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Athar M. Potentials of artificial intelligence in familial hypercholesterolemia: Advances in screening, diagnosis, and risk stratification for early intervention and treatment. Int J Cardiol 2024; 412:132315. [PMID: 38972488 DOI: 10.1016/j.ijcard.2024.132315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 05/21/2024] [Accepted: 07/01/2024] [Indexed: 07/09/2024]
Abstract
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.
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Affiliation(s)
- Mohammad Athar
- Science and Technology Unit, Umm Al-Qura University, Makkah, Saudi Arabia; Department of Medical Genetics, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia.
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Rallidis LS, Rizos CV, Papathanasiou KA, Liamis G, Skoumas I, Garoufi A, Kolovou G, Tziomalos K, Skalidis E, Kotsis V, Sfikas G, Doumas M, Anagnostis P, Lambadiari V, Giannakopoulou V, Kiouri E, Anastasiou G, Petkou E, Koutagiar I, Attilakos A, Kolovou V, Zacharis E, Antza C, Koumaras C, Boutari C, Liberopoulos E. Physical signs and atherosclerotic cardiovascular disease in familial hypercholesterolemia: the HELLAS-FH Registry. J Cardiovasc Med (Hagerstown) 2024; 25:370-378. [PMID: 38526957 DOI: 10.2459/jcm.0000000000001612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
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.
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Affiliation(s)
- Loukianos S Rallidis
- Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens
| | - Christos V Rizos
- Department of Internal Medicine, Medical School, University of Ioannina, Ioannina
| | - Konstantinos A Papathanasiou
- Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens
| | - George Liamis
- Department of Internal Medicine, Medical School, University of Ioannina, Ioannina
| | - Ioannis Skoumas
- 1 Cardiology Department of Athens Medical School, University of Athens, Hippokration Hospital, Athens
| | - Anastasia Garoufi
- Second Department of Pediatrics, Medical School, National and Kapodistrian University of Athens, General Children's Hospital 'P. & A. Kyriakou', Athens
| | - Genovefa Kolovou
- Cardiometabolic Center, Lipid Clinic, LA apheresis Unit, Metropolitan Hospital, Athens
| | - Konstantinos Tziomalos
- 1 Propaedeutic Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, AHEPA Hospital, Thessaloniki
| | | | - Vasileios Kotsis
- 3 Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Papageorgiou General Hospital, Thessaloniki
| | - George Sfikas
- Department of Internal Medicine, 424 General Military Training Hospital, Thessaloniki
| | - Michalis Doumas
- Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Hippokration General Hospital, Thessaloniki
| | | | - Vaia Lambadiari
- 2 Propaedeutic Internal Medicine Department and Diabetes Research Unit, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens
| | | | - Estela Kiouri
- Department of Cardiology, Medical School, National and Kapodistrian University of Athens, Attikon University General Hospital, Athens
| | - Georgia Anastasiou
- Department of Internal Medicine, Medical School, University of Ioannina, Ioannina
| | - Ermioni Petkou
- Department of Internal Medicine, Medical School, University of Ioannina, Ioannina
| | - Iosif Koutagiar
- 1 Cardiology Department of Athens Medical School, University of Athens, Hippokration Hospital, Athens
| | - Achilleas Attilakos
- Department of Pediatrics, Medical School, National and Kapodistrian University of Athens, C' Pediatrics Clinic, Attikon University General Hospital, Athens
| | - Vana Kolovou
- Cardiometabolic Center, Lipid Clinic, LA apheresis Unit, Metropolitan Hospital, Athens
| | | | - Christina Antza
- 3 Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Papageorgiou General Hospital, Thessaloniki
| | - Charalambos Koumaras
- Department of Internal Medicine, 424 General Military Training Hospital, Thessaloniki
| | - Chrysoula Boutari
- Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Hippokration General Hospital, Thessaloniki
| | - Evangelos Liberopoulos
- 1 Propaedeutic Department of Medicine, School of Medicine, National and Kapodistrian University of Athens, Laiko Hospital, Athens, Greece
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Luo RF, Wang JH, Hu LJ, Fu QA, Zhang SY, Jiang L. Applications of machine learning in familial hypercholesterolemia. Front Cardiovasc Med 2023; 10:1237258. [PMID: 37823179 PMCID: PMC10562581 DOI: 10.3389/fcvm.2023.1237258] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023] Open
Abstract
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.
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Affiliation(s)
- Ren-Fei Luo
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jing-Hui Wang
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, China
- Department of Clinical Medicine, Nanchang University Queen Mary School, Nanchang, China
| | - Li-Juan Hu
- Department of Nursing, Nanchang Medical College, Nanchang, China
| | - Qing-An Fu
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Si-Yi Zhang
- Department of Clinical Medicine, Nanchang University Queen Mary School, Nanchang, China
| | - Long Jiang
- Department of Cardiovascular Medicine, the Second Affiliated Hospital of Nanchang University, Nanchang, China
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Abstract
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.
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Affiliation(s)
- Jeffrey J Hsu
- Department of Medicine
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Yin Tintut
- Department of Medicine
- Department of Physiology
- Department of Orthopaedic Surgery
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | - Linda L Demer
- Department of Medicine
- Department of Physiology
- Department of Bioengineering, University of California
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
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