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Integrated Exosomal miRNA and metabolomics model for predicting sarcopenia in patients with malignant tumors
Hong Zhao1, Jing Zhou2, Xu He3
1Practical Training Center, Yiyang Medical College, Yiyang 413000, China.
Background:
Sarcopenia is a common, underdiagnosed complication in malignant tumor (MT) patients, leading to poor treatment tolerance and shortened survival. This study aims to construct and validate a comprehensive prediction model by integrating exosomal microRNAs (miRNAs) and metabolomics biomarkers.
Methods:
A retrospective cohort of 236 MT patients (February 2020-December 2023) was analyzed. Independent risk factors (IRFs) were identified via logistic regression to establish Model 1. Exosomal miRNAs (qRT-PCR) developed Model 2, while differential serum metabolites (LC-MS/MS) built Model 3. Model 4 integrated Models 2 and 3. Performance was assessed using ROC and decision curve analysis (DCA).
Results:
Sarcopenia incidence was 59.32% (140/236). Multivariate analysis identified BMI, diabetes status, and NRS2002 score as IRFs (p < 0.05). Exosomal profiling showed significantly elevated miR 15, miR 21, and miR 223, and downregulated miR 27a, miR 29b, and miR 33a (p < 0.05). Metabolomics identified 10 upregulated and 4 downregulated metabolites (p < 0.05). Model 4 demonstrated the highest accuracy, with AUC 0.992 (95% CI: 0.971-0.999), sensitivity 94.96%, and specificity 97.91%, outperforming other models. DCA confirmed Model 4 provided the greatest standardized net clinical benefit.
Conclusion:
An integrated model combining exosomal miRNA and metabolomic biomarkers offers excellent predictive performance for sarcopenia in MT patients, facilitating early identification and precision care.