A modified algorithm with lipoprotein(a) added for diagnosis of familial hypercholesterolemia

Stratification in Heterozygous Familial Hypercholesterolemia: Imaging, Biomarkers, and Genetic Testing

PURPOSE OF REVIEW

Heterozygous familial hypercholesterolemia (HeFH) is the most common monogenic autosomal dominant disorder. However, the condition is often underdiagnosed and undertreated. The objective of this review is to provide an update on the risk stratification in patients with HeFH, incorporating new cardiovascular imaging techniques, various biomarkers, and genetic studies.

RECENT FINDINGS

The diagnosis of HeFH places patients in a high cardiovascular risk category due to the increased incidence of premature atherosclerotic cardiovascular disease. However, the level of risk varies significantly among different individuals with HeFH. Achieving an optimal stratification of cardiovascular risk is crucial for establishing appropriate and accurate treatment and management strategies. Different new tools such as risk scores have emerged in recent years, aiding physicians in assessing the risk stratification for HeFH using imaging, biomarkers, and genetics. This review emphasizes that not all patients with HeFH face the same cardiovascular risk. By utilizing different assessment tools, we can identify those who require more intensive monitoring, follow-up, and treatment.

Exploring the role of lipoprotein(a) in cardiovascular diseases and diabetes in Chinese population

A high level of lipoprotein (a) in the plasma has been associated with a variety of cardiovascular diseases and is considered to be an independent predictor of some other diseases. Based on recent studies, the concentration levels of Lp(a) in the Chinese population exhibit a distinctive variation from other populations. In the Chinese population, a high level of Lp(a) indicates a higher incidence of revascularization, platelet aggregation, and thrombogenicity following PCI. Increased risk of atherosclerotic cardiovascular disease (ASCVD) in Chinese population has been linked to higher levels of Lp(a), according to studies. More specifically, it has been found that in Chinese populations, higher levels of Lp(a) were linked to an increased risk of coronary heart disease, severe aortic valve stenosis, deep vein thrombosis in patients with spinal cord injuries, central vein thrombosis in patients receiving hemodialysis, and stroke. Furthermore, new and consistent data retrieved from several clinical trials also suggest that Lp (a) might also play an essential role in some other conditions, including metabolic syndrome, type 2 diabetes and cancers. This review explores the clinical and epidemiological relationships among Lp(a), cardiovascular diseases and diabetes in the Chinese population as well as potential Lp(a) underlying mechanisms in these diseases. However, further research is needed to better understand the role of Lp(a) in cardiovascular diseases and especially diabetes in the Chinese population.

Lipoprotein(a) and Atherosclerotic Cardiovascular Diseases: Evidence from Chinese Population

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Multiple factors are involved in CVD, and emerging data indicate that lipoprotein(a) (Lp(a)) may be associated with atherosclerotic cardiovascular disease (ASCVD) independent of other traditional risk factors. Lp(a) has been identified as a novel therapeutic target. Previous studies on the influence of Lp(a) in CVD have mainly used in western populations. In this review, the association of plasma Lp(a) concentration with ASCVD was summarized, with regards to epidemiological, population-based observational, and pathological studies in Chinese populations. Lp(a) mutations and copy number variations in Chinese populations are also explored. Finally, the impact of plasma Lp(a) levels on patients with type 2 diabetes mellitus, cancer, and familial hypercholesterolemia are discussed.

Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients

Background

Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools, for example, DLCN, although their sensitivity and specificity vary among specific populations. We aimed to assess the risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese patients with ASCVD.

Materials and Methods

In total, 5,597 primary patients with ASCVD were assessed for FH risk using 11 tools. The three best performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black box features.

Results

After hybridizing the mDLCN, Taiwan criteria, and DLCN, the HFHRAT was taken as a stacking ensemble method (AUC_class[94.85 ± 0.47], AUC_prob[98.66 ± 0.27]). The interpretation of HFHRAT suggests that patients aged <75 years with LDL-c >4 mmol/L were more likely to be at risk of developing FH.

Conclusion

The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.

Visit-to-visit variability of lipid and cardiovascular events in patients with familial hypercholesterolemia

Background

Visit-to-visit variability in lipid has been suggested as a predictor of major adverse cardiovascular events (MACEs). However, no evidence exists on the prognostic value of lipid variability in patients with familial hypercholesterolemia (FH). This prospective cohort study aimed to investigate whether lipid variability affects future MACEs in patients with FH receiving standard lipid-lowering therapy.

Methods

A total of 254 patients with FH were consecutively enrolled and followed for MACEs. Variability in the triglyceride, total cholesterol, high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C) and lipoprotein (a) [Lp(a)] were evaluated from 3 months after discharge using the standard deviation (SD), coefficient of variation (CV) and variability independent of the mean (VIM).

Results

During a mean follow-up of 49 months, 22 (8.7%) events occurred. Visit-to-visit variability in Lp(a) was significantly higher in the MACE group compared to the non-MACE group. In the multivariate Cox analysis, only Lp(a)-related parameters were independent predictors for MACEs. The hazard ratios and 95% confidence intervals of each 1-SD increase of SD, CV, and VIM of Lp(a) were 1.42 (1.12-1.80), 1.50 (1.11-2.02) and 1.60 (1.16-2.22), respectively. Kaplan-Meier analysis revealed that patients with higher Lp(a) variability presented lower event-free survival. The results were consistent in various subgroups.

Conclusions

Our study firstly suggested that Lp(a) variability was associated with MACEs in real-world patients with FH, which emphasized the importance of regular lipid monitoring in the patients with high risk.

What do we know about the role of lipoprotein(a) in atherogenesis 57 years after its discovery?

Elevated circulating concentrations of lipoprotein(a) [Lp(a)] is strongly associated with increased risk of atherosclerotic cardiovascular disease (CVD) and degenerative aortic stenosis. This relationship was first observed in prospective observational studies, and the causal relationship was confirmed in genetic studies. Everybody should have their Lp(a) concentration measured once in their lifetime. CVD risk is elevated when Lp(a) concentrations are high i.e. > 50 mg/dL (≥100 mmol/L). Extremely high Lp(a) levels >180 mg/dL (≥430 mmol/L) are associated with CVD risk similar to that conferred by familial hypercholesterolemia. Elevated Lp(a) level was previously treated with niacin, which exerts a potent Lp(a)-lowering effect. However, niacin is currently not recommended because, despite the improvement in lipid profile, no improvements on clinical outcomes have been observed. Furthermore, niacin use has been associated with severe adverse effects. Post hoc analyses of clinical trials with proprotein convertase subtilisin/kexin type-9 (PCSK9) inhibitors have shown that these drugs exert clinical benefits by lowering Lp(a), independent of their potent reduction of low-density lipoprotein cholesterol (LDL-C). It is not yet known whether PCSK9 inhibitors will be of clinical use in patients with elevated Lp(a). Apheresis is a very effective approach to Lp(a) reduction, which reduces CVD risk but is invasive and time-consuming and is thus reserved for patients with very high Lp(a) levels and progressive CVD. Studies are ongoing on the practical application of genetic approaches to therapy, including antisense oligonucleotides against apolipoprotein(a) and small interfering RNA (siRNA) technology, to reduce the synthesis of Lp(a).

A modified algorithm with lipoprotein(a) added for diagnosis of familial hypercholesterolemia

Background

Previous studies have observed that high level of lipoprotein (a) [Lp(a)] was common in the phenotypic familial hypercholesterolemia (FH) and may explain part of the clinical diagnosis of FH.

Hypothesis

We aim to develop a modified model including Lp(a) and compare its diagnostic performance with Dutch Lipid Clinic Network (DLCN) criteria.

Methods

Data of 10 449 individuals were utilized for the model establishment (7806 for derivation and 2643 for validation) from January 2011 to March 2018. The novel score model was modified on the basis of DLCN. Furthermore, 718 patients were screened for LDLR, APOB, and PCSK9 gene mutations.

Results

The novel modified model consisted of untreated low‐density lipoprotein cholesterol (LDL‐C) level, Lp(a), personal premature coronary heart disease (CHD), tendon xanthomas and family history of CHD and/or hypercholesterolemia. It has shown high discrimination (area under curve [AUC] 0.991, 95% confidence interval [CI[ 0.988‐0.994, P < .001) for distinguishing clinical FH from non‐FH diagnosed using DLCN. Furthermore, a concordance analysis was performed to compare the modified model with DLCN and it showed a good agreement with DLCN (κ = 0.765). External validation of the novel model also showed good accordance (κ = 0.700). Further genetic analysis showed that the agreements between the new model and mutation improved a little compared to that between DLCN and mutation.

Conclusions

The novel modified model, including Lp(a), could provide new insights into FH diagnosis in Chinese population with more concerns on the patients with high level of Lp(a).