Abstract
Atherosclerotic coronary artery disease (CAD) remains a leading cause of death globally, and individual susceptibility is not fully explained by standard risk factors. The role of plasma fatty acid profiles in relation to imaging-defined CAD burden remains less well established. We performed a cross-sectional cohort analysis of 1,002 participants undergoing coronary computed tomographic angiography (CCTA), with blood samples collected immediately prior to contrast administration. Plasma fatty acids were quantified as a percentage of total plasma fatty acids and evaluated for associations with CAD burden, including coronary artery calcium score (CACS), CACS percentile, Gensini score, soft plaque score, and atrial fibrillation (AF). Associations were assessed using multivariate logistic regression, adjusting for age, sex, and cardiovascular risk factors. Higher plasma omega-6 levels were inversely associated with CACS (CAC+; odds ratio [OR] = 0.93, 95% confidence interval [CI] = 0.90–0.97) with similar inverse associations observed for Gensini and soft plaque scores. In contrast, monounsaturated (CAC + OR = 1.09, 95% CI = 1.03–1.15) and saturated fatty acids (CAC + OR = 1.08, 95% CI = 1.04–1.12) were positively associated with CAD burden. No consistent associations were observed with self-reported atrial fibrillation after multivariable adjustment. Plasma omega-6 fatty acids demonstrated inverse associations with imaging-defined CAD burden, suggesting a potential protective role that warrants investigation in longitudinal studies. Saturated and monounsaturated fatty acids were positively associated with CAD, aligning with prior evidence of their atherogenic potential.
Keywords: Plasma fatty acids, Omega-6, Coronary artery disease, Cardiovascular disease, Biomarker
Subject terms: Biomarkers, Cardiology
Introduction
Atherosclerotic coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, despite widespread efforts to manage standard modifiable cardiovascular risk factors (SMuRFs) such as hypertension, hyperlipidaemia, diabetes mellitus, and tobacco smoking1. Alarmingly, up to 25% of patients presenting with a first-time ST-elevation myocardial infarction (STEMI) have none of these risk factors at thresholds warranting primary prevention, highlighting the need for improved tools to detect individuals at risk2,3.
Circulating fatty acid profiles, including omega-6, saturated and monounsaturated fatty acids, have been investigated for their potential roles in cardiovascular health and disease4–8. Omega-6 fatty acids, particularly linoleic acid (LA), have often been associated with lower cardiovascular risk, although conflicting reports exist, reflecting differences in study design, analytical methods and biomarker compartments9–18. Clarifying their role is important in the context of ongoing debate about dietary recommendations and the balance of polyunsaturated fats. The roles of saturated and monounsaturated fatty acids in atherosclerosis and coronary calcification have been previously studied, although some more recent studies report mixed associations with their atherogenicity, highlighting the need for renewed investigation using imaging-based approaches19,20. Much of the evidence linking fatty acids to cardiovascular outcomes is based on erythrocyte-derived measurements, which capture long-term status but are not widely available in clinical practice16. Plasma-based fatty acid profiling offers greater accessibility and compatibility with biobanked samples, although it is more reflective of short-term intake. The clinical relevance of plasma-derived omega-6, saturated, and monounsaturated fatty acid levels in relation to imaging-defined CAD burden therefore remains uncertain.
Coronary computed tomographic angiography (CCTA) offers high-resolution assessment of both calcified and non-calcified plaque and provides sensitive phenotyping of subclinical coronary atherosclerosis. Relatively few studies have leveraged CCTA to examine how plasma fatty acid profiles relate to imaging-defined CAD burden, especially those extending beyond calcium scoring to include soft plaque and Gensini scoring.
In this study, we evaluated the associations between plasma omega-6, saturated, and monounsaturated fatty acids and coronary atherosclerosis in a large, well-characterised CCTA cohort. CAD imaging markers included coronary artery calcium score (CACS), CACS percentile, Gensini score, and soft plaque score. Given prior reports of pro-arrhythmic potential with certain fatty acids, we also evaluated associations with self-reported atrial fibrillation (AF). We hypothesised that higher levels of saturated and monounsaturated fatty acids and lower levels of omega-6 fatty acids would be associated with greater CAD burden. These analyses were exploratory and conducted within a cross-sectional framework, and causal inferences cannot be made.
Materials and methods
Study population
BioHEART-CT is a prospective, longitudinal cohort study assessing patients with suspected CAD undergoing clinically indicated CCTA across multiple Australian centres (ACTRN12618001322224)21. The study complies with the Declaration of Helsinki and has been approved by the Northern Sydney Local Health District Human Research Ethics Committee (2019/ETH08376). The protocol has been described in detail previously21. In brief, patients were recruited at the time of CCTA. Referral for CCTA was typically based on symptoms such as chest pain or dyspnoea, abnormal stress test results or elevated cardiovascular risk profiles. Approximately, 50% of participants were asymptomatic and referred by their primary care physicians for CAD screening. Written informed consent was obtained, clinical and demographic data were collected via questionnaire, peripheral venous blood samples collected, and CCTA imaging data acquired. Patients with a prior history of myocardial infarction or coronary revascularisation were excluded from this analysis. Anthropometric parameters obtained included height, weight, and body mass index (BMI). Self-reported prior diagnosis of hypertension, hyperlipidaemia, diabetes mellitus, current smoking (having regularly smoked within the previous 12 months), or a smoking history of 10 or more pack-years (significant smoking history) were recorded. In addition to self-reported history of medical diagnoses, participants that were taking cholesterol-lowering or anti-glycaemic pharmacotherapy at the time of recruitment were also considered to have hypercholesterolaemia or diabetes mellitus, respectively.
CCTA images were acquired on a 256-slice scanner using standard clinical protocols22. Radiation dose was minimised in line with current recommendations. Where clinically appropriate, metoprolol or ivabradine were given orally for heart rate optimisation prior to the CCTA, with doses adjusted based on patient body weight and blood pressure. CACS was assessed using vendor specific software via the Agatston method23. Further analysis of each coronary arterial tree was performed using the validated 17-segment Gensini score system, which quantifies the extent of calcified and non-calcified plaque24. A soft plaque score (SPS) was then calculated by subtracting the modified Gensini score from the Gensini score. This modified Gensini score is an adaptation of the original system, incorporating additional factors such as the composition of atherosclerotic plaques25. All images were analysed by cardiologists blinded to the participant’s characteristics.
Biological samples
Peripheral blood samples were collected from fasted participants between 8am and 12pm, following insertion of the venous cannula and immediately before CCTA contrast administration.
Blood was collected into ethylenediaminetetraacetic acid (EDTA) tubes, and plasma was subsequently isolated from whole blood by centrifugation at 1861 x g for 15 min at 4 °C. The isolated plasma was then aliquoted and stored at -80 °C until analysis. The interval between referral for CCTA and the scan was patient-dependent.
Fatty acid analysis of plasma
An established method using dried blood spot (DBS) cards was utilised to measure plasma fatty acids25. For each participant, a drop of plasma (~ 30 µL) was spotted onto separate PUFAcoat™ DBS cards and air dried for 5 h at room temperature (~ 25 °C) in darkness. After drying, blood spot samples (~ 3 mm) discs were punched from the DBS papers. The disc was placed in a 5 mL amber vial, with 200 µL 14 mM ascorbic acid aqueous solution and left for 30 min with intermittent vortexing. Internal standard (tocol, 0.04 µg) and 400 µL of ethanol were then added. After vortexing, 750 µL of hexane was added. The two-phase mixture was vortexed for 5–10 s every 5 min for 15 min. A 600 µL aliquot of saturated NaCl aqueous solution was added to facilitate the phase partition, which was also vortexed vigorously for 5–10 s at least twice before being centrifuged (3000 rpm, 5 min). The upper phase was collected into a fresh amber vial. The hexane extraction was repeated, and the combined hexane layers were dried under nitrogen and reconstituted in 100 µL mobile phase for gas liquid chromatography analysis.
Gas chromatography analysis
Fatty acid methyl esters (FAME) were separated and quantified using a gas chromatography machine (Hewlett-Packard 6890; Palo Alto, California, USA) equipped with a BPX70 capillary column 50 m x 0.32 mm, with film thickness of 0.25 μm (SGC Pty Ltd., Victoria, Australia), PTV injector, and a flame ionisation detector (FID). The injector temperature was set at 250 °C and the FID temperature 300 °C; a programmed temperature ramp (140–240 °C) was used. Helium gas was utilised as a carrier at a flow rate of 35 cm/s in the column and the inlet split ratio was set at 20:1. Identification and quantification of FAME was achieved by comparing the retention times and peak area values of unknown samples to those of commercial lipid standards (Nu-Chek Prep Inc., Elysian, Minnesota, USA) using the Hewlett-Packard Chemstation data system.
Statistical analysis
In the BIOHEART-CT Discovery Cohort, six outcome measures related to CAD burden were considered (refer to our prior methods for full details26:
Clinically Actionable Coronary Artery Calcium (CA-CAC): defined as either a calcium percentile ≥ 75% (adjusted for sex and age group) or a CACS ≥ 100 Agatston units (AU), with participants meeting these criteria classified as CA-CAC + and all others as CA-CAC-. Current primary prevention guidelines recommend lifestyle modifications and statin therapy for CA-CAC + individuals26.
Clinically Actionable Absolute Coronary Artery Calcium Score: Participants with CACS ≥ 100 AU were categorised as CAC+, and the remainder as CAC-.
Calcium Score Percentile (CACS%): Those with an age and sex adjusted calcium score percentile ≥ 75% were categorised as CACP + and the remainder as CACP-.
Gensini Score: Participants were classified as Gensini + if they had a Gensini score > 0, and Gensini- if their Gensini score was 0.
Non-Calcified Plaque [Soft Plaque Score (SPS)]: Individuals with an SPS greater than 0 were labelled as SPS+, and those with an SPS of 0 as SPS-.
Atrial Fibrillation: Participants were categorised as either AF + or AF- based on their self-reported status.
We investigated associations between these binary CAD outcomes and plasma fatty acid variables: total omega-6, total trans, total monounsaturated, and total saturated fatty acids (n = 4) (Fig. 1). Although omega-3 fatty acids and derived ratios were quantified, they were excluded from the present analyses due to susceptibility to reverse causation and potential recent supplementation, which limit their interpretability. Multivariate logistic regression was used to evaluate associations, adjusting for clinical covariates, hypertension, hyperlipidaemia, diabetes mellitus, smoking status, BMI, statin use, LDL cholesterol, and C-reactive protein (CRP). CAD burden variables were assessed for normality using the Shapiro-Wilk test and transformations were applied as needed to meet parametric assumptions. Age and sex were not included as covariates in models for CACS percentile, since these are intrinsic to the calculation. For CACS, Gensini, and SPS, models were adjusted for age and sex, and clinical covariates. Results are reported as adjusted odds ratios (OR) with 95% confidence intervals (CI). Statistical significance was determined using the Benjamini-Hochberg method to control the false discovery rate at 5%, with an adjusted p value < 0.05 considered significant.
Fig. 1.
Workflow of the analysis.
Categorical variables are reported as frequencies and percentages, continuous variables as means ± standard deviations (SD) for normally distributed data, or medians with interquartile ranges otherwise. Pearson’s Chi-squared test and Student’s t test were used to compare categorical and continuous variables respectively. Multivariate logistic regression models were performed, adjusting for age, sex, and prespecified clinical covariates. Statistical analysis was performed in R version 4.2.1, Indianapolis, Indiana, USA, and associated figures produced in GraphPad Prism, version 9.4.1, San Diego, California, USA.
Results
Participant characteristics
The study included 1,002 participants who underwent clinically indicated CCTA. Table 1 presents the clinical and biochemical characteristics of the participants, categorised by absolute calcium presence (CAC+,n = 294; CAC-, n = 708). Those in the CAC- group were observed to be younger and had fewer risk factors, as expected relative to CAC + individuals. A significant male predominance was observed in the CAC + group, comprising 209 out of the 294 participants (71%). In both groups hyperlipidaemia was the most prevalent risk factor, followed by hypertension, obesity, and diabetes. Lipid concentrations did not differ significantly between CAC + and CAC- groups, reflecting the higher use of lipid-lowering therapy in the CAC + group.
Table 1.
Characteristics of participants based on clinically actionable CAD. BMI, body mass index; SMuRFs, standard modifiable risk factors; IQR, interquartile range; HDL, high density lipoproteins; LDL, low density lipoproteins; CRP, C-reactive protein; ACE, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.
| Overall n = 1002 |
Clinically Actionable CAC- n = 708 |
Clinically Actionable CAC+ n = 294 |
P value | |
|---|---|---|---|---|
| Demographics | ||||
| Age, mean ± SD | 61 ± 12 | 58 ± 12 | 69 ± 10 | < 0.0001 |
| Male, n (%) | 556 (55) | 346 (49) | 209 (71) | < 0.0001 |
| BMI (kg/m2), mean ± SD | 26.91 ± 4.80 | 26.79 ± 4.76 | 27.19 ± 4.90 | 0.23 |
| Past Medical History | ||||
| Obesity (BMI ≥ 30 kg/m2) | 227 (23) | 159 (22) | 68 (23) | 0.80 |
| Hypertension, n (%) | 390 (39) | 238 (34) | 152 (52) | < 0.0001 |
| Hyperlipidaemia, n (%) | 590 (59) | 378 (53) | 212 (72) | < 0.0001 |
| Diabetes mellitus, n (%) | 90 (9) | 51 (7) | 36 (12) | 0.01 |
| Atrial fibrillation, n (%) | 119 (12) | 86 (12) | 33 (11) | 0.75 |
| Imaging | ||||
| SMuRFs, mean ± SD | 1.26 ± 0.94 | 1.11 ± 0.89 | 1.65 ± 0.93 | < 0.0001 |
| 0 SMuRFs, n (%) | 216 (22) | 188 (27) | 28 (10) | < 0.0001 |
| 1 SMuRFs, n (%) | 414 (41) | 312 (44) | 102 (35) | 0.006 |
| 2 SMuRFs, n (%) | 276 (28) | 156 (22) | 120 (41) | < 0.0001 |
| 3 SMuRFs, n (%) | 82 (8) | 48 (7) | 34 (12) | 0.02 |
| 4 SMuRFs, n (%) | 14 (1) | 4 (1) | 10 (3) | 0.001 |
| Gensini score, median, IQR | 3.5, 0–11.5 | 1, 0–4.5 | 17.50, 10.50–26 | < 0.0001 |
| Modified Gensini score, median, IQR | 6.5, 0–20 | 1, 0–8 | 29.25, 17–49.50 | < 0.0001 |
| Blood Results | ||||
| Total cholesterol (mmol/L) | 4.50 ± 1.01 | 4.53 ± 1.01 | 4.43 ± 1.02 | 0.18 |
| HDL (mmol/L) | 1.34 ± 0.42 | 1.34 ± 0.42 | 1.34 ± 0.40 | 0.79 |
| LDL (mmol/L) | 2.91 ± 0.94 | 2.93 ± 0.93 | 2.85 ± 0.95 | 0.18 |
| Triglycerides (mmol/L) | 1.27 ± 0.66 | 1.28 ± 0.65 | 1.25 ± 0.67 | 0.33 |
| CRP (µg/mL) | 3.69 ± 22.42 | 4.06 ± 26.41 | 2.82 ± 6.77 | 0.31 |
| Total n-6 | 34.02 ± 4.57 | 34.52 ± 4.48 | 32.82 ± 4.58 | < 0.0001 |
| Total Trans | 0.38 ± 0.14 | 0.38 ± 0.14 | 0.39 ± 0.14 | 0.41 |
| Total Mono | 27.82 ± 3.49 | 27.47 ± 3.48 | 28.64 ± 3.40 | < 0.0001 |
| Total Sat | 31.30 ± 2.23 | 31.15 ± 2.18 | 31.66 ± 2.30 | 0.002 |
| Medication | ||||
| Statins, n (%) | 332 (33) | 181 (26) | 151 (51) | < 0.0001 |
| Anti-platelet agent, n (%) | 179 (18) | 98 (14) | 81 (28) | < 0.0001 |
| Anti-coagulation, n (%) | 91 (9) | 48 (7) | 43 (15) | 0.0002 |
| Beta-blocker, n (%) | 143 (14) | 84 (12) | 59 (20) | 0.001 |
| ACE/ARB agent, n (%) | 321 (32) | 187 (26) | 134 (46) | < 0.0001 |
| Smoking History | ||||
| Non-smoker, n (%) | 551 (55) | 411 (58) | 140 (48) | 0.003 |
| Ex-smoker, n (%) | 387 (39) | 255 (36) | 132 (45) | 0.01 |
| Current smoker, n (%) | 64 (6) | 42 (6) | 22 (7) | 0.39 |
Associations of plasma fatty acid variables with CAD burden
We examined associations between plasma fatty acid composition (omega-6, total trans, total monounsaturated and total saturated) and CCTA derived measures of CAD burden (clinically actionable coronary artery calcium, absolute calcium, calcium percentile, Gensini score, soft plaque score and the addition of AF). Figure 2 presents the results for omega-6, monounsaturated, and saturated fatty acids. Total trans fatty acids did not show significant associations with CAD outcomes. Each association is expressed as an OR with a 95% CI. These ratios are adjusted in a tiered manner, without adjustment for risk factors (illustrated in green); with adjustment for risk factors including hypertension, hyperlipidaemia, diabetes mellitus, current smoking status, BMI, statin usage, LDL and CRP (blue); and factoring in both age and sex (red), as appropriate. Omega-6 fatty acids were inversely associated with CAD burden, including CAC+ (OR = 0.93, 95% CI = 0.90–0.97), with similar inverse associations also seen for Gensini and soft plaque scores. In contrast, monounsaturated fatty acids (CAC + OR = 1.09, 95% CI = 1.03–1.15) and saturated fatty acids (CAC + OR = 1.08, 95% CI = 1.04–1.12) were positively associated with CAD measures. No consistent associations were observed between plasma fatty acid classes and self-reported AF after adjustment.
Fig. 2.
Forest plot of associations between plasma fatty acid profiles and binary CAD burden metrics. Logistic regression models were performed for each CAD outcome, shown unadjusted and adjusted for hypertension, hyperlipidaemia, diabetes mellitus, current smoking status, BMI, statin use, LDL cholesterol and C-reactive protein (CRP), with additional adjustment for age and sex applied to absolute CACS, Gensini and Soft Plaque Score (SPS). Statistical significance was determined using the Benjamini-Hochberg false discovery rate procedure (p < 0.05). Significance thresholds are indicated as * for p < 0.05, ** for p < 0.01, and, *** for p < 0.001. Calcium scores were log-transformed for graphical display.
Discussion
In this cross-sectional study of 1,002 participants undergoing CCTA, we evaluated associations between plasma fatty acid profiles and imaging-defined CAD burden. The main findings were consistent inverse associations between omega-6 fatty acids and multiple CAD endpoints, and positive associations between saturated and monounsaturated fatty acids. Total trans fatty acids were not significantly associated with CAD outcomes.
Omega-6 fatty acids, particularly LA, the dominant dietary omega-6 PUFA, demonstrated consistent inverse associations with CAD burden across multiple imaging endpoints. These associations remained significant after adjustment for conventional risk factors, including LDL cholesterol, suggesting potential LDL-independent mechanisms. Experimental evidence supports that LA can be metabolised into anti-inflammatory and vasodilatory lipid mediators, which may contribute to the observed inverse associations, although our cross-sectional data cannot establish causality27. Importantly, plasma omega-6 levels are less likely than omega-3 to be influenced by short-term supplementation or abrupt dietary changes and may better reflect habitual intake. These findings contribute to an ongoing debate regarding dietary omega-6 intake and cardiovascular risk. Despite concerns about seed oils and potential pro-inflammatory effects, our findings align with prior observational studies, though interpretation is limited by the cross-sectional design and lack of dietary intake data. Recent large-scale biomarker analyses, including UK Biobank and the EPIC-CVD consortium, have similarly reported inverse associations of circulating LA with incident CAD and stroke, providing supportive evidence across diverse populations. In contrast, several downstream metabolites of LA (e.g. DGLA) have been positively associated with cardiovascular risk, suggesting that endogenous fatty acid metabolism may influence the overall impact of n-6 PUFA exposure20.
Saturated and monounsaturated fatty acids were positively associated with CAD burden. These findings are consistent with longstanding evidence linking saturated fats to atherogenesis. However, the role of monounsaturated fats, particularly oleic acid (OA), the major MUFA in olive oil and the Mediterranean diet, is more nuanced. While some population studies suggest that dietary OA may be cardioprotective, others have found positive associations between circulating OA and cardiovascular risk28,29. This paradox may reflect differences in OA sources (dietary versus endogenous), or its elevation as a biomarker of metabolic dysfunction, for example, de novo lipogenesis in insulin resistance29. Together, these findings highlight the importance of considering the source and metabolic context of fatty acids when interpreting associations with cardiovascular outcomes. In our dataset, we could not distinguish between dietary and endogenous sources of OA, which limits interpretation.
Total trans fatty acids were not associated with CAD burden in this study. This contrasts with earlier reports linking dietary trans-fat intake with CAD events. The null finding may be explained by low exposure in contemporary Australian diets following public health regulation and reformulation of food products, which reduces variability in plasma trans levels and limits their discriminative value as a biomarker30. As trans-fatty acid levels are now uniformly low in contemporary diets, there was little variation between participants, which reduced the ability of our study to detect associations with CAD.
Strengths & limitations
This study leveraged a large, well characterised cohort with matched plasma fatty acid profiles and detailed imaging phenotyping using CCTA, enabling sensitive detection of both calcified and non-calcified atherosclerotic plaque. This imaging-based approach provides a more granular assessment of subclinical CAD than event-based outcomes. However, several limitations must be acknowledged. Plasma omega-3 fatty acids were measured but not included in the main analyses because levels are strongly influenced by short-term intake and potential recent supplementation. The interval between referral and scan was patient-dependent (ranging from days to months), raising the possibility of reverse causation if individuals initiated omega-3 supplements after referral. For these reasons, omega-3 fatty acids and related ratios (e.g. n-6:n-3) were excluded from interpretation. Although we adjusted for statin use and major cardiovascular risk factors, the possibility of residual confounding by unmeasured lifestyle, socioeconomic, or dietary variables cannot be excluded. Finally, generalisability may be limited as participants were recruited from those referred for clinically indicated CCTA, representing a higher-risk population than community-based cohorts. Given the cross-sectional design, reverse causation cannot be fully ruled out for other fatty acid associations.
Clinical impact
These findings highlight the potential of broader plasma fatty acid profiling to inform cardiovascular risk research. Inverse associations of omega-6 and positive associations of saturated and monounsaturated fatty acids with CAD burden suggest that fatty acid composition may provide complementary information to imaging markers such as CACS beyond traditional risk factors if validated in prospective studies. While routine clinical use of plasma fatty acid panels is premature, these results support further investigation of lipidomic signatures in prospective, longitudinal studies with diverse populations and serial imaging. Such research could help clarify causal pathways and determine whether fatty acid profiling has value in refining risk stratification or guiding dietary recommendations.
Conclusion
In this cross-sectional study of individuals undergoing CCTA, plasma omega-6 fatty acids were inversely associated with multiple measures of CAD burden, whereas saturated and monounsaturated fatty acids were positively associated. Total trans fatty acids showed no significant associations. These findings suggest the potential of broader plasma fatty acid profiling to inform hypotheses about mechanisms of cardiovascular risk, which require confirmation in longitudinal cohorts. Longitudinal studies incorporating serial imaging and detailed dietary data are needed to validate these associations and clarify causal pathways.
Acknowledgements
We would like to express our sincere gratitude to all individuals and institutions, particularly the study participants, University of Sydney Charles Perkins Centre, and the South Australian Medical and Research Institute (SAHMRI) whose invaluable contributions and support have made this research possible. We are additionally appreciative for the assistance of the radiographers and nursing staff from North Shore Radiology and Nuclear Medicine in St. Leonards, Australia. We would like to thank Zara Ali, Alexander Bate, Meghan Finemore, Jessica Hu, Tung Nguyen, and Luisa Osorio for their current and historic operational support of the BioHEART study and recruitment of participants included in this analysis.
Abbreviations
- AF
Atrial fibrillation
- ASCEND
A study of cardiovascular events in diabetes
- BMI
Body mass index
- CACS
Coronary artery calcium score
- CAD
Coronary artery disease
- CI
Confidence interval
- CRP
C-reactive protein
- CCTA
Coronary computed tomography angiography
- DBS
Dried blood spot
- DHA
Docosahexaenoic acid
- EDTA
Ethylenediaminetetraacetic acid
- EPA
Eicosapentaenoic acid
- FAME
Fatty acid methyl esters
- LDL
Low density lipoprotein
- PUFA
Polyunsaturated fatty acids
- REDUCE-IT
Reduction of cardiovascular events with icosapent ethyl-intervention trial
- SD
Standard deviations
- SMuRFs
Standard modifiable risk factors
- SPS
Soft plaque score
- STEMI
ST-elevation myocardial infarction
- STRENGTH
Statin residual risk reduction with epanova in high cardiovascular risk patients with hypertriglyceridemia
Author contributions
P.C., O.T., P.P., G.F. conceived and designed the study. O.T., B.D., R.G. acquired the data. P.C., D.Z. conducted the statistical analysis. P.C., D.Z., M.G., S.V., K.K., S.G., P.P., G.F., interpreted the data. All authors reviewed the manuscript and provided final approval for submission.
Funding
The authors are grateful for the following financial support for the research, authorship, and publication of this article. PC is supported by an Australian Government Research Training Program (RTP) Postgraduate Research Scholarship; PJP is supported by a Level 3 Future Leader Fellowship from the National Heart Foundation of Australia (ID106656). GAF is supported by grants from the National Health and Medical Research Council (Australia), Heart Research Australia, and the NSW Office of Health and Medical Research.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.