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Lipidomics-based investigation of its impact on the pathogenesis of coronary atherosclerosis: a Mendelian randomization study

Hereditas volume 162, Article number: 13 (2025) Cite this article

Abstract

Background

Considerable attention has been devoted to investigating the association between lipid metabolites and cardiovascular diseases, particularly coronary atherosclerosis.

Methods

A two-sample MR framework was used to investigate the relationship between lipid metabolites and the risk of coronary atherosclerosis. Two GWAS datasets were examined to take intersections of SNPs from 51,589 cases and 343,079 controls, and 14,334 cases and 346,860 controls to determine genetic susceptibility to coronary atherosclerosis. Random-effects inverse variance weighted (IVW) MR analyses were performed by a series of sensitivity assessments to measure the robustness of our findings and to detect any violations of MR assumptions.

Results

Through IVW, MR-Egger and weighted median regression methods, we inferred that these six lipid metabolites: cholesterol levels, sterol ester (27:1/18:2) levels, triacylglycerol (52:4) levels, triacylglycerol (52:5) levels, diacylglycerol (18:1_18.2) levels, triacylglycerol (53:4), could directly impact the development of atherosclerosis.

Conclusion

In conclusion, our study comprehensively illustrates a causal relationship between lipid metabolites and the risk of coronary atherosclerosis. Furthermore, cholesterol levels, sterol ester (27:1/18:2) levels, triacylglycerol (52:4) levels, triacylglycerol (52:5) levels, diacylglycerol (18:1_18.2) levels, and triacylglycerol (53:4) levels are positively correlated with the risk of coronary atherosclerosis. These six lipid metabolites have the potential as new predictors of the risk of atherosclerosis, providing new insights into the treatment and prevention of cardiovascular diseases.

Background

Cardiovascular disease (CVD) ranks as the foremost factor behind global mortality, with an annual toll of around 18 million deaths (31% of all deaths). Atherosclerotic coronary heart disease is the primary contributor to CVD deaths, responsible for nearly 45% of all cases [1]. Coronary atherosclerosis is a long-term refractory disease with a wide range of clinical manifestations, from an asymptomatic state to stable angina, acute coronary syndrome (ACS), heart failure (HF), and sudden cardiac death (SCD) [2]. The principal issues in atherosclerosis are the local deposition of fat within arteries and the development of smooth muscle cells and fibrous matrix, which promote the formation of atherosclerotic plaques over time [3]. Studies have shown that atherosclerosis is characterized by inflammatory responses and arterial lipid accumulation [4]. Inflammation is an important driver of atherosclerosis [5] and atherosclerosis is a chronic inflammatory response that increases the risk of cardiovascular disease [6]. Lipid metabolites, including cholesterol and triglycerides, play significant roles in the development of inflammation. Cholesterol accumulation may promote inflammatory responses and exacerbate diseases associated with chronic metabolic inflammation, such as atherosclerosis and obesity [7]. Therefore, we speculate that inflammatory responses and lipid accumulation directly affect the risk of atherosclerosis.

Recent studies have shown that lipid abnormalities such as cholesterol and triglycerides are implicated in the pathogenesis of atherosclerosis [8]. Lipid metabolites include many types, like cholesterol, triglycerides, and phosphatidylcholine. Phosphatidylcholine is the most abundant phospholipid in all types of mammalian cells and subcellular organelles [9]. One study examined the plasma levels of TMAO biomarkers in Ldlr-/- male mice after dietary phosphatidylcholine supplementation and concluded that dietary phosphatidylcholine supplementation could improve atherosclerosis in mice [10]. Cholesterol is the major sterol in mammals and significantly affects membrane fluidity, permeability, and signaling [11]. All cell membranes require cholesterol, so cholesterol metabolism and its circulating levels are crucial for atherosclerosis [12]. Recent epidemiological data suggest that triglycerides are a causal pathway in the pathogenesis of atherosclerosis.

The important role of lipid metabolism disorders in the pathogenesis of atherosclerosis has been unveiled [13]. However, which lipid metabolites matter most has not been concluded. Therefore, in this study, MR analyses were performed with lipid metabolites as the exposure factor and coronary atherosclerosis as the outcome to explore the potential causal relationship. We aim to provide a theoretical basis for further research on the complex mechanisms and clinical efficacy of lipid metabolites in the risk of atherosclerosis.

Methods

Experimental design

To elucidate the presumed causal relationship between lipid metabolites and atherosclerosis, a two-sample Mendelian randomization (MR) approach was used. Single nucleotide polymorphisms (SNPs) were used as instrumental variables (IVs). This SNP-centered approach reflects the principle of a randomized controlled trial and helps identify the causal relationship between exposure factors (lipid metabolites) and atherosclerotic outcomes.

Study design drawing

figure a
figure b

Ethics and data use statement

Data were incorporated from previous studies that had been approved by the relevant institutional review board. Participants in the original study provided informed consent, so no further ethical review was required for this analysis. Nonetheless, we obtained an ethical statement from the institutional review board and ensured informed consent from all participants involved in the study.

Genetic instrument variants for exposure

Our analyses used 179 SNPs associated with lipid metabolite levels from the PMID37907536. SNPs were selected if p < 5 × 10^−8, and SNPs were excluded if Maf < 0.01 and F > 10.

GWAS Summary Data for Atherosclerosis

We retrieved data from the FinnGen dataset (https://gwas.mrcieu.ac.uk/datasets/finn-R10-I9/) and the UKb dataset (https://gwas.mrcieu.ac.uk/datasets/ukb-d-I9/) using the keyword "Atherosclerosis". The FinnGen dataset included 161 exposures with SNPs ≥ 1, and the combined GWAS summary data for AS could be found at finngen_R10_I9_CORATHER, including 51,589 cases and 343,079 controls. The UKb dataset included 162 exposures with SNPs ≥ 1, and the combined GWAS summary data for atherosclerosis could be found at ukb-d-I9_CORATHER, including 14,334 cases and 346,860 controls. Diagnostic criteria for atherosclerosis were based on a comprehensive assessment of glucose and lipids, calcium deposition on x-ray, and atherosclerotic plaques confirmed by arteriography, or Doppler ultrasound. Through IVW analysis (SNPs > 1) and Wald Ratio (SNP = 1) methods, we explored the potential causal relationship between the levels of each lipid metabolite and atherosclerosis. Also, each genetic marker surpassed the threshold for genome-wide significance (P < 0.001), indicating robust instrument strength (F-statistic > 10).

Screening of IVs

The process of screening SNPs was meticulous attention to detail. Initially, SNPs closely associated with atherosclerosis were selected, with a genome-wide significance threshold of P < 5 × 10^−8. To ensure SNP independence and minimize the influence of linkage disequilibrium (LD), a stringent r2 of 0.01 was implemented within a 10,000 kb range. This step was essential to reduce the potential bias from LD. Additionally, the relationship between (IVs and the exposure was quantified using the F-statistic for each SNP, with IVs exhibiting an F-statistic > 10 indicating unbiased estimates.

Statistical analysis

The primary method was the inverse variance-weighting (IVW) approach, which was predicated on the assumption that all SNPs were valid IVs, thus providing the most accurate estimates. If any SNP did not conform to the IV assumption, a modified version known as the random-effects IVW method was utilized. This method adjusted each estimate based on its standard error, thereby accounting for potential heterogeneity. The weighted median approach required that at least 50% of SNPs are valid to maintain the integrity of the IV assumption. The SNPs were ranked according to their weights and the experimental outcomes were examined to determine the median of the corresponding distribution. Furthermore, the MR-Egger regression, independent of the absence of pleiotropic effects, was used to derive an effect estimate. The intercept from the MR-Egger regression was used to evaluate the pleiotropic effect, with a non-significant deviation from zero indicating no directional pleiotropic bias.

Sensitivity analysis

The random-effects IVW method was the cornerstone of our analysis of the causal links between atherosclerosis and lipid metabolites. This method synthesized the Wald ratio estimates for each SNP to obtain a causal estimate for each risk factor, yielding reliable estimates in the absence of pleiotropy. Sensitivity analyses were conducted to confirm the associations. The weighted median method was employed, which required only half of SNPs to be valid instruments, and the MR-Egger approach was utilized to accommodate a non-zero intercept, indicating pleiotropy. The MR-PRESSO test was employed to identify potential outliers, with adjustments made by excluding such SNPs. If, The IVW-MR estimates were considered robust if the adjusted effect was consistent with the uncorrected effect. However, in case of significant discrepancies, the adjusted effects should be prioritized, as they may be less biased and better reflect the true relationship.

A two-stage MR analysis was performed to evaluate the mediating effects. The first stage used a genetic instrument of the lipid metabolites to estimate the causal effect of the exposure on the mediator. The second stage employed genetic instruments of the mediator to ascertain the causal effect on the risk of atherosclerosis.

The causal effects of lipid metabolites on the risk of atherosclerosis were described using odds ratios (ORs), beta coefficients (β), and 95% confidence intervals (CIs). MR and sensitivity analyses were performed using R software (version 4.2.1) and the "TwoSampleMR" package (version 0.5.6). In the univariate MR analyses, a P-value of < 3.11 × 10^−4 (FinnGen dataset) or a P-value of < 3.09 × 10^−4 (UKb dataset) (adjusted for multiple comparisons as 0.05 divided by the number of exposures and outcomes) implied a statistically significant causal relationship.

Results

MR Analysis: the role of lipid metabolites in atherosclerosis (FinnGen dataset)

Fourteen out of 161 exposures with a P < 0.001 were found. By applying IVW analyses and weighted median methods, we explored potential causal associations between lipid metabolite levels and atherosclerosis. In the Finn database, the IVW methods unveiled significant associations, implying that elevated levels of lipid metabolites may increase the risk of atherosclerosis. The influence of each lipid metabolite on the risk of atherosclerosis is represented in the volcano plot (Fig. 1) and detailed data can be found in Table 1. The P-value for exposure to 14 lipid metabolites on the risk of atherosclerosis was less than 0.001, as shown by a forest plot (Fig. 2).

Fig. 1
figure 1

Volcano Plot of Lipid on Coronary Atherosclerosis (Finngen dataset). Note: The horizontal coordinate represents the relative risk of atherosclerosis and the vertical coordinate represents the p-value

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Table 1 Finngen dataset of lipid metabolites on atherosclerosis risk
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Fig. 2
figure 2

Forest plot of lipid metabolites on atherosclerosis risk (Finngen dataset).Note: Images from left to right the first column is Exposure, the second column is Method, the third column represents nSNP, the fourth column is OR (95% CI) value, and the last column is P value

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MR Analysis: the role of lipid metabolites in atherosclerosis (UKb dataset)

Thirteen out of 162 Exposures were found to have a P < 0.001. By applying (IVW analyses and weighted median methods, we explored potential causal associations between lipid metabolite levels and atherosclerosis. In the UKb database, the IVW methods revealed notable associations, implying that elevated levels of lipid metabolites may increase the risk of atherosclerosis. The influence of each lipid metabolite on atherosclerosis risk is graphically represented in the volcano plot (Fig. 3) and detailed data can be found in Table 2. The P value for exposure to 13 lipid metabolites for atherosclerosis risk was ≤ 0.001, as represented by a forest plot (Fig. 4).

Fig. 3
figure 3

Volcano Plot of Lipid on Coronary Atherosclerosis (UKB dataset). Note: The horizontal coordinate represents the relative risk of atherosclerosis and the vertical coordinate represents the p-value

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Table 2 UKB dataset of lipid metabolites on atherosclerosis risk
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Fig. 4
figure 4

Forest plot of lipid metabolites on atherosclerosis risk (UKB dataset). Note: Images from left to right the first column is Exposure, the second column is Method, the third column represents nSNP, the fourth column is OR (95% CI) value, and the last column is P value

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Identifying key potential lipid metabolites in atherosclerosis

After the crossover of the ADJUSTED < 0.05 fraction of differential lipid metabolites in the above two datasets, we identified six crossover levels of lipid metabolites that affect the risk of atherosclerosis. Finn database:

Cholesterol levels (WR, OR = 2. 310; P = 2.063*10–5; [95% CI: 1.970–2.700]), Sterol ester (27:1/18:2) levels (IVW, OR = 1. 410; P = 8.977*10–5; [95% CI: 1.210–1.630]), Triacylglycerol (52:4) levels (IVW, OR = 1. 270; P = 9.434*10–5; [95% CI: 1.140–1.410]), Triacylglycerol (52:5) levels (IVW, OR = 1. 340; P = 4.603*10–5; [95% CI: 1.160–1.540]), Diacylglycerol (18:1_18:2) levels (IVW, OR = 1. 220; P = 5.797*10–5; [95% CI: 1.110–1.350]), Triacylglycerol (53:4) levels (IVW, OR = 1.320; P = 7.476*10–5; [95% CI: 1.150–1.510]).

UKb database: Cholesterol levels (WR, OR = 1.030; P = 8.444*10–5; [95% CI: 1.020–1.040]), Sterol ester (27:1/18:2) levels (IVW, OR = 1.010; P = 7.102*10–5; [95% CI: 1.010–1.020]), Triacylglycerol (52:4) levels (IVW, OR = 1.010; P = 0.006*10–5; [95% CI: 1.00–1.010]), Triacylglycerol (52:5) levels (IVW, OR = 1.010; P = 2.088*10–5; [95% CI: 1.00–1.010]), Diacylglycerol (18:1_18:2) levels (IVW, OR = 1. 010; P = 1.678*10–5; [95% CI: 1.010–1.010]), Triacylglycerol (53:4) levels (IVW, OR = 1.010; P = 1.776* 10–5; [95% CI: 1.000–1.010]). Detailed information is in Fig. 5, Table 3.

Fig. 5
figure 5

Finngen dataset and UKB dataset take intersection. Sensitivity Analysis (Horizontal Multiple Validity Analysis and Heterogeneity Tests)

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Table 3 Intersection between Finngen dataset and UKB dataset of lipid metabolites on atherosclerosis risk
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Verification of MR Presumptions

In our study, SNPs were selected based on the genome-wide significance threshold (p < 0.001) in these analyses and no directional pleiotropy was noticed, suggesting that the second MR assumption was not violated (p > 0.05, Table 3). Additionally, the MR heterogeneity test showed no heterogeneity in most of our positive outcomes (p > 0.05, Table 3). In summary, the rigorous assessment of the three fundamental assumptions in MR analysis suggested that the selected SNPs were appropriate as genetic instruments, and the relationships between genetically predicted lipid metabolites and atherosclerosis were not influenced by potential confounders or mediators.

Backtesting the MR hypothesis

Eventually, we concluded that the six lipid metabolites directly affected coronary atherosclerosis, and conversely, we wondered whether the six lipid metabolites increased with the development of coronary atherosclerosis. Therefore, we conducted an MR study with coronary atherosclerosis as an exposure factor and the six lipid metabolites as outcome indicators. The results were all P > 0.05, demonstrating that lipid metabolites did not increase with the development of atherosclerosis.

MR Analysis: the role of atherosclerosis on lipid metabolites (FinnGen dataset)

Through IVW analyses and weighted median methods, we explored potential causal associations between atherosclerosis and lipid metabolite levels and obtained the following results:

Cholesterol levels (IVW, OR = 0.960; P = 0.472; [95% CI: 0.870–1.060]),

Sterol ester (27:1/18:2) levels (IVW, OR = 1.010; P = 0.847; [95% CI: 0.910–1.120]), Triacylglycerol (52:4) levels (IVW, OR = 1.010; P = 0.793; [95% CI: 0.930–1.110]),

Triacylglycerol (52:5) levels (IVW, OR = 0.654; P = 0.654; [95% CI: 0.940–1.110]),

Diacylglycerol (18:1_18:2) levels (IVW, OR = 1.000; P = 0.969; [95% CI: 0.910–1.090]), Triacylglycerol (53:4) levels (IVW, OR = 1.040; P = 0.411; [95% CI: 0.950–1.140]).

Detailed information is in Table 4.

Table 4 Intersection between Finngen dataset and UKB dataset of atherosclerosis risk on lipid metabolites
Full size table

MR Analysis: the role of atherosclerosis on lipid metabolites (Ukb dataset)

Through IVW analyses and weighted median methods, we explored the potential causal association between atherosclerosis and lipid metabolite levels and obtained the following results:

Cholesterol levels (IVW, OR = 1.180; 95% CI: 0.100–14.330; P = 0.895),

Sterol ester (27:1/18:2) levels (IVW, OR = 1.040; 95% CI: 0.090–12.420; P = 0.975), Triacylglycerol (52:4) levels (IVW, OR = 0.420; 95% CI: 0.050–3.590; P = 0.429),

Triacylglycerol (52:5) levels (IVW, OR = 0.770; 95% CI: 0.090–6.380; P = 0.808),

Diacylglycerol (18:1_18:2) levels (IVW, OR = 0.280; 95% CI: 0.030–2.380; P = 0.244), Triacylglycerol (53:4) levels (IVW, OR = 1.710; 95% CI:0.190–15.750; P = 0.634). Detailed information is in Table 4.

Discussions

Main findings

Atherosclerosis is attributed to the abnormal deposition of fibers and lipids throughout the endothelium, which leads to the loss of arterial elasticity and disrupts the vascular structure, resulting in ischemia [14]. The mechanism is that macrophages containing oxidized LDL particles release inflammatory substances, cytokines, and growth factors, which induce cell proliferation and promote leukocyte activation and endothelial dysfunction. Our main finding unveiled that the levels of six lipid metabolites, cholesterol, sterol ester (27:1/18:2), triacylglycerol (52:4), triacylglycerol (52:5), diacylglycerol (18:1_18:2), and triacylglycerol (53:4) directly affected the risk of coronary atherosclerosis. However, their levels did not increase with coronary atherosclerosis.

According to national and international guidelines, elevated low-density lipoprotein cholesterol (LDL-C) is a well-known risk factor for atherosclerotic cardiovascular disease [15]. Our study demonstrated that cholesterol levels were positively associated with the risk of coronary atherosclerosis. Consistently, a study reported in 2020 mentioned that crystalline cholesterol and cholesterol crystals in atherosclerosis were regular features within its necrotic core [16]. Likewise, a study found that Rhodiola rosea glycosides attenuated atherosclerosis in mice by reducing SREBP2 levels and cholesterol and triglyceride biosynthesis [17]. Therefore, lowering cholesterol and triglyceride levels reduces the risk of coronary atherosclerosis, which is exactly in line with the conclusion of our study.

Recently, triglyceride-glucose (TyG) has been considered an index for assessing IR and an important predictor of coronary artery disease severity [18]. It means there is a link between triglyceride levels and coronary atherosclerosis. Consistently, our finding noted that triglycerides reduced the risk of coronary atherosclerosis. A clinical study conducted in 2021 compared the TyG index of 424 patients with NAFLD and 255 patients with coronary artery disease, and it concluded that the TyG index of patients with NAFLD was positively correlated with the risk of coronary artery disease, which may reflect the severity of coronary atherosclerosis [19]. Furthermore, a recent meta-study showed that the incidence of CVD was significantly reduced by controlling factors associated with TyG index or triggers (e.g., blood) that elevate TyG [20]. In conclusion, many of these studies support the present study's conclusion that triglycerides are positively associated with the risk of coronary atherosclerosis.

A recent study showed that long-term consumption of dietary diacylglycerol (DAG) enriched in 1,3-species reduced postprandial lipids, thereby modulating monocyte/macrophage migration and aortic lipid accumulation, eventually alleviating atherosclerosis [21], indicating the correlation between DAG levels and coronary atherosclerosis. Consistently, our study revealed a direct relationship between diacylglycerol kinase (DGK) and the risk of coronary atherosclerosis. Likewise, Toshiki Sasaki et al. elucidated the functional role of DGKα in cardiac injury after ischemia/reperfusion in mouse hearts in vivo and finally concluded that DGKα exacerbated I/R injury by inhibiting the cardioprotective effects of PKCε, ERK1/2, and p70S6K activation [22]. These results suggest that diacylglycerol protein kinase (DGKα) has a positive cardioprotective effect on the heart.

Furthermore, we found that sterol ester levels directly affected the risk of coronary atherosclerosis. Similarly, Avery Sengupta et al. observed the composition, osmotic fragility, and antioxidant status of erythrocyte membranes in normal and hypercholesterolemic rats after consumption of EPA-DHA-rich and ALA-rich sterol esters, and concluded that in cholesterol-rich blood, rat erythrocytes appeared to be deformed and become more fragile. This is because sterol esters can alleviate hypercholesterolemia and thus the risk of coronary atherosclerosis, thereby partially reversing this deformity and fragility [23]. This confirms the conclusion of the present study that sterol ester levels directly influence the risk of coronary atherosclerosis.

There are several strengths of our two-sample MR study. Firstly, we used robust MR analysis methods and selected SNP with strong association as IVs, similar to the experimental framework of a randomized controlled trial. Second, we chose independent, validated genetic variants as IVs to avoid potential confounders and increase the accuracy of our results. Finally, our study pooled many MR studies and ultimately screened out the most significant six lipid metabolites, providing a theoretical basis for the clinical treatment of atherosclerosis.

Limitations

However, our study has limitations. The GWAS data include only European people, so further studies are needed to determine the generalizability of our findings to different populations. In addition, there are gender differences in the prevalence of coronary atherosclerosis. Unfortunately, the public databases from which our data were obtained do not allow for detailed subgroup analyses for specific demographics (e.g., age and sex).

Future research

In the future, we will conduct more experiments to investigate the correlation between lipid metabolites and coronary atherosclerosis, with a focus on lipid metabolites that are directly related to the occurrence and development of coronary atherosclerosis, such as cholesterol and triglycerides. The intricate nature of lipid metabolism and its metabolites is not fully understood, underscoring the necessity for additional basic and clinical studies. Improving lipid metabolites presents a promising avenue for addressing coronary atherosclerosis. These six lipid metabolites have the potential as new biomarkers for predicting the risk of atherosclerosis, providing new insights into the treatment and prevention of cardiovascular diseases.

Conclusions

In conclusion, our study comprehensively elucidates the causal relationship between lipid metabolites and the risk of coronary atherosclerosis. Cholesterol levels, sterol ester (27:1/18:2) levels, triacylglycerol (52:4) levels, triacylglycerol (52:5) levels, diacylglycerol (18:1_18. 2) levels, and triacylglycerol (53:4) levels are positively correlated with the risk of coronary atherosclerosis onset. However, the levels of these six lipid metabolites do not increase with the development of coronary atherosclerosis.

Data availability

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

Abbreviations

CVD:

Cardiovascular disease

ACS:

Acute coronary syndrome

HF:

Heart failure

SCD:

Sudden cardiac death

MR:

Mendelian randomization

SNPs:

Single nucleotide polymorphisms

IVs:

Instrumental variables

LD:

Linkage disequilibrium

IVW:

Inverse variance-weighting

ORs:

Odds ratios

Cis:

Confidence intervals

LDL-C:

Low-density lipoprotein cholesterol

TyG:

Triglyceride-glucose

DGK:

Diacylglycerol kinase

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Acknowledgements

Not applicable.

Funding

This work was supported by: Basic Scientific Research Projects of Colleges and Universities of Liaoning Provincial Department of Education (JYTQN2023469 and LJ232410162027), Natural Science Foundation of Liaoning Province 2024JH2/102600194 and National Natural Science Foundation 82474221.

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Author notes

  1. Qun Wang and Yuan Cao contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory of Ministry of Education for TCM Viscera-State Theory and Applications, Ministry of Education of China, Liaoning University of Traditional Chinese Medicine, 79 Chongshan Road, Huanggu District, Shenyang, 110847, Liaoning Province, China

    Qun Wang, Yuan Cao & Lianqun Jia

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Contributions

Conceptualization: [Lianqun Jia]; Methodology: [Qun Wang]; Formal analysis and investigation: [Qun Wang]; Writing—original draft preparation: [Yuan Cao]; Writing—review and editing: [Yuan Cao]; Funding acquisition: [Lianqun Jia]; Resources: [Qun Wang]; Supervision: [Lianqun Jia]. And all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Lianqun Jia.

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Wang, Q., Cao, Y. & Jia, L. Lipidomics-based investigation of its impact on the pathogenesis of coronary atherosclerosis: a Mendelian randomization study. Hereditas 162, 13 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41065-025-00367-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41065-025-00367-x

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Hereditas

ISSN: 1601-5223

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