Biochemical risk markers and 10-year incidence of atherosclerotic cardiovascular disease: independent predictors, improvement in pooled cohort equation, and risk reclassification

Immune-related biochemical markers and 20-year incidence of atherosclerotic cardiovascular disease: the ATTICA study (2002-2022)

BACKGROUND AND AIMS

Inflammation has been associated with increased atherosclerotic cardiovascular disease (ASCVD) risk. We evaluated immune-related biomarkers regarding their ability, individually and as a composite score, to predict 20-year ASCVD incidence in an apparently healthy adult population.

METHODS

A cohort of 1270 adults, who were free of ASCVD at baseline, with a 20-year follow-up from the prospective ATTICA study, were included in this analysis. Immune-related biochemical markers independently predictive of 20-year ASCVD risk were identified, and an aggregate biomarker score was developed. The incremental predictive value of this score beyond the SCORE2 was assessed using area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

Three immune-related biomarkers -interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α) and fibrinogen-showed the best predictive ability of 20-year ASCVD risk and were subsequently integrated into an aggregate biomarker score (ImmActScore), exhibiting a range from 0 (6 % absolute risk) to 4 (63 % absolute risk). Individual ImmActScore was independently associated with 20-year ASCVD risk (multi-adjusted HR per 1-unit:1.24, 95 %CI:1.05-1.46, p = 0.011). The 38.5 % of the 20-year incident ASCVD could be attributed to ImmActScores of ≥1. Integrating ImmActScore into the SCORE2 model yielded a continuous NRI of 0.603 and a categorical NRI of 18.4 % in the 40-50 year age group.

CONCLUSIONS

Assessing immune-related pathways may offer additional potential for long-term ASCVD risk stratification. A combined measure of IL-6, TNF-α and fibrinogen levels was associated with ASCVD events over a 20-year period. Further validation in independent cohorts is warranted.

Systemic inflammation and health outcomes in patients receiving treatment for atherosclerotic cardiovascular disease

BACKGROUND AND AIMS

The burden and outcomes of inflammation in patients with atherosclerotic cardiovascular disease (ASCVD) are not well defined beyond the controlled settings of trials and research cohorts.

METHODS

This was an observational study of ASCVD adults undergoing C-reactive protein testing in Stockholm's healthcare (2007-21). After excluding C-reactive protein tests associated with acute illness or medications/conditions that bias C-reactive protein interpretation, systemic inflammation was evaluated over a 3-month ascertainment window. Determinants of C-reactive protein ≥ 2 mg/L were explored with logistic regression. C-reactive protein categories were compared via negative-binomial/Cox regression for subsequent healthcare resource utilization and occurrence of major adverse cardiovascular events, heart failure hospitalization, and death.

RESULTS

A total of 84 399 ASCVD adults were included (46% female, mean age 71 years, 59% with C-reactive protein ≥ 2 mg/L). Female sex, older age, lower kidney function, albuminuria, diabetes, hypertension, and recent anaemia were associated with higher odds of C-reactive protein ≥ 2 mg/L. The use of renin-angiotensin system inhibitors, antiplatelets, and lipid-lowering therapy was associated with lower odds. Over a median of 6.4 years, compared with C-reactive protein < 2 mg/L, patients with C-reactive protein ≥ 2 mg/L had higher rates of hospitalizations, days spent in hospital, outpatient consultations, and dispensed medications (P < .05 for all). They also had a higher rate of major adverse cardiovascular events [hazard ratio (HR) 1.30; 95% confidence interval (CI) 1.27-1.33], heart failure (HR 1.24; 95% CI 1.20-1.30), and death (HR 1.35; 95% CI 1.31-1.39). Results were consistent across subgroups and granular C-reactive protein categories and robust to the exclusion of extreme C-reactive protein values or early events.

CONCLUSIONS

Three in five adults with ASCVD have systemic inflammation, which is associated with excess healthcare resource utilization and increased rates of cardiovascular events and death.

Development and Validation of the CANHEART Population-Based Laboratory Prediction Models for Atherosclerotic Cardiovascular Disease

BACKGROUND

Prediction of atherosclerotic cardiovascular disease (ASCVD) in primary prevention assessments exclusively with laboratory results may facilitate automated risk reporting and improve uptake of preventive therapies.

OBJECTIVE

To develop and validate sex-specific prediction models for ASCVD using age and routine laboratory tests and compare their performance with that of the pooled cohort equations (PCEs).

DESIGN

Derivation and validation of the CANHEART (Cardiovascular Health in Ambulatory Care Research Team) Lab Models.

SETTING

Population-based cohort study in Ontario, Canada.

PARTICIPANTS

A derivation and internal validation cohort of adults aged 40 to 75 years without cardiovascular disease from April 2009 to December 2015; an external validation cohort of primary care patients from January 2010 to December 2014.

MEASUREMENTS

Age and laboratory predictors measured in the outpatient setting included serum total cholesterol, high-density lipoprotein cholesterol, triglycerides, hemoglobin, mean corpuscular volume, platelets, leukocytes, estimated glomerular filtration rate, and glucose. The ASCVD outcomes were defined as myocardial infarction, stroke, and death from ischemic heart or cerebrovascular disease within 5 years.

RESULTS

Sex-specific models were developed and internally validated in 2 160 497 women and 1 833 147 men. They were well calibrated, with relative differences less than 1% between mean predicted and observed risk for both sexes. The c-statistic was 0.77 in women and 0.71 in men. External validation in 31 697 primary care patients showed a relative difference less than 14% and an absolute difference less than 0.3 percentage points in mean predicted and observed risks for both sexes. The c-statistics for the laboratory models were 0.72 for both sexes and were not statistically significantly different from those for the PCEs in women (change in c-statistic, -0.01 [95% CI, -0.03 to 0.01]) or men (change in c-statistic, -0.01 [CI, -0.04 to 0.02]).

LIMITATION

Medication use was not available at the population level.

CONCLUSION

The CANHEART Lab Models predict ASCVD with similar accuracy to more complex models, such as the PCEs.

PRIMARY FUNDING SOURCE

Canadian Institutes of Health Research.

Cardiovascular Disease and Alzheimer's Disease: The Heart-Brain Axis

Cardiovascular disease (CVD) remains one of the leading causes of morbidity and mortality in aging adults across the United States. Prior studies indicate that the presence of atherosclerosis, the pathogenic basis of CVD, is linked with dementias. Alzheimer's disease (AD) and AD-related dementias are a major public health challenge in the United States. Recent studies indicate that ≈3.7 million Americans ≥65 years of age had clinical AD in 2017, with projected increases to 9.3 million by 2060. Treatment options for AD remain limited. Development of disease-modifying therapies are challenging due, in part, to the long preclinical window of AD. The preclinical incubation period of AD starts in midlife, providing a critical window for identification and optimization of AD risk factors. Studies link AD with CVD risk factors such as hypertension, inflammation, and dyslipidemia. Both AD and CVD are progressive diseases with decades-long development periods. CVD can clinically manifest several years earlier than AD, making CVD and its risk factors a potential predictor of future AD. The current review focuses on the state of literature on molecular and metabolic pathways modulating the heart-brain axis underlying the potential association of midlife CVD risk factors and their effect on AD and related dementias. Further, we explore potential CVD/dementia preventive strategies during the window of opportunity in midlife and the future of research in the field in the multiomics and novel biomarker use era.

Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring

Background

Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.

Methods

We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (S_tr) and internal validation (S_v) sets [S_tr (1): S_v (1): 50:50; S_tr (2): S_v (2): 60:40; S_tr (3): S_v (3): 70:30; S_tr (4): S_v (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from S_tr to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (M_ecg), CAC alone (M_cac) and a combination of eRiS and CAC (M_ecg+cac) for MACE prediction. A nomogram (M_nom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of M_ecg, M_cac, M_ecg+cac and M_nom in predicting CV disease-free survival in ASCVD.

Findings

Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (S_tr). The M_ecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all S_v). The M_ecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that M_ecg+cac was superior to M_ecg (p = 1.8e-10) or M_cac (p < 2.2e-16) alone. The M_nom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, M_nom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.

Conclusion

The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Prognostic Utility of Risk Enhancers and Coronary Artery Calcium Score Recommended in the 2018 ACC/AHA Multisociety Cholesterol Treatment Guidelines Over the Pooled Cohort Equation: Insights From 3 Large Prospective Cohorts

Background Limited data exist on the incremental value of the risk enhancers recommended in the 2018 American Heart Association/American College of Cardiology (ACC/AHA) cholesterol treatment guidelines in addition to the pooled cohort equation. Methods and Results Using pooled individual-level data from 3 epidemiological cohorts involving 22 942 participants (56% women, mean age 59 years), we evaluated the predictive ability of the risk enhancers and coronary artery calcium (CAC) score for atherosclerotic cardiovascular disease, and determined their incremental utility using the C statistic, net reclassification index, and integrated discrimination index. A total of 1960 (8.5%) atherosclerotic cardiovascular disease events were accrued over 10 years. Of the 10 risk enhancers evaluated, only 6 predicted atherosclerotic cardiovascular disease independent of the pooled cohort equation. However, the individual enhancers demonstrated little or no incremental benefit. There was more incremental value from combining the 6 enhancers into an aggregate score (hazard ratio [HR], 1.21; 95% CI, 1.08-1.37 for each additional enhancer), and having ≥3 enhancers represents an optimum threshold for incremental prediction (C statistic, 0.766; net reclassification index, 0.041; integrated discrimination index, 0.010; P≤0.007). On the other hand, CAC was superior to individual enhancers (C statistic, 0.774; net reclassification index, 0.073; integrated discrimination index, 0.010; P<0.001), reliably reclassifies intermediate-risk participants with <3 risk enhancers (event rate, 3.5% if no CAC and 9.8% if positive CAC), but offered no reclassification among participants with ≥3 enhancers. Conclusions The individual risk enhancers evaluated in this study provided no or only marginal incremental information added to the pooled cohort equation. However, the presence of ≥3 risk enhancers reliably identified intermediate-risk patients that will benefit from statin therapy, and further CAC testing may be considered among those with <3 risk enhancers.

Advancements in biomarkers for cardiovascular disease: diagnosis, prognosis, and therapy

Biomarkers are essential tools in the practice of cardiology. They assist with diagnosis, prognosis, and guiding therapy in many different cardiovascular diseases. Numerous biomarkers have become strongly associated with different cardiovascular conditions, such as troponin with acute coronary syndrome and natriuretic peptides with heart failure. Even though these biomarkers have been in practice for almost two decades, their uses continue to expand beyond their original roles. Additionally, many new biomarkers have been discovered with increasing utility in cardiovascular disease, including soluble suppression of tumorigenicity 2, galectin 3, and biomarkers of fibrosis, metabolism, and inflammation. How these old and new biomarkers are being expanded into clinical practice is constantly in evolution. This review will highlight some of the recent major advancements in the rapidly evolving field of biomarkers.

High sensitivity troponin, analytical advantages, clinical benefits and clinical challenges - An update

The measurement of cardiac troponin (cTn) by a high sensitivity method now represents the standard method for cTn measurement in the laboratory. High sensitivity method are not measuring a novel form of troponin but have undergone methodological improvement in assay sensitivity to allow both very low level detection and repeat measurements at low levels with very low degrees of analytical imprecision. The methods identify additional patients with myocardial injury who would benefit from evidence-based interventions. Rapid predictive algorithms utilising measurement on admission as well as short sampling periods (1-2 h) allow much more rapid categorisation of patients to appropriate clinical pathways. The shift in the diagnosis from traditional "cardiac enzymes" to troponin based on the 99th percentile has accounted for the majority of the detection of myocardial injury in patients without acute coronary syndromes. These patients have a worse prognosis irrespective of the underlying cause of their hospital admission. The appropriate management strategy in this group, beyond managing the underlying problem, remains to be defined. Measurement of cTn in otherwise asymptomatic individuals may have a role for patient selection for preventive treatment or for patients monitoring. Clinical trials in this area are awaited.

Clinician's Guide to the Updated ABCs of Cardiovascular Disease Prevention: A Review Part 1

Efforts to better control risk factors for cardiovascular disease and prevent the development of subsequent events are crucial to maintaining healthy populations. In today's busy practice environment and with the overwhelming pace of new research findings, ensuring appropriate emphasis and implementation of evidence-based preventive cardiovascular care can be challenging. The ABCDEF approach to cardiovascular disease prevention is intended to improve dissemination of contemporary best practices and facilitate the implementation of comprehensive preventive strategies for clinicians. This review serves as a succinct yet authoritative overview for internists and subspecialty cardiologists not otherwise focused on cardiovascular prevention. The goal of this 2-part series is to compile a state-of-the-art list of elements central to both primary and secondary prevention of cardiovascular disease, using an ABCDEF checklist, with particular focus on recent society guideline updates. In Part 1 we highlight developments in cardiovascular risk assessment tools, summarize important recent aspirin trials, discuss prevention considerations in atrial fibrillation, and review guidelines for blood pressure categorization, goals, and therapy.

Performance of atherosclerotic cardiovascular risk prediction models in a rural Northern Chinese population: Results from the Fangshan Cohort Study

BACKGROUND

Performance of Pooled Cohort Equations (PCEs) for atherosclerotic cardiovascular disease (ASCVD) risks varied across populations. Whether the recently developed Prediction for ASCVD Risk in China (China-PAR) model could accurately predict cardiovascular risks in real practice remains unclear.

METHODS

A population-based cohort study in rural Beijing in the "stroke belt" in North China was used to externally validate PCE and China-PAR models for 5-year ASCVD risk prediction. Expected 5-year prediction risk using China-PAR model was compared with PCE (white). The models were assessed for calibration, discrimination, and reclassification.

RESULTS

Among 11,169 adults aged 40 to 79 years over a median 6.44 years of follow-up, 1,921 participants developed a first ASCVD event during total 70,951 person-years. China-PAR model fairly predicted ASCVD risk in men but overestimated by 29.4% risk in women (calibration χ² = 81.4, P < .001). Underestimations were shown by PCE as 76.2% in men and 88.2% in women with poor calibration (both P < .001). However, discrimination was similar in both models: C-statistics in men were 0.685 (95% CI 0.660-0.710) for China-PAR and 0.675 (95% CI 0.649-0.701) for PCE; C-statistics in women were 0.711 (95% CI 0.694-0.728) for China-PAR and 0.714 (95% CI 0.697-0.731) for PCE. Moreover, China-PAR did not substantially improve accuracy of reclassification compared with PCE.

CONCLUSIONS

China-PAR outperformed PCE in 5-year ASCVD risk prediction in this rural Northern Chinese population at average population risk level, fairly predicted risk in men, but overestimated risk in women; however, China-PAR did not meaningfully improve the accuracy of discrimination and reclassification at individual risk level.

Low-grade inflammation in first-episode psychosis is determined by increased waist circumference

Psychosis is associated with low-grade inflammation as measured by high-sensitivity C-reactive protein (hs-CRP), a risk factor for cardiovascular events and mortality in the general population. We investigated the relationship between hs-CRP and anthropometric and metabolic changes in first-episode psychosis (FEP) during the first treatment year. We recruited 95 FEP patients and 62 controls, and measured longitudinal changes in hs-CRP, weight, waist circumference, insulin resistance, and lipids. We used linear mixed models to analyze the longitudinal relationship between hs-CRP and clinical, anthropometric and metabolic measures. At baseline, patients with FEP had higher levels of insulin resistance, total and low-density lipoprotein cholesterol, apolipoprotein B, and triglycerides. Baseline weight, waist circumference, hs-CRP, fasting glucose, and high-density lipoprotein cholesterol were similar between patients and controls. Marked increases in anthropometric measures and hs-CRP were observed in FEP during the 12-month follow-up. However, glucose and lipid parameters did not change significantly. In the mixed models, waist circumference and female sex were significant predictors of hs-CRP levels in FEP. Prevention of the early development of abdominal obesity in FEP is crucial, as abdominal obesity is accompanied by chronic low-grade inflammation, which increases further the cardiovascular risk in this vulnerable population.

Toward a panoramic perspective of the association between environmental factors and cardiovascular disease: An environment-wide association study from National Health and Nutrition Examination Survey 1999-2014

OBJECTIVES

An environment-wide association study (EWAS) may be useful to comprehensively test and validate associations between environmental factors and cardiovascular disease (CVD) in an unbiased manner.

APPROACH AND RESULTS

Data from National Health and Nutrition Examination Survey (1999-2014) were randomly 50:50 spilt into training set and testing set. CVD was ascertained by a self-reported diagnosis of myocardial infarction, coronary heart disease or stroke. We performed multiple linear regression analyses associating 203 environmental factors and 132 clinical phenotypes with CVD in training set (false discovery rate < 5%) and significant factors were validated in the testing set (P < 0.05). Random forest (RF) model was used for multicollinearity elimination and variable importance ranking. Discriminative power of factors for CVD was calculated by area under the receiver operating characteristic (AUROC). Overall, 43,568 participants with 4084 (9.4%) CVD were included. After adjusting for age, sex, race, body mass index, blood pressure and socio-economic level, we identified 5 environmental variables and 19 clinical phenotypes associated with CVD in training and testing dataset. Top five factors in RF importance ranking were: waist, glucose, uric acid, and red cell distribution width and glycated hemoglobin. AUROC of the RF model was 0.816 (top 5 factors) and 0.819 (full model). Sensitivity analyses reveal no specific moderators of the associations.

CONCLUSION

Our systematic evaluation provides new knowledge on the complex array of environmental correlates of CVD. These identified correlates may serve as a complementary approach to CVD risk assessment. Our findings need to be probed in further observational and interventional studies.