Monogenic Versus Polygenic Forms of Hypercholesterolemia and Cardiovascular Risk: Are There Any Differences?

Improved Genetic Characterization of Hypercholesterolemia in Latvian Patients with Familial Hypercholesterolemia: A Combined Monogenic and Polygenic Approach Using Whole-Genome Sequencing

Despite the implementation of next-generation sequencing-based genetic testing on patients with clinical familial hypercholesterolemia (FH), most cases lack complete genetic characterization. We aim to investigate the utility of the polygenic risk score (PRS) in specifying the genetic background of patients from the Latvian Registry of FH (LRFH). We analyzed the whole-genome sequencing (WGS) data of the clinically diagnosed FH patients (n = 339) and controls selected from the Latvian reference population (n = 515). Variant pathogenicity in FH patients was classified according to the ACMG/AMP guidelines. The low-density lipoprotein cholesterol (LDL-C) and lipoprotein (a) (LPA) PRS were calculated based on the WGS data. We identified unique causative variants in 80 (23.6%) of the tested individuals (39 variants in FH genes and 4 variants in phenocopy genes, with 6 variants being novel). The LDL-C PRS was highly discriminative compared to the LPA PRS. Nevertheless, both PRS were able to explain the genetic cause of hypercholesterolemia in 26.3% of the remaining non-monogenic patients. The combined genetic analysis of monogenic and polygenic hypercholesterolemia resulted in 43.7% genetically explained hypercholesterolemia cases. Even though the application of PRS alone does not exclude monogenic testing in clinical FH patients, it is a valuable tool for diagnosis specification.

Consensus document on diagnosis and management of familial hypercholesterolemia from the Italian Society for the Study of Atherosclerosis (SISA)

AIMS

Familial Hypercholesterolemia (FH) is a genetic disorder of lipoprotein metabolism that causes an increased risk of premature atherosclerotic cardiovascular disease (ASCVD). Although early diagnosis and treatment of FH can significantly improve the cardiovascular prognosis, this disorder is underdiagnosed and undertreated. For these reasons the Italian Society for the Study of Atherosclerosis (SISA) assembled a Consensus Panel with the task to provide guidelines for FH diagnosis and treatment.

DATA SYNTHESIS

Our guidelines include: i) an overview of the genetic complexity of FH and the role of candidate genes involved in LDL metabolism; ii) the prevalence of FH in the population; iii) the clinical criteria adopted for the diagnosis of FH; iv) the screening for ASCVD and the role of cardiovascular imaging techniques; v) the role of molecular diagnosis in establishing the genetic bases of the disorder; vi) the current therapeutic options in both heterozygous and homozygous FH. Treatment strategies and targets are currently based on low-density lipoprotein cholesterol (LDL-C) levels, as the prognosis of FH largely depends on the magnitude of LDL-C reduction achieved by lipid-lowering therapies. Statins with or without ezetimibe are the mainstay of treatment. Addition of novel medications like PCSK9 inhibitors, ANGPTL3 inhibitors or lomitapide in homozygous FH results in a further reduction of LDL-C levels. LDL apheresis is indicated in FH patients with inadequate response to cholesterol-lowering therapies.

CONCLUSION

FH is a common, treatable genetic disorder and, although our understanding of this disease has improved, many challenges still remain with regard to its identification and management.

Causal relationship between depression and hypercholesterolemia: A bidirectional 2-sample Mendelian randomization study

Although observational studies have found both a positive and negative association between depression and hypercholesterolemia, the findings are mixed and contradictory. To our knowledge, this is the first study that employs the bidirectional Mendelian randomization (MR) and multivariable MR analysis with extensive genome-wide association studies (GWAS) data to examine the causal effect between depression and hypercholesterolemia. Using summary statistics obtained from GWAS of individuals with European ancestry, we utilize a bidirectional 2-sample MR approach to explore the potential causal association between hypercholesterolemia and depressive symptoms. Multivariable Mendelian randomization analysis was used to examine whether the direct causal effect of depression on the risk of hypercholesterolemia can be affected by traits associated with the increased risk of hypercholesterolemia. This MR analysis utilized inverse variance weighted (IVW), MR-Egger regression, weighted mode, and weighted median methods. Data on the summary level of depression were acquired from a GWAS that involved 500,199 participants. We used summary GWAS datasets for hypercholesterolemia including 206,067 participants. We also used another GWAS databases of hypercholesterolemiat (n = 463,010) to validate our results. By utilizing IVW, it was discovered that there is a possibility of a 31% rise in the risk of hypercholesterolemia due to depression (OR = 1.31, 95% CI = 1.10-1.57, P = .002). We found a consistent causal effect of depression on hypercholesterolemia from the IVW analyses using different hypercholesterolemia datasets. After adjustment of smoking, physical activity, and obesity, there remains significant causal relationship between depression and hypercholesterolemia (OR = 1.25, 95% CI = 1.01-1.54, P = .040). However, we did not find any evidence indicating that hypercholesterolemia leads to depression in the opposite direction. Directional pleiotropy was not observed in the MR-Egger regression analysis. Additionally, the MR-PRESSO analysis validated these discoveries. Neither the leave-one-out sensitivity test nor the funnel plots revealed any outliers. In both the unadjusted and adjusted estimates, depression has a consistent direct causal effect on hypercholesterolemia. Our study has led to an improved comprehension of the causal connections between hypercholesterolemia and depression, which could aid in the prevention and treatment of hypercholesterolemia.

Advances in familial hypercholesterolemia

Familial hypercholesterolemia (FH), a semi-dominant genetic disease affecting more than 25 million people worldwide, is associated with severe hypercholesterolemia and premature atherosclerotic cardiovascular disease. Over the last decade, advances in data analysis, screening, diagnosis and cardiovascular risk stratification has significantly improved our ability to deliver precision medicine for these patients. Furthermore, recent updates on guideline recommendations and new therapeutic approaches have also proven to be highly beneficial. It is anticipated that both ongoing and upcoming clinical trials will offer further insights for the care and treatment of FH patients.

Lipoprotein(a) in Familial Hypercholesterolemia

Background

Low density lipoprotein (LDL) and Lipoprotein (Lp)(a) are proatherogenic apolipoprotein (apo) B-containing members of the non-high-density lipoprotein (non-HDL) family of particles. Elevated plasma levels of LDL cholesterol (C), non-HDL-C, and apo B are defining features of heterozygous familial hypercholesterolemia (HeFH), but reports of elevated plasma Lp(a) concentration are inconsistent.

Methods

We performed retrospective chart reviews of 256 genetically characterized patients with hypercholesterolemia and 272 control subjects from the Lipid Genetics Clinic at University Hospital in London, Ontario. We evaluated pairwise correlations between plasma levels of Lp(a) and those of LDL-C, non-HDL-C and apo B.

Results

Mean Lp(a) levels were not different between individuals with hypercholesterolemia and control subjects. No correlations were found between Lp(a) and LDL-C or non-HDL-C levels in controls or patients with hypercholesterolemia; all r values < 0.079 and all P values > 0.193. Borderline weak correlations between Lp(a) and apo B were identified in patients r = 0.103; P = 0.112) and controls (r = 0.175; P = 0.005). Results were similar across genotypic subgroups.

Conclusions

Lp(a) levels are independent of LDL-C and non-HDL-C; in particular Lp(a) levels are not increased in patients with hypercholesterolemia and molecularly proven HeFH. Apo B was only weakly associated with Lp(a). Elevated Lp(a) does not cause FH in our clinic patients. Genetic variants causing HeFH that raise LDL-C do not affect Lp(a), confirming that these lipoproteins are metabolically distinct. Lp(a) cannot be predicted from LDL-C and must be determined separately to evaluate its amplifying effect on atherosclerotic risk in patients with 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.

The effect of adjusting LDL-cholesterol for Lp(a)-cholesterol on the diagnosis of familial hypercholesterolaemia

BACKGROUND

Familial hypercholesterolaemia (FH) diagnostic tools help prioritise patients for genetic testing and include LDL-C estimates commonly calculated using the Friedewald equation. However, cholesterol contributions from lipoprotein(a) (Lp(a)) can overestimate 'true' LDL-C, leading to potentially inappropriate clinical FH diagnosis.

OBJECTIVE

To assess how adjusting LDL-C for Lp(a)-cholesterol affects FH diagnoses using Simon Broome (SB) and Dutch Lipid Clinic Network (DLCN) criteria.

METHODS

Adults referred to a tertiary lipid clinic in London, UK were included if they had undergone FH genetic testing based on SB or DLCN criteria. LDL-C was adjusted for Lp(a)-cholesterol using estimated cholesterol contents of 17.3%, 30% and 45%, and the effects of these adjustments on reclassification to 'unlikely' FH and diagnostic accuracy were determined.

RESULTS

Depending on the estimated cholesterol content applied, LDL-C adjustment reclassified 8-23% and 6-17% of patients to 'unlikely' FH using SB and DLCN criteria, respectively. The highest reclassification rates were observed following 45% adjustment in mutation-negative patients with higher Lp(a) levels. This led to an improvement in diagnostic accuracy (46% to 57% with SB, and 32% to 44% with DLCN following 45% adjustment) through increased specificity. However all adjustment factors led to erroneous reclassification of mutation-positive patients to 'unlikely' FH.

CONCLUSION

LDL-C adjustment for Lp(a)-cholesterol improves the accuracy of clinical FH diagnostic tools. Adopting this approach would reduce unnecessary genetic testing but also incorrectly reclassify mutation-positive patients. Health economic analysis is needed to balance the risks of over- and under-diagnosis before LDL-C adjustments for Lp(a) can be recommended.

The advantages and pitfalls of genetic analysis in the diagnosis and management of lipid disorders

The increasing affordability of and access to next-generation DNA sequencing has increased the feasibility of incorporating genetic analysis into the diagnostic pathway for dyslipidaemia. But should genetic diagnosis be used routinely? DNA testing for any medical condition has potential benefits and pitfalls. For dyslipidaemias, the overall balance of advantages versus drawbacks differs according to the main lipid disturbance. For instance, some patients with severely elevated low-density lipoprotein cholesterol levels have a monogenic disorder, namely heterozygous familial hypercholesterolaemia. In these patients, DNA diagnosis can be definitive, in turn yielding several benefits for patient care that tend to outweigh any potential disadvantages. In contrast, hypertriglyceridaemia is almost always a polygenic condition without a discrete monogenic basis, except for ultrarare monogenic familial chylomicronaemia syndrome. Genetic testing in patients with hypertriglyceridaemia is therefore predominantly non-definitive and evidence for benefit is presently lacking. Here we consider advantages and limitations of genetic testing in dyslipidaemias.

Polygenic Risk of Hypertriglyceridemia Is Modified by BMI

Background: Genetic risk scores (GRSs) have partially improved the understanding of the etiology of moderate hypertriglyceridemia (HTG), which until recently was mainly assessed by secondary predisposing causes. The main objective of this study was to assess whether this variability is due to the interaction between clinical variables and GRS. Methods: We analyzed 276 patients with suspected polygenic HTG. An unweighted GRS was developed with the following variants: c.724C > G (ZPR1 gene), c.56C > G (APOA5 gene), c.1337T > C (GCKR gene), g.19986711A > G (LPL gene), c.107 + 1647T > C (BAZ1B gene) and g.125478730A > T (TRIB gene). Interactions between the GRS and clinical variables (body mass index (BMI), diabetes mellitus, diet, physical activity, alcohol consumption, age and gender) were evaluated. Results: The GRS was associated with triglyceride (TG) concentrations. There was a significant interaction between BMI and GRS, with the intensity of the relationship between the number of alleles and the TG concentration being greater in individuals with a higher BMI. Conclusions: GRS is associated with plasma TG concentrations and is markedly influenced by BMI. This finding could improve the stratification of patients with a high genetic risk for HTG who could benefit from more intensive healthcare interventions.