基于蛋白组学的因果分析，提升亚洲系统性红斑狼疮患者复发预测准确率。
Phenome-wide causal proteomics enhance systemic lupus erythematosus flare prediction: A study in Asian populations
- 采用表型组学孟德尔随机化，找出与疾病活动度相关的5种关键蛋白。
- 整合临床与蛋白组数据的模型预测准确率达AUC 0.769，优于单一模型。
- 发现SAA1蛋白可作为快速区分复发风险的优先生物标志物，适合精准管理。
系统性红斑狼疮（SLE）是一种复杂的自身免疫病，以不可预测的复发为特征。本研究针对亚洲SLE人群，构建基于蛋白组学的风险预测模型，以实现个性化管理和早期干预。研究纳入139名患者，随访48周，每12周评估一次，分为复发组（n=53）和非复发组（n=86）。基线血浆样本通过数据独立采集（DIA）蛋白组学分析，并利用表型组学孟德尔随机化（PheWAS）评估蛋白与临床指标间的因果关系。结合逻辑回归（LR）与随机森林（RF）模型，融合蛋白组与临床数据进行复发风险预测。结果发现5种蛋白（SAA1、B4GALT5、GIT2、NAA15、RPIA）与SLEDAI-2K评分及1年复发风险显著相关，涉及B细胞受体信号通路和血小板脱颗粒等关键通路。其中SAA1对血红蛋白、红细胞计数等复发相关临床指标具有因果效应。联合模型预测准确率最高（AUC=0.769），显著优于单一模型。研究证实，蛋白组与临床数据整合可显著提升亚洲SLE患者复发预测能力，关键蛋白及其因果关系为早期干预和个体化治疗提供了新思路。
原文摘要 · Abstract (English)
Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by unpredictable flares. This study aimed to develop a novel proteomics-based risk prediction model specifically for Asian SLE populations to enhance personalized disease management and early intervention. Methods: A longitudinal cohort study was conducted over 48 weeks, including 139 SLE patients monitored every 12 weeks. Patients were classified into flare (n = 53) and non-flare (n = 86) groups. Baseline plasma samples underwent data-independent acquisition (DIA) proteomics analysis, and phenome-wide Mendelian randomization (PheWAS) was performed to evaluate causal relationships between proteins and clinical predictors. Logistic regression (LR) and random forest (RF) models were used to integrate proteomic and clinical data for flare risk prediction. Results: Five proteins (SAA1, B4GALT5, GIT2, NAA15, and RPIA) were significantly associated with SLE Disease Activity Index-2K (SLEDAI-2K) scores and 1-year flare risk, implicating key pathways such as B-cell receptor signaling and platelet degranulation. SAA1 demonstrated causal effects on flare-related clinical markers, including hemoglobin and red blood cell counts. A combined model integrating clinical and proteomic data achieved the highest predictive accuracy (AUC = 0.769), surpassing individual models. SAA1 was highlighted as a priority biomarker for rapid flare discrimination. Conclusion: The integration of proteomic and clinical data significantly improves flare prediction in Asian SLE patients. The identification of key proteins and their causal relationships with flare-related clinical markers provides valuable insights for proactive SLE management and personalized therapeutic approaches.
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