Referral rate, profile and degree of control of patients with familial hypercholesterolemia: data from a single lipid unit from a Mediterranean area.

BACKGROUND: The challenging rigorous management of hypercholesterolemia promotes referral to specialized units. This study explored the need, based on referral rate and cardiovascular (CV) risk factor control in patients evaluated for familial hypercholesterolemia (FH), for a lipid unit (LU). METHODS: Over a four-year period, 340 referrals to our unit were analyzed to establish the lipid disorder referral rate. Moreover, 118 patients referred for potential FH during the period 2010-2018 (52.4 ± 13.9 years, 47.5% male, Caucasian, 26.3% obese, 33.1% smokers and 51.7% with some glycaemic alteration) were investigated. The Dutch Lipid Clinic Network (DLCN) score, type and dose of lipid-lowering drugs, lipid profile including lipoprotein (a) (Lp(a)) and the presence of plaques with carotid ultrasound (CU) were recorded. RESULTS: Lipids represented 6.2% of referrals (38 patient-years) requiring a 2-3 h weekly monographic outpatient consultation. The potential FH sample displayed a DLCN score ≥ 6 in 78% and modifiable CV risk factors in 51%. Only 22% achieved tight disease control despite intensive treatment. The statin-ezetimibe combination treatment group achieved better goals (73.0% vs. 45.5%, P = 0.003), and the rosuvastatin group had a higher proportion of prediabetes (60.9% vs. 39.1%, P = 0.037). Neither CU plaque presence nor Lp(a) > 50 mg/dL was linked with established CV disease patients, but higher Lp(a) concentrations were detected between them (102.5 (26.3-145.8) vs. 25.0 (13.0-52.0) mg/dL, P = 0.012). CONCLUSIONS: The referral rate, degree of control, and proportion of modifiable CV risk factors in FH patients demonstrate the need for LU in our area as well as optimize control and treatment.

Effectiveness of clinical scores in predicting coronary artery disease in familial hypercholesterolemia: a coronary computed tomography angiography study.

PURPOSE: One of the major challenges in the management of familial hypercholesterolemia (FH) is the stratification of cardiovascular risk in asymptomatic subjects. Our purpose is to investigate the performance of clinical scoring systems, Montreal-FH-score (MFHS), SAFEHEART risk (SAFEHEART-RE) and FH risk score (FHRS) equations and Dutch Lipid Clinic Network (DLCN) diagnostic score, in predicting extent and severity of CAD at coronary computed tomography angiography (CCTA) in asymptomatic FH. MATERIAL AND METHODS: One-hundred and thirty-nine asymptomatic FH subjects were prospectively enrolled to perform CCTA. MFHS, FHRS, SAFEHEART-RE and DLCN were assessed for each patient. Atherosclerotic burden scores at CCTA (Agatston score [AS], segment stenosis score [SSS]) and CAD-RADS score were calculated and compared to clinical indices. RESULTS: Non-obstructive CAD was found in 109 patients, while 30 patients had a CAD-RADS ≥ 3. Classifying the two groups according to AS, values varied significantly for MFHS (p < 0.001), FHRS (p < 0.001) and SAFEHEART-RE (p = 0.047), while according to SSS only MFHS and FHRS showed significant differences (p < 0.001). MFHS, FHRS and SAFEHEART-RE, but not DLCN, showed significant differences between the two CAD-RADS groups (p < .001). MFHS proved to have the best discriminatory power (AUC = 0.819; 0.703-0.937, p < 0.001) at ROC analysis, followed by FHRS (AUC = 0.795; 0.715-0.875, p < .0001) and SAFEHEART-RE (AUC = .725; .61-.843, p < .001). CONCLUSIONS: Greater values of MFHS, FHRS and SAFEHEART-RE are associated to higher risk of obstructive CAD and might help to select asymptomatic patients that should be referred to CCTA for secondary prevention.

Assessing the genetic burden of familial hypercholesterolemia in a large middle eastern biobank.

BACKGROUND: The genetic architecture underlying Familial Hypercholesterolemia (FH) in Middle Eastern Arabs is yet to be fully described, and approaches to assess this from population-wide biobanks are important for public health planning and personalized medicine. METHODS: We evaluate the pilot phase cohort (n = 6,140 adults) of the Qatar Biobank (QBB) for FH using the Dutch Lipid Clinic Network (DLCN) criteria, followed by an in-depth characterization of all genetic alleles in known dominant (LDLR, APOB, and PCSK9) and recessive (LDLRAP1, ABCG5, ABCG8, and LIPA) FH-causing genes derived from whole-genome sequencing (WGS). We also investigate the utility of a globally established 12-SNP polygenic risk score to predict FH individuals in this cohort with Arab ancestry. RESULTS: Using DLCN criteria, we identify eight (0.1%) 'definite', 41 (0.7%) 'probable' and 334 (5.4%) 'possible' FH individuals, estimating a prevalence of 'definite or probable' FH in the Qatari cohort of ~ 1:125. We identify ten previously known pathogenic single-nucleotide variants (SNVs) and 14 putatively novel SNVs, as well as one novel copy number variant in PCSK9. Further, despite the modest sample size, we identify one homozygote for a known pathogenic variant (ABCG8, p. Gly574Arg, global MAF = 4.49E-05) associated with Sitosterolemia 2. Finally, calculation of polygenic risk scores found that individuals with 'definite or probable' FH have a significantly higher LDL-C SNP score than 'unlikely' individuals (p = 0.0003), demonstrating its utility in Arab populations. CONCLUSION: We design and implement a standardized approach to phenotyping a population biobank for FH risk followed by systematically identifying known variants and assessing putative novel variants contributing to FH burden in Qatar. Our results motivate similar studies in population-level biobanks - especially those with globally under-represented ancestries - and highlight the importance of genetic screening programs for early detection and management of individuals with high FH risk in health systems.

Search for familial hypercholesterolemia patients in an Italian community: A real-life retrospective study.

BACKGROUND AND AIMS: Familial hypercholesterolemia (FH) is a common inherited disorder of low-density lipoprotein (LDL) catabolism that causes elevated LDL-cholesterol (LDL-C) and premature atherosclerotic cardiovascular disease (ASCVD). Despite the availability of effective treatments, FH remains underdiagnosed and undertreated. The aims of the study were to identify putative FH subjects using data from laboratory and cardiology databases, genetically characterize suspected FH patients referred to the Lipid Clinic and monitor attainment of treatment goals in identified patients. METHODS AND RESULTS: We retrieved the electronic health records of 221,644 individuals referred to laboratory for routine assessment and of 583 ASCVD patients (age ≤65) who underwent percutaneous transluminal coronary angioplasty (PTCA). We monitored the lipid profiles of subjects with LDL-C ≥ 250 mg/dl identified by laboratory survey (LS-P), PTCA patients and patients from the Lipid Clinic (LC-P). The laboratory survey identified 1.46% of subjects with LDL-C ≥ 190 mg/dl and 0.08% with LDL-C ≥ 250 mg/dl. Probable/definite FH was suspected in 3% of PTCA patients. Molecularly-confirmed FH was found in 44% of LC-P subjects. Five new LDLR mutations were identified. The 50% LDL-C reduction target was achieved by 70.6% of LC-P patients. Only 18.5% of PTCA patients reached the LDL-C < 55 mg/dl target. CONCLUSION: By using a combined approach based on laboratory lipid profiles, documented ASCVD and Lipid Clinic data, we were able to identify subjects with a high probability of being FH. Attainment of LDL-C goals was largely suboptimal. Efforts are needed to improve FH detection and achievement of lipid targets.

Familial Hypercholesterolemia and Its Current Diagnostics and Treatment Possibilities: A Literature Analysis.

Familial hypercholesterolemia (FH) is a common, inherited disorder of cholesterol metabolism. This pathology is usually an autosomal dominant disorder and is caused by inherited mutations in the APOB, LDLR, and PCSK9 genes. Patients can have a homozygous or a heterozygous genotype, which determines the severity of the disease and the onset age of cardiovascular disease (CVD) manifestations. The incidence of heterozygous FH is 1: 200-250, whereas that of homozygous FH is 1: 100.000-160.000. Unfortunately, FH is often diagnosed too late and after the occurrence of a major coronary event. FH may be suspected in patients with elevated blood low-density lipoprotein cholesterol (LDL-C) levels. Moreover, there are other criteria that help to diagnose FH. For instance, the Dutch Lipid Clinical Criteria are a helpful diagnostic tool that is used to diagnose FH. FH often leads to the development of early cardiovascular disease and increases the risk of sudden cardiac death. Therefore, early diagnosis and treatment of this disease is very important. Statins, ezetimibe, bile acid sequestrants, niacin, PCSK9 inhibitors (evolocumab and alirocumab), small-interfering-RNA-based therapeutics (inclisiran), lomitapide, mipomersen, and LDL apheresis are several of the available treatment possibilities that lower LDL-C levels. It is important to say that the timeous lowering of LDL-C levels can reduce the risk of cardiovascular events and mortality in patients with FH. Therefore, it is essential to increase awareness of FH in order to reduce the burden of acute coronary syndrome (ACS).

Association of Clinical Characteristics With Familial Hypercholesterolaemia Variants in a Lipid Clinic Setting: A Case-Control Study.

Objective: Familial hypercholesterolaemia (FH) variant positive subjects have over double the cardiovascular risk of low-density-lipoprotein-cholesterol (LDL-C) matched controls. It is desirable to optimise FH variant detection. Methods: We identified 213 subjects with FH gene panel reports (LDLR, APOB, PCSK9, and APOE) based on total cholesterol >310 mg/dL; excluding triglycerides >400 mg/dL, cascade screening, and patients without pre-treatment LDL-C recorded. Demographic, clinical and lipid parameters were recorded. Results: A 31/213 (14.6%) patients had pathogenic or likely pathogenic FH variants. 10/213 (4.7%) had variants of uncertain significance. Compared with patients without FH variants, patients with FH variants were younger (median age, 39 years vs. 48 years), had more tendon xanthomata (25.0% vs. 11.4%), greater proportion of first degree relatives with total cholesterol >95th percentile (40.6% vs. 16.5%), higher LDL-C (median, 271 mg/dL vs. 236 mg/dL), and lower triglycerides (median, 115 mg/dL vs. 159 mg/dL). The Besseling et al. model (c-statistic 0.798) improved FH variant discrimination over Friedewald LDL-C (c-statistic 0.724), however, Dutch Lipid Clinic Network Score (DLCNS) did not (c-statistic 0.665). Sampson LDL-C (c-statistic 0.734) had similar discrimination to Friedewald. Conclusion: Although tendon xanthomata and first degree relatives with high total cholesterol >95th percentile were associated with FH variants, DLCNS or Simon Broome criteria did not improve FH detection over LDL-C. Sampson LDL-C did not significantly improve discrimination over Friedewald. Although lower triglycerides and younger age of presentation are positively associated with presence of FH variants, this information is not commonly used in FH detection algorithms apart from Besseling et al.

Generation and validation of a classification model to diagnose familial hypercholesterolaemia in adults.

BACKGROUND AND AIMS: The early diagnosis of familial hypercholesterolaemia is associated with a significant reduction in cardiovascular disease (CVD) risk. While the recent use of statistical and machine learning algorithms has shown promising results in comparison with traditional clinical criteria, when applied to screening of potential FH cases in large cohorts, most studies in this field are developed using a single cohort of patients, which may hamper the application of such algorithms to other populations. In the current study, a logistic regression (LR) based algorithm was developed combining observations from three different national FH cohorts, from Portugal, Brazil and Sweden. Independent samples from these cohorts were then used to test the model, as well as an external dataset from Italy. METHODS: The area under the receiver operating characteristics (AUROC) and precision-recall (AUPRC) curves was used to assess the discriminatory ability among the different samples. Comparisons between the LR model and Dutch Lipid Clinic Network (DLCN) clinical criteria were performed by means of McNemar tests, and by the calculation of several operating characteristics. RESULTS: AUROC and AUPRC values were generally higher for all testing sets when compared to the training set. Compared with DLCN criteria, a significantly higher number of correctly classified observations were identified for the Brazilian (p < 0.01), Swedish (p < 0.01), and Italian testing sets (p < 0.01). Higher accuracy (Acc), G mean and F1 score values were also observed for all testing sets. CONCLUSIONS: Compared to DLCN criteria, the LR model revealed improved ability to correctly classify observations, and was able to retain a similar number of FH cases, with less false positive retention. Generalization of the LR model was very good across all testing samples, suggesting it can be an effective screening tool if applied to different populations.

The effect of adjusting LDL-cholesterol for Lp(a)-cholesterol on the diagnosis of familial hypercholesterolaemia.

BACKGROUND: Familial hypercholesterolaemia (FH) diagnostic tools help prioritise patients for genetic testing and include LDL-C estimates commonly calculated using the Friedewald equation. However, cholesterol contributions from lipoprotein(a) (Lp(a)) can overestimate 'true' LDL-C, leading to potentially inappropriate clinical FH diagnosis. OBJECTIVE: To assess how adjusting LDL-C for Lp(a)-cholesterol affects FH diagnoses using Simon Broome (SB) and Dutch Lipid Clinic Network (DLCN) criteria. METHODS: Adults referred to a tertiary lipid clinic in London, UK were included if they had undergone FH genetic testing based on SB or DLCN criteria. LDL-C was adjusted for Lp(a)-cholesterol using estimated cholesterol contents of 17.3%, 30% and 45%, and the effects of these adjustments on reclassification to 'unlikely' FH and diagnostic accuracy were determined. RESULTS: Depending on the estimated cholesterol content applied, LDL-C adjustment reclassified 8-23% and 6-17% of patients to 'unlikely' FH using SB and DLCN criteria, respectively. The highest reclassification rates were observed following 45% adjustment in mutation-negative patients with higher Lp(a) levels. This led to an improvement in diagnostic accuracy (46% to 57% with SB, and 32% to 44% with DLCN following 45% adjustment) through increased specificity. However all adjustment factors led to erroneous reclassification of mutation-positive patients to 'unlikely' FH. CONCLUSION: LDL-C adjustment for Lp(a)-cholesterol improves the accuracy of clinical FH diagnostic tools. Adopting this approach would reduce unnecessary genetic testing but also incorrectly reclassify mutation-positive patients. Health economic analysis is needed to balance the risks of over- and under-diagnosis before LDL-C adjustments for Lp(a) can be recommended.

Elevated Lipoprotein(a) Level Influences Familial Hypercholesterolemia Diagnosis.

Familial hypercholesterolemia (FH) and elevated lipoprotein(a) [Lp(a)] level are the most common inherited disorders of lipid metabolism. This study evaluated the impact of high Lp(a) level on accuracy Dutch Lipid Clinic Network (DLCN) criteria of heterozygous FH diagnosis. A group of 206 individuals not receiving lipid-lowering medication with low-density lipoprotein cholesterol (LDL-C) >4.9 mmol/L was chosen from the Russian FH Registry. LDL-C corrected for Lp(a)-cholesterol was calculated as LDL-C − 0.3 × Lp(a). DLCN criteria were applied before and after adjusting LDL-C concentration. Of the 206 patients with potential FH, a total of 34 subjects (17%) were reclassified to less severe FH diagnosis, 13 subjects of them (6%) were reclassified to "unlike" FH. In accordance with Receiver Operating Characteristic curve, Lp(a) level ≥40 mg/dL was associated with FH re-diagnosing with sensitivity of 63% and specificity of 78% (area under curve = 0.7, 95% CI 0.7−0.8, p < 0.001). The reclassification was mainly observed in FH patients with Lp(a) level above 40 mg/dL, i.e., 33 (51%) with reclassified DLCN criteria points and 22 (34%) with reclassified diagnosis, compared with 21 (15%) and 15 (11%), respectively, in patients with Lp(a) level less than 40 mg/dL. Thus, LDL-C corrected for Lp(a)-cholesterol should be considered in all FH patients with Lp(a) level above 40 mg/dL for recalculating points in accordance with DLCN criteria.

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 >250 mg/dl were identified in subjects >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 >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.