Usage Information
Citation Information: JCI Insight. 2018;3(5):e98019. https://doi.org/10.1172/jci.insight.98019.
Abstract
BACKGROUND. Cirrhosis is associated with gut microbial changes, but current 16S rDNA techniques sequence both dead and live bacteria. We aimed to determine the rRNA content compared with DNA from the same stool sample to evaluate cirrhosis progression and predict hospitalizations. METHODS. Cirrhotics and controls provided stool for RNA and DNA analysis. Comparisons were made between cirrhotics/controls and within cirrhosis (compensated/decompensated, infected/uninfected, renal dysfunction/not, rifaximin use/not) with respect to DNA and RNA bacterial content using linear discriminant analysis. A separate group was treated with omeprazole for 14 days with longitudinal microbiota evaluation. Patients were followed for 90 days for hospitalizations. Multivariable models for hospitalizations with clinical data with and without DNA and RNA microbial data were created. RESULTS. Twenty-six controls and 154 cirrhotics (54 infected, 62 decompensated, 20 renal dysfunction, 18 rifaximin) were included. RNA and DNA analysis showed differing potentially pathogenic taxa but similar autochthonous taxa composition. Thirty subjects underwent the omeprazole study, which demonstrated differences between RNA and DNA changes. Thirty-six patients were hospitalized within 90 days. In the RNA model, MELD score and Enterococcus were independently predictive of hospitalizations, while in the DNA model MELD was predictive and Roseburia protective. In both models, adding microbiota significantly added to the MELD score in predicting hospitalizations. CONCLUSION. DNA and RNA analysis of the same stool sample demonstrated differing microbiota composition, which independently predicts the hospitalization risk in cirrhosis. RNA and DNA content of gut microbiota in cirrhosis are modulated differentially with disease severity, infections, and omeprazole use. TRIAL REGISTRATION. NCT01458990. FUNDING. VA Merit I0CX001076.
Authors
Jasmohan S. Bajaj, Leroy R. Thacker, Andrew Fagan, Melanie B. White, Edith A. Gavis, Phillip B. Hylemon, Robert Brown, Chathur Acharya, Douglas M. Heuman, Michael Fuchs, Swati Dalmet, Masoumeh Sikaroodi, Patrick M. Gillevet
Usage data is cumulative from December 2024 through December 2025.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 515 | 79 |
| 94 | 17 | |
| Figure | 343 | 3 |
| Table | 109 | 0 |
| Supplemental data | 31 | 1 |
| Citation downloads | 141 | 0 |
| Totals | 1,233 | 100 |
| Total Views | 1,333 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
