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一项基于横断面和生物信息学的分析:肾周脂肪厚度作为肾结石疾病的更佳预测指标。

A cross-sectional and bioinformatics-based analysis: perirenal fat thickness as a superior predictor of kidney stone disease.

作者信息

Mao Kaifeng, Xu Xiang, Zhu Yifei, Lin Fenwang, Lu Zhenquan, Luo Bingfeng, Wei Genggeng, Yuan Yuan, Liao Sucai, Xing Yaping, Huang Wenyan, Ji Ruidong, Pan Yige, Li Zhenda, Ye Junsheng, Xiong Lin

机构信息

Division of Urology, Department of Surgery, The University of Hong Kong- Shenzhen Hospital, Shenzhen City, Guangdong Province, China.

Department of Kidney Transplantation, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, 102218, China.

出版信息

Lipids Health Dis. 2025 Aug 29;24(1):269. doi: 10.1186/s12944-025-02686-4.

Abstract

BACKGROUND

Kidney stone disease (KSD) is a growing global health concern, with obesity (OB) as a major risk factor linked to metabolic dysfunction and chronic inflammation. Although the common method for evaluating OB is body mass index (BMI), it is not specific enough when it comes to reflecting visceral fat. The perirenal fat thickness (PFT) might present better predictive capabilities. The goal of this research was to assess the clinical usefulness of PFT in the diagnosis of KSD and to clarify the molecular mechanisms connecting OB to KSD.

METHODS

Analysis was carried out on a retrospective cohort of 413 patients (265 having KSD and 148 controls). Abdominal computed tomography was used to measure PFT. Three machine-learning methods, weighted gene co-expression network analysis, and differential expression analysis were used to evaluate gene expression data for key gene identification. Internal and external datasets were used to develop and validate a diagnostic nomogram. Also, pathway enrichment analysis was carried out.

RESULTS

KSD patients exhibited greater PFT versus controls, with significantly enhanced diagnostic accuracy compared to BMI. Multivariate analysis confirmed PFT as an independent predictor of KSD (OR = 1.20, P < 0.001). Eight genes that are differentially expressed in relation to OB were identified, among which FAM20A and DHRS9 were found to be central hub genes. The nomogram exhibited a high level of predictive accuracy. Analysis of enrichment pointed to the IL-6/JAK/STAT3 and TNF-α/NF-κB signaling pathways in the connection between perirenal fat and KSD.

CONCLUSIONS

PFT serves as a practical and dependable marker for the risk of KSD. It is superior to BMI and can be conveniently incorporated into routine clinical practice. Stone formation may be linked to perirenal fat by FAM20A and DHRS9 via inflammatory pathways, which provides potential targets for the management of OB-related KSD.

摘要

背景

肾结石病(KSD)是一个日益受到全球关注的健康问题,肥胖(OB)是与代谢功能障碍和慢性炎症相关的主要风险因素。虽然评估肥胖的常用方法是体重指数(BMI),但在反映内脏脂肪方面不够特异。肾周脂肪厚度(PFT)可能具有更好的预测能力。本研究的目的是评估PFT在KSD诊断中的临床实用性,并阐明将肥胖与KSD联系起来的分子机制。

方法

对413例患者(265例患有KSD,148例为对照)的回顾性队列进行分析。采用腹部计算机断层扫描测量PFT。使用三种机器学习方法、加权基因共表达网络分析和差异表达分析来评估基因表达数据以识别关键基因。使用内部和外部数据集开发并验证诊断列线图。此外,还进行了通路富集分析。

结果

与对照组相比,KSD患者的PFT更大,与BMI相比诊断准确性显著提高。多变量分析证实PFT是KSD的独立预测因子(OR = 1.20,P < 0.001)。鉴定出8个与肥胖相关的差异表达基因,其中FAM20A和DHRS9被发现是核心枢纽基因。列线图显示出较高的预测准确性。富集分析表明肾周脂肪与KSD之间的联系涉及IL-6/JAK/STAT3和TNF-α/NF-κB信号通路。

结论

PFT是KSD风险的实用且可靠标志物。它优于BMI,可方便地纳入常规临床实践。结石形成可能通过FAM20A和DHRS9经由炎症途径与肾周脂肪相关,这为肥胖相关KSD的管理提供了潜在靶点。

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