Exploring protein relative relations in skeletal muscle proteomic analysis for insights into insulin resistance and type 2 diabetes.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
31 Jul 2024
31 Jul 2024
Historique:
received:
11
12
2023
accepted:
25
07
2024
medline:
1
8
2024
pubmed:
1
8
2024
entrez:
31
7
2024
Statut:
epublish
Résumé
The escalating prevalence of insulin resistance (IR) and type 2 diabetes mellitus (T2D) underscores the urgent need for improved early detection techniques and effective treatment strategies. In this context, our study presents a proteomic analysis of post-exercise skeletal muscle biopsies from individuals across a spectrum of glucose metabolism states: normal, prediabetes, and T2D. This enabled the identification of significant protein relationships indicative of each specific glycemic condition. Our investigation primarily leveraged the machine learning approach, employing the white-box algorithm relative evolutionary hierarchical analysis (REHA), to explore the impact of regulated, mixed mode exercise on skeletal muscle proteome in subjects with diverse glycemic status. This method aimed to advance the diagnosis of IR and T2D and elucidate the molecular pathways involved in its development and the response to exercise. Additionally, we used proteomics-specific statistical analysis to provide a comparative perspective, highlighting the nuanced differences identified by REHA. Validation of the REHA model with a comparable external dataset further demonstrated its efficacy in distinguishing between diverse proteomic profiles. Key metrics such as accuracy and the area under the ROC curve confirmed REHA's capability to uncover novel molecular pathways and significant protein interactions, offering fresh insights into the effects of exercise on IR and T2D pathophysiology of skeletal muscle. The visualizations not only underscored significant proteins and their interactions but also showcased decision trees that effectively differentiate between various glycemic states, thereby enhancing our understanding of the biomolecular landscape of T2D.
Identifiants
pubmed: 39085321
doi: 10.1038/s41598-024-68568-4
pii: 10.1038/s41598-024-68568-4
doi:
Substances chimiques
Proteome
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
17631Subventions
Organisme : Ministerstwo Zdrowia
ID : Excellence Initiative - Research University
Organisme : Narodowe Centrum Nauki
ID : 2019/33/B/ST6/02386
Informations de copyright
© 2024. The Author(s).
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