摘要
背景：免疫球蛋白A肾病（IgAN）的传统风险模型主要依赖于肾脏指标，缺乏全面的评估和治疗指导，因此需要更加精细和综合的方法：本研究将网络生物标志物与无监督学习聚类（基于网络生物标志物的k均值聚类[KMN]）相结合，以完善IgAN的风险分层并探索其临床价值：我们对 1460 名患者进行了多中心前瞻性队列分析，并对另外 200 名患者进行了外部验证。我们从两个不同的队列中获得了更深入的代谢和微生物组学见解：63 名患者接受了超高效液相色谱-质谱分析，另外 45 名患者接受了粪便 16S RNA 测序。我们的方法采用了分层聚类和k-means方法，使用了3组指标：人口统计学和肾脏指标、肾脏和肾外指标以及从所有指标中得出的网络生物标记物：在测试的 6 种聚类方法中，KMN 方案最为有效，它能准确反映患者的严重程度和预后，其预后准确性曲线下面积（AUC）为 0.77，而这仅仅是通过聚类分组实现的，不需要额外的指标。KMN 分层方法的效果明显优于现有的国际 IgA 肾病预测工具（AUC 为 0.72）和肾功能-肾组织学分级方案（AUC 为 0.69）。在临床上，这种分层方法有助于进行个性化治疗，对低危人群推荐使用血管紧张素转换酶抑制剂或血管紧张素受体阻滞剂，对高危人群则考虑使用免疫抑制剂。初步研究结果还表明，IgAN 的进展与血清代谢物和肠道微生物群的改变之间存在相关性，但要确定因果关系还需要进一步研究：结论：KMN 方案的有效性和适用性表明，它在 IgAN 管理中的临床应用潜力巨大。Background: Traditional risk models for immunoglobulin A nephropathy (IgAN), which primarily rely on renal indicators, lack comprehensive assessment and therapeutic guidance, necessitating more refined and integrative approaches.
Objective: This study integrated network biomarkers with unsupervised learning clustering (k-means clustering based on network biomarkers [KMN]) to refine risk stratification in IgAN and explore its clinical value.
Methods: Involving a multicenter prospective cohort, we analyzed 1460 patients and validated the approach externally with 200 additional patients. Deeper metabolic and microbiomic insights were gained from 2 distinct cohorts: 63 patients underwent ultraperformance liquid chromatography-mass spectrometry, while another 45 underwent fecal 16S RNA sequencing. Our approach used hierarchical clustering and k-means methods, using 3 sets of indicators: demographic and renal indicators, renal and extrarenal indicators, and network biomarkers derived from all indicators.
Results: Among 6 clustering methods tested, the KMN scheme was the most effective, accurately reflecting patient severity and prognosis with a prognostic accuracy area under the curve (AUC) of 0.77, achieved solely through cluster grouping without additional indicators. The KMN stratification significantly outperformed the existing International IgA Nephropathy Prediction Tool (AUC of 0.72) and renal function-renal histology grading schemes (AUC of 0.69). Clinically, this stratification facilitated personalized treatment, recommending angiotensin-converting enzyme inhibitors or angiotensin receptor blockers for lower-risk groups and considering immunosuppressive therapy for higher-risk groups. Preliminary findings also indicated a correlation between IgAN progression and alterations in serum metabolites and gut microbiota, although further research is needed to establish causality.
Conclusions: The effectiveness and applicability of the KMN scheme indicate its substantial potential for clinical application in IgAN management.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
