Improving lactose digestion and symptoms of lactose intolerance with a novel galacto-oligosaccharide (RP-G28): a randomized, double-blind clinical trial.

BACKGROUND: Lactose intolerance (LI) is a common medical problem with limited treatment options. The primary symptoms are abdominal pain, diarrhea, bloating, flatulence, and cramping. Limiting dairy foods to reduce symptoms contributes to low calcium intake and the risk for chronic disease. Adaptation of the colon bacteria to effectively metabolize lactose is a novel and potentially useful approach to improve lactose digestion and tolerance. RP-G28 is novel galacto-oligosaccharide (GOS) being investigated to improve lactose digestion and the symptoms of lactose intolerance in affected patients. METHODS: A randomized, double-blind, parallel group, placebo-controlled study was conducted at 2 sites in the United States. RP-G28 or placebo was administered to 85 patients with LI for 35 days. Post-treatment, subjects reintroduced dairy into their daily diets and were followed for 30 additional days to evaluate lactose digestion as measured by hydrogen production and symptom improvements via a patient-reported symptom assessment instrument. RESULTS: Lactose digestion and symptoms of LI trended toward improvement on RP-G28 at the end of treatment and 30 days post-treatment. A reduction in abdominal pain was also demonstrated in the study results. Fifty percent of RP-G28 subjects with abdominal pain at baseline reported no abdominal pain at the end of treatment and 30 days post treatment (p = 0.0190). RP-G28 subjects were also six times more likely to claim lactose tolerance post-treatment once dairy foods had been re-introduced into their diets (p = 0.0389). CONCLUSIONS: Efficacy trends and favorable safety/tolerability findings suggest that RP-G28 appears to be a potentially useful approach for improving lactose digestion and LI symptoms. The concurrent reduction in abdominal pain and improved overall tolerance could be a meaningful benefit to lactose intolerant individuals.

Sparse Regulatory Networks.

In many organisms the expression levels of each gene are controlled by the activation levels of known "Transcription Factors" (TF). A problem of considerable interest is that of estimating the "Transcription Regulation Networks" (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L(1) penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates.

Automated multidimensional phenotypic profiling using large public microarray repositories.

Phenotypes are complex, and difficult to quantify in a high-throughput fashion. The lack of comprehensive phenotype data can prevent or distort genotype-phenotype mapping. Here, we describe "PhenoProfiler," a computational method that enables in silico phenotype profiling. Drawing on the principle that similar gene expression patterns are likely to be associated with similar phenotype patterns, PhenoProfiler supplements the missing quantitative phenotype information for a given microarray dataset based on other well-characterized microarray datasets. We applied our method to 587 human microarray datasets covering >14,000 samples, and confirmed that the predicted phenotype profiles are highly consistent with true phenotype descriptions. PhenoProfiler offers several unique capabilities: (i) automated, multidimensional phenotype profiling, facilitating the analysis and treatment design of complex diseases; (ii) the extrapolation of phenotype profiles beyond provided classes; and (iii) the detection of confounding phenotype factors that could otherwise bias biological inferences. Finally, because no direct comparisons are made between gene expression values from different datasets, the method can use the entire body of cross-platform microarray data. This work has produced a compendium of phenotype profiles for the National Center for Biotechnology Information GEO datasets, which can facilitate an unbiased understanding of the transcriptome-phenome mapping. The continued accumulation of microarray data will further increase the power of PhenoProfiler, by increasing the variety and the quality of phenotypes to be profiled.

A comparison of outcomes among patients with schizophrenia in two mental health systems: a health state approach.

This paper introduces a health state modeling approach using clustering and Markov analysis to compare short- and long-term outcomes among health care populations. We provide a comparison to more conventional mixed effects regression methods and show that discrete state modeling offers a richer portrait of patient outcomes than the standard univariate techniques. We demonstrate our approach using primary data from a three year observational study of patients treated for schizophrenia at a VA Medical Center (VA) and in a Community Mental Health Center (CMHC) in the same urban community. Randomly selected samples of outpatients treated for schizophrenia or schizoaffective disorder were interviewed every six months using standardized psychiatric assessments such as the Positive and Negative Syndrome Scale (PANSS). Items from the PANSS were used to define 7 discrete health states representing different levels of severity and diverse mixtures of psychiatric symptoms. Conventional analysis showed that VA patients exhibited increasingly severe symptoms, while CMHC patients remained more stable over the study period. Health state analysis reinforced these results but also identified which subpopulations of VA patients were deteriorating. In particular they showed that there was little change over time among VA patients in the best and worst health states. Instead the deterioration was caused by VA patients with: a) mild symptoms and hallucinations and b) serious positive and negative symptoms, being more likely to enter a state with severe positive and negative symptoms accompanied by moderate general distress.

Bayesian sparse hidden components analysis for transcription regulation networks.

MOTIVATION: In systems like Escherichia Coli, the abundance of sequence information, gene expression array studies and small scale experiments allows one to reconstruct the regulatory network and to quantify the effects of transcription factors on gene expression. However, this goal can only be achieved if all information sources are used in concert. RESULTS: Our method integrates literature information, DNA sequences and expression arrays. A set of relevant transcription factors is defined on the basis of literature. Sequence data are used to identify potential target genes and the results are used to define a prior distribution on the topology of the regulatory network. A Bayesian hidden component model for the expression array data allows us to identify which of the potential binding sites are actually used by the regulatory proteins in the studied cell conditions, the strength of their control, and their activation profile in a series of experiments. We apply our methodology to 35 expression studies in E.Coli with convincing results. AVAILABILITY: www.genetics.ucla.edu/labs/sabatti/software.html SUPPLEMENTARY INFORMATION: The supplementary material are available at Bioinformatics online.

Discrete state analysis for interpretation of data from clinical trials.

OBJECTIVE: The objective of this study was to demonstrate a multivariate health state approach to analyzing complex disease data that allows projection of long-term outcomes using clustering, Markov modeling, and preference weights. SUBJECTS: We studied patients hospitalized 30 to 364 days with refractory schizophrenia at 15 Veterans Affairs medical centers. STUDY DESIGN: We conducted a randomized clinical trial comparing clozapine, an atypical antipsychotic, and haloperidol, a conventional antipsychotic. METHODS: Health status instruments measuring disease-related symptoms and drug side effects were administered in face-to-face interviews at baseline, 6 weeks, and quarterly follow-up intervals for 1 year. Cost data were derived from Veterans Affairs records supplemented by interviews. K-means clustering was used to identify a small number of health states for each instrument. Markov modeling was used to estimate long-term outcomes. RESULTS: Multivariate models with 7 and 6 states, respectively, were required to describe patterns of psychiatric symptoms and side effects (movement disorders). Clozapine increased the proportion of clients in states characterized by mild psychiatric symptoms and decreased the proportion with severe positive symptoms but showed no long-term benefit for negative symptoms. Clozapine dramatically increased the proportion of patients with no movement side effects and decreased incidences of mild akathisia. Effects on extrapyramidal symptoms and tardive dyskinesia were far less pronounced and slower to develop. Markov modeling confirms the consistency of these findings. CONCLUSIONS: Analyzing complex disease data using multivariate health state models allows a richer understanding of trial effects and projection of long-term outcomes. Although clozapine generates substantially fewer side effects than haloperidol, its impact on psychiatric aspects of schizophrenia is less robust and primarily involves positive symptoms.