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Published on: April 28, 2013
Association between pre-operative computed tomography-based adipose tissue quantification and post-transplant
Yang Feng1,2,3, Ming Liu1,2, Yuechen Shi1,2
1Department of Urology, The Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Background:
There is an established correlation between obesity and dyslipidemia in individuals who have undergone kidney transplantation (KT). Body composition is a more accurate indicator of obesity than body mass index (BMI). However, the relationship between pre-operative body composition and post-transplant dyslipidemia remains unexplored.
Methods:
We analyzed 333 participants who underwent KT between 2021 and 2024. Using pre-transplantation computed tomography (CT), we assessed the cross-sectional areas of skeletal muscle (SM) and adipose tissue at the level of the third lumbar vertebra. Participants were categorized into high and low body composition groups based on the highest quartile, and into dyslipidemia and non-dyslipidemia groups according to post-transplant blood lipid levels. Skeletal muscle (SM) and adipose tissue metrics were subsequently used to model dyslipidemia after KT. Generalized estimating equations (GEE) were then employed to assess the impact of body composition on blood lipid levels at 45 days, and at 3, 6 months, and 1 year post-transplant.
Results:
Univariate analysis revealed that BMI (P = 0.006), visceral adipose tissue (VAT) area (P < 0.001), intermuscular adipose tissue (IMAT; P = 0.005), subcutaneous adipose tissue (SAT; P = 0.003), and skeletal muscle (P = 0.019) were identified as risk factors for dyslipidemia 1 year post-transplant. A predictive model was further developed, indicating that VAT was a more reliable predictor than BMI and other body composition metrics. Statistically significant associations were observed for both VAT (OR = 1.005, 95% CI: 1.001-1.009; P = 0.014) and VATI (OR = 1.013, 95% CI: 1.002-1.025; P = 0.027). Sensitivity analysis indicated that the observed effect sizes were near the detection limit for a study of this sample size, reinforcing the modest magnitude of these associations. REE analysis demonstrated that VAT influenced triglyceride (TG) levels (P = 0.006), high-density lipoprotein cholesterol (HDL-C) levels (P = 0.001), and low-density lipoprotein cholesterol (LDL-C) levels (P = 0.033).
Conclusions:
High pre-transplant VAT was associated with low HDL-C, elevated TG, and LDL-C levels after KT. These findings may have significant implications for reducing the incidence of dyslipidemia post-KT.
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