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Purpose: No information is currently available on genetic determinants of short-term response to drug withdrawal in medication overuse headache (MOH). In the present study, we aimed to evaluate the role of 14 polymorphisms in 8 candidate genes potentially relevant for drug addiction (OPRM1, DRD2, DBH, COMT, BDNF, SLC6A4, 5HT2A, and SLC1A2) as predictors for detoxification outcome of MOH patients at 2 months of follow-up. Methods: Genotyping was conducted by PCR, PCR-RFLP analysis, or real-time PCR allelic discrimination assay on genomic DNA extracted from peripheral blood. The association between gene variants and risk of unsuccessful detoxification was evaluated by univariate and multivariate logistic regression analyses. Results: One hundred and eight MOH patients with effective drug withdrawal therapy and 65 MOH patients with unsuccessful detoxification were available for the analysis. In the multivariable logistic regression analysis, triptan overuse (odds ratio (OR) 0.271, 95 % confidence interval (CI) 0.083-0.890, P = 0.031) and TT genotype carriage of DRD2 NcoI (OR 0.115, 95 % CI 0.014-0.982, P = 0.048) emerged as independent predictors for unsuccessful detoxification. In addition, carriers of at least four of the six top-ranked gene variants ( P < 0.10) were found at higher odds for unsuccessful detoxification than patients with ≤3 high-risk genotypes (OR 3.40, 95 % CI 1.65-7.01, P = 0.001). Conclusion: This exploratory study suggests that DRD2 NcoI may be a genetic determinant of detoxification outcome in MOH patients. Our findings also show that an approach based on the combination of multiple genetic markers could be clinically useful for identification of MOH patients at higher risk for unsuccessful detoxification. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is an adult-onset, progressive, and fatal neurodegenerative disease with unknown etiology. Recent evidence suggests an association between the exposure to toxic environmental factors and sporadic ALS. The flavin-containing monooxygenases (FMOs) and paraoxonase (PONs) genes encode enzymes involved in xenobiotic detoxication and are associated with ALS. FMO and PON gene expression has been examined in the human central nervous system including human brain subregions defined as the spinal cord, medulla, and cerebral cortex and in the peripheral tissues (lymphocytes, fibroblasts) in ALS patients and normal control subjects. FMO expression was generally higher in tissues from ALS subjects than in control tissues, with the largest increases in FMO expression detected in the spinal cord. In peripheral tissues, the FMO mRNA level was found to be lower compared with FMO expression in brain tissue, and no differences were detected between ALS patients and the control tissue. FMO and PON gene expression was low in peripheral tissues. In contrast to FMO5 expression, the PON2 gene was down-regulated in ALS patients compared to the controls. Because FMO and PON are involved in the detoxication processes and their functional activity to bioactivate chemicals to toxins has been documented, the data herein suggest that environmental toxin exposure may play a role in a subset of individuals who contract ALS by altering FMO and PON gene expression. Although the precise pathogenic link is presently unknown, these findings suggest a role at FMO and PON genes in the development of ALS. [ABSTRACT FROM AUTHOR]

Several methods have been developed to detect common prothrombotic mutations, including factor V Leiden (G1691), prothrombin G20210A, and methylenetetrahydrofolate reductase (MTHFR) C677C. In this study, we compared the accuracy of three different molecular techniques, i.e.: (1) restriction enzyme digestion (RFLP), (2) real time with hybridization probes and final melting curve (Fluorescence Resonance Energy Transfer, FRET), and (3) real time with hydrolysis probes (TaqMan). Sequencing was used as the reference standard. Our data showed that RFLPs analysis for the detection of prothrombotic mutations, albeit easy-to-perform, had a limited reliability for assessing correct genotypes. FRET analysis displayed higher resolution than RFLPs. Additionally, FRET analysis was faster and less tedious than sequencing. [ABSTRACT FROM AUTHOR]