Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia

Polygenic risk of high LDL cholesterol and ischemic heart disease in the general population

BACKGROUND AND AIMS

We tested the association of polygenic risk scores (PRS) for low-density lipoprotein cholesterol (LDL-C) and coronary artery disease (CAD) with LDL-C and risk of ischemic heart disease (IHD) in the Danish general population.

METHODS

We included a total of 21,485 individuals from the Copenhagen General Population Study and Copenhagen City Heart Study. For everyone, LDL-PRS and CAD-PRS were calculated, each based on >400,000 variants. We also genotyped four rare variants in LDLR or APOB known to cause familial hypercholesterolemia (FH).

RESULTS

Heterozygous carriers of FH-causing variants in APOB or LDLR had a mean LDL-C of 5.40 and 6.09 mmol/L, respectively, and an odds ratio for IHD of 2.27 (95 % CI 1.43-3.51) when compared to non-carriers. The LDL-PRS explained 13.8 % of the total variation in LDL-C in the cohort. Individuals in the lowest and highest 1 % of LDL-PRS had a mean LDL-C of 2.49 and 4.75 mmol/L, respectively. Compared to those in the middle 20-80 %, those in the lowest and highest 1 % of LDL-PRS had odds ratios for IHD of 0.58 (95 % CI, 0.38-0.88) and 1.83 (95 % CI, 1.33-2.53). The corresponding odds ratios for CAD-PRS were 0.61 (95 % CI, 0.41-0.92) and 2.06 (95 % CI, 1.49-2.85).

CONCLUSIONS

The top 1 % of LDL-PRS and CAD-PRS conferred effects on LDL-C and risk of IHD comparable to those seen for carriers of rare FH-causing variants in APOB or LDLR. These results highlight the potential value of implementing such PRS clinically.

Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment

Familial hypercholesterolemia (FH) poses a global health challenge due to high incidence rates and underdiagnosis, leading to increased risks of early-onset atherosclerosis and cardiovascular diseases. Early detection and treatment of FH is critical in reducing the risk of cardiovascular events and improving the long-term outcomes and quality of life for affected individuals and their families. Traditional therapeutic approaches revolve around lipid-lowering interventions, yet challenges persist, particularly in accurate and timely diagnosis. The current diagnostic landscape heavily relies on genetic testing of specific LDL-C metabolism genes, often limited to specialized centers. This constraint has led to the adoption of alternative clinical scores for FH diagnosis. However, the rapid advancements in artificial intelligence (AI) and machine learning (ML) present promising solutions to these diagnostic challenges. This review explores the intricacies of FH, highlighting the challenges that are encountered in the diagnosis and management of the disorder. The revolutionary potential of ML, particularly in large-scale population screening, is highlighted. Applications of ML in FH screening, diagnosis, and risk stratification are discussed, showcasing its ability to outperform traditional criteria. However, challenges and ethical considerations, including algorithmic stability, data quality, privacy, and consent issues, are crucial areas that require attention. The review concludes by emphasizing the significant promise of AI and ML in FH management while underscoring the need for ethical and practical vigilance to ensure responsible and effective integration into healthcare practices.

An 8-SNP LDL Cholesterol Polygenic Score: Associations with Cardiovascular Risk Traits, Familial Hypercholesterolemia Phenotype, and Premature Coronary Heart Disease in Central Romania

Familial hypercholesterolemia (FH) is the most significant inherited risk factor for coronary heart disease (CHD). Current guidelines focus on monogenic FH, but the polygenic form is more common and less understood. This study aimed to assess the clinical utility of an 8-SNP LDLC polygenic score in a central Romanian cohort. The cohort included 97 healthy controls and 125 patients with premature (P)CHD. The weighted LDLC polygenic risk score (wPRS) was analyzed for associations with relevant phenotypic traits, PCHD risk, and clinical FH diagnosis. The wPRS positively correlated with LDLC and DLCN scores, and LDLC concentrations could be predicted by wPRS. A trend of increasing LDLC and DLCN scores with wPRS deciles was observed. A +1 SD increase in wPRS was associated with a 36% higher likelihood of having LDLC > 190 mg/dL and increases in LDLC (+0.20 SD), DLCN score (+0.16 SD), and BMI (+0.15 SD), as well as a decrease in HDLC (-0.14 SD). Although wPRS did not predict PCHD across the entire spectrum of values, individuals above the 90th percentile were three times more likely to have PCHD compared to those within the 10th or 20th percentiles. Additionally, wPRS > 45th percentile identified "definite" clinical FH (DLCN score > 8) with 100% sensitivity and 45% specificity. The LDLC polygenic score correlates with key phenotypic traits, and individuals with high scores are more likely to have PCHD. Implementing this genetic tool may enhance risk prediction and patient stratification. These findings, the first of their kind in Romania, are consistent with the existing literature.

Cardiovascular Risk Estimation and Stratification Among Individuals with Hypercholesterolemia

PURPOSE OF THE REVIEW

This review aims to assess the variability in considering hypercholesterolemia for cardiovascular risk stratification in the general population. Recent literature on the integration of hypercholesterolemia into clinical risk scores and its interaction with other risk factors will be explored.

RECENT FINDINGS

The impact of hypercholesterolemia on risk estimation varies among different cardiovascular risk calculators. Elevated lipid levels during early life stages contribute to atherosclerotic plaque development, influencing disease severity despite later treatment initiation. The interplay between low-density lipoprotein cholesterol (LDLc), inflammatory markers and non-LDL lipid parameters enhances cardiovascular risk stratification. Studies have also examined the role of coronary artery calcium (CAC) score as a negative risk marker in populations with severe hypercholesterolemia. Furthermore, polygenic risk scores (PRS) may aid in diagnosing non-monogenic hypercholesterolemia, refining cardiovascular risk stratification and guiding lipid-lowering therapy strategies. Understanding the heterogeneity in risk estimation and the role of emerging biomarkers and imaging techniques is crucial for optimizing cardiovascular risk prediction and guiding personalized treatment strategies in individuals with hypercholesterolemia.

SEA 2024 Standards for Global Control of Vascular Risk

One of the objectives of the Spanish Society of Arteriosclerosis is to contribute to the knowledge, prevention and treatment of vascular diseases, which are the leading cause of death in Spain and entail a high degree of disability and health expenditure. Atherosclerosis is a multifactorial disease and its prevention requires a global approach that takes into account the associated risk factors. This document summarises the current evidence and includes recommendations for patients with established vascular disease or at high vascular risk: it reviews the symptoms and signs to evaluate, the laboratory and imaging procedures to request routinely or in special situations, and includes the estimation of vascular risk, diagnostic criteria for entities that are vascular risk factors, and general and specific recommendations for their treatment. Finally, it presents aspects that are not usually referenced in the literature, such as the organisation of a vascular risk consultation.

Applications of machine learning in familial hypercholesterolemia

Familial hypercholesterolemia (FH) is a common hereditary cholesterol metabolic disease that usually leads to an increase in the level of low-density lipoprotein cholesterol in plasma and an increase in the risk of cardiovascular disease. The lack of disease screening and diagnosis often results in FH patients being unable to receive early intervention and treatment, which may mean early occurrence of cardiovascular disease. Thus, more requirements for FH identification and management have been proposed. Recently, machine learning (ML) has made great progress in the field of medicine, including many innovative applications in cardiovascular medicine. In this review, we discussed how ML can be used for FH screening, diagnosis and risk assessment based on different data sources, such as electronic health records, plasma lipid profiles and corneal radian images. In the future, research aimed at developing ML models with better performance and accuracy will continue to overcome the limitations of ML, provide better prediction, diagnosis and management tools for FH, and ultimately achieve the goal of early diagnosis and treatment of FH.

Assessment of multi-population polygenic risk scores for lipid traits in African Americans

Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.

Subclinical atherosclerosis burden in non-diabetic hypertensives treated in primary care center: the IMTABI study

BACKGROUND

Identifying patients at high risk of cardiovascular disease in primary prevention is a challenging task. This study aimed at detecting subclinical atherosclerosis burden in non-diabetic hypertensive patients in a primary care centre.

METHODS

Clinical, anthropometric and analytical data were collected from patients with hypertension who were free from clinical vascular disease and diabetes. The cardiovascular risk was assessed using the SCORE system. Subclinical atherosclerosis burden was assessed by carotid ultrasonography (intima-medial thickness [IMT] and plaque) and measurement of the ankle-brachial index (ABI).

RESULTS

Out of 140 patients, 59 (42%) have carotid plaque, 32 (23%) have IMT higher than 75% and 12 (9%) have an ABI < 0.9. Total atherosclerosis burden was present in 91 (65%) of the subjects. Consequently, 59 (42%) patients were re-classified into the very high-risk category. In multivariate analyses, smoking, creatinine levels and duration of hypertension were associated with atherosclerosis burden. In contrast, only smoking and age were associated with the presence of carotid plaque. Almost 90% of patients were treated with hypotensive drugs, half of them combined several drugs and 60% were well-controlled. Only 30% received statins in monotherapy and only less than 20% had an LDL cholesterol < 100 mg/dL.

CONCLUSIONS

In non-diabetic hypertensive patients managed at a primary care centre, 4 out of 10 had subclinical atherosclerosis burden and were re-classified into the very high- risk category. There was clear undertreatment with lipid-lowering drugs of most LDL cholesterol inappropriate levels, according to current clinical guidelines.

Implementation of a biochemical, clinical, and genetic screening programme for familial hypercholesterolemia in 26 centres in Spain: The ARIAN study

Background: Familial hypercholesterolemia (FH) is clearly underdiagnosed and undertreated. The aim of this present study is to assess the benefits of FH screening through a joint national program implemented between clinical laboratories and lipid units.

Methods: All clinical laboratory tests from 1 January 2017 to 31 December 2018 were reviewed, and those with LDL cholesterol (LDL-C) levels &gt;250 mg/dl were identified in subjects &gt;18 years of age of both sexes. Once secondary causes had been ruled out, the treating physician was contacted and advised to refer the patient to an LU to perform the Dutch Lipid Clinic Network score and to request genetic testing if the score was ≥6 points. Next Generation Sequencing was used to analyse the promoter and coding DNA sequences of four genes associated with FH (LDLR, APOB, PCSK9, APOE) and two genes that have a clinical overlap with FH characteristics (LDLRAP1 and LIPA). A polygenic risk score based on 12 variants was also obtained.

Results: Of the 3,827,513 patients analyzed in 26 centers, 6,765 had LDL-C levels &gt;250 mg/dl. Having ruled out secondary causes and known cases of FH, 3,015 subjects were included, although only 1,205 treating physicians could be contacted. 635 patients were referred to an LU and genetic testing was requested for 153 of them. This resulted in a finding of sixty-seven pathogenic variants for FH, 66 in the LDLR gene and one in APOB. The polygenic risk score was found higher in those who had no pathogenic variant compared to those with a pathogenic variant.

Conclusion: Despite its limitations, systematic collaboration between clinical laboratories and lipid units allows for the identification of large numbers of patients with a phenotypic or genetic diagnosis of FH, which will reduce their vascular risk. This activity should be part of the clinical routine.