Gastrointestinal well-being in subjects reporting mild gastrointestinal discomfort: characteristics and properties of a global assessment measure.

There is a lack of recognised markers for measuring gastrointestinal (GI) well-being and digestive symptoms in the general population. The aim of the present study was to demonstrate construct validity of a global assessment tool of GI well-being. In this randomised double-blind study, 197 adult women consumed either a probiotic fermented milk or a control dairy product daily during 4 weeks. GI well-being was assessed weekly using a single question and subjects indicated whether their GI well-being remained the same, improved or worsened compared with the baseline period. Responders for GI well-being were subjects reporting improvement for at least 2 weeks of the 4 weeks of intervention. Frequency of individual digestive symptoms was assessed weekly. Health-related quality of life (HRQoL) was measured at baseline and at the end of the study. Subjects reporting improvement of their GI well-being had a significantly (P<0·05) lower frequency of combined digestive symptoms than individuals with no change, whereas subjects with worsened GI well-being had a significantly (P<0·05) higher digestive symptom frequency. Number of weeks with reported GI well-being improvement was significantly (P<0·05) correlated with the decrease in digestive symptoms (r 0·58) and the HRQoL digestive comfort dimension (r 0·47). When compared with non-responders, responders had significantly (P<0·0001) fewer average digestive symptoms and higher scores on digestive comfort of the HRQoL questionnaire. The data provide construct validity for a single-item questionnaire as a measure of GI well-being improvement. These data support the use of this questionnaire as an end point for nutritional intervention in the general population.

Hydration biomarkers in free-living adults with different levels of habitual fluid consumption.

Little is known about the impact of habitual fluid intake on physiology. Specifically, biomarkers of hydration status and body water regulation have not been adequately explored in adults who consume different fluid volumes in everyday conditions, without prolonged exercise or environmental exposure. The purpose of the present study was to compare adults with habitually different fluid intakes with respect to biomarkers implicated in the assessment of hydration status, the regulation of total body water and the risk of kidney pathologies. In the present cross-sectional study, seventy-one adults (thirty-two men, thirty-nine women, age 25­40 years) were classified according to daily fluid intake: thirty-nine low drinkers (LD; ≤ 1·2 litres/d) and thirty-two high drinkers (HD; 2­4 litres/d). During four consecutive days, urinary parameters (first morning urine (FMU) on day 1 and subsequent 24 h urine (24hU) collections), blood parameters, and food and beverage intake were assessed. ANOVA and non-parametric comparisons revealed significant differences between the LD and HD groups in 24hU volume (1·0 (se 0·1) v. 2·4 (se 0·1) litres), specific gravity (median 1·023 v. 1·010), osmolality (767 (se 27) v. 371 (se 33) mOsm/kg) and colour (3·1 (se 0·2) v. 1·8 (se 0·2)). Similarly, in the FMU, the LD group produced a smaller amount of more concentrated urine. Plasma cortisol, creatinine and arginine vasopressin concentrations were significantly higher among the LD. Plasma osmolality was similar between the groups, suggesting physiological adaptations to preserve plasma osmolality despite low fluid intake. The long-term impact of adaptations to preserve plasma osmolality must be examined, particularly in the context of renal health.

Dietary patterns, digestive symptoms, and health-related quality of life in women reporting minor digestive symptoms.

OBJECTIVE: Digestive symptoms are reported to result from a wide range of dietary components. Dietary pattern analysis is a useful method when considering the entire diet, rather than individual foods or nutrients, providing an opportunity to take interactions into account. The aim of the present study was to investigate, using a dietary pattern approach, the relationship between diet, digestive symptoms, and health-related quality of life (HRQoL) in women reporting minor digestive symptoms. METHODS: Analysis was performed on dietary and digestive symptoms data collected in France. Women (N = 308, ages 18-60 y) reporting a bowel movement frequency within the normal range (3-21 stools/wk) but with minor digestive symptoms in the previous month were studied. Dietary data was collected using three 24-h recalls. K-means was used to divide the dietary data into clusters. The frequency of digestive symptoms (abdominal discomfort or pain, bloating, flatulence, borborygmi) and bowel movements were evaluated over a 2-wk period. HRQoL was also assessed. RESULTS: Four dietary clusters were identified and characterized as unhealthy, balance, healthy, and convenience. No differences were found in the frequency of digestive symptoms according to dietary cluster, except for flatulence (P = 0.030), which was more prevalent in the unhealthy and convenience clusters. No significant differences were observed in HRQoL according to dietary clusters. CONCLUSIONS: Results from the present study demonstrated that even within a relatively homogeneous sample of French women, distinct dietary patterns can be identified but without significant differences in digestive symptoms (except for flatulence) or HRQoL.

MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities.

Culture independent techniques, such as shotgun metagenomics and 16S rRNA amplicon sequencing have dramatically changed the way we can examine microbial communities. Recently, changes in microbial community structure and dynamics have been associated with a growing list of human diseases. The identification and comparison of bacteria driving those changes requires the development of sound statistical tools, especially if microbial biomarkers are to be used in a clinical setting. We present mixMC, a novel multivariate data analysis framework for metagenomic biomarker discovery. mixMC accounts for the compositional nature of 16S data and enables detection of subtle differences when high inter-subject variability is present due to microbial sampling performed repeatedly on the same subjects, but in multiple habitats. Through data dimension reduction the multivariate methods provide insightful graphical visualisations to characterise each type of environment in a detailed manner. We applied mixMC to 16S microbiome studies focusing on multiple body sites in healthy individuals, compared our results with existing statistical tools and illustrated added value of using multivariate methodologies to fully characterise and compare microbial communities.