Multi-omics approaches to identify molecular targets and disease mechanisms of chronic diabetic foot ulcerations
Long-standing hyperglycemia results in a wide panel of diabetic complications. Diabetic neuropathy (DNP), the most common chronic microvascular complication of diabetes, includes a panel of foot problems caused by damage to the peripheral and autonomic nervous system. Due to their exposed position, the plantar surfaces of the feet are most prone to abrasions and lesions. Skin damage may result in diabetic foot ulcers (DFU), a complex blend of neuropathy, peripheral arterial diseases, foot deformities, and invasive infections. Approximately 50% of diabetic patients suffer from DNP, and 15-20% progress to foot ulcerations, leading to a substantial burden on morbidity and mortality rates, besides being the most expensive of all complications. The medical regimen for diabetic foot ulcers is constrained due to the recurrence of the ulcerations, and the absence of any reliable risk predictors or known molecular mechanisms.
Hypothesizing that wound effluents provide valuable information about bioactive molecules (e.g., chemokines, cytokines, host defense peptides) that can serve as predictive markers of healing trajectory in DFU, I will screen the full proteome profile of wound bed by using LC/MS proteomics method. This will result in the largest clinical proteomics dataset from DFU patients up to date. The dataset will further be used to pinpoint biomarkers associated with the risk of developing ulcers. Having information from the wound bed would benefit patients in two ways, first by providing a much better risk prediction to allow better care for the most vulnerable high-risk patients, and second by identifying potentially targetable proteins or pathways for pharmacological treatments. Data will be further integrated with information about DFU obtained with high-end multi-omics (transcriptomic, proteomic, and genomic) approaches. These approaches involve other sample types like blood and wound biopsies from well-characterized clinical cohorts of people with type 1 or type 2 diabetes, thus the DFU screening or risk assessment will be based on multiple omics’ approaches.
Moreover, based on information acquired from data integration, I will be able to propose a panel of DFU biomarkers of healing/non-healing trajectory. Finally, validation studies will dissect the mechanistic pathways underlying the development and progression of this devastating disease.
Tarunveer Singh Ahluwalia, Steno Diabetes Center Copenhagen