Association of small dense LDL-cholesterol with disease severity, hypertension status and clinical outcome in patients with coronary artery disease

An increase in calculated small dense low-density lipoprotein cholesterol predicts new onset of hypertension in a Japanese cohort

A disorder of lipid metabolism is involved in cardiovascular diseases including hypertension. A high level of small dense low-density lipoprotein cholesterol (sdLDL-C) is a strong risk factor for atherosclerotic cardiovascular disease. However, the association between sdLDL-C and hypertension has not been fully investigated. We investigated the associations between the development of hypertension during a 10-year period and levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides (TG), and LDL-C and sdLDL-C calculated by using the Sampson equations in 28,990 Japanese subjects who received annual health examinations. After exclusion of subjects with missing data, those with hypertension, and those with TG ≥ 800 mg/dL at baseline, a total of 15,177 subjects (men/women: 9374/5803, mean age: 46 years) were recruited. During the 10-year period, 2379 men (25.4%) and 724 women (12.5%) had new onset of hypertension. Multivariable Cox proportional hazard model analyses showed that levels of HDL-C, non-HDL-C, TG and sdLDL-C, but not levels of TC and LDL-C, were independent risk factors for the development of hypertension after adjustment of age, sex, family history of hypertension, systolic blood pressure, obesity, current smoking habit, alcohol drinking habit, estimated glomerular filtration rate, diagnosis of diabetes mellitus and use of lipid-lowering drugs and that the adjusted risk of sdLDL-C (per 1-standard deviation) was highest (hazard ratio [95% confidence interval: 1.09 [1.05-1.13]). The addition of sdLDL-C to traditional risk factors for hypertension significantly improved the discriminatory capability, which was better than that of other lipid fractions. In conclusion, a high level of calculated sdLDL-C predicts the development of hypertension.

Pharmacotherapeutics for dyslipidemia management

ABSTRACT

Effective management of dyslipidemia is of paramount importance to prevent cardiovascular (CV) complications. Using current clinical practice guidelines is recommended to correct lipid levels and prevent further pathologic processes. This article presents an overview of treatment options for patients with dyslipidemia and CV disease, with a special focus on the following drug classes: HMG-CoA reductase inhibitors (also called statins), cholesterol absorption inhibitors (ezetimibe), bile acid sequestrants, fibrates, icosapent ethyl, and PCSK9 inhibitors.

Small Dense LDL Level and LDL/HDL Distribution in Acute Coronary Syndrome Patients

This study aimed to determine the size and distribution of LDL and HDL particles in North African acute coronary syndrome (ACS) patients and to compare the level of small dense LDL (sdLDL) to other markers used in cardiovascular risk prediction.

METHODS

A total of 205 ACS patients and 100 healthy control subjects were enrolled. LDL particle size and LDL and HDL subclass distributions were measured using Quantimetric Lipoprint^® linear polyacrylamide gel electrophoresis. Lipid ratios (total cholesterol, LDL cholesterol, non-HDL cholesterol, and HDL cholesterol) were determined to calculate the atherogenic index of plasma (AIP), the atherogenic coefficient (AC), Castelli's Risk-I (CR-I), and Castelli's Risk-II (CR-II). Receiver operating characteristic (ROC) curve analyses and area under the curve (AUC) were used to assess the predictive value of sdLDL as a marker for cardiovascular disease.

RESULTS

The ACS patients, compared to the healthy control subjects, displayed an alteration of LDL particle distribution, with a significant increase in sdLDL serum concentrations (0.303 ± 0.478 mmol/L vs. 0.0225 ± 0.043 mmol/L, respectively, p < 0.001). The sdLDL levels had a high discrimination accuracy [AUC = 0.847 ± 0.0353 (95% CI 0.778 to 0.916, p < 0.0001)]. The best predictive cutoff value of ACS determined with the maximum Youden index (J) [(sensitivity + specificity) - 1 = 0.60] was 0.038 mmol/L. A Spearman correlation analysis showed that sdLDL levels were moderately but significantly and positively correlated with AC and CR-I (r = 0.37, p < 0.001) and weakly but significantly correlated with PAI and CR-II; r = 0.32 (p < 0.001) and r = 0.30 (p < 0.008), respectively. The subclass distribution of HDL particles from ACS patients was also altered, with a decrease in large HDL particles and an increase in small HDL particles compared to HDL from healthy control subjects.

CONCLUSION

Due to their high atherogenicity, sdLDL levels could be used as a valuable marker for the prediction cardiovascular events.

Evaluation of (sdLDLc*HCYc)/HDLc ratio in clinical auxiliary diagnosis of primary cerebral infarction

BACKGROUND

Timely detection of cerebral infarction is of vital importance in planning intervention effect of rapid rehabilitation. The clinical auxiliary diagnosis value of single biomarker, including small dense low-density lipoprotein concentration (sdLDLc), homocysteine concentration (HCYc) and high-density lipoprotein cholesterol concentration (HDLc) for cerebral infarction has been confirmed by many studies. Whether the use of three biomarkers in combination by calculating (sdLDLc*HCYc)/HDLc ratio could improve the diagnosis ability for primary cerebral infarction remains to be unclear. In the present study, we conducted a cross-sectional study to evaluate the value of (sdLDLc*HCYc)/HDLc ratio in clinical auxiliary diagnosis of primary cerebral infarction.

METHODS

A total of 583 participants, including 299 healthy participants as control group and 284 participants diagnosed with first cerebral infarction as experiment group, were included in this respective study. The serum sdLDLc, HDLc and HCYc were measured by peroxidase method, enzyme-linked immunosorbent assay and an enzymatic method, respectively.

RESULTS

The average concentration of sdLDL and HCY (0.69 ± 0.29 mmol/L and 18.14 ± 6.62 μmol/L) in experiment group was significantly higher than those in the control group (0.55 ± 0.22 mmol/L and 10.77 ± 2.67 μmol/L, P < 0.05). However, the average concentration of HDL (1.47 ± 0.25 mmol/L) in the control group was higher than that in the experiment group (1.33 ± 0.28 mmol/L, P < 0.05). Spearman correlation coefficient showed the three indicators are independent of each other. The positive predictive value of (sdLDLc*HCYc)/HDLc ratio (61.27%, 95% CI: 55.31-66.92) is higher than that in single biomarker (sdLDLc: 6.69 95% CI: 4.19-10.42, HCYc: 38.38%, 95% CI: 32.75-44.33, HDLc: 3.87%, 95% CI: 2.05-7.02). Receiver-operating characteristic curve (ROC) analysis illustrated that predictive power of (sdLDLc*HCYc)/HDLc was higher than single biomarker, including sdLDLc, HCYc and HDLc, in primary cerebral infarction.

CONCLUSIONS

Therefore, (sdLDLc*HCYc)/HDLc ratio might be a better new indicator in clinical auxiliary diagnosis of primary cerebral infarction, which could be contributed to predicting cerebral infarction occurrence and provide a scientific basis for early prevention.

Comprehensive Statistical and Bioinformatics Analysis in the Deciphering of Putative Mechanisms by Which Lipid-Associated GWAS Loci Contribute to Coronary Artery Disease

The study was designed to evaluate putative mechanisms by which lipid-associated loci identified by genome-wide association studies (GWAS) are involved in the molecular pathogenesis of coronary artery disease (CAD) using a comprehensive statistical and bioinformatics analysis. A total of 1700 unrelated individuals of Slavic origin from the Central Russia, including 991 CAD patients and 709 healthy controls were examined. Sixteen lipid-associated GWAS loci were selected from European studies and genotyped using the MassArray-4 system. The polymorphisms were associated with plasma lipids such as total cholesterol (rs12328675, rs4846914, rs55730499, and rs838880), LDL-cholesterol (rs3764261, rs55730499, rs1689800, and rs838880), HDL-cholesterol (rs3764261) as well as carotid intima-media thickness/CIMT (rs12328675, rs11220463, and rs1689800). Polymorphisms such as rs4420638 of APOC1 (p = 0.009), rs55730499 of LPA (p = 0.0007), rs3136441 of F2 (p < 0.0001), and rs6065906 of PLTP (p = 0.002) showed significant associations with the risk of CAD, regardless of sex, age, and body mass index. A majority of the observed associations were successfully replicated in large independent cohorts. Bioinformatics analysis allowed establishing (1) phenotype-specific and shared epistatic gene–gene and gene–smoking interactions contributing to all studied cardiovascular phenotypes; (2) lipid-associated GWAS loci might be allele-specific binding sites for transcription factors from gene regulatory networks controlling multifaceted molecular mechanisms of atherosclerosis.

Association between small dense low-density lipoprotein cholesterol and neuroimaging markers of cerebral small vessel disease in middle-aged and elderly Chinese populations

BACKGROUND

Cerebral small vascular disease (CSVD) is one of the leading causes of death in the aged population and is closely related to abnormalities in low-density lipoprotein cholesterol (LDL-C). Our study aims to clarify the relationship between small and dense low-density lipoprotein cholesterol (sdLDL-C) (a subcomponent of LDL-C) and neuroimaging markers of CSVD.

METHODS

In total, 1211 Chinese adults aged ≥45 years with cranial magnetic resonance imaging (MRI) were recruited in this retrospective study from January 2018 to May 2021. Serum lipids and other baseline characteristics were investigated in relation to the occurrence of CSVD. A logistic regression model was performed to analyze the relationships between LDL subtypes and CSVD risk, and the Pearson correlation coefficient was used to analyze the correlation between clinical characteristics and CSVD risk. ROC curves and AUCs were created and depicted to predict the best cutoff value of LDL-C subtypes for CSVD risk. Based on these data, we performed comprehensive analyses to investigate the risk factors for CSVD.

RESULTS

Ultimately, 623 eligible patients were included in the present study. Of the 623 eligible patients, 487 were included in the CSVD group, and 136 were included in the group without CSVD (control group). We adjusted for confounders in the multivariate logistic regression model, and LDL-C3 was still higher in the CSVD patients than in the group of those without CSVD (OR (95% CI), 1.22(1.08-1.38), P < 0.05). Pearson correlation showed that there was a positive correlation between the levels of LDL-C3, LDL-C4, LDL-C5, glucose, age, hypertension, previous ischemic stroke and CSVD risk (r > 0.15, P < 0.01). Moreover, the best cutoff value of LDL-C3 to predict CSVD was 9.5 mg/dL with 68.4% sensitivity and 72.8% specificity, and the best cutoff value of LDL-C4 to predict CSVD was 5.5 mg/dL with 50.5% sensitivity and 90.4% specificity.

CONCLUSION

The results indicate that LDL-C3 is an independent risk factor for CSVD. A new prediction model based on LDL-C3 and LDL-C4 can help clinicians identify high-risk CSVD, even in people with normal LDL-C levels. The levels of sdLDL-C should be considered in the assessment and management of CSVD.