Importance of so called "novel cardiovascular risk factors" in severity of coronary artery calcification; how serious they should be taken: a systematic review and metaanalysis

Hematologic and biochemical inflammatory markers increase with body mass and positively correlate in adolescents

BACKGROUND

Atherosclerosis is a chronic inflammatory disease that has its origins in childhood. The goal of this study was to explore the relationships of hematologic inflammatory markers to body mass, biochemical inflammatory markers and cardiometabolic risk factors.

METHODS

Healthy, white, non-Hispanic identifying adolescents (n = 75, age 12 to 18 years) were enrolled. Measures studied included body mass index percentile (BMI%), neutrophil and platelet to lymphocyte ratio (NLR, PLR), pan immune inflammation value (PIV), lipids, augmentation index, reactive hyperemia, inflammatory markers (interleukin 6: IL6, c-reactive protein: CRP), complement (C3, C3a, C4, C4a, C5a) insulin secretion and insulin sensitivity (oral glucose tolerance test: Matusda index, and disposition index (DI)).

RESULTS

NLR (rS = 0.31, p < 0.01), PLR (rS = 0.32, p < 0.01), PIV (rS = 0.32, p < 0.01) and CRP (rS = 0.51, p < 0.001) all positively correlated with BMI% but IL-6 did not. NLR, PLR and PIV all positively correlated with each other. NLR correlated with the reactive hyperemia response (rS = 0.29, p < 0.02) but this relationship was lost when BMI% was included. NLR positively correlated with C3a, C4, CRP and IL6 even when BMI% was included.

CONCLUSION

In healthy adolescents hematologic markers of inflammation increase with increasing body mass and neutrocyte to lymphocyte ratio is associated with increased complement and inflammatory markers independent of obesity.

IMPACT STATEMENT

Hematologic and biochemical markers of inflammation increase with increased body mass in healthy adolescents. Hematologic and biochemical markers of inflammation are positively related independent of body mass in healthy adolescents. Hematologic inflammatory markers are not related to markers of cardiometabolic risk in healthy adolescents.

Neutrophil and lymphocyte count as predictors of the location of calcific coronary lesions in patients treated with rotational atherectomy

Introduction

The role of immune system activation in development and progression of atherosclerotic plaques has been postulated. Previous studies on inflammation have focused on the severity of coronary disease, the risk of acute coronary syndrome, and the prediction of outcomes based on hematological indices obtained from whole blood count analysis.

Aim

To analyze simple data of the whole blood count in relation to the location of calcified atherosclerotic lesions in patients treated with coronary rotational atherectomy (RA).

Material and methods

Eighty-one patients (57 (70%) males, mean (SD) age of 70.4 ±8 years) who underwent RA were enrolled in the study. The study group was divided into two subgroups depending on the proximal (group 1) and non-proximal (group 2) location of the culprit lesions in coronary arteries. The angiographic results were compared with demographic and clinical data and whole blood count analysis.

Results

The multivariable analysis revealed the predictive value of low neutrophil (OR = 0.75, 95% CI: 0.58-0.97, p = 0.030) and lymphocyte counts (OR = 0.27, 95% CI: 0.11-0.68, p = 0.005) for the proximal location of the culprit lesions. In the ROC analysis, combined neutrophil and lymphocyte counts showed the best prediction of proximal location, with the area under the curve of 0.747 (p < 0.001), yielding a sensitivity of 79.07% and specificity of 73.68%.

Conclusions

The lower neutrophil and lymphocyte counts in peripheral blood count analysis may be more representative of proximal calcified coronary lesions. The relationship between neutrophil and lymphocyte blood count and calcific atherosclerotic plaque location can indicate the inflammatory background of epicardial atheroma formation and distribution.

Machine learning approaches that use clinical, laboratory, and electrocardiogram data enhance the prediction of obstructive coronary artery disease

Pretest probability (PTP) for assessing obstructive coronary artery disease (ObCAD) was updated to reduce overestimation. However, standard laboratory findings and electrocardiogram (ECG) raw data as first-line tests have not been evaluated for integration into the PTP estimation. Therefore, this study developed an ensemble model by adopting machine learning (ML) and deep learning (DL) algorithms with clinical, laboratory, and ECG data for the assessment of ObCAD. Data were extracted from the electronic medical records of patients with suspected ObCAD who underwent coronary angiography. With the ML algorithm, 27 clinical and laboratory data were included to identify ObCAD, whereas ECG waveform data were utilized with the DL algorithm. The ensemble method combined the clinical-laboratory and ECG models. We included 7907 patients between 2008 and 2020. The clinical and laboratory model showed an area under the curve (AUC) of 0.747; the ECG model had an AUC of 0.685. The ensemble model demonstrated the highest AUC of 0.767. The sensitivity, specificity, and F1 score of the ensemble model ObCAD were 0.761, 0.625, and 0.696, respectively. It demonstrated good performance and superior prediction over traditional PTP models. This may facilitate personalized decisions for ObCAD assessment and reduce PTP overestimation.