The aim of this study was to investigate local muscle O₂consumption (muscV˙o ₂) and forearm blood flow (FBF) in resting and exercising muscle by use of near-infrared spectroscopy (NIRS) and to compare the results with the global muscV˙o ₂ and FBF derived from the well-established Fick method and plethysmography. muscV˙o ₂ was derived from 1) NIRS using venous occlusion, 2) NIRS using arterial occlusion, and 3) the Fick method [muscV˙o _{2 (Fick)}]. FBF was derived from 1) NIRS and 2) strain-gauge plethysmography. Twenty-six healthy subjects were tested at rest and during sustained isometric handgrip exercise. Local variations were investigated with two independent and simultaneously operating NIRS systems at two different muscles and two measurement depths. muscV˙o ₂ increased more than fivefold in the active flexor digitorum superficialis muscle, and it increased 1.6 times in the brachioradialis muscle. The average increase in muscV˙o _{2 (Fick)} was twofold. FBF increased 1.4 times independent of the muscle or the method. It is concluded that NIRS is an appropriate tool to provide information about local muscV˙o ₂ and local FBF because both place and depth of the NIRS measurements reveal local differences that are not detectable by the more established, but also more global, Fick method.

Cerebrotendinous xanthomatosis (CTX) is a lipid storage disease caused by a deficiency of the mitochondrial enzyme 27-sterol hydroxylase (CYP 27), due to mutations in its gene. In this study we report on mutations in 58 patients with CTX out of 32 unrelated families. Eight of these were novel mutations, two of which were found together with two already known pathogenic mutations. Twelve mutations found in this patient group have been described in the literature. In the patients from 31 families, mutations were found in both alleles. In the literature, 28 mutations in 67 patients with CTX out of 44 families have been described. Pooling our patient group and the patients from the literature together, 37 different mutations in 125 patients out of 74 families were obtained. Identical mutations have been found in families from different ethnic backgrounds. In 41% of all the patients, CYP 27 gene mutations are found in the region of exons 6–8. This region encodes for adrenodoxin and haem binding sites of the protein. Of these 125 patients, a genotype–phenotype analysis was done for 79 homozygous patients harbouring 23 different mutations, out of 45 families. The patients with compound heterozygous mutations were left out of the genotype–phenotype analysis. The genotype–phenotype analysis did not reveal any correlation.

The implementation of whole‐exome sequencing in clinical diagnostics has generated a need for functional evaluation of genetic variants. In the field of inborn errors of metabolism (IEM), a diverse spectrum of targeted biochemical assays is employed to analyze a limited amount of metabolites. We now present a single‐platform, high‐resolution liquid chromatography quadrupole time of flight (LC‐QTOF) method that can be applied for holistic metabolic profiling in plasma of individual IEM‐suspected patients. This method, which we termed “next‐generation metabolic screening” (NGMS), can detect >10,000 features in each sample. In the NGMS workflow, features identified in patient and control samples are aligned using the “various forms of chromatography mass spectrometry (XCMS)” software package. Subsequently, all features are annotated using the Human Metabolome Database, and statistical testing is performed to identify significantly perturbed metabolite concentrations in a patient sample compared with controls. We propose three main modalities to analyze complex, untargeted metabolomics data. First, a targeted evaluation can be done based on identified genetic variants of uncertain significance in metabolic pathways. Second, we developed a panel of IEM‐related metabolites to filter untargeted metabolomics data. Based on this IEM‐panel approach, we provided the correct diagnosis for 42 of 46 IEMs. As a last modality, metabolomics data can be analyzed in an untargeted setting, which we term “open the metabolome” analysis. This approach identifies potential novel biomarkers in known IEMs and leads to identification of biomarkers for as yet unknown IEMs. We are convinced that NGMS is the way forward in laboratory diagnostics of IEMs.