Wang Xiaoqi, Yuan Dan, Shao Feng, Zhou Jingjing, Zhang Xiao, Li Zhongxin
Beijing Luhe Hospital, Capital Medical University, Beijing, China.
Front Cardiovasc Med. 2025 Apr 10;12:1574649. doi: 10.3389/fcvm.2025.1574649. eCollection 2025.
To investigate the relationship between Visceral Fat Area (VFA) and cardiac valve calcification (CVC) in Maintenance Hemodialysis (MHD) patients.
This cross-sectional study included MHD patients enrolled at our hospital between July 2023 and February 2024. Body composition analysis was performed on recruited patients. According to echocardiography results, the participants were classified into 2 groups. We then compared their clinical characteristics and identified independent factors influencing CVC through multivariate logistic regression. The ROC curve was employed to assess the ability of influencing factors to predict CVC in MHD patients.
There are 154 MHD patients were recruited, including 76 with CVC and 78 without CVC. Significant differences were observed between CVC and non-CVC participants in age, the proportion of diabetic nephropathy, the proportion of diabetes mellitus, the levels of Hs-CRP, fasting blood glucose, blood phosphorus, iPTH, HDL-C and VFA ( < 0.05). Advanced age, diabetes, increased VFA and iPTH all have the ability to predict individuals with CVC in MHD patients based on Multivariate Logistic regression. ROC curve indicated that VFA could accurately identify individuals with CVC among MHD patients (AUC = 0.713). When age, diabetes, iPTH, and VFA were combined for predicting CVC, the AUC was 0.776 ( < 0.01), which was greater than any single indicator.
For MHD patients, increased VFA may serve as a potential marker for detecting CVC and can assist in clinical decision-making.
探讨维持性血液透析（MHD）患者内脏脂肪面积（VFA）与心脏瓣膜钙化（CVC）之间的关系。
本横断面研究纳入了2023年7月至2024年2月在我院登记的MHD患者。对招募的患者进行身体成分分析。根据超声心动图结果，将参与者分为2组。然后比较他们的临床特征，并通过多因素逻辑回归确定影响CVC的独立因素。采用ROC曲线评估影响因素预测MHD患者CVC的能力。
共招募了154例MHD患者，其中76例有CVC，78例无CVC。CVC组和非CVC组在年龄、糖尿病肾病比例、糖尿病比例、Hs-CRP水平、空腹血糖、血磷、iPTH、HDL-C和VFA方面存在显著差异（P<0.05）。根据多因素逻辑回归，高龄、糖尿病、VFA增加和iPTH均有能力预测MHD患者中患有CVC的个体。ROC曲线表明，VFA能够准确识别MHD患者中患有CVC的个体（AUC = 0.713）。当将年龄、糖尿病、iPTH和VFA联合用于预测CVC时，AUC为0.776（P<0.01），大于任何单一指标。
对于MHD患者，VFA增加可能是检测CVC的潜在标志物，并有助于临床决策。
