Biomarkers of Hepatic Dysfunction and Cardiovascular Risk

PURPOSE OF REVIEW

The objective of this manuscript is to examine the current literature on non-alcoholic fatty liver disease (NAFLD) biomarkers and their correlation with cardiovascular disease (CVD) outcomes and cardiovascular risk scores.

RECENT FINDINGS

There has been a growing appreciation for an independent link between NAFLD and CVD, culminating in a scientific statement by the American Heart Association in 2022. More recently, studies have begun to identify biomarkers of the three NAFLD phases as potent predictors of cardiovascular risk. Despite the body of evidence supporting a connection between hepatic biomarkers and CVD, more research is certainly needed, as some studies find no significant relationship. If this relationship continues to be robust and readily reproducible, NAFLD and its biomarkers may have an exciting role in the future of cardiovascular risk prediction, possibly as risk-enhancing factors or as components of novel cardiovascular risk prediction models.

New Way to "SCORE" Risk: Updates on the ESC Scoring System and Incorporation into ESC Cardiovascular Prevention Guidelines

PURPOSE OF REVIEW

Prior European Society of Cardiology (ESC) guidelines endorsed the SCORE 10-year cardiovascular disease (CVD) risk calculator to inform the total risk approach to CVD prevention, including the use of preventive interventions like lipid lowering therapies. However, SCORE was released in 2003, did not allow for estimation of fatal and non-fatal CVD events, and was limited to adults aged 40 to 70 years. The ESC's Cardiovascular Risk Collaboration (CRC) was tasked with updating SCORE (SCORE2) and with extending the upper age range of adults eligible for risk estimation (SCORE2-OP). This review summarises these two deliverables.

RECENT FINDINGS

Published in 2021, these updated risk scores allow for estimation of 10 year total (fatal + non-fatal) risks of a first atherosclerotic cardiovascular event in adults (SCORE2) and older persons (SCORE2-OP), calibrated for use in four European risk regions. The models account for competing risk of non-CVD death. These were extensively validated with excellent calibration and C-statistics ranging from 0.67 to 0.81. SCORE2 and SCORE2-OP have informed the 2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. In addition to endorsing these two updated risk calculators, these guidelines have, for the first time, recommended the use of age-related risk categories. This change was motivated to prevent overreliance on age when making CVD prevention decisions.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Nursing workload. Calculation of cardiovascular risk and therapeutic objectives

Therapeutic intervention should be determined by the risk of developing atheromatous cardiovascular disease (CVD). The higher the risk, the more intense the action should be. This is the reason for the stratification of patient risk. In primary prevention, the two main guidelines used, the American Heart Association and the American College of Cardiology (ACC/AHA) use the Pooled cohort equations (PCE) and the guidelines of the European societies use the SCORE tables. The PCE calculates the risk of fatal and non-fatal CVD, and the SCORE calculates risk of fatal CVD only. In young people, it is useful to consider the lifetime risk calculation. The Spanish Society of Arteriosclerosis (SEA) recommends the SCORE system in Spain. SCORE and PCE calculate the risk for people up to 70 and 75 years of age. Prediction and potentials are available for 80 years of age and above, with the data available being much more scarce. Risk stratification in secondary prevention may be useful to identify the subgroup of patients who may benefit from more intensive treatment. Imaging tests, especially coronary calcium scans and vascular ultrasound, can help to better the profile risk. European guidelines identify LDL cholesterol as a therapeutic target. They recommend initiating treatment with statins, and increasing dose and potency until targets are achieved, and then to treatment with potent statins at a maximum tolerated dose, and ezetimibe if targets are not achieved. As a third step, PCSK9 inhibitors are indicated. They set very ambitious targets, as low as 40 mg/dL in those subjects with recurrences before two years of CVD despite high-intensity statin therapy, and below 55 mg/dL for all very high-risk subjects.

Achilles tendon ultrasonography - A useful screening tool for cardiovascular risk estimation in patients with severe hypercholesterolemia

BACKGROUND AND AIMS

Achilles tendon lesions have long been associated with genetic defects in lipid metabolism and increased risk of cardiovascular diseases (CVD). With this study we aimed to evaluate the usefulness of Achilles tendon ultrasonography in identifying people at greater risk among subjects with severe hypercholesterolemia (SH) in a high-risk population.

METHODS

During the period of 2016-2017 a total of 213 participants were enrolled in this case-control study. Data of 110 patients with SH and 103 age and sex matched controls without dyslipidaeplemia and established CVD was collected.

RESULTS

Achilles tendinopathy (AT) was present in 42.7% of subjects with SH and in 29.1% of controls (p = 0.039). Stronger association between SH and AT was seen in women - 24.1% vs 2.0% (p = 0.001). SH increased odds of AT by 1.815 (95% CI, 1.028-3.206). Prevalence of AT was higher in males despite presence (SH⁺) or absence (SH^-) of severe hypercholesterolemia (SH⁺ 60.7% vs 24.1%, SH^- 55.8% vs 2.0%, p < 0.001). AT was associated with higher proportion of subjects exceeding normal mean values of TC (80.5% vs 52.9%, p = 0.001), LDL-C (76.6% vs 52.2%), TG (54.5% vs. 22.1%), ApoB (57.1% vs 22.2%), ApoE (44.0% vs 22.4%) levels and ApoB/ApoA ratio (46.1% vs 21.5%) (p = 0.001) and family history of premature coronary heart disease (CHD).

CONCLUSIONS

AT is more prevalent among subjects with SH and is associated with higher levels of TC, TG, LDL-C, ApoB, ApoE, ApoB/ApoA ratio, family history of premature CHD. SH increases the odds of developing AT.

Functional slit lamp biomicroscopy metrics correlate with cardiovascular risk

PURPOSE

Our aim was to correlate cardiovascular risk factor estimation with bulbar conjunctival blood flow metrics as measured through Functional Slit Lamp Biomicroscopy (FSLB).

METHODS

Cross-sectional study of individuals with otherwise healthy eyelid and corneal anatomy recruited from the Miami Veterans Affairs (VA) Healthcare System eye clinic. We measured conjunctival microvascular hemodynamics by mounting a camera on a slit lamp and cardiovascular risk using the Framingham risk score. Our main outcome measures were correlations between conjunctival vessel parameters (axial and cross-sectional blood flow velocity, blood flow rate) and Framingham score.

RESULTS

We included 84 patients who underwent FSLB. The mean age was 60 years, the majority were male (88%) and approximately half the patients were black (54%). Mean vessel diameter was similar between all Framingham score categories. Axial and cross-sectional blood flow velocities and blood flow rate were lower in individuals with higher Framingham risk score. Specifically, mean cross-sectional blood flow velocity in individuals with a low Framingham risk score was 0.37 ± 0.0.9 mm/s, with an intermediate score was 0.30 ± 0.09 mm/s, and with a high score was 0.29 ± 0.10 mm/s, p = 0.04. Mean blood flow rate in individuals with a low Framingham risk score was 133.4 ± 59.6 pl/s, with an intermediate score was 123.6 ± 39.3 pl/s, and with a high score was 121.9 ± 52.6 pl/s, p = 0.04. The beta coefficient of the blood flow rate for change in Framingham score was -0.73; 95% CI-1.34-0.13, p = 0.02, adjusted for race.

CONCLUSION

FSLB correlates with cardiovascular risk estimation. Future studies should evaluate if FSLB can predict cardiovascular outcomes.

Which Lipids Should Be Analyzed for Diagnostic Workup and Follow-up of Patients with Hyperlipidemias?

PURPOSE OF REVIEW

To summarize and discuss the clinical use of lipid and apolipoprotein tests in the settings of diagnosis and therapeutic follow-up of hyperlipidemia.

RECENT FINDINGS

The joint consensus panel of the European Atherosclerosis Society (EAS) and the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) recently produced recommendations on the measurement of atherogenic lipoproteins, taking into account the strengths and weaknesses of analytical and clinical performances of the tests. Total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol, and calculated non-HDL cholesterol (= LDL + remnant cholesterol) constitute the primary lipid panel for hyperlipidemia diagnosis and cardiovascular risk estimation. LDL cholesterol is the primary target of lipid-lowering therapies. Non-HDL cholesterol or apolipoprotein B should be used as secondary therapeutic target in patients with mild-to-moderate hypertriglyceridemia, 2-10 mmol/l (175-880 mg/dl). Lipoprotein (a) is included in LDL cholesterol and should be measured at least once in all patients at cardiovascular risk, including to explain poor response to statin treatment.

Cardiovascular Risk Stratification in Patients with Metabolic Syndrome Without Diabetes or Cardiovascular Disease: Usefulness of Metabolic Syndrome Severity Score

INTRODUCTION

The estimated cardiovascular risk determined by the different risk scores, could be heterogeneous in patients with metabolic syndrome without diabetes or vascular disease. This risk stratification could be improved by detecting subclinical carotid atheromatosis.

AIMS

To estimate the cardiovascular risk measured by different scores in patients with metabolic syndrome and analyze its association with the presence of carotid plaque.

METHODS

Non-diabetic patients with metabolic syndrome (Adult Treatment Panel III definition) without cardiovascular disease were enrolled. The Framingham score, the Reynolds score, the new score proposed by the 2013 ACC/AHA Guidelines and the Metabolic Syndrome Severity Calculator were calculated. Prevalence of carotid plaque was determined by ultrasound examination. A Receiver Operating Characteristic analysis was performed.

RESULTS

A total of 238 patients were enrolled. Most patients were stratified as "low risk" by Framingham score (64%) and Reynolds score (70.1%). Using the 2013 ACC/AHA score, 45.3% of the population had a risk ≥7.5%. A significant correlation was found between classic scores but the agreement (concordance) was moderate. The correlation between classical scores and the Metabolic Syndrome Severity Calculator was poor. Overall, the prevalence of carotid plaque was 28.2%. The continuous metabolic syndrome score used in our study showed a good predictive power to detect carotid plaque (area under the curve 0.752).

CONCLUSION

In this population, the calculated cardiovascular risk was heterogenic. The prevalence of carotid plaque was high. The Metabolic Syndrome Severity Calculator showed a good predictive power to detect carotid plaque.

Cardiovascular risk stratification in overweight or obese patients in primary prevention. Implications for use of statins

BACKGROUND AND OBJECTIVES

Cardiovascular risk estimation in patients with overweight/obesity is not standardized. Our objectives were to stratify cardiovascular risk using different scores, to analyze use of statins, to report the prevalence of carotid atherosclerotic plaque (CAP), and to determine the optimal cut-off point (OCP) of scores that discriminate between subjects with or without CAP.

MATERIAL AND METHODS

Non-diabetic patients with overweight or obesity in primary prevention were enrolled. The Framingham score (FS), the European score (ES), and the score proposed by the new American guidelines (NS) were calculated, and statin indication was evaluated. Prevalence of CAP was determined by ultrasound examination. A ROC analysis was performed.

RESULTS

A total of 474 patients (67% with overweight and 33% obese) were enrolled into the study. The FS classified the largest number of subjects as low risk. PAC prevalence was higher in obese as compared to overweight subjects (44.8% vs. 36.1%, P=.04). According to the FS, ES, and NS respectively, 26.7%, 39.1%, and 39.1% of overweight subjects and 28.6%, 39.0%, and 39.0% of obese subjects had an absolute indication for statins. All three scores were shown to acceptably discriminate between subjects with and without CAP (area under the curve>0.7). The OCPs evaluated did not agree with the risk category values.

CONCLUSIONS

Risk stratification and use of statins varied in the overweight/obese population depending on the function used. Understanding of the relationship between scores and presence of CAP may optimize risk estimate.