabetes and cardiovascular disease development was assessed based on the data obtained from blood plasma analysis and clinical and vital parameters of participants.
Lauber et al., PLOS Biology (2022), 10.1371/journal.pbio.3001561
Overall cardiovascular disease risk stratification worked very well when the authors combined the lipidomics risk score with other standard clinical measurements (C), such as BMI, blood pressure, fasting blood glucose, glycated hemoglobin, LDL, HDL, and triacylglycerol levels, as well as polygenic (P) parameters, and age and sex (N). This combined model (N + L + P + C) improved the risk stratification for these diseases compared to the other scores.
Risk scores and case rate correlation. Various risk scores for CVD case rates are allocated to deciles. Dot represents the mean for 10 repetitions and the bars represent the standard errors. The risk scores are based on the models utilizing the following parameters: age and sex (N), lipidome (L), polygenic score (P), and clinical parameters (C). The combinations of particular variables in the models are N+L+P and N+L+P+C. The dashed line represents the average incidence rate within the cohort. CVD, cardiovascular disease.
Lauber et al., PLOS Biology (2022), 10.1371/journal.pbio.3001561
This study emphasizes the potential significance of the blood plasma lipidome data in predicting the risk of diseases. The authors propose to use the lipidomic risk scores for early assessment of CVD risk. The findings from this cohort study suggest that regular monitoring of the lipidome over time could provide valuable insights into an individual’s health status and shed light on the impact of lifestyle and dietary factors on their well-being. By identifying changes in the lipidome at an early stage, it may be possible to intervene and prevent the onset or progression of diseases. Additionally, adjustments to lifestyle or dietary habits and other interventions could be made to reduce the likelihood of developing diseases in the future.
Cardiac lipid metabolism in heart failure
Heart failure is considered one of the acute cardiovascular diseases, characterized by a loss of the heart’s ability to effectively pump blood to meet the body’s needs. This condition often arises due to damage to the cardiac tissue caused by myocardial infarction or coronary artery disease. In the presence of heart failure, there is an observed elevation in lipid metabolism within adipose tissue, cardiac tissue, and various other tissues and organs.
The research by Salatzki and has proposed that analyzing lipidomic profiles during heart failure could provide insights into the mechanisms underlying this particular cardiovascular disease. Furthermore, it may offer the potential to identify new therapeutic targets and serve as a non-invasive diagnostic tool. A team of scientists and clinicians with diverse expertise from Charité conducted a study focusing on lipid metabolism during left-sided systolic heart failure in both mice and humans. The findings of their study indicate a potential regulatory role of adipose tissue lipid metabolism in cardiac tissue function.
Altered lipid species levels in human blood plasma samples: Lipidomics analysis of blood plasma samples from patients with left-sided systolic heart failure (HFrEF) compared against patients without heart failure (control).
Salatzki & Foryst-Ludwig et al., PGen (2018), doi: 10.1371/journal.pgen.1007171
In blood plasma from 13 patients with heart failure and 10 patients from the control group, eight out of the 147 lipid species were upregulated. Three of these were particular phosphatidylethanolamines 34:1;0, 34:2;0, and 36:2;0 – notably the same three phosphatidylethanolamine species as in the cardiac tissue from the mouse model experiment. This is a strong indicator of a link between the blood lipidome and the cardiac tissue lipidome.
The study provides evidence of significant alterations in the lipidome during heart failure. Furthermore, the findings from the investigation of murine cardiac tissue suggest that these changes are influenced, to some extent, by lipid metabolism in adipose tissue. This discovery opens up the possibility of leveraging lipid metabolism in adipose tissue as a potential therapeutic target for heart failure.
Multiomics and CVD
As discussed above, plasma lipids are often used to estimate an individual’s risk of developing cardiovascular diseases or to differentiate one type of CVD from another. Combining the plasma lipidome with other omic data may enhance cardiovascular disease prediction and/or differential diagosis. By combining multiple omic endpoints, researchers can gain a more comprehensive understanding of the complex nature of cardiovascular diseases, leading to improved patient care and outcomes.
An international team of scientists analyzed the associations between genotypes, the plasma lipidome, and risk for cardiovascular disease in over 2000 samples from patients with CVD and the control group.
The analysis revealed that single nucleotide polymorphisms (SNPs), which are common genetic variations, are associated with specific lipid species in plasma. Notably, ceramides and lipids containing polyunsaturated fatty acids exhibited a high level of heritability. On the other hand, phosphatidylinositols demonstrated the lowest level of heritability among the analyzed lipid species.
The analysis also demonstrated a strong association between genetic variants and lipid species containing polyunsaturated fatty acids. Genetic variants were also associated with certain cardiovascular disease phenotypes.
Heritability of lipidomic profiles and genetic correlations among the lipid species: A Median heritability estimates in each lipid class. B Heritability estimates based on selected fatty acid chains in lipid species.
Rubina Tabassum et al., Nature Communications (2019), doi: 10.1038/s41467-019-11954-8
In summary, this investigation demonstrated the heritability of plasma lipid levels and their connection to the risk of developing cardiovascular disease. The inclusion of lipidomics in omics-based studies proved to be adding diagnostic and prognostic values. This report utilized such data to establish novel correlations between genetic variants, specific plasma lipid species, and the likelihood of experiencing cardiovascular diseases.
How to use lipidomics in cardiovascular disease research?
Quantitative lipidomics analysis allows not only to detect the smallest changes in blood plasma lipidome but also links these changes to the pathophysiological processes in the body. An in-depth analysis of lipidome allows not only to distinguish between various CVDs but also to predict their development in the future.
Lipotype Lipidomics technology can be used to characterize lipidomes of blood plasma, cell cultures, and tissues deriving from patients with cardiovascular and metabolic diseases, such as diabetes and obesity. This data can be helpful in the treatment efficacy evaluation and disease progression.
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