Cardiovascular risk assessment and lipid modification: NICE guideline

Elevated remnant cholesterol and non-HDL cholesterol concentrations from real-world laboratory results: a cross-sectional study in Southeast Asians

Introduction

Triglyceride-rich remnant lipoproteins (TRLs) are considered atherogenic due to the presence of remnant cholesterol, which is transported by apolipoprotein B. In clinical practice, the concentration of TRLs can be estimated by calculating remnant cholesterol or non-HDL cholesterol levels.

Aim

This study aims to investigate the proportion of patients who have low LDL cholesterol (LDL-C) concentration but elevated remnant cholesterol concentration, stratified by the presence of hypertriglyceridaemia and ethnicity, using real-world hospital data. Our secondary aim is to investigate the proportion of patients with elevated non-HDL cholesterol levels using guideline-recommended goals.

Methods

A 2-year retrospective study was conducted at a single centre, analyzing lipid blood tests of all patients, including directly measured LDL-C. Fasting for blood tests was not mandatory.

Results

The study included a total of 21,605 consecutive patients with plasma lipid profiles analyzed in our hospital laboratory. The median age was 61 years. In patients with ASCVD (n = 14,704), 23.7% had an LDL-C level of <1.8 mmol/L, 11.3% had elevated remnant cholesterol concentrations at ≥0.65 mmol/L, and 48.8% were at the non-high-density lipoprotein cholesterol (non-HDL-C) goal (<2.6 mmol/L). Among patients diagnosed with atherosclerotic cardiovascular disease (ASCVD) with LDL-C levels of <1.8 mmol/L (n = 3,484), only 11.9% had high levels of remnant cholesterol, but 96% of the ASCVD patients also achieved the recommended non-HDL-C target of <2.6 mmol/L. When the LDL-C level was <1.8 mmol/L, the mean concentration of remnant cholesterol was 0.214 mmol/L when the triglyceride level was <1.7 mmol/L (n = 3,380), vs. 0.70 mmol/L when the triglyceride level was elevated (n = 724), p < 0.001. Among patients with a triglyceride level of ≥1.7 mmol/L and an LDL-C level of <.8 mmol/L, there were 254 patients with elevated remnant cholesterol concentration and 71 patients with suboptimal non-HDL levels. Malays had a higher mean remnant cholesterol concentration compared with both Chinese and Indians across all LDL-C levels, particularly in the presence of hypertriglyceridaemia.

Conclusions

An elevated remnant cholesterol concentration of >0.65 mmol/L was present in 11% of all patients. The current guideline-recommended non-HDL-C goal, which uses a 0.8 mmol/L estimate of remnant cholesterol concentration, was achieved in >92% of patients, suggesting that it is unlikely to be clinically useful for the majority of our patient population except where there is concomitant hypertriglyceridaemia. Further studies are needed to establish the appropriate non-HDL-C goal or calculated remnant cholesterol concentration, paired with the LDL-C goal or otherwise, in a Southeast Asian population.

National study of NAFLD management identifies variation in delivery of care in the UK between 2019 to 2022

Background & Aims

Non-alcoholic fatty liver disease (NAFLD) is associated with liver and cardiovascular morbidity and mortality. Recently published NAFLD Quality Standards include 11 key performance indicators (KPIs) of good clinical care. This national study, endorsed by British Association for the Study of the Liver (BASL) and British Society of Gastroenterology (BSG), aimed to benchmark NAFLD care in UK hospitals against these KPIs.

Methods

This study included all new patients with NAFLD reviewed in the outpatient clinic in the months of March 2019 and March 2022. Participating UK hospitals self-registered for the study through BASL/BSG. KPI outcomes were compared using Fisher's exact or Chi-square tests.

Results

Data from 776 patients with NAFLD attending 34 hospitals (England [25], Scotland [four], Wales [three], Northern Ireland [two]) were collected. A total of 85.3% of hospitals reported established local liver disease assessment pathways, yet only 27.9% of patients with suspected NAFLD had non-invasive fibrosis assessment documented at the point of referral to secondary care. In secondary care, 79.1% of patients had fibrosis assessment. Assessment of cardiometabolic risk factors including obesity, type 2 diabetes, hypertension, and smoking were conducted in 73.2%, 33.0%, 19.3%, and 54.9% of all patients, respectively. There was limited documentation of diet (35.7%) and exercise advice (55.1%). Excluding those on statins, only 9.1% of patients with NAFLD at increased cardiovascular risk (T2DM and/or QRISK-3 >10%) had documented discussion of statin treatment. Significant KPI improvements from 2019 to 2022 were evident in use of non-invasive fibrosis assessment before secondary care referral, statin recommendations, and diet and exercise recommendations.

Conclusions

This national study identified substantial variation in NAFLD management in the UK with clear areas for improvement, particularly fibrosis risk assessment before secondary care referral and management of associated cardiometabolic risk factors.

Impact and implications

This study identified significant variation in the management of NAFLD in the UK. Only 27.9% of patients with suspected NAFLD had non-invasive fibrosis assessment performed to identify those at greater risk of advanced liver disease before specialist referral. Greater emphasis is needed on the management of associated cardiometabolic risk factors in individuals with NAFLD. Hospitals with multidisciplinary NAFLD service provision had higher rates of fibrosis evaluation and assessment and management of cardiometabolic risk than hospitals without multidisciplinary services. Further work is needed to align guideline recommendations and real-world practice in NAFLD care.

Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care

Objectives: Machine learning has potential to improve the management of lipid disorders. We explored the utility of machine learning in high-risk patients in primary care receiving cholesterol-lowering medications. Methods: Machine learning algorithms were created based on lipid management guidelines for England [National Institute for Health and Care Excellence (NICE) CG181] to reproduce the guidance with >95% accuracy. Natural language processing and therapy identification algorithms were applied to anonymized electronic records from six South London primary care general practices to extract medication information from free text fields. Results: Among a total of 48,226 adult patients, a subset of 5630 (mean ± standard deviation, age = 67 ± 13 years; male:female = 55:45) with a history of lipid-lowering therapy were identified. Additional major cardiometabolic comorbidities included type 2 diabetes in 13% (n = 724) and hypertension in 32% (n = 1791); all three risk factors were present in a further 28% (n = 1552). Of the 5630 patients, 4290 (76%) and 1349 (24%) were in primary and secondary cardiovascular disease prevention cohorts, respectively. Statin monotherapy was the most common current medication (82%, n = 4632). For patients receiving statin monotherapy, 71% (n = 3269) were on high-intensity therapy aligned with NICE guidance with rates being similar for the primary and secondary prevention cohorts. In the combined cohort, only 46% of patients who had been prescribed lipid-lowering therapy in the previous 12 months achieved the NICE treatment goal of >40% reduction in non-high-density lipoprotein cholesterol from baseline pretreatment levels. Based on the most recent data entry for patients not at goal the neural network recommended either increasing the dose of statin, adding complementary cholesterol-lowering medication, or obtaining an expert lipid opinion. Conclusions: Machine learning can be of value in (a) quantifying suboptimal lipid-lowering prescribing patterns, (b) identifying high-risk patients who could benefit from more intensive therapy, and (c) suggesting evidence-based therapeutic options.

Physicians' perceptions and beliefs on the current dyslipidemia management practices within Saudi Arabia

Background

Limited reports addressing physicians' understanding of the various low-density lipoprotein cholesterol (LDL-C) targets/statin intensity required for treating the various dyslipidemia patient populations in Saudi Arabia are available. Therefore, the current study assessed the perceptions and beliefs of practicing clinicians in Saudi Arabia regarding the current practice for management of dyslipidemia and potential perceived barriers to adherence to lipid guidelines encountered in their regular clinical practice. Knowledge of different clinical practices and beliefs could have a positive impact on improving the quality of future care provided by physicians.

Methods

A survey questionnaire was designed to assess physicians' familiarity, usage, and adherence to seven different international guidelines and used to evaluate the management of dyslipidemia, practice of patient treatment, and perceived obstacles to adhering to lipid guidelines related to specific patients, doctors, and practice issues.

Results

A total of 467 physicians were recruited for the study: (1) 57.2% were primary care physicians (PCPs) and (2) 42.8% were specialists. About 90.8% of them followed lipid guidelines of which the most common set were based on those by the American College of Cardiology/American Heart Association. The most utilized risk assessment tool was the atherosclerotic cardiovascular disease (ASCVD) risk calculator. About 60% of the physicians set an LDL-C target for their patients based on a combination of patients' risk factors and lipid profiles. In all, 42.1% of the physicians chose not to change existing therapy among patients with dyslipidemia to attain a non-high-density lipoprotein goal with controlled LDL-C level. Atorvastatin accounted for the greatest percentage of primary and secondary prevention choices (71.9% and 69.6%, respectively). Rosuvastatin was mostly preferred by physicians for patients with familial hypercholesterolemia. About two-thirds of the physicians (77.9%) prescribed statins to diabetic patients aged 40-75 years. Statin intolerance was encountered by 62.9% of the physicians in ≤ 10% of patients by 62.9%. Therapeutic strategies included switching to an alternative statin (40.1%) followed by reducing the statin dose (35.3%). Ezetimibe was prescribed by most physicians (77.9%) as an add-on to statin if the LDL-C target was not achieved. Fibrate was most preferred by physicians (62.7%) for hypertriglyceremia treatment followed by statins (28.7% of the physicians). Sixty-six percent reported not using proprotein convertase subtilisin/kexin type 9 serine protease inhibitors in their clinical practice due to unavailability at their institute (51.8%), high costs (26.3%), and/or lack of knowledge (20.6%). Perceived barriers to guideline adherence identified by physicians were lack of familiarity and knowledge of the guidelines, patient non-adherence, medication costs, and lack of timely follow-up appointments and educational tools. Multiple similarities and differences were observed after comparisons were made between specialists and PCPs in terms of guideline preference, clinical practice, and perceived barriers.

Conclusion

Different perceptions and attitudes among physicians in Saudi Arabia were found due to variable recommendations by international lipid guidelines. Perceived barriers that included the patient, physician, and practice were identified by physicians at multiple levels. Multiple challenges and different action gaps were observed when comparing specialists to PCPs. It is recommended that standardized practices be followed by clinicians in Saudi Arabia, and actions to address the outlined barriers are essential for optimizing health outcomes and ASCVD prevention.

Non-HDL-cholesterol in dyslipidemia: Review of the state-of-the-art literature and outlook

Dyslipidemia refers to unhealthy changes in blood lipid composition and is a risk factor for atherosclerotic cardiovascular diseases (ASCVD). Usually, low-density lipoprotein-cholesterol (LDL-C) is the primary goal for dyslipidemia management. However, non-high-density lipoprotein cholesterol (non-HDL-C) has gained attention as an alternative, reliable goal. It encompasses all plasma lipoproteins like LDL, triglyceride-rich lipoproteins (TRL), TRL-remnants, and lipoprotein a [Lp(a)] except high-density lipoproteins (HDL). In addition to LDL-C, several other constituents of non-HDL-C have been reported to be atherogenic, aiding the pathophysiology of atherosclerosis. They are acknowledged as contributors to residual ASCVD risk that exists in patients on statin therapy with controlled LDL-C levels. Therefore, non-HDL-C is now considered an independent risk factor or predictor for CVD. The popularity of non-HDL-C is attributed to its ease of estimation and non-dependency on fasting status. It is also better at predicting ASCVD risk in patients on statin therapy, and/or in those with obesity, diabetes, and metabolic disorders. In addition, large follow-up studies have reported that individuals with higher baseline non-HDL-C at a younger age (<45 years) were more prone to adverse CVD events at an older age, suggesting a predictive ability of non-HDL-C over the long term. Consequently, non-HDL-C is recommended as a secondary goal for dyslipidemia management by most international guidelines. Intriguingly, geographical patterns in recent epidemiological studies showed remarkably high non-HDL-C attributable mortality in high-risk countries. This review highlights the independent role of non-HDL-C in ASCVD pathogenesis and prognosis. In addition, the need for a country-specific approach to dyslipidemia management at the community/population level is discussed. Overall, non-HDL-C can become a co-primary or primary goal in dyslipidemia management.

Study protocol for development and validation of a single tool to assess risks of stroke, diabetes mellitus, myocardial infarction and dementia: DemNCD-Risk

INTRODUCTION

Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners.

METHODS AND ANALYSIS

Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression.

ETHICS AND DISSEMINATION

Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool will be accessible as a public health resource. Knowledge translation and implementation work will explore strategies to apply the tool in clinical practice.

Longitudinal patterns of lifestyle risk behaviours among UK adults with established cardiovascular disease: a latent transition analysis

Background

People with cardiovascular disease (CVD) need to engage in healthy lifestyle behaviours. However, there is a gap in identifying longitudinal patterns of change in lifestyle behaviours among people with CVD. This study aimed to identify clustering of lifestyle risk behaviours and their 4 ± year changes among UK adults with CVD, and to determine the associated factors.

Methods

We used the UK Biobank data collected at two time points (2006-2010/baseline data = T0 and 2014+/third visit data = T4). Six key lifestyle risk behaviours were assessed: smoking, high alcohol intake, poor fruit and vegetable consumption, physical inactivity, poor sleep balance (<7 or >8 h/night) and prolonged sitting. A random intercept latent transition analysis was performed to identify patterns of lifestyle risk behaviours at T0 and their changes from T0 to T4.

Results

We included 5,304 participants with CVD whose data on lifestyle risk behaviours were collected at two-time points. Alcohol intake and current smoking were 75.7% and 5.4% at baseline, respectively, and 67.4% and 3.0% at follow-up. Three latent classes emerged: Latent class (LC) 1-"high alcohol intake, poor sleep balance and poor fruit and vegetable intake", LC2-"high alcohol intake and poor fruit and vegetable intake", and LC3-"high alcohol intake". Most adults remained in the same LC over the 4 + years (range: 83.9%-100.0%). After 4 + years, 3.5% from LC3 and 10.4% from LC2 at baseline moved into LC1. The odds of transitioning to LC2 relative to staying in LC1 and LC3 were 2.22 and 4.13 times higher for males than for females, respectively. A single-year increase in participants' age was associated with a 1.16 times increase in the odds of moving to LC1 relative to staying in LC2.

Conclusion

People with CVD did not show improvement in lifestyle risk behaviours, and interventions targeting multiple lifestyle risk behaviours are needed to improve CVD.

Association Between Lipid Profile Measurements and Mortality Outcomes Among Older Adults in a Primary Care Setting: A Retrospective Cohort Study

Background Lipid profile components play a role in predicting the development of cardiovascular disease and hence mortality, but recent studies have shown mixed results in the older population. The aim of our study was to investigate the association between levels of lipid profile components with all-cause mortality and cardiovascular outcomes among older adults in a primary care setting in Riyadh, Saudi Arabia. Methods A retrospective cohort study was performed among 485 individuals aged 60 years and older who visited the family medicine clinics linked to a tertiary care hospital during the first six months of 2010. The electronic charts of the participants were reviewed up to April 2022 to gather relevant data. Each lipid profile component, including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TGs), was categorized into four quartiles. LDL was calculated using the Friedewald formula. Cardiovascular outcomes included ischemic heart disease (IHD), heart failure (HF), and stroke. Results The mean follow-up period was 12 years. The elderly participants with the lowest HDL-C quartile (<1.1 mmol/L) were at higher risk of all-cause mortality (adjusted hazard ratio of 2.023 (95% CI 1.21-3.38)) and IHD (adjusted hazard ratio 3.2 (95% CI 1.6-6.2)). High TC (≥5.7 mmol/L) was associated with an increased risk of HF (adjusted hazard ratio 2.1 (95% CI 1.1-4.0)). Conclusion In patients aged 60 years and older, low HDL-C (<1.1 mmol/L) was associated with a higher risk for all-cause mortality and IHD, and high TC was associated with an increased risk of having HF. No significant association was found for LDL-C, TC, and TGs with all-cause mortality.

Cost-Effectiveness of Evolocumab in Adult Patients with Atherosclerotic Cardiovascular Disease from Chinese Healthcare Perspective

INTRODUCTION

To assess the cost-effectiveness of evolocumab with statins versus placebo combined with statins in the treatment of adult patients with atherosclerotic cardiovascular disease (ASCVD) and low-density lipoprotein cholesterol (LDL-C) levels > 70 mg/dl after the maximum tolerable dose of statin therapy in China.

METHODS

A Markov model, based on data from the FOURIER trial, claims databases, and published literature, was used to compare the health outcomes of the two therapies from the perspective of Chinese healthcare system. The time horizon in the model was a lifetime, the cycle length was a year, and the discount rate was 5%. The output indicators of the model included direct medical costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). Sensitivity analyses were conducted on critical parameters such as cost, utility, and incidence of cardiovascular events to evaluate the effect of uncertainty in parameters and the robustness of the model.

RESULTS

In Chinese adult patients with ASCVD and LDL-C levels > 70 mg/dl, evolocumab was associated with incremental QALYs of 1.25 and incremental costs of 18,714 CNY versus placebo, both with a statin therapy, resulting in an ICER of 14,969 CNY/QALY gained, which was less than the willingness to pay (WTP) threshold (80,976 CNY/QALY, a capita GDP of China, 2021). The result of one-way sensitivity analysis indicated that when the effect of evolocumab on myocardial infarction (MI) rate after the first year varied, ICER changed the most. The results of probabilistic sensitivity analysis showed that the probability of evolocumab added to statins being cost-effective at a threshold of 80,976 CNY/QALY was 100%.

CONCLUSION

Compared with placebo and statin therapy combination, evolocumab added to statin therapy for adult patients with ASCVD and LDL-C > 70 mg/dl in China is cost-effective.

Assessment of indices of conjunctival microvascular function in patients with and without obstructive coronary artery disease

BACKGROUND

Atherosclerotic heart disease often remains asymptomatic until presentation with a major adverse cardiovascular event. Primary preventive therapies improve outcomes, but conventional screening often misattributes risk. Vascular imaging can be utilised to detect atherosclerosis, but often involves ionising radiation. The conjunctiva is a readily accessible vascular network allowing non-invasive hemodynamic evaluation.

AIM

To compare conjunctival microcirculatory function in patients with and without obstructive coronary artery disease.

METHODS

We compared the conjunctival microcirculation of myocardial infarction patients (MI-cohort) to controls with no obstructive coronary artery disease (NO-CAD cohort). Conjunctival imaging was performed using a smartphone and slit-lamp biomicroscope combination. Microvascular indices of axial (V_a) and cross-sectional (V_cs) velocity; blood flow rate (Q); and wall shear rate (WSR) were compared in all conjunctival vessels between 5 and 45 μm in diameter.

RESULTS

A total of 127 patients were recruited (66 MI vs 61 NO-CAD) and 3602 conjunctival vessels analysed (2414 MI vs 1188 NO-CAD). Mean V_a, V_cs and Q were significantly lower in the MI vs NO-CAD cohort (V_a 0.50 ± 0.17 mm/s vs 0.55 ± 0.15 mm/s, p < 0.001; V_cs 0.35 ± 0.12 mm/s vs 0.38 ± 0.10 mm/s, p < 0.001; Q 154 ± 116 pl/s vs 198 ± 130 pl/s, p < 0.001). To correct for differences in mean vessel diameter, WSR was compared in 10-36 μm vessels (3268/3602 vessels) and was lower in the MI-cohort (134 ± 64 s^-1 vs 140 ± 63 s^-1, p = 0.002).

CONCLUSIONS

Conjunctival microcirculatory alterations can be observed in patients with obstructive coronary artery disease. The conjunctival microvasculature merits further evaluation in cardiovascular risk screening.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

The Immunomodulatory Effects of Statins on Macrophages

Statins are 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase inhibitors used worldwide to manage dyslipidaemia and thus limit the development of atherosclerotic disease and its complications. These atheroprotective drugs are now known to exert pleiotropic actions outside of their cholesterol-lowering activity, including altering immune cell function. Macrophages are phagocytic leukocytes that play critical functional roles in the pathogenesis of atherosclerosis and are directly targeted by statins. Early studies documented the anti-inflammatory effects of statins on macrophages, but emerging evidence suggests that these drugs can also enhance pro-inflammatory macrophage responses, creating an unresolved paradox. This review comprehensively examines the in vitro, in vivo, and clinical literature to document the statin-induced changes in macrophage polarization and immunomodulatory functions, explore the underlying mechanisms involved, and offer potential explanations for this paradox. A better understanding of the immunomodulatory actions of statins on macrophages should pave the way for the development of novel therapeutic approaches to manage atherosclerosis and other chronic diseases and conditions characterised by unresolved inflammation.

Polygenic risk for coronary artery disease in the Scottish and English population

Background

Epidemiological studies have repeatedly observed a markedly higher risk for coronary artery disease (CAD) in Scotland as compared to England. Up to now, it is unclear whether environmental or genetic factors might explain this phenomenon.

Methods

Using UK Biobank (UKB) data, we assessed CAD risk, based on the Framingham risk score (FRS) and common genetic variants, to explore the respective contribution to CAD prevalence in Scotland (n = 31,963) and England (n = 317,889). We calculated FRS based on sex, age, body mass index (BMI), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), antihypertensive medication, smoking status, and diabetes. We determined the allele frequency of published genome-wide significant risk CAD alleles and a weighted genetic risk score (wGRS) for quantifying genetic CAD risk.

Results

Prevalence of CAD was 16% higher in Scotland as compared to England (8.98% vs. 7.68%, P < 0.001). However, the FRS only predicted a marginally higher CAD risk (less than 1%) in Scotland (12.5 ± 10.5 vs.12.6 ± 10.6, P = 0.03). Likewise, the overall number of genome-wide significant variants affecting CAD risk (157.6 ± 7.7 and 157.5 ± 7.7; P = 0.12) and a wGRS for CAD (2.49 ± 0.25 in both populations, P = 0.14) were remarkably similar in the English and Scottish population. Interestingly, we observed substantial differences in the allele frequencies of individual risk variants. Of the previously described 163 genome-wide significant variants studied here, 35 variants had higher frequencies in Scotland, whereas 37 had higher frequencies in England (P < 0.001 each).

Conclusions

Neither the traditional risk factors included in the FRS nor a genetic risk score (GRS) based on established common risk alleles explained the higher CAD prevalence in Scotland. However, we observed marked differences in the distribution of individual risk alleles, which emphasizes that even geographically and ethnically closely related populations may display relevant differences in the genetic architecture of a common disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02398-4.

Ten-year risk of fatal cardiovascular disease and its association with metabolic risk factors among waste pickers in South Africa

Background

Cardiovascular disease (CVD) is the leading cause of death among non-communicable diseases in South Africa. Several metabolic risk factors contribute to the development of CVD. Informal workers such as waste pickers could be unhealthy lifestyle naive, and most public health research on CVD does not include this understudied population. This study estimated the 10-year risk of fatal CVD and its association with metabolic risk factors in an understudied study population of waste pickers in Johannesburg, South Africa.

Methods

A cross-sectional survey was conducted among waste pickers in two landfill sites in Johannesburg. We used the Systematic Coronary Risk Evaluation (SCORE) risk charts to estimate the 10-year risk of fatal CVD. We then employed ordinary least squares regression to assess the association between the 10-year risk of fatal CVD with metabolic risk factors. Other variables adjusted in the regression model were HIV status, education, income, injuries from work, clinic visits in the previous 12 months, and alcohol consumption.

Results

A total of 370 waste pickers were included in this analysis, 265 (73.41%) were males. The mean age of the participants was 34 years. The majority were between the age of 20 and 39 years. More than 55% of the waste pickers did not visit a clinic in the previous 12 months, and 68.57% were smoking. The 10-year survival probability from CVD was more than 99% for both males and females. In the multivariable regression model, elevated blood glucose showed a non-significant increase in the mean percentage of 10-year risk of fatal CVD. Waste pickers who were overweight/obese, and hypertensive had high statistically significant mean percentages of the 10-year risk of fatal CVD compared to those who did not have the metabolic risk factors.

Conclusions

Prevention of 10-year risk of fatal CVD in this understudied population of waste pickers should target the control of obesity, hypertension, and diabetes. Health awareness and education for waste pickers will be an important step in reducing the burden of these metabolic risk factors. We further recommend that health systems should recognize waste pickers as a high-risk group and consider extensive CVDs surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02150-y.

Chinese Guideline on the Primary Prevention of Cardiovascular Diseases

Abstract

Cardiovascular disease is the leading cause of mortality in China. Primary prevention of cardiovascular disease with a focus on lifestyle intervention and risk factor control has been shown to effectively delay or prevent the occurrence of cardiovascular events. To promote a healthy lifestyle and enhance the detection, diagnosis, and treatment of cardiovascular risk factors such as hypertension, dyslipidemia, and diabetes, and to improve the overall capacity of primary prevention of cardiovascular disease, the Chinese Society of Cardiology of Chinese Medical Association has collaborated with multiple societies to summarize and evaluate the latest evidence with reference to relevant guidelines and subsequently to develop recommendations for primary cardiovascular disease prevention in Chinese adults. The guideline consists of 10 sections: introduction, methodology for developing the guideline, epidemiology of cardiovascular disease in China and challenges in primary prevention, general recommendations for primary prevention, assessment of cardiovascular risk, lifestyle intervention, blood pressure control, lipid management, management of type 2 diabetes, and use of aspirin. The promulgation and implementation of this guideline will play a key role in promoting the practice of primary prevention for cardiovascular disease in China.

Use of Coronary Artery Calcium Scoring to Improve Cardiovascular Risk Stratification and Guide Decisions to Start Statin Therapy in People Living With HIV

BACKGROUND

Cardiovascular disease (CVD) risk assessment remains a critical step in guiding decisions to initiate primary prevention interventions in people living with HIV (PLWH).

SETTING

We investigated whether coronary artery calcium (CAC) scoring allowed a more accurate selection of patients who may benefit from statin therapy, compared with current risk assessment tools alone.

METHODS

Cross-sectional analysis of PLWH over 50 years old who underwent CAC scoring between 2009 and 2019. Framingham Risk score (FRS), QRISK2 and D:A:D scores were calculated for each participant at the time of CAC scoring and statin eligibility determined based on current European guidelines on the prevention of CVD in PLWH.

RESULTS

A total of 739 patients were included (mean age 56 ± 5, 92.8% male, 84% white). Among 417 (56.4%) candidates for statin therapy based on FRS ≥10%, 174 (23.5%) had no detectable calcification (CAC = 0). Conversely, 145 (19.6%) patients with detectable calcification (CAC > 0) were identified as low-risk (FRS < 10%). When compared with FRS, CAC scoring reclassified CVD risk in 43.1% of patients, 145 (19.6%) to a higher risk group that could benefit from statin therapy and 174 (23.5%) statin candidates to a lower risk group. QRISK2 and D:A:D scores performed similarly to FRS, underestimating the presence of significant coronary calcification in 21.1% and 24.9% respectively and overestimating risk in 16.9% and 18.8% patients with CAC = 0.

CONCLUSIONS

Establishing a decision-model based on the combination of conventional risk tools and CAC scoring improves risk assessment and the selection of PLWH who would benefit from statin therapy.

Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound

RECENT FINDINGS

Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called "integrated predictive CVD risk models."

PURPOSE

of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework.

CONCLUSION

Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Cardiovascular/stroke risk predictive calculators: a comparison between statistical and machine learning models

Background

Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC_ML) and compares against statistically derived CVRC (CVRC_Stat) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).

Methods

The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC_Stat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC_Stat, the patients were again risk-stratified using CVRC_ML based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.

Results

An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC_Stat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS_cardio (AUC 0.67, P<0.01), FRS_stroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC_ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC_ML with integrated risk factors was 92.52% which was higher compared all the other CVRC_Stat.

Conclusions

ML-based CVD/stroke risk calculator provided a higher predictive ability of 10-year CVD/stroke compared to the 13 different types of statistically derived risk calculators including integrated model AECRS 2.0.

Ultrasound-based stroke/cardiovascular risk stratification using Framingham Risk Score and ASCVD Risk Score based on "Integrated Vascular Age" instead of "Chronological Age": a multi-ethnic study of Asian Indian, Caucasian, and Japanese cohorts

Background

Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA.

Methods

The system follows a four-step process: (I) VA using cIMT based using linear-regression (LR) model and its coefficients; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area-under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin.

Results

The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1,287 ultrasound scans. The 10-year FRS using IVA reported higher AUC (AUC =0.78) compared with 10-year FRS using CA (AUC =0.66) by ~18%.

Conclusions

IVA is an efficient biomarker for risk stratifications for patients in routine practice.

A 10-Year Risk of Cardiovascular Disease among Patients with Severe Mental Illness at Mbarara Regional Referral Hospital, Southwestern Uganda

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide. Patients with severe mental illness (SMI) are at a higher risk for developing CVD and have a higher risk for harboring factors related to CVD. In addition to the effects of antipsychotic medications, unhealthy lifestyle factors, such as poor diet, inadequate physical activity, cigarette smoking, and sedentary behaviors, are known to be risk factors that may contribute to poor cardiovascular health in patients with SMI. Early identification of individuals at elevated risk of CVD is essential so that dietary and lifestyle modifications or pharmacological interventions can be prescribed to alleviate the risk of cardiovascular disease. The objective of the study was to determine the 10-year risk of cardiovascular disease among patients with severe mental illness at Mbarara Regional Referral Hospital, southwestern Uganda. We conducted a cross-sectional study at the outpatient mental health clinic of Mbarara Regional Referral Hospital, between October 2018 and March 2019. We used the Globorisk CVD risk score to estimate the 10-year risk of CVD among patients with SMI, using the online Globorisk calculator. Participants were then assigned to one of three categories depending on their 10-year CVD risk score: <3% (low), 3-10% (intermediate), and >10% (high). We calculated the risk scores of 125 participants aged 40-74 years. Most of the participants were female 75 (60%), had a diagnosis of bipolar disorder 75 (60%), and had mental illness for ≥10 years 57 (46%). Eighty five percent (85%) of the participants had intermediate to high 10-year risk of CVD (64% with intermediate and 21% with high risk). The average risk score was significantly higher in males compared to females, 8.82% versus 6.43%, p = 0.016. We detected a high 10-year risk of CVD in a significant proportion of patients with SMI in southwestern Uganda. We recommend lifestyle modifications and pharmacological interventions to reverse risk or delay progression to CVD in this patient population.

Exercise versus fixed-dose combination therapy for cardiovascular risk factors control and atherosclerotic disease prevention: a network meta-analysis protocol

INTRODUCTION

Despite the consistent evidence of the benefits of physical activity on preventing atherosclerotic cardiovascular diseases (ASCVD) and some cardiovascular risk factors, such as hypertension and dyslipidaemia, the prescription of drugs remains the most widely used approach to prevent ASCVD in clinical settings. The purpose of this study protocol is to provide a meta-synthesis methodology for comparing the effect of fixed-dose combination therapy and physical exercise on controlling cardiovascular risk factors and preventing ASCVD.

METHODS AND ANALYSIS

This protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols and the recommendations of the Cochrane Collaboration Handbook. We plan to conduct a computerised search in Medline, Web of Science, Embase, Cochrane Database of Systematic Reviews and SPORTDiscus from inception to May 2020 for studies testing the effectiveness of physical exercise or fixed-dose combination drug therapy in preventing ASCVD, all-cause and cardiovascular mortality and controlling some cardiovascular risk factors (hypertension and dyslipidaemia). Since performing network meta-analyses (NMA) is a statistical approach that allows direct and indirect comparisons of interventions, where sufficient studies are included, we plan to perform the following NMA comparing the effect of fixed-dose combination therapy and physical exercise interventions on (1) improving lipid profile, (2) reducing blood pressure, (3) preventing cardiovascular events and all-cause and cardiovascular mortality and (4) improving compliance with the therapeutic strategy and reducing adverse events.

ETHICS AND DISSEMINATION

Ethical approval will not be needed because data included in the NMA will be extracted from published trials that meet accepted ethical standards. The results will be published in academic peer-reviewed journals, and the evidence gathered by this project could be included in the preventive cardiovascular disease guidelines.

PROSPERO REGISTRATION NUMBER

CRD42019122794.

Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease

BACKGROUND

Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment.

METHODS

The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC).

RESULTS

South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m²). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients.

CONCLUSIONS

An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.

Statins and muscle pain

Introduction: Statins remain among the most frequently prescribed drugs and constitute a cornerstone in the prevention of cardiovascular disease. However, muscle symptoms are often reported from patients on statins. Muscle symptoms are frequently reported as adverse events associated with statin therapy.Areas covered: In the present narrative review, statin-associated muscle pain is discussed. It elucidates potential mechanisms and possible targets for management.Expert opinion: In general, the evidence in support of muscle pain caused by statins is in some cases equivocal and not particularly strong. Reported symptoms are difficult to quantify. Rarely is it possible to establish a causal link between statins and muscle pain. In randomized controlled trials, statins are well tolerated, and muscle-pain related side-effects is similar to placebo. There are also nocebo effects of statins. Exchange of statin may be beneficial although all statins have been associated with muscle pain. In some patients reduction of dose is worth trying, especially in primary prevention Although the benefits of statins outweigh potential risks in the vast majority of cases, careful clinical judgment may be necessary in certain cases to manage potential side effects on an individual basis.

Effects of aged garlic extract on arterial elasticity in a placebo-controlled clinical trial using EndoPAT™ technology

Cardiovascular diseases are the main cause of death in the industrialized world, with the main risk factors being elevated blood pressure and blood lipid levels, leading to arterial stiffness and arteriosclerosis. In this study, we examined the effect of aged garlic extract (AGE) on arterial elasticity, using the EndoPAT™ technology in subjects with slightly elevated blood pressure. This randomized double-blind, placebo-controlled clinical trial examined 57 subjects over a period of 12 weeks, with EndoPAT™ measurements taken at 0 and 12 weeks; in addition, changes in blood pressure were analyzed. The positive effect of AGE on blood pressure values previously reported was confirmed. The results revealed a significant decrease in blood pressure in the AGE group, and in particular diastolic blood pressure. Using the EndoPAT™ technology, the augmentation index (AI) was analyzed, which measures arterial stiffness calculated via pulse waveform analysis of the PAT signal; lower AI values reflect better arterial elasticity. The AGE group exhibited a significant improvement in arterial elasticity, measured as AI₇₅, by 21.6%. The result of this well-controlled clinical trial confirmed the positive effect of AGE on blood pressure. To the best of our knowledge, for the first time, the effect of AGE on arterial elasticity could be proven using the EndoPAT™ methodology. These results not only demonstrate the positive effects of AGE on the relevant risk factors of cardiovascular diseases, but also the direct effect on arterial elasticity. These data clearly indicate that AGE may exert several positive direct effects on the development and progression of cardiovascular diseases.

Treatment of hypercholesterolaemia in older adults calls for a patient-centred approach

Due to an increasing number of older adults with (risk factors for) cardiovascular disease (CVD), the sum of older adults eligible for lipid-lowering drugs will increase. This has risen questions about benefits and harms of lipid-lowering therapy in older adults with a varying number of (cardiovascular) comorbidities and functional status. The heterogeneity in physical and functional health increases with age, leading to a much wider variety in cardiovascular risk and life expectancy than in younger adults. We suggest treatment decisions on hypercholesterolaemia in adults aged ≥75 years should shift from a strictly 10-year cardiovascular risk-driven approach to a patient-centred and lifetime benefit-based approach. With this, estimated 10-year risk of CVD should be placed into the perspective of life expectancy. Moreover, frailty and safety concerns must be taken into account for a risk–benefit discussion between clinician and patient. Based on the Dutch addendum ‘Cardiovascular Risk Management in (frail) older adults’, our approach offers more detailed information on when not to initiate or deprescribe therapy than standard guidelines. Instead of using traditional risk estimating tools which tend to overestimate risk of CVD in older adults, use a competing risk adjusted, older adults-specific risk score (available at https://u-prevent.com). By filling in a patient’s (cardiovascular) health profile (eg, cholesterol, renal function), the tool estimates risk of CVD and models the effect of medication in terms of absolute risk reduction for an individual patient. Using this tool can guide doctors and patients in making shared decisions on initiating, continuing or deprescribing lipid-lowering therapy.

Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0

Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients' left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators. Graphical abstract AECRS1.0: Carotid ultrasound image phenotype-based 10-year cardiovascular risk calculator. The figure provides brief overview of the proposed carotid image phenotype-based 10-year cardiovascular risk calculator called AECRS1.0. AECRS1.0 was also benchmarked against five conventional cardiovascular risk calculators (Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score).

Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

MOTIVATION

AtheroEdge Composite Risk Score (AECRS1.0_10yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.0_10yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs).

METHODS

The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.0_10yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.0_10yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics.

RESULTS

AECRS1.0_10yr reported the highest area-under-the-curve (0.927;P < 0.001) among all the risk calculators. The top three CCVRCs closest to AECRS1.0_10yr were QRISK3, FRS, and UKPDS60 with cumulative ranking scores of 2.1, 3.0, and 3.8, respectively.

CONCLUSION

AECRS1.0_10yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.0_10yr, which is also consistent with the industry trend.

Criteria for referring patients to Spanish Atherosclerosis Society lipid units

The Spanish Arteriosclerosis Society has accredited more than 70 lipid units across the country. The main criteria for patients to be referred to these units are presented. These are not only grouped by the type of dyslipidaemia or the lipid levels, but also on certain clinical characteristics suggesting primary hyperlipidaemia, a complex diagnosis, or difficult management due to inefficacy, or side effects.

Excessive Erythrocytosis and Cardiovascular Risk in Andean Highlanders

Corante, Noemí, Cecilia Anza-Ramírez, Rómulo Figueroa-Mujíca, José Luis Macarlupú, Gustavo Vizcardo-Galindo, Grzegorz Bilo, Gianfranco Parati, Jorge L. Gamboa, Fabiola León-Velarde, and Francisco C. Villafuerte. Excessive erythrocytosis and cardiovascular risk in Andean highlanders. High Alt Med Biol. 19:221–231, 2018.—Cardiovascular diseases are the main cause of death worldwide. Life under high-altitude (HA) hypoxic conditions is believed to provide highlanders with a natural protection against cardiovascular and metabolic diseases compared with sea-level inhabitants. However, some HA dwellers become intolerant to chronic hypoxia and develop a progressive incapacitating syndrome known as chronic mountain sickness (CMS), characterized by excessive erythrocytosis (EE; Hb ≥21 g/dL in men, Hb ≥19 g/dL in women). Evidence from HA studies suggests that, in addition to CMS typical signs and symptoms, these highlanders may also suffer from metabolic and cardiovascular disorders. Thus, we hypothesize that this syndrome is also associated to the loss of the cardiometabolic protection observed in healthy highlanders (HH), and therefore to a higher cardiovascular risk (CVR). The aim of the present work was to evaluate the association between EE and CVR calculated using the Framingham General CVR Score and between EE and CVR factors in male highlanders. This cross-sectional study included 342 males from Cerro de Pasco, Peru at 4340 m (HH = 209, CMS = 133). Associations were assessed by multiple logistic regressions adjusted for potential confounders (BMI, pulse oxygen saturation and age). The adjusted models show that the odds of high CVR (>20%) in highlanders with EE was 3.63 times the odds in HH (CI 95%:1.22–10.78; p = 0.020), and that EE is associated to hypertension, elevated fasting serum glucose, insulin resistance, and elevated fasting serum triglycerides. Our results suggest that individuals who suffer from EE are at increased risk of developing cardiovascular events compared with their healthy counterparts.

Early changes of blood lipid levels during psychotropic drug treatment as predictors of long-term lipid changes and of new onset dyslipidemia

BACKGROUND

Cardiovascular diseases and dyslipidemia represent a major health issue in psychiatry. Many psychotropic drugs can induce a rapid and substantial increase of blood lipid levels.

OBJECTIVE

This study aimed to determine the potential predictive power of an early change of blood lipid levels during psychotropic treatment on long-term change and on dyslipidemia development.

METHODS

Data were obtained from a prospective study including 181 psychiatric patients with metabolic parameters monitored during the first year of treatment and with adherence ascertained. Blood lipid levels (ie, total cholesterol [TC], low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], non-high-density lipoprotein cholesterol [non-HDL-C], and fasting triglycerides [TGs]) were measured at baseline and after 1, 3, and/or 12 months of treatment.

RESULTS

Receiver-operating characteristic analyses indicated that early (ie, after 1 month of psychotropic treatment) increases (≥5%) for TC, LDL-C, TG, and non-HDL-C and decrease (≥5%) for HDL-C were the best predictors for clinically relevant modifications of blood lipid levels after 3 months of treatment (≥30% TC, ≥40% LDL-C, ≥45% TG, ≥55% non-HDL-C increase, and ≥20% HDL-C decrease; sensitivity 70%-100%, specificity 53%-72%). Predictive powers of these models were confirmed by fitting longitudinal multivariate models in the same cohort (P ≤ .03) as well as in a replication cohort (n = 79; P ≤ .003). Survival models showed significantly higher incidences of new onset dyslipidemia (TC, LDL-C, and non-HDL-C hypercholesterolemia, HDL-C hypocholesterolemia, and hypertriglyceridemia) for patients with early changes of blood lipid levels compared to others (P ≤ .01).

CONCLUSION

Early modifications of blood lipid levels following prescription of psychotropic drugs inducing dyslipidemia should therefore raise questions on clinical strategies to control long-term dyslipidemia.

Clustering of cardiovascular risk factors and carotid intima-media thickness: The USE-IMT study

Background

The relation of a single risk factor with atherosclerosis is established. Clinically we know of risk factor clustering within individuals. Yet, studies into the magnitude of the relation of risk factor clusters with atherosclerosis are limited. Here, we assessed that relation.

Methods

Individual participant data from 14 cohorts, involving 59,025 individuals were used in this cross-sectional analysis. We made 15 clusters of four risk factors (current smoking, overweight, elevated blood pressure, elevated total cholesterol). Multilevel age and sex adjusted linear regression models were applied to estimate mean differences in common carotid intima-media thickness (CIMT) between clusters using those without any of the four risk factors as reference group.

Results

Compared to the reference, those with 1, 2, 3 or 4 risk factors had a significantly higher common CIMT: mean difference of 0.026 mm, 0.052 mm, 0.074 mm and 0.114 mm, respectively. These findings were the same in men and in women, and across ethnic groups. Within each risk factor cluster (1, 2, 3 risk factors), groups with elevated blood pressure had the largest CIMT and those with elevated cholesterol the lowest CIMT, a pattern similar for men and women.

Conclusion

Clusters of risk factors relate to increased common CIMT in a graded manner, similar in men, women and across race-ethnic groups. Some clusters seemed more atherogenic than others. Our findings support the notion that cardiovascular prevention should focus on sets of risk factors rather than individual levels alone, but may prioritize within clusters.