Long-term statin therapy could be efficacious in reducing the lipoprotein (a) levels in patients with coronary artery disease modified by some traditional risk factors

Recommendations of the Experts of the Polish Cardiac Society (PCS) and the Polish Lipid Association (PoLA) on the diagnosis and management of elevated lipoprotein(a) levels

Lipoprotein(a) [Lp(a)] is made up of a low-density lipoprotein (LDL) particle and a specific apolipoprotein(a). The blood concentration of Lp(a) is approximately 90% genetically determined, and the main genetic factor determining Lp(a) levels is the size of the apo(a) isoform, which is determined by the number of KIV2 domain repeats. The size of the apo(a) isoform is inversely proportional to the blood concentration of Lp(a). Lp(a) is a strong and independent cardiovascular risk factor. Elevated Lp(a) levels ≥ 50 mg/dl (≥ 125 nmol/l) are estimated to occur in more than 1.5 billion people worldwide. However, determination of Lp(a) levels is performed far too rarely, including Poland, where, in fact, it is only since the 2021 guidelines of the Polish Lipid Association (PoLA) and five other scientific societies that Lp(a) measurements have begun to be performed. Determination of Lp(a) concentrations is not easy due to, among other things, the different sizes of the apo(a) isoforms; however, the currently available certified tests make it possible to distinguish between people with low and high cardiovascular risk with a high degree of precision. In 2022, the first guidelines for the management of patients with elevated lipoprotein(a) levels were published by the European Atherosclerosis Society (EAS) and the American Heart Association (AHA). The first Polish guidelines are the result of the work of experts from the two scientific societies and their aim is to provide clear, practical recommendations for the determination and management of elevated Lp(a) levels.

Cholesterol Metabolism in Aging and Age-Related Disorders

All mammalian cell membranes contain cholesterol to maintain membrane integrity. The transport of this hydrophobic lipid is mediated by lipoproteins. Cholesterol is especially enriched in the brain, particularly in synaptic and myelin membranes. Aging involves changes in sterol metabolism in peripheral organs and also in the brain. Some of those alterations have the potential to promote or to counteract the development of neurodegenerative diseases during aging. Here, we summarize the current knowledge of general principles of sterol metabolism in humans and mice, the most widely used model organism in biomedical research. We discuss changes in sterol metabolism that occur in the aged brain and highlight recent developments in cell type-specific cholesterol metabolism in the fast-growing research field of aging and age-related diseases, focusing on Alzheimer's disease. We propose that cell type-specific cholesterol handling and the interplay between cell types critically influence age-related disease processes.

Attainment of low-density lipoprotein cholesterol goals in statin treated patients: real-world evidence from Australia

OBJECTIVE

Little is known about the attainment of low-density lipoprotein cholesterol (LDL-C) targets in patients treated with statins in Australian primary healthcare setting that are at increased risk of cardiovascular disease.

METHODS

A retrospective cohort study was conducted using data from electronic medical records of patients treated by general practitioners across Australia. LDL-C target attainment was defined as LDL-C levels ≤ 2 mmol/L for all risk groups, in line with Australian guidelines. Multivariable logistic regression was used to identify the factors associated with LDL-C target attainment.

RESULTS

Overall, 61,407 patients were included in the analysis. The mean age was 65 years (± standard deviation [SD] 12.1); 52.0% were males.. Overall, the median LDL-C level was 2.3 mmol/L (IQR = 1.8 - 2.8) and 36.0% of the study population met therapeutic targets. Increased likelihood to achieve LDL-C targets was observed in patients diagnosed with type 2 diabetes (OR 2.07, 95% CI 1.92 - 2.24), stroke (OR = 1.58, 95% CI 1.39 - 1.79, p< 0.001) or chronic heart disease (OR = 1.67, 95% CI 1.55 - 1.81, p<0.001). Patients diagnosed with dyslipidaemia (OR = 0.59, 95% CI 0.55 - 0.64, p < 0.001), hypertension (OR = 0.91, 95% CI 0.83 - 1.00, p< 0.05) and current smokers (OR = 0.71, 95% CI 0.71 - 1.00, p <0.05), were less likely to attain LDL-C targets, regardless of the type, intensity and length of use of the prescribed statin.

CONCLUSION

Longer duration and higher intensity statin were associated with more patients achieving targeted LDL-C goal, however nearly two thirds of Australians still failed to achieve targeted outcome even after 24 months of statin therapy.

Effect of different types and dosages of statins on plasma lipoprotein(a) levels: A network meta-analysis

BACKGROUND AND AIM

Studies differ with respect to the effects of statins and their on lipoprotein(a)[Lp(a)] levels. The aim of the present study was to resolve these differences by determining the effect of various types and dosages of statins on Lp(a) levels.

METHODS

We searched PubMed, Embase and the Cochrane library for randomized controlled trials (RCTs) investigating the efficacy of statins on plasma Lp(a) levels. Study selection, data extraction and risk of bias assessment were conducted independently by four authors. We conducted pairwise meta-analysis and network meta-analysis (NMA). Consistency models were applied to NMA and the ranking probabilities for each treatment's efficacy were calculated. Node-splitting analysis was used to test inconsistency. This study was registered with PROSPERO, number CRD42020167612.

RESULTS

Twenty RCTs with 23,605 participants were included, involving 11 interventions. Most of the included studies presented some risks of bias, especially risks of performance and detection bias. In the pairwise meta-analysis, pooled results showed a small but statistically significant difference between high-intensity rosuvastatin and placebo on Lp(a) levels (MD = 1.81, 95 % CI [0.43, 3.19], P = 0.01). In the NMA, different types and dosages of statins showed no significant effect on the level of Lp(a), and there was no obvious difference between them. Subgroup analysis based on different populations and treatment durations did not provide any statistically significant findings about different statins on Lp(a) levels. Node-splitting analysis showed that no significant inconsistency existed (P > 0.05).

CONCLUSIONS

Statins have no clinically significant effect on Lp(a) levels, and there is no significant difference in the effect on Lp(a) levels between different types and dosages of statins. Moderate-intensity pitavastatin tended to have the best effect on reducing Lp(a) levels; nevertheless, it was insignificant. Our findings highlight the necessity for further study of the effect of statins on Lp(a) levels in future studies.

A selective androgen receptor modulator SARM-2f activates androgen receptor, increases lean body mass, and suppresses blood lipid levels in cynomolgus monkeys

SARM-2f a selective androgen receptor (AR) modulator, increases skeletal muscle mass and locomotor activity in rats. This study aimed to clarify its pharmacological effects in monkeys. In reporter assays, the EC50 values of SARM-2f for rat, monkey, and human AR were 2.5, 3, and 3.6 nmol/L, respectively; those of testosterone were 12, 3.2, and 11 nmol/L, respectively. A single oral administration (10 mg/kg SARM-2f) produced a maximal plasma concentration of 3011 ng/mL, with an area under the 24 hours concentration-time curve of 8152 ng·h/mL in monkeys. Body weight (BW), lean body mass (LBM), and plasma levels of total cholesterol, triglyceride, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, lipoprotein (a), alanine aminotransferase, and asparagine aminotransferase were measured after 4 weeks of treatment with SARM-2f (1, 3, and 10 mg/kg/day, QD, p.o.) or testosterone enanthate (TE; 2 mg/kg/2 weeks, s.c.) in monkeys. BW and LBM were significantly increased by 12% each by SARM-2f at 10 mg/kg, and by 5% and 8%, respectively, by TE, but these effects were not statistically significant. Plasma levels of all lipids were either decreased or showed a tendency to be decreased by SARM-2f. TE decreased the triglyceride level and increased the low-density lipoprotein cholesterol level. Liver marker levels were not changed by either SARM-2f or TE. Our data demonstrated that SARM-2f exerted anabolic effects and produced a lipid profile that differed from that produced by testosterone in monkeys, suggesting that SARM-2f might be useful for diseases such as sarcopenia.

Effect of Ezetimibe Monotherapy on Plasma Lipoprotein(a) Concentrations in Patients with Primary Hypercholesterolemia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND AND AIMS

Ezetimibe reduces plasma low-density lipoprotein cholesterol (LDL-C) levels by up to 20%. However, its effect on plasma lipoprotein(a) [Lp(a)] concentrations in patients with primary hypercholesterolemia has not been defined.

OBJECTIVE

Therefore, we performed a systematic review and meta-analysis to assess this effect based on the available randomized controlled trials (RCTs).

METHODS

We searched the PubMed and SCOPUS databases from inception until 28 February 2017 to identify RCTs that investigated the effect of ezetimibe monotherapy on plasma Lp(a) concentrations in patients with primary hypercholesterolemia. We pooled mean percentage changes in plasma Lp(a) concentrations as a mean difference (MD) with a 95% confidence interval (CI).

RESULTS

Seven RCTs with 2337 patients met the selection criteria and were included in the analysis. Overall pooled analysis suggested that ezetimibe 10 mg significantly reduced plasma Lp(a) concentrations in patients with primary hypercholesterolemia by - 7.06% (95% CI - 11.95 to - 2.18; p = 0.005) compared with placebo. No significant heterogeneity was observed (χ² = 5.34; p = 0.5). Excluding one study from the analysis resulted in insignificant differences between the two groups (p = 0.2). Meta-regression did not find a significant association between the mean percentage changes in Lp(a) and other potential moderator variables, which included the mean percentage changes of LDL-C concentrations (p = 0.06) and baseline Lp(a) mean values (p = 0.46).

CONCLUSIONS

Ezetimibe monotherapy (10 mg/day) showed a small (7.06%) but statistically significant reduction in the plasma levels of Lp(a) in patients with primary hypercholesterolemia. According to current literature, this magnitude of reduction seems to have no clinical relevance. However, further studies are warranted to clarify the mechanism mediating this effect of ezetimibe and to investigate its efficacy in combination with other drugs that have shown promise in lowering Lp(a) levels.

Modeling analysis of the relationship between atherosclerosis and related inflammatory factors

Objective: To establish early diagnosis model of inflammatory factors for atherosclerosis (AS), providing theoretical evidence for early detection of AS and development of plaques. Methods: Serum samples were collected to detect the inflammatory factors including CysC, Hcy, hs-CRP, UA, FIB, D-D, LP (a), IL-6, SAA, sCD40L and MDA. Using Logistic regression analysis, the inflammatory factors used for modeling were screened out, and then the AS early diagnosis models were established based on receiver operating characteristic (ROC) curve, support vector machine and BP neural network respectively. Results: No significant difference exists between the general materials of two groups. All 11 inflammatory factors had higher level in AS group than in control group. As shown in ROC curve, all inflammatory factors were helpful in AS diagnosis. In terms of sensitivity, UA ranked first (98) and FIB ranked last (55.5); in terms of specificity, UA ranked first (99) and FIB ranked last (78); in terms of area under the curve, UA and SAA ranked first (both were 0.995) and FIB ranked last (0.721). Based on Logistic regression equation, six factors were screened out, including Hcy, Hs-CRP, IL-6, D-D, CysC and MDA. According to classification, the final sixth steps had a prediction accuracy of 99%. When six inflammatory factors included in Logistic regression equation were detected jointly, the sensitivity, specificity and area under the curve were 57%, 97% and 0.821 respectively, while those of the model excluding D-D were 64%, 90% and 0.828, generally superior to results of joint detection including six factors. The ROC curve based on Hcy, Hs-CRP and MDA had a sensitivity of 87%, a specificity of 94% and an area under the curve of 0.869, being inferior to those of the ROC curve based on IL-6, D-D and Cys C, which were 87%, 92% and 0.936 respectively. The accuracy of SVM-AS diagnosis model and BP neural network model were 82.5% and 77.5% respectively. Conclusion: All 11 inflammatory factors are valuable in AS diagnosis. AS early diagnosis models based on Logistic regression analysis, ROC curve, support vector machine and BP neural network possess diagnostic value and can provide reference for clinical diagnosis.