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Obesity, Acute Kidney Injury, and Mortality in Critical Illness

Danziger, John MD, MPhil1; Chen, Ken P. MD1; Lee, Joon PhD2,3; Feng, Mengling PhD2,4; Mark, Roger G. MD, PhD2; Celi, Leo Anthony MD, MPH1,2; Mukamal, Kenneth J. MD, MPH1

doi: 10.1097/CCM.0000000000001398
Clinical Investigations

Objectives: Although obesity is associated with risk for chronic kidney disease and improved survival, less is known about the associations of obesity with risk of acute kidney injury and post acute kidney injury mortality.

Design: In a single-center inception cohort of almost 15,000 critically ill patients, we evaluated the association of obesity with acute kidney injury and acute kidney injury severity, as well as in-hospital and 1-year survival. Acute kidney injury was defined using the Kidney Disease Outcome Quality Initiative criteria.

Measurements and Main Results: The acute kidney injury prevalence rates for normal, overweight, class I, II, and III obesity were 18.6%, 20.6%, 22.5%, 24.3%, and 24.0%, respectively, and the adjusted odds ratios of acute kidney injury were 1.18 (95% CI, 1.06–1.31), 1.35 (1.19–1.53), 1.47 (1.25–1.73), and 1.59 (1.31–1.87) when compared with normal weight, respectively. Each 5-kg/m2 increase in body mass index was associated with a 10% risk (95% CI, 1.06–1.24; p < 0.001) of more severe acute kidney injury. Within-hospital and 1-year survival rates associated with the acute kidney injury episodes were similar across body mass index categories.

Conclusion: Obesity is a risk factor for acute kidney injury, which is associated with increased short- and long-term mortality.

1Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA.

2Division of Health Sciences and Technology, Harvard-MIT , Cambridge, MA.

3School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada.

4Institute for Infocomm Research, A*STAR, Singapore, Singapore.

Danziger helped in study concept and design. Chen, Lee, Feng, Celi, and Mark helped in acquisition of data. Danziger and Mukamal helped in analysis and interpretation of data. Danziger and Mukamal helped in drafting of the article. Danziger and Mukamal helped in study supervision and overall study responsibility.

Supported, in part, by National Institutes of Health grant R01 EB001659.

Dr. Danziger was supported by a Normon S. Coplon Extramural Grant from Satellite Healthcare. Dr. Feng was supported by Agency for Science, Technology and Research (A*STAR) Graduate Scholarship. He received support for article research from the National Institutes of Health (NIH) and disclosed work for hire. Dr. Mark received support for article research from the NIH. His institution received funding from the NIH. Dr. Celi’s work in the Laboratory for Computational Physiology at MIT is funded by the National Institute of Biomedical Imaging and Bioengineering under National Institute of Biomedical Imaging and Bioengineering grant 2R01 EB001659. Dr. Mukamal received support for article research from the NIH. The remaining authors have disclosed that they do not have any potential conflicts of interest.

Address requests for reprints to: John Danziger, MD, MPhil, 185 Pilgrim Road, Farr 8, Boston, MA 02215. E-mail: jdanzige@bidmc.harvard.edu

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