scholarly journals ROS constitute a convergence nexus in the development of IGF1 resistance and impaired wound healing in a rat model of type 2 diabetes

2012 ◽  
Vol 5 (3) ◽  
pp. 375-388 ◽  
Author(s):  
M. S. Bitar ◽  
F. Al-Mulla
Keyword(s):  
Type 2 Diabetes ◽  
Wound Healing ◽  
Rat Model ◽  
Impaired Wound Healing

Abstract 199: Type 2 Diabetes Impairs Wound Healing By DNMT1-dependent Reduction of Hematopoietic Stem Cell Differentiation towards Monocytes/Macrophages and Skewing of the M1/M2 Polarization

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Jinglian Yan ◽  
Guodong Tie ◽  
Lyne Khair ◽  
Elena Filippova ◽  
Louis Messina
Keyword(s):  
Type 2 Diabetes ◽  
Wound Healing ◽  
Epigenetic Modification ◽  
Stem Cell Differentiation ◽  
M1 Macrophages ◽  
Hematopoietic Stem ◽  
Impaired Wound Healing ◽  
Inflammatory Phase ◽  
M2 Polarization

Rationale: People with Type 2 Diabetes Mellitus (T2DM) have a 25x higher risk of limb loss than non-diabetics due in large part to impaired wound healing. The mechanisms that cause impaired wound healing remain incompletely characterized. Objective: We hypothesize that T2DM impairs wound healing by epigenetic modifications in hematopoietic stem cells (HSC) that reduce their differentiation towards monocytes/macrophages and disrupts the balance in M1/M2 polarization during the three phases of wound healing. Methods and Results: Wounds were created on the back of mice. Wound healing was significantly slower in diabetic db/db than in WT mice. During the early inflammatory phase, db/db wounds exhibited a significant decrease in total macrophages and M1 macrophages. Then, total macrophages and M2 macrophages were decreased, while M1 macrophages increased in tissue formation phase. In the late tissue remodeling phase, total macrophages and M1 macrophages were persistently increased. The impaired wound healing phenotype of db/db mice was recapitulated in WT recipients which were resconstituted with db/db HSCs, demonstrating that the impaired differentiation of HSCs towards macrophages as well as their M1/M2 polarization was due to a cell autonomous mechanism. Epigenetic studies indicated that DNMT1-dependent hypermethylation of Notch1, Pu.1 and KLF4 in T2D HSCs was responsible for the impaired differentiation towards monocytes/macrophages as well as the skewed M1/M2 polarization. Knockdown of DNMT1 in HSCs from db/db mice transplanted into lethally irradiated WT mice led to improved wound healing by an increase in macrophage infiltration as well as a normalization of the M1/M2 polarization. Conclusion: This study indicates that the dynamic changes of macrophage concentration and M1/M2 polarization in wound healing are regulated at the level of HSCs. Moreover, T2DM impairs wound healing by inducing DNMT1-dependent reduction of HSCs’ differentiation towards macrophages and their M1/M2 polarization. This novel finding indicates that inflammation is regulated at the level of HSCs, which creates new opportunities to develop epigenetic modification related therapies for T2DM and potentially other conditions that result from dysinflammation.

scholarly journals Macrophage PPARγ and impaired wound healing in type 2 diabetes

2015 ◽  
Vol 236 (4) ◽  
pp. 433-444 ◽  
Author(s):  
Rita E Mirza ◽  
Milie M Fang ◽  
Margaret L Novak ◽  
Norifumi Urao ◽  
Audrey Sui ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Wound Healing ◽  
Impaired Wound Healing

scholarly journals Volumetric quantification of wound healing by machine learning and optical coherence tomography in adults with type 2 diabetes: the GC-SHEALD RCT

2021 ◽  
Author(s):  
Yinhai Wang ◽  
Ramzi Ajjan ◽  
Adrian Freeman ◽  
Paul M Stewart ◽  
Francesco Del Galdo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Wound Healing ◽  
Optical Coherence Tomography ◽  
Granulation Tissue ◽  
Wound Repair ◽  
Optical Coherence ◽  
Impaired Wound Healing ◽  
Volumetric Quantification

Type 2 diabetes mellitus is associated with impaired wound healing, which contributes substantially to patient morbidity and mortality. Glucocorticoid (stress hormone) excess is also known to delay wound repair. Optical coherence tomography (OCT) is an emerging tool for monitoring healing by 'virtual biopsy', but largely requires manual analysis, which is labour-intensive and restricts data volume processing. This limits the capability of OCT in clinical research. Using OCT data from the GC-SHEALD trial, we developed a novel machine learning algorithm for automated volumetric quantification of discrete morphological elements of wound healing (by 3mm punch biopsy) in patients with type 2 diabetes. This was able to differentiate between early / late granulation tissue, neo-epidermis and clot structural features and quantify their volumetric transition between day 2 and day 7 wounds. Using OCT, we were able to visualize differences in wound re-epithelialisation and re-modelling otherwise indistinguishable by gross wound morphology between these time points. Automated quantification of maximal early granulation tissue showed a strong correlation with corresponding (manual) GC-SHEALD data. Further, % re-epithelialisation was improved in patients treated with oral AZD4017, an inhibitor of systemic glucocorticoid-activating 11β-hydroxysteroid dehydrogenase type 1 enzyme action, with a similar trend in neo-epidermis volume. Through the combination of machine learning and OCT, we have developed a highly sensitive and reproducible method of automated volumetric quantification of wound healing. This novel approach could be further developed as a future clinical tool for the assessment of wound healing e.g. diabetic foot ulcers and pressure ulcers.

Effect of Metformin on Periimplant Wound Healing in a Rat Model of Type 2 Diabetes

2014 ◽  
pp. 1 ◽  
Author(s):  
Kimberly Ann S. Inouye ◽  
Frederick C. Bisch ◽  
Mohammed E. Elsalanty ◽  
Ibrahim Zakhary ◽  
Rania M. Khashaba ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Wound Healing ◽  
Rat Model

1045: Pioglitazone Ameliorates Penile Corpora Veno-Occlusive Dysfunction (CVOD) in a Rat Model of Type 2 Diabetes

2005 ◽  
Vol 173 (4S) ◽  
pp. 283-284
Author(s):  
Istvan Kovanecz ◽  
Monica G. Ferrini ◽  
Hugo H. Davila ◽  
Jacob Rajfer ◽  
Nestor F. Gonzalez-Cadavid
Keyword(s):  
Type 2 Diabetes ◽  
Rat Model
Sign in / Sign up

Export Citation Format

Share Document