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This preclinical AD CSF proteome study identified a panel of 12-CSF markers detecting amyloid positivity and clinical progression to AD with high accuracy; some of these CSF proteins related to immune function, neurotrophic processes, energy metabolism and endolysosomal functioning (e.g., ITGB2, CLEC5A, IGFBP-1, CST3) changed before amyloid positivity is established.

Objectives To investigate the association between leisure time physical activity (LTPA) and MRI-based diastolic function and the mediating role of metabolic health. Methods This cross-sectional analysis comprised 901 participants (46%women, mean age (SD): 56 (6) years (The Netherlands, 2008–2012)). LTPA was assessed via questionnaire, quantified in metabolic equivalent of tasks (METs)-minutes per week and participants underwent abdominal and cardiovascular MRI. Confirmatory factor analysis was used to construct the metabolic load factor. Piecewise structural equation model with adjustments for confounders was used to determine associations between LTPA and diastolic function and the mediating effect of metabolic load. Results Significant differences in mitral early/late peak filling rate (E/A) ratio per SD of LTPA (men=1999, women=1870 MET-min/week) of 0.18, (95% CI= 0.03 to 0.33, p=0.021) were observed in men, but not in women: −0.01 (−0.01 to 0.34, p=0.058). Difference in deceleration time of mitral early filling (E-DT) was 0.13 (0.01 to 0.24, p=0.030) in men and 0.17 (0.05 to 0.28, p=0.005) in women. Metabolic load, including MRI-based visceral and subcutaneous adipose tissue, fasting glucose, high-density lipoprotein cholesterol and triglycerides, mediated these associations as follows: E/A-ratio of 0.030 (0.000 to 0.067, 19% mediated, p=0.047) in men but not in women: 0.058 (0.027 to 0.089, p<0.001) and E-DT not in men 0.004 (−0.012 to 0.021, p=0.602) but did in women 0.044 (0.013 to 0.057, 27% mediated, p=0.006). Conclusions A larger amount of LTPA was associated with improved diastolic function where confirmatory factor analysis-based metabolic load partly mediated this effect. Future studies should assess whether improving indicators of metabolic load alongside LTPA will benefit healthy diastolic function even more.

Intoduction: In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks. Objective: In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves. Methods: The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data. Conclusions: We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

Diagnosis of dementia with Lewy bodies (DLB) is challenging and specific biofluid biomarkers are highly needed. We employed proximity extension-based assays to measure 665 proteins in the cerebrospinal fluid (CSF) from patients with DLB (n = 109), Alzheimer´s disease (AD, n = 235) and cognitively unimpaired controls (n = 190). We identified over 50 CSF proteins dysregulated in DLB, enriched in myelination processes among others. The dopamine biosynthesis enzyme DDC was the strongest dysregulated protein, and could efficiently discriminate DLB from controls and AD (AUC:0.91 and 0.81 respectively). Classification modeling unveiled a 7-CSF biomarker panel that better discriminate DLB from AD (AUC:0.93). A custom multiplex panel for six of these markers (DDC, CRH, MMP-3, ABL1, MMP-10, THOP1) was developed and validated in independent cohorts, including an AD and DLB autopsy cohort. This DLB CSF proteome study identifies DLB-specific protein changes and translates these findings to a practicable biomarker panel that accurately identifies DLB patients, providing promising diagnostic and clinical trial testing opportunities.

BackgroundFrontotemporal dementia (FTD) is caused by frontotemporal lobar degeneration (FTLD) and the most common forms are characterized by either tau (FTLD-Tau) or TDP43 (FTLD-TDP) brain aggregates. However, FTD-specific fluid biomarkers are lacking. Furthermore, the pathological subtypes are not distinct in their presentation, hampering accurate subtyping at clinical diagnosis. Therefore, there is a strong need to identify fluid biomarkers that could aid in FTD diagnosis and to discriminate the pathological subtypes.MethodWe employed an antibody-based proteomic technology to analyze >600 proteins in a large multicenter cohort including cerebrospinal fluid (CSF) samples from FTD (n = 189), AD (n = 235) and cognitively unimpaired individuals (n = 196). For a subset of cases the underlying neuropathology was known or could be predicted (FTLD-Tau = 85 and FTLD-TDP = 57). Differences in protein expression profiles were analyzed by nested linear models. Penalized generalized linear modeling was used to identify classification protein panels. Protein panels were then validated in independent clinical cohorts (cohort 1: n = 157; cohort 2: n = 165) and a neuropathology cohort (n = 100) using customized assays.ResultWe observed 65 differentially regulated proteins in FTD versus controls and AD patients, associated with axonogenesis, synapse assembly, or locomotory behavior pathways. We identified panels of 14 and 13 proteins that could discriminate FTD from controls (AUC = 0.96, 95%CI:0.91-0.99) and AD patients (AUC = 0.91, 95%CI:0.85-0.96), respectively. Most of these proteins (21 out of 27) were translated into customized panels, which discriminated between groups with high accuracy for all three cohorts (FTDvsCon: AUCs > 0.96, FTDvsAD: AUCs > 0.88). When comparing the FTLD-Tau and FTLD-TDP subtypes, we observed that 86 proteins were increased in FTLD-Tau, and associated with developmental and cellular processes and locomotion pathways. A panel of 8 proteins could discri

The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data., Statistics, Education and Student Affairs

Contains fulltext : 283883.pdf (Publisher’s version ) (Open Access)

Contains fulltext : 283875.pdf (Publisher’s version ) (Open Access)

The features in a high-dimensional biomedical prediction problem are often well described by low-dimensional latent variables (or factors). We use this to include unlabeled features and additional information on the features when building a prediction model. Such additional feature information is often available in biomedical applications. Examples are annotation of genes, metabolites, or p-values from a previous study. We employ a Bayesian factor regression model that jointly models the features and the outcome using Gaussian latent variables. We fit the model using a computationally efficient variational Bayes method, which scales to high dimensions. We use the extra information to set up a prior model for the features in terms of hyperparameters, which are then estimated through empirical Bayes. The method is demonstrated in simulations and two applications. One application considers influenza vaccine efficacy prediction based on microarray data. The second application predicts oral cancer metastasis from RNAseq data.

Genomic selection entails the estimation of phenotypic traits of interest for plants without phenotype based on the association between single-nucleotide polymorphisms (SNPs) and phenotypic traits for plants with phenotype. Typically, the number of SNPs far exceeds the number of samples (high-dimensionality) and, therefore, usage of regularization methods is common. The most common approach to estimate marker-trait associations uses the genomic best linear unbiased predictor (GBLUP) method, where a mixed model is fitted to the data. GBLUP has also been alternatively parameterized as a ridge regression model (RRBLUP). GBLUP/RRBLUP is based on the assumption of independence between predictor variables. However, it is to be expected that variables will be associated due to their genetic proximity. Here, we propose a regularized linear model (namely psBLUP: proximity smoothed BLUP) that explicitly models the dependence between predictor effects. We show that psBLUP can improve accuracy compared to the standard methods on both Arabidopsis thaliana data and Barley data.

Contains fulltext : 239291.pdf (Publisher’s version ) (Closed access)

Purpose: Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. Methods: Data consisted of self-reports of cancer survivors who used a fully automated online application ‘Oncokompas’ that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. Results: When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. Conclusion: There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.

Objectives: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. Materials and Methods: Native T1-weighted images of four independent, retrospective (2005–2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). Results: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). Conclusions: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. Key Points: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisi