Survival advantages of obesity in dialysis patients
Open accessAbstract
In the general population, a high body mass index (BMI; in kg/m(2)) is associated with increased cardiovascular disease and all-cause mortality. However, the effect of overweight (BMI: 25-30) or obesity (BMI: >30) in patients with chronic kidney disease (CKD) undergoing maintenance hemodialysis (MHD) is paradoxically in the opposite direction; ie, a high BMI is associated with improved survival. Although this "reverse epidemiology" of obesity or dialysis-risk-paradox is relatively consistent in MHD patients, studies in CKD patients undergoing peritoneal dialysis have yielded mixed results. Growing confusion has developed among physicians, some of whom are no longer confident about whether to treat obesity in CKD patients. A similar reverse epidemiology of obesity has been described in geriatric populations and in patients with chronic heart failure (CHF). Possible causes of the reverse epidemiology of obesity include a more stable hemodynamic status, alterations in circulating cytokines, unique neurohormonal constellations, endotoxin-lipoprotein interaction, reverse causation, survival bias, time discrepancies among competitive risk factors, and malnutrition-inflammation complex syndrome. Reverse epidemiology may have significant clinical implications in the management of dialysis, CHF, and geriatric patients, ie, populations with extraordinarily high mortality. Exploring the causes and consequences of the reverse epidemiology of obesity in dialysis patients can enhance our insights into similar paradoxes observed for other conventional risk factors, such as blood pressure and serum cholesterol and homocysteine concentrations, and in other populations such as those with CHF, advanced age, cancer, or AIDS. Weight-gaining interventional studies in dialysis patients are urgently needed to ascertain whether they can improve survival and quality of life.
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A protective effect of obesity has been seen in hemodialysis patients, evidenced by improved survival in those with higher BMIs.
“Lastly, a protective effect of obesity has been seen in hemodialysis patients evidenced by improved survival in those with higher BMIs (164, 307, 311).”
From Cardiac remodeling in obesity (2008) · cites this paper as [164]
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- Authors
- Kalantar-Zadeh, K., Abbott, K. C., Salahudeen, A. K., Kilpatrick, R. D., & Horwich, T. B.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- The American Journal of Clinical Nutrition
- Publisher
- Elsevier BV
- Published
- 2005-03
- Volume
- 81
- Issue
- 3
- Pages
- 543-554
- ISSN
- 0002-9165 (ISSN-L)
- OpenAlex
- W1910718411
- Cited by
- 610
- References
- 116
- Citations by year
- 2026: 15 · 2025: 9 · 2024: 13 · 2023: 17 · 2022: 18 · 2021: 26 · 2020: 21 · 2019: 20 · 2018: 33 · 2017: 30 · 2016: 31 · 2015: 44 · 2014: 44 · 2013: 51 · 2012: 28
- FWCI
- 23.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- American Journal of Clinical Nutrition (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- U.S. Department of Defense
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases (K23DK061162, DK61162)
- Topics
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Obesity, Physical Activity, Diet
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- obesity paradox, dialysis patients, maintenance hemodialysis, peritoneal dialysis, chronic kidney disease, body mass index, survival, reverse epidemiology, malnutrition-inflammation complex syndrome, cytokines, weight gain, chronic heart failure, elderly patients
- MeSH terms
- Chronic Disease, Renal Dialysis, Hemodynamics, Humans, Kidney Failure, Chronic, Obesity, Peritoneal Dialysis, Risk Factors, Body Mass Index, Survival Rate, Risk Assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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