Białek Piotr, Żuberek Michał, Dobek Adam, Falenta Krzysztof, Kurnatowska Ilona, Stefańczyk Ludomir
1st Department of Radiology and Diagnostic Imaging, Medical University of Lodz, Kopcinskiego 22 Street, 90-153 Lodz, Poland.
Department of Internal Diseases and Transplant Nephrology, Medical University of Lodz, Kopcinskiego 22 Street, 90-153 Lodz, Poland.
J Clin Med. 2025 Aug 20;14(16):5888. doi: 10.3390/jcm14165888.
Chronic kidney disease (CKD) is a prevalent condition with many cases remaining undiagnosed, although early detection is essential. Adipose tissue distribution-particularly perirenal fat thickness (PrFT)-has recently been linked to renal pathophysiology. This study assessed the association between CT-derived parameters of fat distribution and kidney morphology with CKD. This retrospective study included 237 patients (117 subjects, 120 controls) who underwent abdominal CT and had serum creatinine data. The dataset was randomly split (70% training, 30% test) to develop and evaluate a logistic regression model. CKD was defined as estimated Glomerular Filtration Rate (eGFR) < 60 mL/min/1.73 m. PrFT was measured as the distance from the posterior renal capsule to the posterior abdominal wall; renal hilum fat was segmented using a -195 to -45 HU range. Additional parameters (measured using automated segmentation tools) included kidney volume (KV), visceral/subcutaneous fat areas, skeletal muscle area and attenuation, and liver attenuation. Bilateral measurements were averaged. KV (OR = 0.249, 95% CI: 0.146-0.422, < 0.001) and PrFT (2nd tercile: OR = 7.720, 95% CI: 2.860-20.839; 3rd tercile: OR = 16.892, 95% CI: 5.727-49.822; both < 0.001) were identified as independent predictors of CKD. These variables were used to construct a simplified model, which demonstrated moderate clinical applicability (AUC = 0.894) when evaluated on the test subset. KV and PrFT emerged as independent predictors of CKD, forming the basis of a simplified model with potential for opportunistic clinical application. This approach may facilitate earlier detection of CKD in patients undergoing CT imaging for unrelated clinical reasons. These imaging parameters are not intended to replace serum creatinine or eGFR but may serve as complementary predictors in specific clinical contexts.
慢性肾脏病（CKD）是一种常见疾病，许多病例仍未被诊断出来，尽管早期检测至关重要。脂肪组织分布——尤其是肾周脂肪厚度（PrFT）——最近已与肾脏病理生理学联系起来。本研究评估了CT衍生的脂肪分布参数和肾脏形态与CKD之间的关联。这项回顾性研究纳入了237例接受腹部CT检查并拥有血清肌酐数据的患者（117例受试者，120例对照）。数据集被随机拆分（70%用于训练，30%用于测试）以开发和评估逻辑回归模型。CKD被定义为估计肾小球滤过率（eGFR）<60 mL/min/1.73 m²。PrFT测量为从肾后包膜到后腹壁的距离；肾门脂肪使用-195至-45 HU范围进行分割。其他参数（使用自动分割工具测量）包括肾脏体积（KV）、内脏/皮下脂肪面积、骨骼肌面积和衰减以及肝脏衰减。双侧测量值取平均值。KV（OR = 0.249，95% CI：0.146 - 0.422，P < 0.001）和PrFT（第二三分位数：OR = 7.720，95% CI：2.860 - 20.839；第三三分位数：OR = 16.892，95% CI：5.727 - 49.822；两者P < 0.001）被确定为CKD的独立预测因素。这些变量被用于构建一个简化模型，该模型在测试子集中评估时显示出中等的临床适用性（AUC = 0.894）。KV和PrFT成为CKD独立预测因素，构成了一个具有机会性临床应用潜力的简化模型的基础。这种方法可能有助于在因无关临床原因接受CT成像的患者中更早地检测出CKD。这些成像参数并非旨在替代血清肌酐或eGFR，但在特定临床情况下可作为补充预测指标。
