Bayesian variable selection logistic regression with paired proteomic measurements.

We explore the problem of variable selection in a case-control setting with mass spectrometry proteomic data consisting of paired measurements. Each pair corresponds to a distinct isotope cluster and each component within pair represents a summary of isotopic expression based on either the intensity or the shape of the cluster. Our objective is to identify a collection of isotope clusters associated with the disease outcome and at the same time assess the predictive added-value of shape beyond intensity while maintaining predictive performance. We propose a Bayesian model that exploits the paired structure of our data and utilizes prior information on the relative predictive power of each source by introducing multiple layers of selection. This allows us to make simultaneous inference on which are the most informative pairs and for which-and to what extent-shape has a complementary value in separating the two groups. We evaluate the Bayesian model on pancreatic cancer data. Results from the fitted model show that most predictive potential is achieved with a subset of just six (out of 1289) pairs while the contribution of the intensity components is much higher than the shape components. To demonstrate how the method behaves under a controlled setting we consider a simulation study. Results from this study indicate that the proposed approach can successfully select the truly predictive pairs and accurately estimate the effects of both components although, in some cases, the model tends to overestimate the inclusion probability of the second component.

Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies.

Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

Impact of synbiotics on gut microbiota during early life: a randomized, double-blind study.

Human milk is considered the optimal nutrition for infants and found to contain significant numbers of viable bacteria. The aim of the study was to assess the effects of a specific synbiotic combination at doses closer to the bacterial cells present in human milk, on intestinal bifidobacteria proportions (relative abundance), reduction of potential pathogens and gut physiological conditions. A clinical study was conducted in 290 healthy infants aged from 6 to 19 weeks. Infants received either a control infant formula or one of the two investigational infant formulas (control formula with 0.8 g/100 ml scGOS/lcFOS and Bifidobacterium breve M-16V at either 1 × 104 cfu/ml or 1 × 106 cfu/ml). Exclusively breastfed infants were included as a reference. Analyses were performed on intention-to-treat groups and all-subjects-treated groups. After 6 weeks of intervention, the synbiotics at two different doses significantly increased the bifidobacteria proportions in healthy infants. The synbiotic supplementation also decreased the prevalence (infants with detectable levels) and the abundance of C. difficile. Closer to the levels in the breastfed reference group, fecal pH was significantly lower while L-lactate concentrations and acetate proportions were significantly higher in the synbiotic groups. All formulas were well tolerated and all groups showed a comparable safety profile based on the number and severity of adverse events and growth. In healthy infants, supplementation of infant-type bifidobacterial strain B. breve M-16V, at a dose close to bacterial numbers found in human milk, with scGOS/lcFOS (9:1) created a gut environment closer to the breastfed reference group. This specific synbiotic mixture may also support gut microbiota resilience during early life.Clinical Trial Registration This clinical study named Color Synbiotics Study, was registered in ClinicalTrials.gov on 18 March 2013. Registration number is NCT01813175. https://clinicaltrials.gov/ct2/show/NCT01813175 .

Frailty, Sarcopenia, and Malnutrition Frequently (Co-)occur in Hospitalized Older Adults: A Systematic Review and Meta-analysis.

OBJECTIVES: The purpose of this systematic review and meta-analysis was to summarize the prevalence of, and association between, physical frailty or sarcopenia and malnutrition in older hospitalized adults. DESIGN: A systematic literature search was performed in 10 databases. SETTING AND PARTICIPANTS: Articles were selected that evaluated physical frailty or sarcopenia and malnutrition according to predefined criteria and cutoffs in older hospitalized patients. MEASURES: Data were pooled in a meta-analysis to evaluate the prevalence of prefrailty and frailty [together (pre-)frailty], sarcopenia, and risk of malnutrition and malnutrition [together (risk of) malnutrition], and the association between either (pre-)frailty or sarcopenia and (risk of) malnutrition. RESULTS: Forty-seven articles with 18,039 patients (55% female) were included in the systematic review, and 39 articles (8868 patients, 62% female) were eligible for the meta-analysis. Pooling 11 studies (2725 patients) revealed that 84% [95% confidence interval (CI): 77%, 91%, I2 = 98.4%] of patients were physically (pre-)frail. Pooling 15 studies (4014 patients) revealed that 37% (95% CI: 26%, 48%, I2 = 98.6%) of patients had sarcopenia. Pooling 28 studies (7256 patients) revealed a prevalence of 66% (95% CI: 58%, 73%, I2 = 98.6%) (risk of) malnutrition. Pooling 10 studies (2427 patients) revealed a high association [odds ratio (OR): 5.77 (95% CI: 3.88, 8.58), P < .0001, I2 = 42.3%] and considerable overlap (49.7%) between physical (pre-)frailty and (risk of) malnutrition. Pooling 7 studies (2506 patients) revealed a high association [OR: 4.06 (95% CI: 2.43, 6.80), P < .0001, I2 = 71.4%] and considerable overlap (41.6%) between sarcopenia and (risk of) malnutrition. CONCLUSIONS AND IMPLICATIONS: The association between and prevalence of (pre-)frailty or sarcopenia and (risk of) malnutrition in older hospitalized adults is substantial. About half of the hospitalized older adults suffer from 2 and perhaps 3 of these debilitating conditions. Therefore, standardized screening for these conditions at hospital admission is highly warranted to guide targeted nutritional and physical interventions.

Adapting censored regression methods to adjust for the limit of detection in the calibration of diagnostic rules for clinical mass spectrometry proteomic data.

In this paper, we consider the problem of calibrating diagnostic rules based on high-resolution mass spectrometry data subject to the limit of detection. The limit of detection is related to the limitation of instruments in measuring low-concentration proteins. As a consequence, peak intensities below the limit of detection are often reported as missing during the quantification step of proteomic analysis. We propose the use of censored data methodology to handle spectral measurements within the presence of limit of detection, recognizing that those have been left-censored for low-abundance proteins. We replace the set of incomplete spectral measurements with estimates of the expected intensity and use those as input to a prediction model. To correct for lack of information and measurement uncertainty, we combine this approach with borrowing of information through the addition of an individual-specific random effect formulation. We present different modalities of using the above formulation for prediction purposes and show how it may also allow for variable selection. We evaluate the proposed methods by comparing their predictive performance with the one achieved using the complete information as well as alternative methods to deal with the limit of detection.

Combination approaches improve predictive performance of diagnostic rules for mass-spectrometry proteomic data.

We consider a proteomic mass spectrometry case-control study for the construction of a diagnostic rule for patients' disease status allocation. We propose an approach for combining a collection of classifiers for the construction of a "combined" classification rule in order to enhance calibration and prediction ability. In a first stage this is achieved by building individual classifiers separately, each one using the entire proteomic data set. A double leave-one-out cross-validatory approach is used to estimate the class-predicted probabilities on which the combination method will be calibrated. The performance of the combination approach is examined both through a breast cancer proteomic data set and through simulation studies. Our experimental results indicate that in many circumstances gains in classification performance and predictive accuracy can be achieved.