2. Academic Validation
  3. Multi-omics and machine learning reveal a critical mediator of PRKCA in quercetin-mediated protection against Sarcopenia

Multi-omics and machine learning reveal a critical mediator of PRKCA in quercetin-mediated protection against Sarcopenia

  • Phytomedicine. 2026 Jun:155:158121. doi: 10.1016/j.phymed.2026.158121.
Xinjie Wu 1,  Zhonghao Li 2,  Xin Xu 3,  Zhimu Yuan 1,  Zhizhuo Li 4,  Benlong Shi 1,  Saihu Mao 1,  Jun Qiao 1,  Zhenhua Feng 1,  Qingyu Zhang 5
Affiliations

Affiliations

  • 1 Department of Orthopaedic Surgery, Nanjing Drum Tower Hospital The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, Jiangsu, China.
  • 2 Department of Neurosurgery, Dongfang Hospital Beijing University of Chinese Medicine 100078, Beijing, China.
  • 3 Department of Orthopaedics, Qilu Hospital of Shandong University, Jinan 250012 Shandong, China.
  • 4 Division of Sports Medicine and Adult Reconstructive Surgery Department of Orthopedic Surgery, Nanjing Drum Tower Hospital Affiliated Hospital of Medical School, Nanjing 210008, Jiangsu, China.
  • 5 Department of Orthopedics, Shandong Provincial Hospital affiliated to Shandong First Medical University, Jinan 250021 Shandong, China. Electronic address: [email protected].
PMID: 41936173 DOI: 10.1016/j.phymed.2026.158121
Abstract

Background: Sarcopenia, the age-associated loss of skeletal muscle mass and function, poses a growing public health challenge. Although dietary Flavonoids have been proposed as protective agents due to their antioxidant and anti-inflammatory properties, their precise roles in sarcopenia prevention remain unclear.

Methods: We integrated data from the National Health and Nutrition Examination Survey (NHANES, 2017-2018) and the Food and Nutrient Database for Dietary Studies (FNDDS) to examine the relationship between flavonoid intake and sarcopenia risk. Using machine learning, LASSO, CatBoost, and SHAP interpretation, we identified key predictive variables. Drug-target genes were retrieved from Drug-Gene Interaction Database (DGIdb), followed by Mendelian randomization using GWAS and QTL summary data. Bulk RNA-seq and single-cell RNA-seq were analyzed to explore molecular pathways. Experimental validation was performed using C2C12 myoblast differentiation assays.

Results: After propensity score matching, BMI, gender, and PIR remained significantly associated with sarcopenia. Machine learning identified Flavonoids, particularly quercetin, as key predictors. SHAP values revealed an inverse, dose-dependent association between quercetin intake and sarcopenia predictions in the model. SMR analysis linked quercetin to genes such as PRKCA. Bulk RNA-seq enrichment analysis revealed enrichment of protein kinase C (PKC)-related signaling and downstream MAPK/ERK pathways after quercetin treatment, while scRNA-seq data identified Prkca as a quercetin-responsive gene in muscle fiber populations. Experimental results confirmed that quercetin promotes myoblast differentiation via PRKCA upregulation, accompanied by enhanced ERK1/2 phosphorylation, an effect abolished by the PRKCA inhibitor Go6976.

Conclusions: Our integrative multi-omics and experimental approach reveals that quercetin is associated with sarcopenia and influence muscle differentiation through PRKCA-ERK signaling. These findings support the potential of Flavonoids as targeted dietary interventions for age-related muscle decline.

Keywords

Flavonoids; Machine Learning; Mendelian Randomization; PRKCA; Quercetin; Sarcopenia.

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