Carotid plaque is strongly associated with coronary artery calcium and predicts incident coronary heart disease in a population-based cohort

Non-linear association and benchmark dose of blood pressure on carotid artery intima-media thickening in a general population of southern China

Background and aims

This study aimed to evaluate whether there is a J-curve association between blood pressure (BP) and carotid artery intima-media thickening (CAIT) and estimate the effect of the turning point of BP on CAIT.

Methods and results

Data from 111,494 regular physical examinations conducted on workers and retirees (aged 18 years or older) between January 2011 and December 2016, exported from the hospital information system, were analyzed. Restricted cubic splines (RCS) logistic regression was employed to access the association of BP with CAIT, and Bayesian benchmark dose methods were used to estimate the benchmark dose as the departure point of BP measurements. All the pnon-linear values of BP measurements were less than 0.05 in the RCS logistic regression models. Both systolic blood pressure (SBP) and diastolic blood pressure (DBP) had J-curve associations with the risk of CAIT at a turning point around 120/70 mmHg in the RCS. The benchmark dose for a 1% change in CAIT risk was estimated to be 120.64 mmHg for SBP and 72.46 mmHg for DBP.

Conclusion

The J-curve associations between SBP and DBP and the risk of CAIT were observed in the general population in southern China, and the turning point of blood pressure for significantly reducing the risk of CAIT was estimated to be 120.64/72.46 mmHg for SBP/DBP.

User Engagement, Acceptability, and Clinical Markers in a Digital Health Program for Nonalcoholic Fatty Liver Disease: Prospective, Single-Arm Feasibility Study

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease in the world. Common comorbidities are central obesity, type 2 diabetes mellitus, dyslipidemia, and metabolic syndrome. Cardiovascular disease is the most common cause of death among people with NAFLD, and lifestyle changes can improve health outcomes.

OBJECTIVE

This study aims to explore the acceptability of a digital health program in terms of engagement, retention, and user satisfaction in addition to exploring changes in clinical outcomes, such as weight, cardiometabolic risk factors, and health-related quality of life.

METHODS

We conducted a prospective, open-label, single-arm, 12-week study including 38 individuals with either a BMI >30, metabolic syndrome, or type 2 diabetes mellitus and NAFLD screened by FibroScan. An NAFLD-specific digital health program focused on disease education, lowering carbohydrates in the diet, food logging, increasing activity level, reducing stress, and healthy lifestyle coaching was offered to participants. The coach provided weekly feedback on food logs and other in-app activities and opportunities for participants to ask questions. The coaching was active throughout the 12-week intervention period. The primary outcome was feasibility and acceptability of the 12-week program, assessed through patient engagement, retention, and satisfaction with the program. Secondary outcomes included changes in weight, liver fat, body composition, and other cardiometabolic clinical parameters at baseline and 12 weeks.

RESULTS

In total, 38 individuals were included in the study (median age 59.5, IQR 46.3-68.8 years; n=23, 61% female). Overall, 34 (89%) participants completed the program and 29 (76%) were active during the 12-week program period. The median satisfaction score was 6.3 (IQR 5.8-6.7) of 7. Mean weight loss was 3.5 (SD 3.7) kg (P<.001) or 3.2% (SD 3.4%), with a 2.2 (SD 2.7) kg reduction in fat mass (P<.001). Relative liver fat reduction was 19.4% (SD 23.9%). Systolic blood pressure was reduced by 6.0 (SD 13.5) mmHg (P=.009). The median reduction was 0.14 (IQR 0-0.47) mmol/L for triglyceride levels (P=.003), 3.2 (IQR 0.0-5.4) µU/ml for serum insulin (s-insulin) levels (P=.003), and 0.5 (IQR -0.7 to 3.8) mmol/mol for hemoglobin A1c (HbA1c) levels (P=.03). Participants who were highly engaged (ie, who used the app at least 5 days per week) had greater weight loss and liver fat reduction.

CONCLUSIONS

The 12-week-long digital health program was feasible for individuals with NAFLD, receiving high user engagement, retention, and satisfaction. Improved liver-specific and cardiometabolic health was observed, and more engaged participants showed greater improvements. This digital health program could provide a new tool to improve health outcomes in people with NAFLD.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05426382; https://clinicaltrials.gov/study/NCT05426382.

A machine learning based approach to identify carotid subclinical atherosclerosis endotypes

AIMS

To define endotypes of carotid subclinical atherosclerosis.

METHODS AND RESULTS

We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.

CONCLUSIONS

We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Carotid Plaque Score for Stroke and Cardiovascular Risk Prediction in a Middle-Aged Cohort From the General Population

Background We aimed to explore the predictive value of the carotid plaque score, compared with the Systematic Coronary Risk Evaluation 2 (SCORE2) risk prediction algorithm, on incident ischemic stroke and major adverse cardiovascular events and establish a prognostic cutoff of the carotid plaque score. Methods and Results In the prospective ACE 1950 (Akershus Cardiac Examination 1950 study), carotid plaque score was calculated with ultrasonography at inclusion in 2012 to 2015. The largest plaque diameter in each extracranial segment of the carotid artery on both sides was scored from 0 to 3 points. The sum of points in all segments provided the carotid plaque score. The cohort was followed up by linkage to national registries for incident ischemic stroke and major adverse cardiovascular events (nonfatal ischemic stroke, nonfatal myocardial infarction, and cardiovascular death) throughout 2020. Carotid plaque score was available in 3650 (98.5%) participants, with mean±SD age of 63.9±0.64 years at inclusion. Only 462 (12.7%) participants were free of plaque, and and 970 (26.6%) had a carotid plaque score of >3. Carotid plaque score predicted ischemic stroke (hazard ratio [HR], 1.25 [95% CI, 1.15-1.36]) and major adverse cardiovascular events (HR, 1.21 [95% CI, 1.14-1.27]) after adjustment for SCORE2 and provided strong incremental prognostic information to SCORE2. The best cutoff value of carotid plaque score for ischemic stroke was >3, with positive predictive value of 2.5% and negative predictive value of 99.3%. Conclusions The carotid plaque score is a strong predictor of ischemic stroke and major adverse cardiovascular events, and it provides incremental prognostic information to SCORE2 for risk prediction. A cutoff score of >3 seems to be suitable to discriminate high-risk subjects. Registration Information clinicaltrials.gov. Identifier: NCT01555411.

Is coronary artery calcium an independent risk factor for white matter hyperintensity?

BACKGROUND

Cardiovascular diseases have been considered the primary cause of disability and death worldwide. Coronary artery calcium (CAC) is an important indicator of the severity of coronary atherosclerosis. This study is aimed to investigate the relationship between CAC and white matter hyperintensity (WMH) in the context of diagnostic utility.

METHODS

A retrospective analysis was conducted on 342 patients with a diagnosis of WMH on magnetic resonance images (MRI) who also underwent chest computed tomography (CT) scans. WMH volumes were automatically measured using a lesion prediction algorithm. Subjects were divided into four groups based on the CAC score obtained from chest CT scans. A multilevel mixed-effects linear regression model considering conventional vascular risk factors assessed the association between total WMH volume and CAC score.

RESULTS

Overall, participants with coronary artery calcium (CAC score > 0) had larger WMH volumes than those without calcium (CAC score = 0), and WMH volumes were statistically different between the four CAC score groups, with increasing CAC scores, the volume of WMH significantly increased. In the linear regression model 1 of the high CAC score group, for every 1% increase in CAC score, the WMH volume increases by 2.96%. After including other covariates in model 2 and model 3, the β coefficient in the high CAC group remains higher than in the low and medium CAC score groups.

CONCLUSION

In elderly adults, the presence and severity of CAC is related to an increase in WMH volume. Our findings suggest an association between two different vascular bed diseases in addition to traditional vascular risk factors, possibly indicating a comorbid mechanism.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

Mathematical Analysis of the Healthcare Treatment of 215 Patients with Coronary Heart Disease

The main risk factors for CHD and the comorbidity include hyperlipidemia (HL), hypertension, smoking, dietary factors, and genetic factors. In this work, 215 patients with coronary heart disease, including 128 males and 87 females, were analyzed for a better understanding of the related clinical pharmacology. Nonparametric test, analysis of variance, chi-square test, correlation analysis, and other methods were used to sort out the data. From the analysis, there are significant differences in age among different gender samples. The incidence of coronary heart disease in men is five years younger than that in women. The sample pairs from different regions showed differences in the presence of family history of diabetes, indicating that a series of patients in some regions concentrated on the disease status of family history of diabetes. Age has a significant positive effect on cardiac functional classification. The older you are, the larger the cardiac functional classification is and the worse the cardiac function is. Age was negatively correlated with VTE score, diastolic blood pressure, CAR, TG, neutrophil, and TC. The older you are, the lower these six values are. Samples of different types of CHD showed significant differences in the presence of comorbidity and family history of CHD. The most significant are unstable angina pectoris and ischemic cardiomyopathy. Samples of different CHD types showed significant effects on VTE score, creatine kinase, low-density lipoprotein cholesterol (LDL⁃C), and lactate dehydrogenase. The highest lactate dehydrogenase is ischemic cardiomyopathy. The highest LDL cholesterol is ST-segment elevation angina. The highest creatine kinase is ischemic cardiomyopathy. The VTE score was the highest for ischemic cardiomyopathy, followed by non-ST-segment elevation angina. Samples taken with or without lipid-lowering drugs showed significant differences in lactate dehydrogenase, creatinine, and TC. There was a significant positive correlation between VTE scores and lactate dehydrogenase, myoglobin, and creatine kinase. High VTE score indicates high lactate dehydrogenase, myoglobin, and creatine kinase. TC has a significant positive correlation with HDL⁃C and TG, respectively. Higher TC values indicate higher HDL⁃C and TG values.