The mainstay of treating patients with phenylketonuria (PKU) is based on a Phe-restricted diet, restrictive in natural protein combined with Phe-free L-amino acid supplements and low protein foods. This PKU diet seems to reduce atherogenesis and confer protection against cardiovascular diseases but the results from the few published studies have been inconclusive. The aim of our study was to evaluate the relationship between the lipid profile and several treatment-related risk factors in patients with hyperphenylalaninaemia (HPA) in order to optimize their monitoring. We conducted a cross-sectional multicentre study. A total of 141 patients with HPA were classified according to age, phenotype, type of treatment and dietary adherence. Annual median blood phenylalanine (Phe) levels, Phe tolerance, anthropometric measurements, blood pressure (BP) and biochemical parameters [(triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), apolipoprotein A (ApoA), apolipoprotein B (ApoB), vitamin B12, total homocysteine (tHcy), Methionine (Met), high sensitivity C-Reactive Protein (hsCRP)] were collected for each patient. Plasma TC levels were lower in patients with PKU than in the mild-HPA group (150 ± 31 vs. 164 ± 22 mg/dL), and there was a weak inverse correlation between plasma TC and Phe levels. HDL-C, LDL-C, ApoA and ApoB levels were lower in the PKU group than in mild-HPA. Patients with PKU had higher systolic BP than the mild-HPA group and there was found a quadratic correlation between median Phe levels and systolic BP (p = 6.42e-5) and a linear correlation between median Phe levels and diastolic BP (p = 5.65e-4). In overweight or obese PKU patients (24.11 %), biochemical parameters such as TC, triglycerides, LDL-C, tHcy, hsCRP and BP were higher. By contrast, HDL-C was lower in these patients. Our data show a direct correlation between lipid profile parameters and good adherence to the diet in PKU patients. However, lipid profile in overweight or obese patients displayed an atherogenic profile, in addition to higher hsCRP concentrations and BP. Our study contributes to a better understanding of the relationship between phenotype and treatment in patients with HPA, which could be useful in improving follow-up strategies and clinical outcome. Research Ethics Committee of Santiago-Lugo 2015/393. Registered 22 September 2015, retrospectively registered.
The biggest challenge geneticists face when applying next-generation sequencing technology to the diagnosis of rare diseases is determining which rare variants, from the dozens or hundreds detected, are potentially implicated in the patient’s phenotype. Thus, variant prioritization is an essential step in the process of rare disease diagnosis. In addition to conducting the usual in-silico analyses to predict variant pathogenicity (based on nucleotide/amino-acid conservation and the differences between the physicochemical features of the amino-acid change), three important concepts should be borne in mind. The first is the “mutation tolerance” of the genes in which variants are located. This describes the susceptibility of a given gene to any functional mutation and depends on the strength of purifying selection acting against it. The second is the “mutational architecture” of each gene. This describes the type and location of mutations previously identified in the gene, and their association with different phenotypes or degrees of severity. The third is the mode of inheritance (inherited vs. de novo) of the variants detected. Here, we discuss the importance of each of these concepts for variant prioritization in the diagnosis of rare diseases. Using real data, we show how genes, rather than variants, can be prioritized by calculating a gene-specific mutation tolerance score. We also illustrate the influence of mutational architecture on variant prioritization using five paradigmatic examples. Finally, we discuss the importance of familial variant analysis as final step in variant prioritization.