Balancing versus modelling in weighted analysis of non-randomised studies with survival outcomes: A simulation study.

Weighting methods are widely used for causal effect estimation in non-randomised studies. In general, these methods use the propensity score (PS), the probability of receiving the treatment given the covariates, to arrive at the respective weights. All of these "modelling" methods actually optimize prediction of the respective outcome, which is, in the PS model, treatment assignment. However, this does not match with the actual aim of weighting, which is eliminating the association between covariates and treatment assignment. In the "balancing" approach, covariates are thus balanced directly by solving systems of numerical equations, explicitly without fitting a PS model. To compare modelling, balancing and hybrid approaches to weighting we performed a large simulation study for a binary treatment and a survival outcome. For maximal practical relevance all simulation parameters were selected after a systematic review of medical studies that used PS methods for analysis. We also introduce a new hybrid method that uses the idea of the covariate balancing propensity score and matching weights, thus avoiding extreme weights. In addition, we present a corrected robust variance estimator for some of the methods. Overall, our simulations results indicate that balancing approach methods work worse than expected. However, among the considered balancing methods, entropy balancing consistently outperforms the variance balancing approach. All methods estimating the average treatment effect in the overlap population perform well with very little bias and small standard errors even in settings with misspecified propensity score models. Finally, the coverage using the standard robust variance estimator was too high for all methods, with the proposed corrected robust variance estimator improving coverage in a variety of settings.

Evaluation of tree-based statistical learning methods for constructing genetic risk scores.

BACKGROUND: Genetic risk scores (GRS) summarize genetic features such as single nucleotide polymorphisms (SNPs) in a single statistic with respect to a given trait. So far, GRS are typically built using generalized linear models or regularized extensions. However, these linear methods are usually not able to incorporate gene-gene interactions or non-linear SNP-response relationships. Tree-based statistical learning methods such as random forests and logic regression may be an alternative to such regularized-regression-based methods and are investigated in this article. Moreover, we consider modifications of random forests and logic regression for the construction of GRS. RESULTS: In an extensive simulation study and an application to a real data set from a German cohort study, we show that both tree-based approaches can outperform elastic net when constructing GRS for binary traits. Especially a modification of logic regression called logic bagging could induce comparatively high predictive power as measured by the area under the curve and the statistical power. Even when considering no epistatic interaction effects but only marginal genetic effects, the regularized regression method lead in most cases to inferior results. CONCLUSIONS: When constructing GRS, we recommend taking random forests and logic bagging into account, in particular, if it can be assumed that possibly unknown epistasis between SNPs is present. To develop the best possible prediction models, extensive joint hyperparameter optimizations should be conducted.

Chronic air pollution-induced subclinical airway inflammation and polygenic susceptibility.

BACKGROUND: Air pollutants can activate low-grade subclinical inflammation which further impairs respiratory health. We aimed to investigate the role of polygenic susceptibility to chronic air pollution-induced subclinical airway inflammation. METHODS: We used data from 296 women (69-79 years) enrolled in the population-based SALIA cohort (Study on the influence of Air pollution on Lung function, Inflammation and Aging). Biomarkers of airway inflammation were measured in induced-sputum samples at follow-up investigation in 2007-2010. Chronic air pollution exposures at residential addresses within 15 years prior to the biomarker assessments were used to estimate main environmental effects on subclinical airway inflammation. Furthermore, we calculated internally weighted polygenic risk scores based on genome-wide derived single nucleotide polymorphisms. Polygenic main and gene-environment interaction (GxE) effects were investigated by adjusted linear regression models. RESULTS: Higher exposures to nitrogen dioxide (NO2), nitrogen oxides (NOx), particulate matter with aerodynamic diameters of ≤ 2.5 µm, ≤ 10 µm, and 2.5-10 µm significantly increased the levels of leukotriene (LT)B4 by 19.7% (p-value = 0.005), 20.9% (p = 0.002), 22.1% (p = 0.004), 17.4% (p = 0.004), and 23.4% (p = 0.001), respectively. We found significant effects of NO2 (25.9%, p = 0.008) and NOx (25.9%, p-value = 0.004) on the total number of cells. No significant GxE effects were observed. The trends were mostly robust in sensitivity analyses. CONCLUSIONS: While this study confirms that higher chronic exposures to air pollution increase the risk of subclinical airway inflammation in elderly women, we could not demonstrate a significant role of polygenic susceptibility on this pathway. Further studies are required to investigate the role of polygenic susceptibility.

Core body temperatures during final stages of life-an evaluation of data from in-hospital decedents.

Temperature-based methods are widely accepted as the gold standard for death time estimation. In the absence of any other information, the nomogram method generally assumes that a person died with a core body temperature of approximately 37.2 °C. Nevertheless, several external and internal factors may alter the body temperature during agony. A retrospective medical record analysis was carried out on in-hospital death cases from two consecutive years of surgical intensive care units to determine the effects of factors influencing the core body temperature at the point of death. Data from 103 case files were included in the statistical data evaluation. The body temperature fluctuated between and within individuals over time. No clear correlation to certain death groups was observed. Even primary cardiac deaths showed broad intervals of temperatures at the point of death. Men seem to die with higher body temperatures than women. The presented data highlight potential biases for death time estimations when generally assuming a core body temperature of 37.2 °C. In conclusion, the estimation of the time of death should include various methods, including a non-temperature-dependent method. Any uncertainties regarding the body temperature at point of death need to be resolved (e.g. by identifying fever constellations) and elucidated if elimination is not possible.

Cycling under the influence of alcohol-criminal offenses in a German metropolis.

INTRODUCTION: Real or simulated cycling tests under the influence of alcohol might be biased by laboratory settings. Accident analyses consider incidents with injuries only. Herein, criminal offenses consisting of drunk cycling are evaluated in detail to fill this gap. MATERIAL AND METHODS: All police-recorded cases of cycling under the influence of alcohol that took place in Düsseldorf, Germany, from 2009 to 2018 were identified. A total of 388 respective prosecutor's files were available for analyses. RESULTS: Mean blood alcohol concentrations were approximately 2 g/kg in both men and women. Men were overrepresented (6:1). Almost 60% of the cases were recorded between Friday and Sunday (the "weekend"). The average blood alcohol concentration (BAC) at night (01:00-05:59) was 0.39 g/kg lower than that during the day (06:00-17:59). Drinking after cycling allegations appear almost irrelevant among (German) cyclists. On average, the legal outcomes show 33 daily rates (median: 30). Additionally, the presented data raise doubts about whether the utilized medical tests or the ways in which they are carried out reliably discriminate between different grades of intoxication. Negative tests did not exclude high BACs, nor did positive tests correlate well with BACs. DISCUSSION/CONCLUSION: In practice, CUI is seen with BACs above 1.60 g/kg in most cases. BACs below 1.60 g/kg either seem to be a minor problem or they have been incompletely addressed thus far. In summary, to be prosecuted, drunk cyclists have to ride their bikes in either a highly insecure or rude manner or they must cause an accident.

E-scooter driving under the acute influence of alcohol-a real-driving fitness study.

PURPOSE: To assess the effects of alcohol on the ability to drive an e-scooter, driving tests reflecting real-life situations accompanied by medical examinations focusing on balance were conducted at different blood alcohol concentrations (BACs). METHODS: Fifty-seven subjects who consumed alcohol (28 female, 29 male) and 6 consistently sober subjects (3 female, 3 male) participated in the study. Alcohol was administered on a fixed schedule, and the individual drinking quantity was individually calculated in advance using the Widmark formula. Repeated runs through a fixed course were performed. Following each ride, a blood sample was taken for BAC determination, and medical tests were performed. RESULTS: Even at low BACs (0.21-0.60 g/kg), subjects showed a significant decrease in driving performance, to approximately 60% of the initial level. Differences in driving performance at different BAC ranges were observed for different obstacles, especially for the narrowing track, gate passage, slalom, and driving in circles obstacles. Furthermore, worse Romberg and Unterberger test results were correlated with worse driving performance. It cannot be assumed that learning effects during the study had a relevant effect, as shown in the comparison of the driving performance of the alcohol-consuming group with that of the control group. Sex-specific differences were not found. DISCUSSION: Significant deteriorations in driving performance at BACs below 1.10 g/kg confirmed alcohol-related risk potential when using e-scooters. At this time, these findings may lead to the assumption of "relative driving impairment" in Germany. The Romberg and Unterberger tests could be considered a complementary investigation method for the assessment of e-scooter driving impairment. CONCLUSION: Even at rather low BACs between 0.21 and 0.40 g/kg, there was a significant deterioration in driving performance under the influence of alcohol compared to sober, which highlights the negative effects of alcohol on e-scooter driving.

A Connectivity-Based Psychometric Prediction Framework for Brain-Behavior Relationship Studies.

The recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions' connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions' connectivity profiles. We first illustrate two main applications: 1) single brain region's predictive power for a range of psychometric variables and 2) single psychometric variable's predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships, as well as the unique insight into brain-behavior relationships offered by this approach.

Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.

Biomedical research studies have generated large multi-omic datasets to study complex diseases like Alzheimer's disease (AD). An important aim of these studies is the identification of candidate genes that demonstrate congruent disease-related alterations across the different data types measured by the study. We developed a new method to detect such candidate genes in large multi-omic case-control studies that measure multiple data types in the same set of samples. The method is based on a gene-centric integrative coefficient quantifying to what degree consistent differences are observed in the different data types. For statistical inference, a Bayesian hierarchical model is used to study the distribution of the integrative coefficient. The model employs a conditional autoregressive prior to integrate a functional gene network and to share information between genes known to be functionally related. We applied the method to an AD dataset consisting of histone acetylation, DNA methylation, and RNA transcription data from human cortical tissue samples of 233 subjects, and we detected 816 genes with consistent differences between persons with AD and controls. The findings were validated in protein data and in RNA transcription data from two independent AD studies. Finally, we found three subnetworks of jointly dysregulated genes within the functional gene network which capture three distinct biological processes: myeloid cell differentiation, protein phosphorylation and synaptic signaling. Further investigation of the myeloid network indicated an upregulation of this network in early stages of AD prior to accumulation of hyperphosphorylated tau and suggested that increased CSF1 transcription in astrocytes may contribute to microglial activation in AD. Thus, we developed a method that integrates multiple data types and external knowledge of gene function to detect candidate genes, applied the method to an AD dataset, and identified several disease-related genes and processes demonstrating the usefulness of the integrative approach.

BayMAP: a Bayesian hierarchical model for the analysis of PAR-CLIP data.

MOTIVATION: Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites. RESULTS: We have developed BayMAP, a procedure based on a fully Bayesian hierarchical model that takes other sources of substitutions into account. Furthermore, this model enables the incorporation of additional information into the analysis of PAR-CLIP data. This incorporation does not only permit a better detection of binding sites, but also a better understanding of the data and the biology of binding sites. In applications to simulated PAR-CLIP data, BayMAP distinguishes binding sites from noise better than existing methods. Additionally, it yields good estimates of the influence of the additional information. We here demonstrate BayMAP's usability for real datasets even when noisy data is present. AVAILABILITY AND IMPLEMENTATION: BayMAP is freely available as an R package at http://stat.math.uni-duesseldorf.de/baymap. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Benefits of improved air quality on ageing lungs: impacts of genetics and obesity.

INTRODUCTION: The beneficial effect of improving air quality on lung function in the elderly remains unclear. We examined associations between decline in air pollutants and lung function, and effect modifications by genetics and body mass index (BMI), in elderly German women. METHODS: Data were analysed from the prospective SALIA (Study on the influence of Air pollution on Lung function, Inflammation and Aging) study (n=601). Spirometry was conducted at baseline (1985-1994; age 55 years), in 2007-2010 and in 2012-2013. Air pollution concentrations at home addresses were determined for each time-point using land-use regression models. Global Lung Initiative 2012 z-scores were calculated. Weighted genetic risk scores (GRSs) were determined from lung function-related risk alleles and used to investigate interactions with improved air quality. Multiple linear mixed models were fitted. RESULTS: Air pollution levels decreased substantially during the study period. Reduction of air pollution was associated with an increase in z-scores for forced expiratory volume in 1 s (FEV1) and the FEV1/forced vital capacity ratio. For a decrease of 10 µg·m-3 in nitrogen dioxide (NO2), the z-score for FEV1 increased by 0.14 (95% CI 0.01-0.26). However, with an increasing number of lung function-related risk alleles, the benefit from improved air quality decreased (GRS×NO2 interaction: p=0.029). Interactions with BMI were not significant. CONCLUSIONS: Reduction of air pollution is associated with a relative improvement of lung function in elderly women, but also depends on their genetic make-up.

On the impact of cannabis consumption on traffic safety: a driving simulator study with habitual cannabis consumers.

To contribute to the ongoing discussion about threshold limits of Δ9-tetrahydrocannabinol (THC) in road traffic, a driving simulator study with 15 habitually cannabis consuming test persons was conducted. Probands were tested on different routes after consumption of a maximum of three cannabis joints, each containing 300 µg THC/kg body weight (sober testing as well as testing directly, 3 and 6 h after cannabis consumption). Accompanying the drives, medical examinations including a blood sampling were performed. Driving faults and distinctive features in the medical examinations were allocated certain penalty points, which were then summed up and evaluated using the ANOVA model. The results showed that very high CIF values > 30 as well as serum THC concentrations > 15 ng/ml significantly increased the number of penalty points, but no direct correlation to the THC concentrations in serum and/or CIF values was detected. Instead, the point in time after cannabis consumption seems to play an important role concerning driving safety: significantly more driving faults were committed directly after consumption. Three hours after consumption, no significant increase of driving faults was seen. Six hours after consumption (during the so-called subacute phase), an increase of driving faults could be noted although not significant. Considering the limitation of our study (e.g. small test group, no placebo test persons, long lasting test situation with possible tiredness), further studies focusing on the time dependant impact of cannabis consumption on road traffic are required.

Identification of interactions of binary variables associated with survival time using survivalFS.

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel's concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

Pharmacotherapy in paediatric heart failure: a Delphi process.

BACKGROUND: Little evidence exists to support pharmacotherapeutic strategies for heart failure management in paediatrics. A recent Europe-wide survey suggests that this translates into substantial variability in clinical practice. OBJECTIVE: To conduct a formal discussion among an expert group of paediatric cardiology physicians on controversial aspects regarding the pharmacotherapy of children heart failure, facilitate consensus, and highlight areas of agreement and disagreement. METHODS: A two-round modified Delphi process was conducted between July and August 2015. Topics addressed were predominantly selected from the results of a previous Europe-wide survey. Fourteen statements were presented for discussion grouped under three categories; Angiotensin-converting-enzyme-inhibitors: Considerations for optimal dosage; Angiotensin-converting-enzyme-inhibitors for the management of CHDs; Neurohumoral antagonists for the management of dilated cardiomyopathy-related heart failure. RESULTS: A total of 13 paediatricians dedicated to cardiology from across Europe and the United States of America completed the study; of them, 92% had a working experience in the field of more than 10 years and were working in a specific paediatric cardiology unit. Agreement on the acceptance/rejection of 11 statements was achieved. Results show agreement on the importance of a set of topics relevant to the standardisation of the therapy as well as consensus upon specific therapeutic attitudes. CONCLUSIONS: We have found areas of common thinking and motivation, which can provide a means of triggering scientific collaboration. Our results might also contribute to disseminate available paediatric evidence and promote reducing unjustified variability in everyday practice. Until solid evidence is available, other research methods can contribute to advancing the goal of safe and effective paediatric heart failure pharmacotherapy.

Mining the Secretome of C2C12 Muscle Cells: Data Dependent Experimental Approach To Analyze Protein Secretion Using Label-Free Quantification and Peptide Based Analysis.

Secretome analysis faces several challenges including detection of low abundant proteins and the discrimination of bona fide secreted proteins from false-positive identifications stemming from cell leakage or serum. Here, we developed a two-step secretomics approach and applied it to the analysis of secreted proteins of C2C12 skeletal muscle cells since the skeletal muscle has been identified as an important endocrine organ secreting myokines as signaling molecules. First, we compared culture supernatants with corresponding cell lysates by mass spectrometry-based proteomics and label-free quantification. We identified 672 protein groups as candidate secreted proteins due to their higher abundance in the secretome. On the basis of Brefeldin A mediated blocking of classical secretory processes, we estimated a sensitivity of >80% for the detection of classical secreted proteins for our experimental approach. In the second step, the peptide level information was integrated with UniProt based protein information employing the newly developed bioinformatics tool "Lysate and Secretome Peptide Feature Plotter" (LSPFP) to detect proteolytic protein processing events that might occur during secretion. Concerning the proof of concept, we identified truncations of the cytoplasmic part of the protein Plexin-B2. Our workflow provides an efficient combination of experimental workflow and data analysis to identify putative secreted and proteolytic processed proteins.

Evidence for SNP-SNP interaction identified through targeted sequencing of cleft case-parent trios.

Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans, affecting 1 in 700 live births. This malformation has a complex etiology where multiple genes and several environmental factors influence risk. At least a dozen different genes have been confirmed to be associated with risk of NSCL/P in previous studies. However, all the known genetic risk factors cannot fully explain the observed heritability of NSCL/P, and several authors have suggested gene-gene (G × G) interaction may be important in the etiology of this complex and heterogeneous malformation. We tested for G × G interactions using common single nucleotide polymorphic (SNP) markers from targeted sequencing in 13 regions identified by previous studies spanning 6.3 Mb of the genome in a study of 1,498 NSCL/P case-parent trios. We used the R-package trio to assess interactions between polymorphic markers in different genes, using a 1 degree of freedom (1df) test for screening, and a 4 degree of freedom (4df) test to assess statistical significance of epistatic interactions. To adjust for multiple comparisons, we performed permutation tests. The most significant interaction was observed between rs6029315 in MAFB and rs6681355 in IRF6 (4df P = 3.8 × 10-8 ) in case-parent trios of European ancestry, which remained significant after correcting for multiple comparisons. However, no significant interaction was detected in trios of Asian ancestry.

Evaluation of non-negative matrix factorization of grey matter in age prediction.

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.

Integrative analysis of multiple genomic variables using a hierarchical Bayesian model.

MOTIVATION: Genes showing congruent differences in several genomic variables between two biological conditions are crucial to unravel causalities behind phenotypes of interest. Detecting such genes is important in biomedical research, e.g. when identifying genes responsible for cancer development. Small sample sizes common in next-generation sequencing studies are a key challenge, and there are still only very few statistical methods to analyze more than two genomic variables in an integrative, model-based way. Here, we present a novel bioinformatics approach to detect congruent differences between two biological conditions in a larger number of different measurements such as various epigenetic marks or mRNA transcript levels. RESULTS: We propose a coefficient quantifying the degree to which genes present consistent alterations in multiple (more than two) genomic variables when comparing samples presenting a condition of interest (e.g. cancer) to a reference group. A hierarchical Bayesian model is employed to assess uncertainty on a gene level, incorporating information on functional relationships between genes. We demonstrate the approach on different data sets containing RNA-seq gene transcripton and up to four ChIP-seq histone modification measurements. Both the coefficient-based ranking and the inference based on the model lead to a plausible prioritizing of candidate genes when analyzing multiple genomic variables. AVAILABILITY AND IMPLEMENTATION: BUGS code in the Supplement. CONTACT: m.schaefer@uni-duesseldorf.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effective Interventions for Diabetes Patients by Community Pharmacists: A Meta-analysis of Pharmaceutical Care Components.

OBJECTIVE: To evaluate randomized controlled trials (RCTs) that included interventions provided by community pharmacists for patients with type 1 and 2 diabetes, the analysis of each component of the intervention(s), and the description of the training that the pharmacists received. DATA SOURCES: The literature research was conducted in PubMed and in the Cochrane Central Register of Controlled Trials (January 2000 to April 2016) for RCTs with interventions provided by community pharmacists for patients with diabetes. Corresponding authors were contacted about missing data and intervention and training design. STUDY SELECTION AND DATA EXTRACTION: RCTs published in English or German were included if pharmaceutical care or medication therapy management was conducted by community pharmacists with diabetes patients. Basic information, intervention and training design data were extracted. DATA SYNTHESIS: The literature research resulted in 11 eligible studies for further analysis. The corresponding authors of 6 studies responded to our request and sent their raw data. The calculated meta-analytical effect of 640 analyzed patients was a hemoglobin A1C (A1C) difference of -0.66%, with a 95% CI of -0.86% to -0.45%. The analysis revealed that most intervention elements had a significant positive meta-analytical effect on the A1C values. CONCLUSIONS: Our meta-analysis suggests that community pharmacist-led interventions can improve glycemic control in patients with type 1 and 2 diabetes. The most effective intervention components were patient centered and interdisciplinary. Pharmaceutical care interventions should, therefore, include the following components: sending feedback to the physician, setting individual goals, reviewing medication, and assessing patients' health beliefs and medication knowledge.

Evaluating the effect of nicotinic cholinergic receptor genes on the risk of nonsyndromic cleft lip with or without cleft palate.

OBJECTIVE: Multiple studies have suggested nonsyndromic cleft lip with or without cleft palate (NSCL/P), and lung cancer may have common genetic etiology. Previous studies have showed genetic variants in nicotinic cholinergic receptor genes (CHRNs) may influence risk of lung cancer. We aimed to explore the effect of CHRNs on risk of NSCL/P considering gene-gene (GxG) interaction for these genes. SUBJECTS AND METHODS: We selected 120 markers in 14 CHRNs to test for GxG interaction using 806 Chinese case-parent trios recruited from an international consortium established for a GWAS of oral clefts. RESULTS: Totally, two pairs of SNPs yielded significant GxG interactions after Bonferroni correction (rs935865 and rs2337980 with p = 4.04 × 10-5 , rs2741335 and rs3743077 with p = 4.80 × 10-4 ), and these pairwise interactions were confirmed in permutation tests. In addition, the relative risk (RR) of the putative interaction between rs935865 and rs2337980 was 1.10 (95% CI: 0.92~1.31). CONCLUSIONS: While the single SNP association and the gene-environment interaction analysis of 14 CHRN genes yielded no signal, this study did demonstrate the importance of considering potential GxG interaction for exploring etiology of NSCL/P. This study suggests an important role for particular combinations of SNPs in CHRN genes in influencing risk to NSCL/P, which needs further study.

Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities.

We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as a diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data. Analysis of three Fastq batches downloaded from ArrayExpress indicated experimental effects. Analysis of RNAseq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced in our facility indicate presence of batch effects. The observed batch effects were also present in reads mapped to the human genome and also in reads filtered for high quality (Phred > 30). We propose, that hierarchical clustering of DNA k-mer counts provides an unspecific diagnostic tool for RNAseq experiments. Further exploration is required once samples are identified as outliers in HC derived trees.