ArticleInsights into imaging2024
Body composition as a potential imaging biomarker for predicting the progression risk of chronic kidney disease.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Automated 3D chest CT muscle segmentation-derived model development and explanation in discrimination of normal, preserved ratio impaired spirometry, and COPD: a multicenter study.Insights into imaging · 2026Article
- Quantification of PET activation in adipose tissue from non-contrast CT scans.NPJ digital medicine · 2026Article
- CT-based renal and body-composition radiomics model to improve the detection ability of diabetic kidney disease in patients with type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- Article
- Letter to the Editor Reply to "Accurate Predictory Role of Sarcopenia for the Progression-Free Survival in Patients with Localized Papillary Renal Cell Carcinoma," by Zhang.Annals of surgical oncology · 2025Article
- Dynamic CT-based body composition analysis predicts surgical risk in Crohn's disease with small bowel stenosis: a retrospective cohort study.Frontiers in medicine · 2025Article
- Emerging insights into the role of visceral adipose tissue in diabetic kidney disease.Frontiers in nutrition · 2025Review
- Development of a nomogram for predicting renal survival in patients with biopsy-proven diabetic nephropathy.Frontiers in endocrinology · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
purposeTo investigate whether the body composition parameters can be employed as potential biomarkers for predicting the progression risk of chronic kidney disease (CKD). MATERIALS AND
methodsFour hundred sixteen patients diagnosed with CKD were included in this retrospective study. Patients with a greater than 50% decline in estimated glomerular filtration rate or progression to end-stage kidney disease were in the high-risk group, otherwise, they were in a low-risk group. Body composition area, the index, and radiodensities in the Hounsfield unit (HU), which reflect the degree of X-ray absorption, were measured on abdominal CT images. Risk factors in body composition and clinical parameters of CKD were identified by Cox regression and utilized to construct the nomogram. The performance of the nomogram was assessed using time receiver operating characteristics curves, calibration curves, and decision curve analysis.
resultsThere were 254 patients in low-risk group and 162 in high-risk group (268 males, 148 females, mean age: 55.89 years). Urea, diabetes, 24 h-urinary protein, mean arterial pressure, and subcutaneous adipose tissue radiodensity (SATd) were valuable indicators for predicting the high-risk group. The area under curve values for the nomogram of training/validation set at 1 year, 2 years, and 3 years were 0.805/0.753, 0.784/0.783, and 0.846/0.754, respectively. For diabetic CKD patients, extra attention needs to be paid to visceral to subcutaneous fat ratio and renal sinus fat radiodensity.
conclusionSATd was the most valuable noninvasive indicator of all body composition parameters for predicting high-risk populations with CKD. The nomogram we constructed has generalization with easily obtainable indicators, good performance, differentiation, and clinical practicability. CRITICAL RELEVANCE STATEMENT: Radiodensity rather than an area of adipose tissue can be used as a new biomarker of prognosis for CKD patients, providing new insights into risk assessment, stratified management, and treatment for CKD patients. KEY POINTS: Obesity is an independent risk factor for the development and prognosis of CKD. Adipose tissue radiodensity is more valuable than fat area in prognosticating for kidney disease. Parameters that prognosticate in diabetic CKD patients are different from those in other CKD patients.
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