Deciphering Risk of Recurrent Bone Stress Injury in Female Runners Using Serum Proteomics Analysis and Predictive Models.

Genevieve E Romanowicz, Kristin Popp, Ethan Dinh, Isabella R Harker, Kelly Leguineche, Julie M Hughes, Kathryn E Ackerman, Mary L Bouxsein, Robert E Guldberg
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Abstract

Up to 40% of elite athletes experience bone stress injuries (BSIs), with 20-30% facing reinjury. Early identification of runners at high risk of subsequent BSI could improve prevention strategies. However, the complex etiology and multifactorial risk factors of BSIs makes identifying predictive risk factors challenging. In a study of 30 female recreational athletes with tibial BSIs, 10 experienced additional BSIs over a 1-year period, prompting investigation of systemic biomarkers of subsequent BSIs using aptamer-based proteomic technology. We hypothesized that early proteomic signatures could discriminate runners who experienced subsequent BSIs. 1,500 proteins related to metabolic, immune, and bone healing pathways were examined. Using supervised machine learning and genetic programming methods, we analyzed serum protein signatures over the 1-year monitoring period. Models were also created with clinical metrics, including standard-of-care blood analysis, bone density measures, and health histories. Protein signatures collected within three weeks of BSI diagnosis achieved the greatest separation by sparse partial least squares discriminant analysis (sPLS-DA), clustering single and recurrent BSI individuals with a mean accuracy of 96 ± 0.02%. Genetic programming models independently verified the presence of candidate biomarkers, including fumarylacetoacetase, osteopontin, and trypsin-2, which significantly outperformed clinical metrics. Time-course differential expression analysis highlighted 112 differentially expressed proteins in individuals with additional BSIs. Gene set enrichment analysis mapped these proteins to pathways indicating increased fibrin clot formation and decreased immune signaling in recurrent BSI individuals. These findings provide new insights into biomarkers and dysregulated protein pathways associated with recurrent BSI and may lead to new preventative or therapeutic intervention strategies.

One sentence summary: Our study identified candidate serum biomarkers to predict subsequent bone stress injuries in female runners, offering new insights for clinical monitoring and interventions.

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利用血清蛋白质组学分析和预测模型解读女性跑步者复发性骨应力损伤的风险
高达40%的优秀运动员经历过骨应力性损伤(bsi),其中20-30%面临再损伤。早期识别有BSI高风险的跑步者可以改善预防策略。然而,复杂的病因和多因素的危险因素使得识别预测危险因素具有挑战性。在一项对30名患有胫骨脑损伤的女性休闲运动员的研究中,10人在1年的时间里经历了额外的脑损伤,这促使人们使用基于适体的蛋白质组学技术研究随后的脑损伤的系统生物标志物。我们假设早期蛋白质组学特征可以区分随后经历脑损伤的跑步者。研究人员检测了1500种与代谢、免疫和骨愈合途径相关的蛋白质。使用监督机器学习和遗传编程方法,我们分析了1年监测期间的血清蛋白特征。模型还包括临床指标,包括标准护理血液分析、骨密度测量和健康史。通过稀疏偏最小二乘判别分析(sPLS-DA), BSI诊断后三周内收集的蛋白质特征获得了最大的分离,聚类单一和复发性BSI个体,平均准确率为96±0.02%。遗传规划模型独立验证了候选生物标志物的存在,包括富马酰乙酰酶、骨桥蛋白和胰蛋白酶-2,这些标志物的表现明显优于临床指标。时间过程差异表达分析强调了112种差异表达蛋白在附加bsi个体中。基因集富集分析将这些蛋白定位到表明复发性BSI个体中纤维蛋白凝块形成增加和免疫信号减少的途径。这些发现为与复发性BSI相关的生物标志物和失调蛋白通路提供了新的见解,并可能导致新的预防或治疗干预策略。一句话总结:我们的研究确定了候选血清生物标志物,以预测女性跑步者随后的骨应激损伤,为临床监测和干预提供了新的见解。
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