Department of Electrical and Computer Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
Genomics, Proteomics, Bioinformatics and Systems Biology Center, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA.
Biomolecules. 2024 Jul 30;14(8):924. doi: 10.3390/biom14080924.
Preoperative risk biomarkers for delirium may aid in identifying high-risk patients and developing intervention therapies, which would minimize the health and economic burden of postoperative delirium. Previous studies have typically used single omics approaches to identify such biomarkers. Preoperative cerebrospinal fluid (CSF) from the Healthier Postoperative Recovery study of adults ≥ 63 years old undergoing elective major orthopedic surgery was used in a matched pair delirium case-no delirium control design. We performed metabolomics and lipidomics, which were combined with our previously reported proteomics results on the same samples. Differential expression, clustering, classification, and systems biology analyses were applied to individual and combined omics datasets. Probabilistic graph models were used to identify an integrated multi-omics interaction network, which included clusters of heterogeneous omics interactions among lipids, metabolites, and proteins. The combined multi-omics signature of 25 molecules attained an AUC of 0.96 [95% CI: 0.85-1.00], showing improvement over individual omics-based classification. We conclude that multi-omics integration of preoperative CSF identifies potential risk markers for delirium and generates new insights into the complex pathways associated with delirium. With future validation, this hypotheses-generating study may serve to build robust biomarkers for delirium and improve our understanding of its pathophysiology.
术前谵妄风险生物标志物有助于识别高危患者并开发干预疗法,从而最大程度地降低术后谵妄的健康和经济负担。先前的研究通常使用单一的组学方法来识别此类生物标志物。在一项匹配的对谵妄病例-无谵妄对照设计中,使用了来自≥63 岁接受择期大型骨科手术的成年人的健康术后恢复研究中的术前脑脊液 (CSF)。我们进行了代谢组学和脂质组学分析,并将其与我们之前在相同样本上报告的蛋白质组学结果相结合。对个体和综合组学数据集应用了差异表达、聚类、分类和系统生物学分析。概率图模型用于识别综合多组学相互作用网络,其中包括脂质、代谢物和蛋白质之间异质组学相互作用的聚类。25 种分子的组合多组学特征的 AUC 为 0.96[95%CI:0.85-1.00],表明优于基于个体组学的分类。我们得出结论,术前 CSF 的多组学整合可识别谵妄的潜在风险标志物,并为与谵妄相关的复杂途径提供新的见解。随着未来的验证,这项假说生成研究可能有助于构建用于谵妄的稳健生物标志物,并加深我们对其病理生理学的理解。