Medication-overuse headache : a widely recognized entity amidst ongoing debate

Medication overuse in primary headache disorders is a worldwide phenomenon and has a role in the chronification of headache disorders. The burden of disease on individuals and societies is significant due to high costs and comorbidities. In the Third Edition of the International Classification of Headache Disorders, medication-overuse headache is recognized as a separate secondary entity next to mostly primary headache disorders, although many clinicians see the disease as a sole complication of primary headache disorders. In this review, we explore the historical background of medication-overuse headache, its epidemiology, phenomenology, pathophysiology and treatment options. The review explores relevant unanswered questions and summarizes the current debates in medication-overuse headache

Development and validation of a comprehensive genomic diagnostic tool for myeloid malignancies.

The diagnosis of hematologic malignancies relies on multidisciplinary workflows involving morphology, flow cytometry, cytogenetic, and molecular genetic analyses. Advances in cancer genomics have identified numerous recurrent mutations with clear prognostic and/or therapeutic significance to different cancers. In myeloid malignancies, there is a clinical imperative to test for such mutations in mainstream diagnosis; however, progress toward this has been slow and piecemeal. Here we describe Karyogene, an integrated targeted resequencing/analytical platform that detects nucleotide substitutions, insertions/deletions, chromosomal translocations, copy number abnormalities, and zygosity changes in a single assay. We validate the approach against 62 acute myeloid leukemia, 50 myelodysplastic syndrome, and 40 blood DNA samples from individuals without evidence of clonal blood disorders. We demonstrate robust detection of sequence changes in 49 genes, including difficult-to-detect mutations such as FLT3 internal-tandem and mixed-lineage leukemia (MLL) partial-tandem duplications, and clinically significant chromosomal rearrangements including MLL translocations to known and unknown partners, identifying the novel fusion gene MLL-DIAPH2 in the process. Additionally, we identify most significant chromosomal gains and losses, and several copy neutral loss-of-heterozygosity mutations at a genome-wide level, including previously unreported changes such as homozygosity for DNMT3A R882 mutations. Karyogene represents a dependable genomic diagnosis platform for translational research and for the clinical management of myeloid malignancies, which can be readily adapted for use in other cancers

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM)

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations

R.I.S.C.L: A Holistic Molecular Diagnostic Tool for Myeloid Malignancies

The genomic landscapes of acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), myeloproliferative disorders (MPD) and other related myeloid malignancies are now amongst the best characterized cancer genomes. These malignancies share most of their somatic driver mutations, many of which have therapeutic and prognostic significance (Patel et al, NEJM 2012). Patient prognostication and clinical decision-making can be greatly facilitated by testing for these mutations in parallel with established diagnostic assays. Here, we describe and validate RISCL (Rearrangements, Indels, Substitutions, Copy number and Loss-of-heterozygosity), a novel methodological and bioinformatic tool for the molecular diagnosis of myeloid malignancies. This tool employs targeted DNA capture to simultaneously: 1) identify coding mutations in 49 genes, 2) detect the four most important translocations in AML and 3) derive genome-wide copy number and zygosity data. Samples & methods 1. Samples Genomic DNA was extracted from bone marrow samples of 62 patients with AML (n=86 samples, including 24 remission samples) and 68 patients with MDS; and from blood granulocytes and mononuclear cells from 5 cord blood samples and 18 adults with normal hematopoiesis. 2. cRNA baits and sequencing The bait library (Agilent) contained 53,613 probes to capture: 1) all exons from 49 genes 2) intronic breakpoint sites for PML-RARA, CBFb-MYH11 and RUNX1-RUNX1T1 and MLL breakpoints 3) 9958 SNPs (minor allele frequency 0.40-0.45) for genome wide copy number and zygosity analysis. Barcoded sequencing was performed using Illumina HiSeq 2000 (100bp paired-end). 3. Bioinformatic analysis We used bespoke bioinformatics for detecting coding substitutions and indels (MIDAS; Conte et al, Leukemia 2013), chromosomal translocations (SMALT-FIT), copy number analysis (Avadis software) and detection of specific mutations such as MLL-PTD and FLT3-ITD (in-house scripts). 4. Verification of results To validate the sensitivity and specificity of our approach, we compared our findings to conventional diagnostic data and are also validating 30% of randomly selected variants. Results A mean of 94% of targeted bases were covered at least by 30x. In AML samples, the four most common coding mutations identified affected NPM1 (n=10), CEBPA (n=8), IDH1 (n=8) and NRAS (n=7). By comparison to conventional diagnostics, we detected 5/5 IDH1R132, 4/4 CEBPA, 1/1 IDH2R172K and 8/9 NPM1 mutations. In MDS samples, the top four mutations affected TP53 (n=15), TET2 (n=13), SRSF2 (n=9), ASXL1 (n=8) and mutations affecting spliceosome genes (n=18) that were mutually exclusive, as previously described (Yoshida et al, Nature 2011). RISCL detected 100% of known translocations (28/28) in AML patients, namely CBFb-MYH11 (n=8/8), PML-RARA (n=9/9), RUNX1-RUNX1T1 (n=4/4) and rearrangements of MLL (n=7/7). In every case of MLL rearrangement the gene partner was identified and in one case with t(X;11) we identified a novel gene partner to MLL, DIAPH2. Furthermore, we identified one patient with an MLL rearrangement not identified at diagnosis. Copy number analysis efficiently detected known large chromosomal deletions or monosomies in chromosome 5 (18/18) and 7 (10/12). Overall 47/54 large deletions were detected using Avadis software. Furthermore in one MDS patient we were able to detect a submicroscopic heterozygous deletion in chromosome 4 which included TET2. However this method was much less sensitive for detecting trisomies (13/27 trisomies detected overall). The reasons for this disparity between detection of deletions and amplifications using a standardized depth of coverage algorithm are unclear, but may include subclonal mutations, selection of karyotypically abnormal cells during metaphase preparation or limitations of our bioinformatic analysis, which we are currently investigating. Figure 1 shows an example of the results of our holistic analysis using RISCL from an informative case of AML. In summary we describe RISCL, a novel powerful holistic NGS tool for detailed characterization of myeloid malignancies that can be used for patient stratification and a personalized approach to malignancy in the molecular era. The same approach can be extended to other malignancies

Combined Metabolomic Analysis of Plasma and Urine Reveals AHBA, Tryptophan and Serotonin Metabolism as Potential Risk Factors in Gestational Diabetes Mellitus (GDM)

Gestational diabetes mellitus during pregnancy has severe implications for the health of the mother and the fetus. Therefore, early prediction and an understanding of the physiology are an important part of prenatal care. Metabolite profiling is a long established method for the analysis and prediction of metabolic diseases. Here, we applied untargeted and targeted metabolomic protocols to analyze plasma and urine samples of pregnant women with and without GDM. Univariate and multivariate statistical analyses of metabolomic profiles revealed markers such as 2-hydroxybutanoic acid (AHBA), 3-hydroxybutanoic acid (BHBA), amino acids valine and alanine, the glucose-alanine-cycle, but also plant-derived compounds like sitosterin as different between control and GDM patients. PLS-DA and VIP analysis revealed tryptophan as a strong variable separating control and GDM. As tryptophan is biotransformed to serotonin we hypothesized whether serotonin metabolism might also be altered in GDM. To test this hypothesis we applied a method for the analysis of serotonin, metabolic intermediates and dopamine in urine by stable isotope dilution direct infusion electrospray ionization mass spectrometry (SID-MS). Indeed, serotonin and related metabolites differ significantly between control and GDM patients confirming the involvement of serotonin metabolism in GDM. Clustered correlation coefficient visualization of metabolite correlation networks revealed the different metabolic signatures between control and GDM patients. Eventually, the combination of selected blood plasma and urine sample metabolites improved the AUC prediction accuracy to 0.99. The detected GDM candidate biomarkers and the related systemic metabolic signatures are discussed in their pathophysiological context. Further studies with larger cohorts are necessary to underpin these observations.© 2017 Leitner, Fragner, Danner, Holeschofsky, Leitner, Tischler, Doerfler, Bachmann, Sun, Jaeger, Kautzky-Willer and Weckwert