期刊
CLINICAL TRANSPLANTATION
卷 28, 期 11, 页码 1271-1278出版社
WILEY-BLACKWELL
DOI: 10.1111/ctr.12456
关键词
activity; Bayesian network modeling; diet; kidney transplant; obesity; weight gain
资金
- National Institutes of Health/National Institute of Nursing Research [R01NR009270, 3R01NR009270-03S2]
Clinically useful predictors of weight gain could be used to reduce the epidemic of post-kidney transplant obesity and resulting co-morbidities. The purpose of this study was to identify predictors of weight gain at 12 months following kidney transplant in a cohort of 96 recipients. Demographic, clinical, and environmental data were obtained at transplant and 12 months. Descriptive, correlational, and Bayesian network analysis were used to identify predictors. For the 52 (55.9%) recipients who gained weight, the average amount gained was 9.18 +/- 6.59kg. From the 15 baseline factors that met inclusion criteria, Bayesian network modeling identified four baseline predictors for weight gain: younger age, higher carbohydrate consumption, higher trunk fat percentage, and higher perception of mental health quality of life. Three are modifiable through either pre- or immediate post-transplant clinical intervention programs.
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