Low-Density Lipoprotein Cholesterol Level, Statin Use and Myocardial Infarction Risk in Young Adults

Statin therapy in individuals with intermediate cardiovascular risk

BACKGROUND

As intermediate cardiovascular risk group accounts for a large part of the total population, determining appropriate cholesterol target in this population is critical. Herein, we investigated the optimal low-density lipoprotein cholesterol (LDL-C) level in individuals with intermediate cardiovascular risk after statin therapy.

METHODS

This was a nationwide observational and validation cohort study (median duration of follow-up: 7.5 and 8.7 years, respectively), using data from the Korean National Health Insurance Service and a tertiary hospital database. Among individuals who underwent regular health examinations, those with ≥2 cardiovascular risk factors except diabetes mellitus, LDL-C 100-189 mg/dL, and newly used statins were enrolled. Of the 358,694 screened people, 57,594 met the inclusion criteria, of whom 27,793 were finally analyzed. The study population was stratified according to post-treatment LDL-C levels as follows: <100, 100-119, 120-139, and ≥ 140 mg/dL. The primary outcome variable was composite cardiovascular events (myocardial infarction, coronary revascularization, and ischemic stroke). From the patients screened of Severance Hospital cohort, 1859 meeting inclusion criteria were used for validation.

RESULTS

The rates of composite events ranged from 7.74 to 9.10 (mean 8.38)/1000 person-years in the three lower LDL-C groups. Adjusted hazard ratios (aHRs) ranged from 0.78 to 0.95 in the three groups with lower LDL-C, and a lower event risk was more evident in the groups that achieved LDL-C levels <120 mg/dL (p = 0.001-0.009). The total mortality risk did not differ between groups. In the validation cohort, the mean rate of composite events was 10.83/1000 person-years. aHRs ranged from 0.52 to 0.78 in the groups with lower LDL-C, and a lower risk was more obvious in patients who achieved LDL-C levels <100 mg/dL (p = 0.006-0.03).

CONCLUSIONS

Individuals with intermediate cardiovascular risk who achieved LDL-C levels <120 mg/dL after statin therapy had lower event risk. This result provides clinically useful evidence on target LDL-C levels in this population.

Clinical Characteristics of Patients With Statin Discontinuation in Korea: A Nationwide Population-Based Study

Objective

To investigate the clinical characteristics of patients with statin discontinuation in Korea, using a nationwide database.

Methods

We analyzed 1,308,390 patients treated with statin for the first time in their life between 2016 and 2017 using the Korean National Health Information Database. The patients participated in the Korean National Health Screening Program within two years before taking statin. Patients with statin discontinuation were defined as those who were not prescribed statin between 365 days and 730 days after the initial statin prescription.

Results

The overall prevalence of statin discontinuation was 39.44%. Patients with statin discontinuation were younger, had lower body mass index (BMI), included a higher number of smokers and drinkers, did not exercise regularly, with fewer cases of hypertension and diabetes mellitus than those without statin discontinuation (p<0.001). Compared with patients aged 20-29 years, the risk of statin discontinuation showed a U-shaped relationship with age (odds ratios [ORs]: 0.619 in 30-39 years; 0.454 in 40-49 years; 0.345 in 50-59 years; 0.307 in 60-69 years; 0.324 in 70-79 years; and 0.415 in ≥80 years). In addition, increased BMI was associated with decreased risk of statin discontinuation (ORs: 0.969 with 25.0-29.9 kg/m2, and 0.890 with ≥30.0 kg/m2). Patients with hypertension and diabetes mellitus were at a lower risk of statin discontinuation (OR: 0.414 for hypertension; 0.416 for diabetes mellitus).

Conclusion

The prevalence of patients with statin discontinuation in Korea was 39.44% at 1 to 2 years after initial statin treatment.

From multi-omics approaches to personalized medicine in myocardial infarction

Myocardial infarction (MI) is a prevalent cardiovascular disease characterized by myocardial necrosis resulting from coronary artery ischemia and hypoxia, which can lead to severe complications such as arrhythmia, cardiac rupture, heart failure, and sudden death. Despite being a research hotspot, the etiological mechanism of MI remains unclear. The emergence and widespread use of omics technologies, including genomics, transcriptomics, proteomics, metabolomics, and other omics, have provided new opportunities for exploring the molecular mechanism of MI and identifying a large number of disease biomarkers. However, a single-omics approach has limitations in understanding the complex biological pathways of diseases. The multi-omics approach can reveal the interaction network among molecules at various levels and overcome the limitations of the single-omics approaches. This review focuses on the omics studies of MI, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and other omics. The exploration extended into the domain of multi-omics integrative analysis, accompanied by a compilation of diverse online resources, databases, and tools conducive to these investigations. Additionally, we discussed the role and prospects of multi-omics approaches in personalized medicine, highlighting the potential for improving diagnosis, treatment, and prognosis of MI.

Are Only p-Values Less Than 0.05 Significant? A p-Value Greater Than 0.05 Is Also Significant!

Statistical hypothesis testing compares the significance probability value and the significance level value to determine whether or not to reject the null hypothesis. This concludes "significant or not significant." However, since this process is a process of statistical hypothesis testing, the conclusion of "statistically significant or not statistically significant" is more appropriate than the conclusion of "significant or not significant." Also, in many studies, the significance level is set to 0.05 to compare with the significance probability value, p-value. If the p-value is less than 0.05, it is judged as "significant," and if the p-value is greater than 0.05, it is judged as "not significant." However, since the significance probability is a value set by the researcher according to the circumstances of each study, it does not necessarily have to be 0.05. In a statistical hypothesis test, the conclusion depends on the setting of the significance level value, so the researcher must carefully set the significance level value. In this study, the stages of statistical hypothesis testing were examined in detail, and the exact conclusions accordingly and the contents that should be considered carefully when interpreting them were mentioned with emphasis on statistical hypothesis testing and significance level. In 11 original articles published in the Journal of Lipid and Atherosclerosis in 2022, the interpretation of hypothesis testing and the contents of the described conclusions were reviewed from the perspective of statistical hypothesis testing and significance level, and the content that I would like to be supplemented was mentioned.