Lipid and lipoprotein parameters for detection of familial hypercholesterolemia in childhood. The DECOPIN Project

Genetic Counseling and Genetic Testing for Familial Hypercholesterolemia

Familial hypercholesterolemia (FH) is one of the most common autosomal codominant Mendelian diseases. The major complications of FH include tendon and cutaneous xanthomas and coronary artery disease (CAD) associated with a substantial elevation of serum low-density lipoprotein levels (LDL). Genetic counseling and genetic testing for FH is useful for its diagnosis, risk stratification, and motivation for further LDL-lowering treatments. In this study, we summarize the epidemiology of FH based on numerous genetic studies, including its pathogenic variants, genotype-phenotype correlation, prognostic factors, screening, and usefulness of genetic counseling and genetic testing. Due to the variety of treatments available for this common Mendelian disease, genetic counseling and genetic testing for FH should be implemented in daily clinical practice.

Correlation of cardiovascular risk predictors with overweight and obesity in patients with familial hypercholesterolemia

Purpose

We aimed to analyze the correlation between overweight and obesity-related indicators and cardiovascular risk predictors in patients with familial hypercholesterolemia (FH) and to evaluate their mutual predictive properties.

Methods

A total of 103 patients with FH included from 2004 to 2017 were retrospectively analyzed. Pearson correlation analysis and multiple linear regression analysis were used to assess the correlation between overweight and obesity-related indicators and cardiovascular risk predictors in FH patients. Subject operating characteristic (ROC) curve was used to analyze their reciprocal predictive performance.

Results

(1) Atherogenic index of plasma (AIP) (β = 0.020) and ApoB/ApoA1 Ratio (BAR) (β = 0.015) were independently correlated with body mass index (BMI) (P < 0.05); AIP (β = 1.176) was independently correlated with waist-to-hip ratio (WHR) (P < 0.01); AIP (β = 1.575), BAR (β = 0.661) and atherogenic coefficient (AC) (β = 0.427) were independently correlated with waist-to-height ratio (WHtR) (P < 0.05). (2) The area under the ROC (AUC) for overweight corresponding to AIP, BAR, and AC were 0.695 (95% CI = 0.593–0.797, P < 0.01), 0.660 (95% CI = 0.555–0.766, P < 0.01), and 0.632 (95% CI = 0.525–0.740, P < 0.05), respectively; and AUCs for central obesity corresponding to AIP, BAR and AC were 0.757 (95% CI = 0.656–0.857, P < 0.001), 0.654 (95% CI = 0.536–0.771, P < 0.05) and 0.651 (95% CI = 0.538–0.764, P < 0.05), respectively. The AUCs for moderate risk of AIP corresponding to BMI, WHR, and WHtR were 0.709 (95% CI = 0.608–0.811, P < 0.001), 0.773 (95% CI = 0.678–0.867, P < 0.001), 0.739 (95% CI = 0.641–0.836, P < 0.001), respectively, and BMI, WHR and WHtR corresponded to an AUC of 0.691 (95% CI = 0.585–0.797, P < 0.01), 0.734 (95% CI = 0.632–0.835, P < 0.001), and 0.706 (95% CI = 0.603–0.810, P < 0.01) for high risk of AIP, respectively.

Conclusion

AIP has independent positive linear correlation with indicators related to overweight and obesity in FH patients; AIP has good predictive performance for overweight and obesity in FH patients, and WHR has good performance for identifying moderate and high risk of AIP in FH patients.

Targeted profiling of amino acid metabolome in serum by a liquid chromatography-mass spectrometry method: application to identify potential markers for diet-induced hyperlipidemia

To better understand the mechanism of hyperlipidemia and discover potential biomarkers, we have used targeted metabolomics to analyze eight amino acid profiles of control and hyperlipidemia rats by a liquid chromatography-mass spectrometry method. With high fat diet, the concentrations of serum of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (ApoB) were increased by 666.7%, 99.0%, 61.7% and 51.0%, whereas the concentrations of high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-I (ApoA-I) were decreased by 46.3% and 58.9%. The concentrations of alanine, arginine, lysine, methionine, serine, tyrosine and valine in hyperlipidemia rats were significantly decreased by 21.8%, 19.72%, 26.5%, 19.6%, 48.7%, 19.8% and 24.91%, while there was no striking change in threonine. Combined with experimental results and previous literature, we inferred that alanine and serine were gradually disordered and subsequently generated abundant acetyl-CoA through pyruvate, which resulted in energy metabolism deficiency. Furthermore, Spearman correlation analysis shows that TC was negatively associated with methionine (r = -0.640, p < 0.05), suggesting that the lowered level of methionine caused by the homocysteine pathway enhances absorption and synthesis of TC. Meanwhile, the reduction of tyrosine demonstrated that rapid metabolism of cholesterol in vivo was caused by high levels of exogenous cholesterol. Furthermore, the observed ApoB and lysine changes indicated that lysine was largely incorporated into ApoB particles during the disease process. In addition, the levels of arginine, SOD and MDA reflected the behavior of oxidative stress. Finally, the metabolism fluctuation of valine demonstrated that abnormal lipid metabolism could cause abnormal glucose metabolism. In general, disordered energy metabolism, lipid metabolism, glucose metabolism and elevated oxidative stress were important characteristics of metabolic perturbations in hyperlipidemia. Herein, the discovery of biomarkers and the biological explanations mentioned above could be used to analyze the pathogenesis of hyperlipidemia through metabolic pathways, and these results could play an important role in assisting the clinical diagnosis of hyperlipidemia.