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Background: Interferon and ribavirin therapy for chronic hepatitis C virus (HCV) infection yields sustained virological response (SVR) rates of 50–80%. Several factors such as non-1 genotype, beneficial IL28B genetic variants, low baseline IP-10, and the functionality of HCV-specific T cells predict SVR. With the pending introduction of new therapies for HCV entailing very rapid clearance of plasma HCV RNA, the importance of baseline biomarkers likely will increase in order to tailor therapy. CD26 (DPPIV) truncates the chemokine IP-10 into a shorter antagonistic form, and this truncation of IP-10 has been suggested to influence treatment outcome in patients with chronic HCV infection patients. In addition, previous reports have shown CD26 to be a co-stimulator for T cells. The aim of the present study was to assess the utility of CD26 as a biomarker for treatment outcome in chronic hepatitis C and to define its association with HCV-specific T cells.
Methods: Baseline plasma from 153 genotype 1 and 58 genotype 2/3 infected patients enrolled in an international multicenter phase III trial (DITTO-HCV) and 36 genotype 1 infected patients participating in a Swedish trial (TTG1) were evaluated regarding baseline soluble CD26 (sCD26) and the functionality of HCV-specific CD8+ T cells.
Results: Genotype 1 infected patients achieving SVR in the DITTO (P = 0.002) and the TTG1 (P = 0.02) studies had lower pretreatment sCD26 concentrations compared with non-SVR patients. Sixty-five percent of patients with sCD26 concentrations below 600 ng/mL achieved SVR compared with 39% of the patients with sCD26 exceeding 600 ng/mL (P = 0.01). Patients with sCD26 concentrations below 600 ng/mL had significantly higher frequencies of HCV-specific CD8+ T cells (P = 0.02).
Conclusions: Low baseline systemic concentrations of sCD26 predict favorable treatment outcome in chronic HCV infection and may be associated with higher blood counts of HCV-specific CD8+ T cells.
Men and women differ substantially regarding height, weight, and body fat. Interestingly, previous work detecting genetic effects for waist-to-hip ratio, to assess body fat distribution, has found that many of these showed sex-differences. However, systematic searches for sex-differences in genetic effects have not yet been conducted. Therefore, we undertook a genome-wide search for sexually dimorphic genetic effects for anthropometric traits including 133,723 individuals in a large meta-analysis and followed promising variants in further 137,052 individuals, including a total of 94 studies. We identified seven loci with significant sex-difference including four previously established (near GRB14/COBLL1, LYPLAL1/SLC30A10, VEGFA, ADAMTS9) and three novel anthropometric trait loci (near MAP3K1, HSD17B4, PPARG), all of which were significant in women, but not in men. Of interest is that sex-difference was only observed for waist phenotypes, but not for height or body-mass-index. We found no evidence for sex-differences with opposite effect direction for men and women. The PPARG locus is of specific interest due to its link to diabetes genetics and therapy. Our findings demonstrate the importance of investigating sex differences, which may lead to a better understanding of disease mechanisms with a potential relevance to treatment options.
Background: Standard treatment for venous thromboembolism (VTE) consists of a heparin combined with vitamin K antagonists. Direct oral anticoagulants have been investigated for acute and extended treatment of symptomatic VTE; their use could avoid parenteral treatment and/or laboratory monitoring of anticoagulant effects.
Methods: A prespecified pooled analysis of the EINSTEIN-DVT and EINSTEIN-PE studies compared the efficacy and safety of rivaroxaban (15 mg twice-daily for 21 days, followed by 20 mg once-daily) with standard-therapy (enoxaparin 1.0 mg/kg twice-daily and warfarin or acenocoumarol). Patients were treated for 3, 6, or 12 months and followed for suspected recurrent VTE and bleeding. The prespecified noninferiority margin was 1.75.
Results: 8282 patients were enrolled. 4151 received rivaroxaban and 4131 received standard-therapy. The primary efficacy outcome occurred in 86 rivaroxaban-treated patients (2.1%) compared with 95 (2.3%) standard-therapy-treated patients (hazard ratio, 0.89; 95% confidence interval [CI], 0.66-1.19; pnoninferiority<0.001). Major bleeding was observed in 40 (1.0%) and 72 (1.7%) patients in the rivaroxaban and standard-therapy groups, respectively (hazard ratio, 0.54; 95% CI, 0.37-0.79; p=0.002). In key subgroups, including fragile patients, cancer patients, patients presenting with large clots and those with a history of recurrent VTE, the efficacy and safety of rivaroxaban was similar compared with standard-therapy.
Conclusion: The single-drug approach with rivaroxaban resulted in similar efficacy to standard-therapy and was associated with a significantly lower rate of major bleeding. Efficacy and safety results were consistent among key patient subgroups.
Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample.