Comparison of Newly Proposed LDL-Cholesterol Estimation Equations

The effect of sarpogrelate compared to aspirin in high- or very-high-risk diabetes for primary prevention

The benefit of aspirin in primary prevention for atherosclerotic cardiovascular diseases (ASCVD) is questionable due to bleeding complications. We analyzed the Korean National Health Insurance data to compare the efficacy and overall bleeding of sarpogrelate, an antiplatelet agent with lower bleeding risk, versus aspirin in high-/very-high-risk diabetic populations without prior ASCVD. The primary endpoint was net adverse clinical events (NACE), defined as a composite of efficacy and overall bleeding. The efficacy was a composite of all-cause death, myocardial infarction (MI) and stroke, whereas overall bleeding included intracranial hemorrhage (ICH) and gastrointestinal (GI) bleeding. A total of 10,778 high-/very-high-risk diabetic patients (9550 on aspirin, 1228 on sarpogrelate) were analyzed. After propensity score matching, sarpogrelate was linked to a lower incidence of NACE (HR:0.71; 95% CI 0.57-0.88), mainly driven by 62% reductions in overall bleeding (0.38; 0.17-0.81), a composite of 64% and 72% lower rate of GI bleeding and ICH, respectively. Additionally, there was no significant differences in MI or stroke between groups. In high- or very-high-risk diabetic patients without ASCVD, sarpogrelate use was associated with net clinical benefit mainly due to the reduction of significant reduction in overall bleeding events.

Comparative Analysis of Biochemical and Lipid Profile Markers at Different Stages of Lung Cancer: A Cross-Sectional Study

BACKGROUND

 Systemic inflammation, metabolic dysregulation, and changes in biochemical markers are closely associated with the progression of lung cancer. This study focuses on evaluating serum parathyroid hormone (PTH), C-reactive protein (CRP), lipid profile parameters, and interleukin-6 (IL-6) in relation to the stages of lung cancer, exploring their potential as biomarkers for assessing disease severity.

METHODS

A total of 160 lung cancer patients were selected for a cross-sectional study and equally distributed into four clinical stages (Stages 1-4). PTH, CRP, low-density lipoproteins (LDLs), high-density lipoproteins (HDLs), triacylglycerides (TAGs), and IL-6 were analyzed in serum by the enzyme-linked immunosorbent assay, chemiluminescent immunoassay, and enzymatic techniques. Statistical analysis included analysis of variance with post hoc Tukey's test and Pearson correlation analysis for body mass index (BMI) and Eastern Cooperative Oncology Group (ECOG) performance status.

RESULTS

 PTH levels increased significantly as the disease progressed (Stage 1: 10.8 ± 2.5 pg/mL; Stage 4: 498 ± 89.4 pg/mL; p < 0.001). Similarly, CRP (Stage 1: 5.8 ± 1.2 mg/L; Stage 4: 8.6 ± 2.4 mg/L; p = 0.004) and IL-6 (Stage 1: 3.8 ± 0.9 pg/mL; Stage 4: 8.5 ± 2.1 pg/mL; p < 0.001) levels showed significant increases, indicating elevated systemic inflammation in the advanced stages. Lipid profile analysis revealed a decrease in HDL levels (p = 0.042) and an increase in TAG levels (p = 0.031), while LDL levels did not show statistically significant variations (p = 0.085). The ECOG performance status significantly deteriorated with advancing stages (p = 0.001) and was strongly correlated with BMI (r = 0.915; p < 0.001).

CONCLUSION

 Lung cancer progression is strongly linked to elevated levels of PTH, CRP, and IL-6, alongside dysregulated lipid profiles. The interplay of systemic inflammation and metabolic alterations underscores key pathophysiological mechanisms underlying the disease. Emerging evidence suggests that PTH and IL-6 hold promise as potential biomarkers for assessing disease severity and progression.

A comparative evaluation of low-density lipoprotein cholesterol estimation: Machine learning algorithms versus various equations

BACKGROUND

Given the critical importance of Low-density lipoprotein cholesterol (LDL-C) levels in determining cardiovascular risk, it is essential to measure LDL-C accurately. Since the Friedewald formula generates incorrect predictions in many circumstances, new equations have been developed to overcome the Friedewald equations' shortcomings. This study aimed to compare estimated LDL-C with directly measured LDL-C (dLDL-C), as well as their performance in predicting LDL-C, utilizing Friedewald, extended Martin-Hopkins, Sampson, de Cordova, and Vujovic formulas and five machine learning (ML) algorithms.

METHODS

A total of 29,504 samples from the ISLAB-2 Core Laboratory were included in the study. All statistical analysis was performed using R version 4.1.2. Statistical Language.

RESULTS

Bayesian-Regularized Neural Network (BRNN) (r = 0.957) and Random Forest (RF) (r = 0.957) algorithms showed a higher correlation with dLDL-C than the other equations in all-testing dataset. All ML algorithms demonstrated less bias than pre-existing LDL-C equations with dLDL-C and outperformed the LDL-C estimation equations in terms of concordance in all-testing dataset.

CONCLUSIONS

The results of our research indicate that when compared to conventional equations, ML algorithms are much more effective in predicting LDL-C. ML algorithms, aided by a vast dataset, could have the capability to predict LDL-C levels even in cases where triglyceride levels are high, unlike the limited usage of Friedewald formula.