Pharmacological inhibition of NOX reduces atherosclerotic lesions, vascular ROS and immune-inflammatory responses in diabetic Apoe(-/-) mice

AIMS/HYPOTHESIS

Enhanced vascular inflammation, immune cell infiltration and elevated production of reactive oxygen species (ROS) contribute significantly to pro-atherogenic responses in diabetes. We assessed the immunomodulatory role of NADPH oxidase (NOX)-derived ROS in diabetes-accelerated atherosclerosis.

METHODS

Diabetes was induced in male Apoe(-/-) mice with five daily doses of streptozotocin (55 mg kg(-1) day(-1)). Atherosclerotic plaque size, markers of ROS and immune cell accumulation were assessed in addition to flow cytometric analyses of cells isolated from the adjacent mediastinal lymph nodes (meLNs). The role of NOX-derived ROS was investigated using the NOX inhibitor, GKT137831 (60 mg/kg per day; gavage) administered to diabetic and non-diabetic Apoe(-/-) mice for 10 weeks.

RESULTS

Diabetes increased atherosclerotic plaque development in the aortic sinus and this correlated with increased lesional accumulation of T cells and CD11c(+) cells and altered T cell activation in the adjacent meLNs. Diabetic Apoe(-/-) mice demonstrated an elevation in vascular ROS production and expression of the proinflammatory markers monocyte chemoattractant protein 1, vascular adhesion molecule 1 and IFNγ. Blockade of NOX-derived ROS using GKT137831 prevented the diabetes-mediated increase in atherosclerotic plaque area and associated vascular T cell infiltration and also significantly reduced vascular ROS as well as markers of inflammation and plaque necrotic core area.

CONCLUSIONS/INTERPRETATION

Diabetes promotes pro-inflammatory immune responses in the aortic sinus and its associated lymphoid tissue. These changes are associated with increased ROS production by NOX. Blockade of NOX-derived ROS using the NOX inhibitor GKT137831 is associated with attenuation of these changes in the immune response and reduces the diabetes-accelerated development of atherosclerotic plaques in Apoe(-/-) mice.

A Simplification and Implementation of Random-effects Meta-analyses Based on the Exact Distribution of Cochran’s Q

Background: The random-effects (RE) model is the standard choice for meta-analysis in the presence of heterogeneity, and the stand ard RE method is the DerSimonian and Laird (DSL) approach, where the degree of heterogeneity is estimated using a moment-estimator. The DSL approach does not take into account the variability of the estimated heterogeneity variance in the estimation of Cochran’s Q. Biggerstaff and Jackson derived the exact cumulative distribution function (CDF) of Q to account for the variability of Ť 2.

Objectives: The first objective is to show that the explicit numerical computation of the density function of Cochran’s Q is not required. The second objective is to develop an R package with the possibility to easily calculate the classical RE method and the new exact RE method.

Methods: The novel approach was validated in extensive simulation studies. The different approaches used in the simulation studies, including the exact weights RE meta-analysis, the I 2 and T 2 estimates together with their confidence intervals were implemented in the R package metaxa.

Results: The comparison with the classical DSL method showed that the exact weights RE meta-analysis kept the nominal type I error level better and that it had greater power in case of many small studies and a single large study. The Hedges RE approach had inflated type I error levels. Another advantage of the exact weights RE meta-analysis is that an exact confidence interval for T 2is readily available. The exact weights RE approach had greater power in case of few studies, while the restricted maximum likelihood (REML) approach was superior in case of a large number of studies. Differences between the exact weights RE meta-analysis and the DSL approach were observed in the re-analysis of real data sets. Application of the exact weights RE meta-analysis, REML, and the DSL approach to real data sets showed that conclusions between these methods differed.

Conclusions: The simplification does not require the calculation of the density of Cochran’s Q, but only the calculation of the cumulative distribution function, while the previous approach required the computation of both the density and the cumulative distribution function. It thus reduces computation time, improves numerical stability, and reduces the approximation error in meta-analysis. The different approaches, including the exact weights RE meta-analysis, the I 2 and T 2estimates together with their confidence intervals are available in the R package metaxa, which can be used in applications.

Challenges in planning and conducting diagnostic studies with molecular biomarkers

Biomarkers are of increasing importance for personalized medicine in many areas of application, such as diagnosis, prognosis, or the selection of targeted therapies. In many molecular biomarker studies, intensity values are obtained from large scale ‑omics experiments. These intensity values, such as protein concentrations, are often compared between at least two groups of subjects to determine the diagnostic ability of the molecular biomarker. Various prospective or retrospective study designs are available for molecular biomarker studies, and the biomarker used may be univariate or may even consist in a multimarker rule. In this work, several challenges are discussed for the planning and conduct of biomarker studies. The phases of diagnostic biomarker studies are closely related to levels of evidence in diagnosis, and they are therefore discussed upfront. Different study designs for molecular biomarker studies are discussed, and they primarily differ in the way subjects are selected. Using two systematic reviews from the literature, common sources of bias of molecular diagnostic studies are illustrated. The extreme selection of patients and controls and verification bias are specifically discussed. The pre-analytical and technical variability of biomarker measurements is usually expressed in terms of the coefficient of variation, and is of great importance for subsequent validation studies for molecular biomarkers. It is finally shown that the required sample size for biomarker validation quadratically increases with the coefficient of variation, and the effect is illustrated using real data from different laboratory technologies.

Anti-JC virus antibodies in a large German natalizumab-treated multiple sclerosis cohort

OBJECTIVE

To investigate the rate of seropositivity of anti-JC virus (JCV) antibodies in a German multiple sclerosis (MS) cohort treated with natalizumab in the postmarketing setting and to assess anti-JCV serostatus in samples obtained before diagnosis of progressive multifocal leukoencephalopathy (PML).

METHODS

This was a blinded, retrospective cross-sectional and longitudinal analysis for anti-JCV antibodies using a confirmatory 2-step ELISA on 2,782 blood samples obtained from 2,253 patients nationwide for routine testing for anti-natalizumab antibodies during open-label treatment between 2007 and 2010.

RESULTS

Of the natalizumab-treated patients with MS, 58.8% tested positive for anti-JCV antibodies. The rate of seropositivity was higher in males and increased with age, with a plateau between age intervals 20-29 and 30-39 years. In longitudinal analyses, 19 of 194 (9.8%) patients converted from anti-JCV antibody-negative to seropositive status over 7.7 months; 4.7% reverted from antibody-positive to seronegative status over 7.9 months. Antibody levels, especially in the latter group, were low, indicating fluctuations around the lower cut point of the assay. Neither anti-JCV serostatus nor antibody levels were associated with immunosuppressive pretreatment, duration of natalizumab treatment, or anti-natalizumab antibodies. All samples obtained from 10 patients who developed PML were seropositive (13 samples before PML diagnosis [2.0-37.6 months]; 2 samples at diagnosis). Antibody levels in these samples were higher than those in samples from seropositive patients who did not develop PML.

CONCLUSIONS

These data argue for the potential clinical utility of JCV serology for PML risk stratification. However, further investigations of fluctuations in serostatus and of antibody levels for a more precise understanding of the predictive value are warranted.

Data analysis and data mining: current issues in biomedical informatics

BACKGROUND

Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.

OBJECTIVES

To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics.

METHODS

On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.

RESULTS

The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.

CONCLUSIONS

Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

Country-specific birth weight and length in type 1 diabetes high-risk HLA genotypes in combination with prenatal characteristics

OBJECTIVE

To examine the relationship between high-risk human leukocyte antigen (HLA) genotypes for type 1 diabetes and birth size in combination with prenatal ch aracteristics in different countries.

STUDY DESIGN

Four high-risk HLA genotypes were enrolled in the Environmental determinants of Diabetes in the Young study newborn babies from the general population in Finland, Germany, Sweden and the United States. Stepwise regression analyses were used to adjust for country, parental physical characteristics and environmental factors during pregnancy.

RESULT

Regression analyses did not reveal differences in birth size between the four type 1 diabetes high-risk HLA genotypes. Compared with DQ 4/8 in each country, (1) DQ 2/2 children were heavier in the United States (P=0.028) mostly explained however, by parental weight; (2) DQ 2/8 (P=0.023) and DQ 8/8 (P=0.046) children were longer in Sweden independent of parents height and as well as (3) in the United States for DQ 2/8 (P=0.023), but again dependent on parental height.

CONCLUSION

Children born with type 1 diabetes high-risk HLA genotypes have comparable birth size. Longitudinal follow-up of these children should reveal whether birth size differences between countries contribute to the risk for islet autoimmunity and type 1 diabetes.

[3T-AI: a new treatment algorithm for anal incontinence with a higher evidence level]

BACKGROUND

The evidence for conservative treatment of anal incontinence is poor. In our first publication [Schwandner et al. Dis Colon Rectum 2010; 53: 1007-1016] we demonstrated that a novel therapeutic concept, termed triple target treatment (3T), combining amplitude-modulated medium frequency stimulation and electromyography biofeedback (EMG-BF) was superior to EMG-BF alone. Questions about the required treatment duration and the relevant subgroups of patients with sphincter damage and damaged anal sensibility were not addressed.

METHODS

We enrolled 158 patients with anal incontinence in this randomized study. Here, we -report on the important subgroup analyses of patients with and without sphincter damage and damaged anal sensibility for the endpoints Cleveland Clinic Score (CCS) and success record. Using the results of this study we propose a novel treatment algorithm which is open for discussion.

RESULTS

In patients with sphincter damage, the median difference on the CCS from baseline to 9 months was 5 points higher for 3T than for EMG-BF (95 % confidence interval 0-8; p = 0.0168). While 47 % of the patients with sphincter damage became continent with 3T, only 18 % did with EMG-BF (p = 0.0036). Ten of 17 patients in the 3T group regained anal sensibility after 3 months stimulation. There was tendency towards improved continence in patients with neuropathy upon 3T treatment (p = 0.1219).

CONCLUSIONS

3T is superior to EMG-BF alone for patients with sphincter damage and neuropathic anal incontinence. It is a successful key element within our treatment algorithm, even in patients with sphincter damage and neuropathic anal incontinence.

Probability machines: consistent probability estimation using nonparametric learning machines

BACKGROUND

Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem.

OBJECTIVES

The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities.

METHODS

Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians.

RESULTS

Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software.

CONCLUSIONS

Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications.

[Clinical trials with medical devices: lack of quality illustrated for faecal incontinence]

BACKGROUND

Medical devices must be safe and functioning states the law. Treatments with medical devices need not be efficacious to be allowed. We investigated special requirements and problems arising from the law.

METHODS

The market for medical devices is contrasted with that for drugs. The requirements of relevant laws are discussed. Finally, published clinical studies on anal incontinence are analysed with respect to their methodological quality.

RESULTS

Clinical trials of medical devices for treat-ing anal incontinence are of poor methodological quality thus preventing evaluation of the devices' utility.

CONCLUSION

Large, high quality clinical studies of the efficacy of medical devices for treating anal incontinence are urgently needed. Only such studies enable health technology assessment and comprehensible decisions on reimbursement by health insurance.