Clinical, Anthropometric and Biochemical Characteristics of Patients with or without Genetically Confirmed Familial Hypercholesterolemia

Prevalence and factors associated with possible cases of familial hypercholesterolemia in Brazilian adults: a cross-sectional study

This study aimed to estimate the prevalence of possible cases of FH and analyze associated factors in the adult Brazilian population. Cross-sectional study with laboratory data from the Brazilian National Health Survey, with 8521 participants. Possible cases of FH were defined according to the Dutch Lipid Clinic Network criteria. The prevalence and 95% confidence intervals (95% CI) of possible cases of FH were estimated according to sociodemographic variables, lifestyle, diabetes, hypertension, altered tests, treatment and self-rated health. Logistic regression was used to analyze the associations. The prevalence of possible cases of FH was 0.96%, higher in women, between 45 and 59 years, white race/skin color and others, less education, people with diabetes, hypertension and total cholesterol ≥ 310 mg/dL. The presence of FH was positively associated with regular self-rated health (OR 1.96; 95% CI 0.99-3.84), poor/very poor (OR 3.02; 95% CI 1.30-7.03) and negatively with black race/skin color (OR 0.10; 95% CI 0.02-0.46) and complete elementary school, incomplete high school (OR 0.47; 95% CI 0.23-0.98) and complete high school and more (OR 0.45; 95% CI 0.21-0.95). FH affects 1:104 Brazilian adults, these findings contribute to understanding the burden of disease in Brazil. Due to the scarcity of studies on FH in low- and middle-income countries, further studies are desirable.

LDLR and PCSK9 3´UTR variants and their putative effects on microRNA molecular interactions in familial hypercholesterolemia: a computational approach

BACKGROUND

Familial hypercholesterolemia (FH) is caused by pathogenic variants in low-density lipoprotein (LDL) receptor (LDLR) or its associated genes, including apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), and LDLR adaptor protein 1 (LDLRAP1). However, approximately 40% of the FH patients clinically diagnosed (based on FH phenotypes) may not carry a causal variant in a FH-related gene. Variants located at 3' untranslated region (UTR) of FH-related genes could elucidate mechanisms involved in FH pathogenesis. This study used a computational approach to assess the effects of 3'UTR variants in FH-related genes on miRNAs molecular interactions and to explore the association of these variants with molecular diagnosis of FH.

METHODS AND RESULTS

Exons and regulatory regions of FH-related genes were sequenced in 83 FH patients using an exon-target gene sequencing strategy. In silico prediction tools were used to study the effects of 3´UTR variants on interactions between miRNAs and target mRNAs. Pathogenic variants in FH-related genes (molecular diagnosis) were detected in 44.6% FH patients. Among 59 3'UTR variants identified, LDLR rs5742911 and PCSK9 rs17111557 were associated with molecular diagnosis of FH, whereas LDLR rs7258146 and rs7254521 and LDLRAP1 rs397860393 had an opposite effect (p < 0.05). 3´UTR variants in LDLR (rs5742911, rs7258146, rs7254521) and PCSK9 (rs17111557) disrupt interactions with several miRNAs, and more stable bindings were found with LDLR (miR-4435, miR-509-3 and miR-502) and PCSK9 (miR-4796).

CONCLUSION

LDLR and PCSK9 3´UTR variants disturb miRNA:mRNA interactions that could affect gene expression and are potentially associated with molecular diagnosis of FH.

Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females

Hypercholesterolemia is characterized by high plasma LDL cholesterol and often caused by genetic mutations in LDL receptor (LDLR), APOB, or proprotein convertase subtilisin/kexin type 9 (PCSK9). However, a substantial proportion of hypercholesterolemic subjects do not have any mutations in these canonical genes, leaving the underlying pathobiology to be determined. In this study, we investigated to determine whether combining plasma metabolomics with genetic information increases insight in the biology of hypercholesterolemia. For this proof of concept study, we combined plasma metabolites from 119 hypercholesterolemic females with genetic information on the LDL canonical genes. Using hierarchical clustering, we identified four subtypes of hypercholesterolemia, which could be distinguished along two axes represented by triglyceride and large LDL particle concentration. Subjects with mutations in LDLR or APOB preferentially clustered together, suggesting that patients with defects in the LDLR pathway show a distinctive metabolomics profile. In conclusion, we show the potential of using metabolomics to segregate hypercholesterolemic subjects into different clusters, which may help in targeting genetic analysis.