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Reproducibility of Assessing Fecal Microbiota in Chronic Constipation

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Date 2017 Jul 29
PMID 28752633
Citations 11
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Abstract

Background: While limited data suggest that the fecal microbiota in healthy people is stable over time, the intraindividual variability of the fecal microbiota in constipated patients is unknown.

Methods: This study evaluated the intraindividual reproducibility of fecal microbiota analyzed with 16S rRNA gene sequencing in two stool samples collected without and after a laxative, respectively, in 25 healthy people and 25 constipated women. Participants completed a food record for 3 d before the stool collection. Colonic transit was measured with scintigraphy.

Key Results: The constipated patients were older (48±15 vs 39±10 y, P=.02) than healthy participants but had a similar BMI. The total daily caloric intake was less (P=.005) in constipated (1265±350 kcal) than healthy participants (1597±402 kcal). Fourteen patients but only two controls (P<.005), had delayed colonic transit. For most measures of alpha (eg, Observed OTU number, Shannon index) and beta diversity (eg, Bray-Curtis dissimilarity, UniFrac, phyla level abundance), the ICCs between two stool samples were high, indicating moderate or strong agreement, and similar in healthy people and constipated patients. The ICC for the weighted UniFrac distance, which is weighted by abundance, was lower than its unweighted counterpart, indicating that the unweighted measure is more robust and reproducible.

Conclusions And Inferences: The intraindividual reproducibility of fecal microbiota in constipated patients is high and comparable to healthy participants. For most purposes, evaluating the fecal microbiota in a single stool sample should generally suffice in adequately powered studies of healthy and constipated patients.

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References
1.
Wu G, Chen J, Hoffmann C, Bittinger K, Chen Y, Keilbaugh S . Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011; 334(6052):105-8. PMC: 3368382. DOI: 10.1126/science.1208344. View

2.
Vogtmann E, Chen J, Amir A, Shi J, Abnet C, Nelson H . Comparison of Collection Methods for Fecal Samples in Microbiome Studies. Am J Epidemiol. 2016; 185(2):115-123. PMC: 5253972. DOI: 10.1093/aje/kww177. View

3.
Jalanka J, Salonen A, Salojarvi J, Ritari J, Immonen O, Marciani L . Effects of bowel cleansing on the intestinal microbiota. Gut. 2014; 64(10):1562-8. DOI: 10.1136/gutjnl-2014-307240. View

4.
Sandler R, Jordan M, Shelton B . Demographic and dietary determinants of constipation in the US population. Am J Public Health. 1990; 80(2):185-9. PMC: 1404600. DOI: 10.2105/ajph.80.2.185. View

5.
Kelly B, Gross R, Bittinger K, Sherrill-Mix S, Lewis J, Collman R . Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA. Bioinformatics. 2015; 31(15):2461-8. PMC: 4514928. DOI: 10.1093/bioinformatics/btv183. View