Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia

International Atherosclerosis Society guidance for implementing best practice in the care of familial hypercholesterolaemia

This contemporary, international, evidence-informed guidance aims to achieve the greatest good for the greatest number of people with familial hypercholesterolaemia (FH) across different countries. FH, a family of monogenic defects in the hepatic LDL clearance pathway, is a preventable cause of premature coronary artery disease and death. Worldwide, 35 million people have FH, but most remain undiagnosed or undertreated. Current FH care is guided by a useful and diverse group of evidence-based guidelines, with some primarily directed at cholesterol management and some that are country-specific. However, none of these guidelines provides a comprehensive overview of FH care that includes both the lifelong components of clinical practice and strategies for implementation. Therefore, a group of international experts systematically developed this guidance to compile clinical strategies from existing evidence-based guidelines for the detection (screening, diagnosis, genetic testing and counselling) and management (risk stratification, treatment of adults or children with heterozygous or homozygous FH, therapy during pregnancy and use of apheresis) of patients with FH, update evidence-informed clinical recommendations, and develop and integrate consensus-based implementation strategies at the patient, provider and health-care system levels, with the aim of maximizing the potential benefit for at-risk patients and their families worldwide.

Artificial intelligence in cardiovascular prevention: new ways will open new doors

Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.

Genetic and molecular architecture of familial hypercholesterolemia

Atherosclerotic cardiovascular disease is the leading cause of death globally. Despite its important risk of premature atherosclerosis and cardiovascular disease, familial hypercholesterolemia (FH) is still largely underdiagnosed worldwide. It is one of the most frequently inherited diseases due to mutations, for autosomal dominant forms, in either of the LDLR, APOB, and PCSK9 genes or possibly a few mutations in the APOE gene and, for the rare autosomal forms, in the LDLRAP1 gene. The discovery of the genes implicated in the disease has largely helped to improve the diagnosis and treatment of FH from the LDLR by Brown and Goldstein, as well as the introduction of statins, to PCSK9 discovery in FH by Abifadel et al., and the very rapid availability of PCSK9 inhibitors. In the last two decades, major progress has been made in clinical and genetic diagnostic tools and the therapeutic arsenal against FH. Improving prevention, diagnosis, and treatment and making them more accessible to all patients will help reduce the lifelong burden of the disease.

Clinical decision support for familial hypercholesterolemia (CDS-FH): Rationale and design of a cluster randomized trial in primary care

BACKGROUND

Familial hypercholesterolemia (FH) is an underdiagnosed and undertreated genetic disorder with high risk of premature atherosclerotic cardiovascular disease and death. Clinical decision support (CDS) systems have the potential to aid in the identification and management of patients with FH. Prior studies using computer-based systems to screen patients for FH have shown promising results, but there has been no randomized controlled trial conducted. The aim of the current cluster randomized study is to evaluate if a CDS can increase the identification of FH.

METHODS

We have developed a CDS integrated in the electronic health records that will be activated in patients with elevated cholesterol levels (total cholesterol >8 mmol/L or low-density lipoprotein-cholesterol >5.5 mmol/L, adjusted for age, ongoing lipid lowering therapy and presence of premature coronary artery disease) at increased risk for FH. When activated, the CDS will urge the physician to send an automatically generated referral to the local lipid clinic for further evaluation. To evaluate the effects of the CDS, all primary care clinics will be cluster randomized 1:1 to either CDS intervention or standard care in a Swedish region with almost 500,000 inhabitants. The primary endpoint will be the number of patients diagnosed with FH at 30 months. Resource use and long-term health consequences will be estimated to assess the cost-effectiveness of the intervention.

CONCLUSION

Despite increasing awareness of FH, the condition remains underdiagnosed and undertreated. The present study will investigate whether a CDS can increase the number of patients being diagnosed with FH.

Novel Biomarkers of Atherosclerotic Vascular Disease-Latest Insights in the Research Field

The atherosclerotic vascular disease is a cardiovascular continuum in which the main role is attributed to atherosclerosis, from its appearance to its associated complications. The increasing prevalence of cardiovascular risk factors, population ageing, and burden on both the economy and the healthcare system have led to the development of new diagnostic and therapeutic strategies in the field. The better understanding or discovery of new pathophysiological mechanisms and molecules modulating various signaling pathways involved in atherosclerosis have led to the development of potential new biomarkers, with key role in early, subclinical diagnosis. The evolution of technological processes in medicine has shifted the attention of researchers from the profiling of classical risk factors to the identification of new biomarkers such as midregional pro-adrenomedullin, midkine, stromelysin-2, pentraxin 3, inflammasomes, or endothelial cell-derived extracellular vesicles. These molecules are seen as future therapeutic targets associated with decreased morbidity and mortality through early diagnosis of atherosclerotic lesions and future research directions.