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Mendelian randomization suggests that aggregate cholesterol content of HDL particles, or HDL-C, is not causal in CHD. 31 However, potential anti-atherogenic functions attributed to HDL include RCT, reduction of inflammation in the arterial wall, inhibition of LDL oxidation, and improvement of endothelial function. 5–7 Given that HDL functions vary by subclass, size, and composition of HDL, 32 it may be informative to conduct Mendelian randomization studies mapped to HDL subclass or particle concentration measures if genes can be identified to allow for such analysis.

First, while we performed extensive bivariate comparisons of relevant factors in our well-characterized samples and used adjusted multivariable modelling, unmeasured confounders are possible in this observational analysis. Secondly, a one-time lipid profile at the time of MI or coronary angiography may not represent levels of HDL-C parameters over a lifetime and does not address the relationship of changes in HDL-C parameters with outcomes. Third, HDL-C subclassification by ultracentrifugation is one method of measurement among a heterogeneous set of available technologies for more granular characterization of HDL. 32 Nuclear magnetic resonance spectroscopy and 2D gel electrophoresis provide information on additional subfractions, and there may be discernible differences between these technologies in risk associations. Further research is necessary to compare these techniques with ultracentrifugation, delineate which subfractions are prone to change functionality (and when), while examining their clinical implications. 30 Fourth, while all-cause mortality is a reliable and important hard endpoint, competing non-cardiovascular risks may contribute. We also did not study cardiovascular endpoints other than recurrent MI, such as angina or revascularization. Finally, we address the question of prognosis, not management of HDL, wherein there is currently clinical uncertainty, and novel approaches are needed. 33 , 34

These contemporary, rigorouslyadjusted analyses, supplemented with prior biological, epidemiological, and clinical trial evidence, appear to implicate the HDL 3 density subclass as being the primary marker or mediator of risk. In secondary prevention populations, these data provide evidence that, by ultracentrifugation, a worse prognosis is associated with low HDL 3 -C, but not HDL 2 -C or HDL-C. Our results highlight the value of subclassifying HDL for risk stratification and provide a strong impetus for further research leveraging technologies that characterize HDL at a more granular level and map this information to clinical outcomes.

Supplementary material is available at European Heart Journal online .

Figure1
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Adjusted spline curves of HDL parameters in association with predicted mortality and mortality/myocardial infarction. Restricted cubic spline curves are presented. The x-axis represents observed values for HDL parameters and the y-axis represents predicted mortality at 2 years in TRIUMPH and mortality/myocardial infarction at 5 years in IHCS after adjusting for all covariates in the fully adjusted model 3.*Dotted lines indicate the 95% confidence interval. () HDL-C, () HDL-C, and () HDL-C. Results were consistent across models 1, 2, and 3; therefore, results from model 3 only are highlighted. Model 1 was unadjusted in both cohorts. Model 2 was adjusted for GRACE score in TRIUMPH and for age and sex in IHCS. TRIUMPH model 3 was adjusted for GRACE score, age, sex, race, insurance, education, tobacco use, diabetes mellitus, hypertension, AUDIT alcohol use scores, physical activity, body mass index, non-HDL-C, log-transformed triglycerides, statins, and non-statin lipid-modifying medications (including ezetimibe, bile acid sequestrants, fibrates, niacin, and fish oil), and site. IHCS model 3 was adjusted for age, sex, tobacco use, diabetes mellitus, hypertension, hyperlipidaemia, family history of CHD, renal failure, heart failure, prior MI, prior stroke, reason for angiography, angiographic CHD, non-HDL-C, and log-transformed triglycerides, statins, and non-statin lipid-modifying medications. Of note, the GRACE score is the Global Registry of Acute Coronary Events score, a composite score of the following components: age, heart rate, systolic blood pressure, creatinine, congestive heart failure, in-hospital percutaneous coronary intervention, in-hospital coronary artery bypass surgery, prior MI, ST-segment depression on electrocardiogram, and elevated cardiac biomarkers.

Figure1
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Adjusted spline curves of HDL parameters in association with predicted mortality and mortality/myocardial infarction. Restricted cubic spline curves are presented. The x-axis represents observed values for HDL parameters and the y-axis represents predicted mortality at 2 years in TRIUMPH and mortality/myocardial infarction at 5 years in IHCS after adjusting for all covariates in the fully adjusted model 3.*Dotted lines indicate the 95% confidence interval. () HDL-C, () HDL-C, and () HDL-C. Results were consistent across models 1, 2, and 3; therefore, results from model 3 only are highlighted. Model 1 was unadjusted in both cohorts. Model 2 was adjusted for GRACE score in TRIUMPH and for age and sex in IHCS. TRIUMPH model 3 was adjusted for GRACE score, age, sex, race, insurance, education, tobacco use, diabetes mellitus, hypertension, AUDIT alcohol use scores, physical activity, body mass index, non-HDL-C, log-transformed triglycerides, statins, and non-statin lipid-modifying medications (including ezetimibe, bile acid sequestrants, fibrates, niacin, and fish oil), and site. IHCS model 3 was adjusted for age, sex, tobacco use, diabetes mellitus, hypertension, hyperlipidaemia, family history of CHD, renal failure, heart failure, prior MI, prior stroke, reason for angiography, angiographic CHD, non-HDL-C, and log-transformed triglycerides, statins, and non-statin lipid-modifying medications. Of note, the GRACE score is the Global Registry of Acute Coronary Events score, a composite score of the following components: age, heart rate, systolic blood pressure, creatinine, congestive heart failure, in-hospital percutaneous coronary intervention, in-hospital coronary artery bypass surgery, prior MI, ST-segment depression on electrocardiogram, and elevated cardiac biomarkers.

In TRIUMPH, the overall covariate missing rate was 9.7%, mainly due to missing information on race, insurance, education, tobacco use, AUDIT scores, physical activity, or body mass index. Data were assumed to be missing at random and were imputed using an imputation model that contained all of the variables from the multivariable model. For IHCS, all patients had values for all covariates used in the multivariable modelling.

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