Relations of physical signs to genotype, lipid and inflammatory markers, coronary stenosis or calcification, and outcomes in patients with heterozygous familial hypercholesterolemia

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.

Physical signs and atherosclerotic cardiovascular disease in familial hypercholesterolemia: the HELLAS-FH Registry

AIMS

Three physical signs, namely tendon xanthomas, corneal arcus and xanthelasma, have been associated with heterozygous familial hypercholesterolemia (heFH). The prevalence and clinical significance of these signs are not well established among contemporary heFH individuals. This study explored the frequency as well as the association of these physical signs with prevalent atherosclerotic cardiovascular disease (ASCVD) in heFH individuals.

METHODS

Data from the Hellenic Familial Hypercholesterolemia Registry were applied for this analysis. The diagnosis of heFH was based on the Dutch Lipid Clinic Network Score. Multivariate logistic regression analysis was conducted to examine the association of heFH-related physical signs with prevalent ASCVD.

RESULTS

Adult patients ( n  = 2156, mean age 50 ± 15 years, 47.7% women) were included in this analysis. Among them, 14.5% had at least one heFH-related physical sign present. The prevalence of corneal arcus before the age of 45 years was 6.6%, tendon xanthomas 5.3%, and xanthelasmas 5.8%. Among physical signs, only the presence of corneal arcus before the age of 45 years was independently associated with the presence of premature coronary artery disease (CAD). No association of any physical sign with total CAD, stroke or peripheral artery disease was found. Patients with physical signs were more likely to receive higher intensity statin therapy and dual lipid-lowering therapy, but only a minority reached optimal lipid targets.

CONCLUSION

The prevalence of physical signs is relatively low in contemporary heFH patients. The presence of corneal arcus before the age of 45 years is independently associated with premature CAD.

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.

Regulation of cardiovascular calcification by lipids and lipoproteins

PURPOSE OF REVIEW

Lipids and lipoproteins have long been known to contribute to atherosclerosis and cardiovascular calcification. One theme of recent work is the study of lipoprotein (a) [Lp(a)], a lipoprotein particle similar to LDL-cholesterol that carries a long apoprotein tail and most of the circulating oxidized phospholipids.

RECENT FINDINGS

In-vitro studies show that Lp(a) stimulates osteoblastic differentiation and mineralization of vascular smooth muscle cells, while the association of Lp(a) with coronary artery calcification continues to have varying results, possibly because of the widely varying threshold levels of Lp(a) chosen for association analyses. Another emerging area in the field of cardiovascular calcification is pathological endothelial-to-mesenchymal transition (EndMT), the process whereby endothelial cell transition into multipotent mesenchymal cells, some of which differentiate into osteochondrogenic cells and mineralize. The effects of lipids and lipoproteins on EndMT suggest that they modulate cardiovascular calcification through multiple mechanisms. There are also emerging trends in imaging of calcific vasculopathy, including: intravascular optical coherence tomography for quantifying plaque characteristics, PET with a radiolabeled NaF tracer, with either CT or MRI to detect coronary plaque vulnerability.

SUMMARY

Recent work in this field includes studies of Lp(a), EndMT, and new imaging techniques.