Comparative Analysis of Biomarker Profiles in Type 2 Diabetes Mellitus: Insights Into Inflammation, Wound Healing, and Clinical Implications
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: The purpose of this study is to compare biomarker profiles between Type 2 Diabetes Mellitus (T2DM), prediabetic, and healthy controls. It investigates the involvement of inflammation, metabolic dysfunction and wound healing mechanisms in the progression of the disease and the potential use of the latter tools as diagnostic and prognostic markers for personalized medicine interventions in diabetes management. Methods: We recruited a total of 200 participants (100 T2DM patients studied, 50 prediabetic individuals studied and 50 healthy controls as controls). Inflammatory (IL6, TNFa, hsCRP, IL10), metabolic (LDL, HDL, triglycerides) and wound healing biomarkers (NLR. PLR) were tested in blood and breath samples. Volatile organic compounds (VOCs) in exhaled breath were identified via gas chromatography mass spectrometry (GCMS). The relative associations of biomarkers with disease progression were evaluated through statistical analyses, such as principle component analysis (PCA) and logistic regression. Results: Inflamed inflammatory markers (IL-6, TNF-α, hs-CRP) and dyslipidemias (increased LDL, triglycerides, reduced HDL) were significantly elevated in T2DM patients. Non-invasive diagnostic markers were found with breath metabolomics (isopropanol and 2,3,4-trimethylhexane, 97.9% sensitivity, 100% specificity). Elevated NLR and PLR were associated with impaired wound healing, and predicted delayed tissue repair and increased amputation risk. These reductions in systemic inflammation were most notable in the resistance exercise group, where CRP levels were reduced by -0.59 mg/dL. Conclusion: Distinct biomarker profiles in T2DM are identified in this study with clinical relevance regarding early detection, risk stratification and personalized interventions. Multi-omics approaches for the integration of data help to further understand diabetes pathophysiology and assist with precision medicine approaches for better patient outcomes.
Authors: Darlington David Faijue, Tooba Adil, Muhammad Mohsin Dilshad, Razia Virk, Bhavna Singla, Sheeba Sattar
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410009243