Usage Information
Citation Information: JCI Insight. 2021;6(4):e143299. https://doi.org/10.1172/jci.insight.143299.
Abstract
Background Mitochondrial DNA (MT-DNA) are intrinsically inflammatory nucleic acids released by damaged solid organs. Whether circulating cell-free MT-DNA quantitation could be used to predict the risk of poor COVID-19 outcomes remains undetermined.Methods We measured circulating MT-DNA levels in prospectively collected, cell-free plasma samples from 97 subjects with COVID-19 at hospital presentation. Our primary outcome was mortality. Intensive care unit (ICU) admission, intubation, vasopressor, and renal replacement therapy requirements were secondary outcomes. Multivariate regression analysis determined whether MT-DNA levels were independent of other reported COVID-19 risk factors. Receiver operating characteristic and area under the curve assessments were used to compare MT-DNA levels with established and emerging inflammatory markers of COVID-19.Results Circulating MT-DNA levels were highly elevated in patients who eventually died or required ICU admission, intubation, vasopressor use, or renal replacement therapy. Multivariate regression revealed that high circulating MT-DNA was an independent risk factor for these outcomes after adjusting for age, sex, and comorbidities. We also found that circulating MT-DNA levels had a similar or superior area under the curve when compared against clinically established measures of inflammation and emerging markers currently of interest as investigational targets for COVID-19 therapy.Conclusion These results show that high circulating MT-DNA levels are a potential early indicator for poor COVID-19 outcomes.Funding Washington University Institute of Clinical Translational Sciences COVID-19 Research Program and Washington University Institute of Clinical Translational Sciences (ICTS) NIH grant UL1TR002345.
Authors
Davide Scozzi, Marlene Cano, Lina Ma, Dequan Zhou, Ji Hong Zhu, Jane A. O’Halloran, Charles Goss, Adriana M. Rauseo, Zhiyi Liu, Sanjaya K. Sahu, Valentina Peritore, Monica Rocco, Alberto Ricci, Rachele Amodeo, Laura Aimati, Mohsen Ibrahim, Ramsey Hachem, Daniel Kreisel, Philip A. Mudd, Hrishikesh S. Kulkarni, Andrew E. Gelman
Usage data is cumulative from December 2022 through December 2023.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 2,204 | 514 |
| 115 | 126 | |
| Figure | 134 | 9 |
| Table | 109 | 0 |
| Supplemental data | 62 | 11 |
| Citation downloads | 41 | 0 |
| Totals | 2,665 | 660 |
| Total Views | 3,325 | |
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.
