Background and objectives: Despite reducing low-density lipoprotein cholesterol (LDL-C) to the normal range, residual cardiovascular risk remain. Remnant cholesterol (RC) exerts a potential residual risk for cardiovascular disease (CVD) prevention, and the long-term longitudinal association between RC and mortality has yet to be well elucidated.Methods: This study examined a nationally representative sample of 13,383 adults aged 20 years or older (mean age 45.7 and 52% women) who participated in the NHANES III (from1988 to1994). Causes of death were ascertained by linkage to death records through December 31, 2019. The relations of RC with all-cause and CVD mortality were tested using weighted Cox proportional hazard models.Results: Through a median follow-up of 26.6 years, 5,044 deaths were reported, comprising 1,741 deaths of CVD [1,409 deaths of ischemic heart disease (IHD) and 332 deaths of stroke] and 1,126 of cancer. Compared to those with RC <14.26 mg/dl (lowest quartile), participants with RC ≥29.80 mg/dl (highest quartile) had multivariable-adjusted HRs of 1.23 (95% CI: 1.07–1.42) for all-cause mortality, 1.22 (95% CI: 0.97–1.53) for CVD mortality, and 1.32 (95% CI: 1.03–1.69) for IHD mortality, and 0.89 (95% CI: 0.55–1.43) for stroke mortality, and 1.17 (95% CI 0.90–1.52) for cancer mortality. We observed that elevated RC levels increased CVD risk and IHD mortality despite LDL-C being in the normal range.Conclusions: Elevated blood RC was associated with an increased long-term risk of all-cause, CVD, and IHD mortality. These associations were independent of socioeconomic factors, lifestyles, and history of diseases, and remained robust across the LDL-C stratum. Measuring RC levels might favor clinical assessment of early CVD risk. Further investigation is needed to elucidate the optimal range of RC levels for cardiovascular disease health in the general population.

Background: With the disappointing results associated with the use of cardiac myosin inhibitors in the treatment of hypertrophic cardiomyopathy (HCM), the development of new therapies in clinical trials for HCM has rapidly increased. We assessed the characteristics of therapeutic intervention in HCM registered on ClinicalTrials.gov and the International Clinical Trials Registry Platform (ICTRP). Methods: We conducted a cross-sectional, descriptive study of clinical trials for therapeutic intervention in HCM registered on ClinicalTrials.gov and ICTRP. Results: This study analyzed 137 registered trials. Regarding study designs of these trials, 77.37% were purpose of treatment, 59.12% were randomized, 50.36% were parallel assignment, 45.26% were performed with masking, 48.18% recruited less than 50 participants, and 27.74% were Phase 2 trials. In total, 67 trials were new drug trials, of which 35 drugs were tested in these trials, and 13 trials involved treatment with mavacamten. Of these 67 clinical drug trials, 44.78% of trials involved the study of amines, and 16.42% involved 1-ring heterocyclic compounds. Regarding the NCI Thesaurus Tree, 23.81% of trials involved myosin inhibitors, 23.81% of trials involved drugs belonging to agents affecting the cardiovascular system, and 20.63% were involved in testing cation channel blockers. The drug-target network showed that myosin-7, potassium voltage-gated channel subfamily h member 2, beta-1 adrenergic receptor, carnitine o-palmitoyltransferase 1, and liver isoform were the most targeted pathways of the clinical trials analyzed in the drug-target network. Conclusion: The number of clinical trials investigating therapeutic interventions for HCM has increased in recent years. Ultimately, recent HCM therapeutic clinical trials generally did not incorporate either randomized controlled trials or masking and were small studies recruiting fewer than 50 participants. Although recent research has focused on targeting myosin-7, the molecular signaling mechanisms involved in the pathogenesis of HCM have the potential to elucidate novel target pathways.

Objective: Researching the prognostic value of myocardial enzymes in COVID-19 patients. Materials & methods: We collected 113 confirmed COVID-19 patients. The dynamic changes of CK, LDH and α-HBDH ...

Background: This study aimed to develop and test an explainable machine learning (ML) predictive model based on lipid-related biomarkers to predict acute coronary syndrome (ACS) in hospitalized patients. Methods: A total of 10,127 consecutive hospitalized patients at three large hospitals were retrospectively studied between 2022 and 2024. ACS incidence was recorded as the primary outcome. Eight ML models were used to calculate the risk of ACS during hospitalization and to distribute patients into low-, intermediate-, and high-risk groups. Results: All patients were randomly divided into a 70% training set (n = 7088) and a 30% test set (n = 3039). ACS occurred in 1119 (15.8%) and 461 (15.2%) patients, respectively. The Light Gradient Boosting Machine (LightGBM) exhibited the best predictive performance (area under the curve, 0.829) for ACS in the training set. The final model, which included the top 10 features from the LightGBM model, including lipid-related markers and clinical features, achieved a C-index of 0.781 on the test set and demonstrated a significant ability to stratify patients into low-, intermediate-, and high-risk groups. Conclusion: We constructed a risk-stratification model based on lipid-related biomarkers derived from ML models to predict ACS in hospitalized patients, which could assist in identifying patients with high discriminatory capacity.