Exploring adipogenic and myogenic circulatory biomarkers of recurrent pressure injury risk for persons with spinal cord injury

Purpose: To investigate linkages between circulatory adipogenic and myogenic biomarkers, gluteal intramuscular adipose tissue (IMAT), and pressure injury (PrI) history following spinal cord injury (SCI). Methods: This is an observational repeated-measures study of 30 individuals with SCI. Whole b...

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Bibliographic Details
Main Authors: Kath M. Bogie, Katelyn Schwartz, Youjin Li, Shengxuan Wang, Wei Dai, Jiayang Sun
Format: Article
Language:English
Published: AboutScience Srl 2020-09-01
Series:Journal of Circulating Biomarkers
Subjects:
Online Access:https://journals.aboutscience.eu/index.php/jcb/article/view/2121
Description
Summary:Purpose: To investigate linkages between circulatory adipogenic and myogenic biomarkers, gluteal intramuscular adipose tissue (IMAT), and pressure injury (PrI) history following spinal cord injury (SCI). Methods: This is an observational repeated-measures study of 30 individuals with SCI. Whole blood was collected regularly over 2-3 years. Circulatory adipogenic and myogenic gene expression was determined. IMAT was defined as above/below 15% (IMATd) or percentage (IMAT%). PrI history was defined as recurrent PrI (RPrI) or PrI number (nPrI). Model development used R packages (version 3.5.1). Univariate analysis screened for discriminating genes for downstream multivariate and combined models of averaged and longitudinal data for binary (RPrI/IMATd) and finer scales (nPrI/IMAT%). Results: For adipogenesis, Krüppel-like factor 4 was the top RPrI predictor together with resistin and cyclin D1, and sirtuin 2 was the top IMAT predictor. For myogenesis, the top RPrI predictor was dysferlin 2B, and pyruvate dehydrogenase kinase-4 was the top IMAT predictor together with dystrophin. Conclusion: Circulatory adipogenic and myogenic biomarkers have statistically significant relationships with PrI history and IMAT for persons with SCI. Biomarkers of interest may act synergistically or additively. Variable importance rankings can reveal nonlinear correlations among the predictors. Biomarkers of interest may act synergistically or additively, thus multiple genes may need to be included for prediction with finer distinction.
ISSN:1849-4544