Investigation of Lactylation-Associated Genes and Their Relationship with the Development of Diabetic Foot Ulcers and Immune Cell Infiltration - Scorecard - MDSpire

Investigation of Lactylation-Associated Genes and Their Relationship with the Development of Diabetic Foot Ulcers and Immune Cell Infiltration

  • By

  • Xiaolong Hu

  • Junpeng Zhou

  • Meng Guo

  • Wei Peng

  • Chen Yang

  • Fang Wang

  • Wei Zhang

  • Jiaqi Liu

  • April 29, 2026

  • 0 min

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Clinical Scorecard: Investigation of Lactylation-Associated Genes and Their Relationship with the Development of Diabetic Foot Ulcers and Immune Cell Infiltration

At a Glance

CategoryDetail
ConditionDiabetic Foot Ulcers (DFUs)
Key MechanismsLactylation as a post-translational modification influencing immune response and gene expression.
Target PopulationPatients with diabetes at risk of developing DFUs.
Care SettingClinical settings managing diabetic complications.

Key Highlights

  • Identification of 1,234 differentially expressed genes (DEGs) in DFUs.
  • Three core lactylation-associated genes (CHD4, EEF1A1, EEF1G) significantly downregulated in DFUs.
  • Positive correlation of identified genes with natural killer cells and negative correlation with neutrophil infiltration.
  • Increased global lactylation observed under diabetic conditions.
  • Lactylation may serve as a regulatory mechanism in diabetic wound pathology.

Guideline-Based Recommendations

Diagnosis

  • Early diagnosis of DFUs is critical to prevent severe complications.

Management

  • Focus on lactylation-targeted biomarkers and therapeutic strategies for DFU management.

Monitoring & Follow-up

  • Monitor immune cell infiltration and lactylation levels in DFU patients.

Risks

  • DFUs can lead to severe infections and lower limb amputations if not managed effectively.

Patient & Prescribing Data

Diabetic patients with a high risk of developing foot ulcers.

Lactylation modifications may influence wound healing and immune responses.

Clinical Best Practices

  • Integrate lactylation assessment in the management of DFUs.
  • Utilize machine learning approaches for identifying key biomarkers in DFUs.

References

Original Source(s)

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