Manipulation of dietary short chain carbohydrates alters the pattern of gas production and genesis of symptoms in irritable bowel syndrome

Background and Aim:  Reduction of short-chain poorly absorbed carbohydrates (FODMAPs) in the diet reduces symptoms of irritable bowel syndrome (IBS). In the present study, we aimed to compare the patterns of breath hydrogen and methane and symptoms produced in response to diets that differed only in FODMAP content.
Methods:  Fifteen healthy subjects and 15 with IBS (Rome III criteria) undertook a single-blind, crossover intervention trial involving consuming provided diets that were either low (9 g/day) or high (50 g/day) in FODMAPs for 2 days. Food and gastrointestinal symptom diaries were kept and breath samples collected hourly over 14 h on day 2 of each diet.
Results:  Higher levels of breath hydrogen were produced over the entire day with the high FODMAP diet for healthy volunteers (181 ± 77 ppm.14 h vs 43 ± 18; mean ± SD P < 0.0001) and patients with IBS (242 ± 79 vs 62 ± 23; P < 0.0001), who had higher levels during each dietary period than the controls (P < 0.05). Breath methane, produced by 10 subjects within each group, was reduced with the high FODMAP intake in healthy subjects (47 ± 29 vs 109 ± 77; P = 0.043), but was not different in patients with IBS (126 ± 153 vs 86 ± 72). Gastrointestinal symptoms and lethargy were significantly induced by the high FODMAP diet in patients with IBS, while only increased flatus production was reported by healthy volunteers.
Conclusions:  Dietary FODMAPs induce prolonged hydrogen production in the intestine that is greater in IBS, influence the amount of methane produced, and induce gastrointestinal and systemic symptoms experienced by patients with IBS. The results offer mechanisms underlying the efficacy of the low FODMAP diet in IBS.

Dimensionality reduction of protein mass spectrometry data using random projection

Protein mass spectrometry (MS) pattern recognition has recently emerged as a new method for cancer diagnosis. Unfortunately, classification performance may degrade owing to the enormously high dimensionality of the data. This paper investigates the use of Random Projection in protein MS data dimensionality reduction. The effectiveness of Random Projection (RP) is analyzed and compared against Principal Component Analysis (PCA) by using three classification algorithms, namely Support Vector Machine, Feed-forward Neural Networks and K-Nearest Neighbour. Three real-world cancer data sets are employed to evaluate the performances of RP and PCA. Through the investigations, RP method demonstrated better or at least comparable classification performance as PCA if the dimensionality of the projection matrix is sufficiently large. This paper also explores the use of RP as a pre-processing step prior to PCA. The results show that without sacrificing classification accuracy, performing RP prior to PCA significantly improves the computational time.