Algorithm for Detection and Screening of Familial Hypercholesterolemia in Lithuanian Population

Abstract

Abstract BACKGROUND Familial hypercholesterolemia (FH) is one of the most common autosomal dominant diseases. FH causes a lifelong increase in low-density lipoprotein cholesterol (LDL-C) levels, which in turn leads to atherosclerotic cardiovascular disease. FH incidence is widely underestimated and undertreated, despite the availability and effectiveness of lipid-lowering therapy. Patients with FH have an increased cardiovascular risk; therefore, early diagnosis and treatment are vital. To address the burden of FH, several countries have implemented national FH screening programmes. The currently used method for FH detection in Lithuania is mainly opportunistic screening with subsequent cascade screening of index cases’ first-degree relatives.METHODS A total of 428 patients were included in this study. Patients with suspected FH are referred to a lipidology center for thorough evaluation. Patients who met the criteria for probable or definite FH according to Dutch Lipid Clinic Network (DLCN) score system and/or had LDL-C > = 6.5 mmol/l were subjected to genetic testing. Laboratory and instrumental tests, vascular marker data of early atherosclerosis, and consultations by other specialists, such as radiologists and ophthalmologists, were also recorded.RESULTS 127 (30%) patients were genetically tested. FH-related mutations were found in 38.6% (n = 49) of the patients. Coronary artery disease (CAD) was diagnosed in 13% (n = 57) of the included patients, whereas premature CAD was found in 47 (11%) patients. CAD was diagnosed in 19% (n = 9) of patients with FH-related mutations, and this diagnosis was premature for all of them.Conclusions Despite the well-known socioeconomic burden of FH worldwide, it is underdiagnosed and undertreated. Accurate diagnosis of FH, as well as detailed examination and evaluation of the FH patient, are important for initiating cascade screening of first-degree relatives. Furthermore, the implementation of such an algorithm is likely to be a cost-effective method for detecting and screening FH cases.