ABSTRACT
Wound research has typically been performed without regard for where the wounds are located on the body, despite well-known heterogeneities in physical and biological properties between different skin areas. The skin covering the palms and soles is highly specialized, and plantar ulcers are one of the most challenging and costly wound types to manage. Using primarily the porcine model, we show that plantar skin is molecularly and functionally more distinct from nonplantar skin than previously recognized, with unique gene and protein expression profiles, broad alterations in cellular functions, constitutive activation of many wound-associated phenotypes, and inherently delayed healing. This unusual physiology is likely to play a significant but underappreciated role in the pathogenesis of plantar ulcers as well as the last 25+ years of futility in therapy development efforts. By revealing this critical yet unrecognized pitfall, we hope to contribute to the development of more effective therapies for these devastating nonhealing wounds.
Subject(s)
Phenotype , Skin , Wound Healing , Animals , Wound Healing/physiology , Swine , Skin/pathology , Skin/injuries , Skin/metabolism , Disease Models, Animal , Foot Ulcer/physiopathology , Foot Ulcer/pathology , Humans , Female , Skin Physiological Phenomena , FootABSTRACT
OBJECTIVE: We aimed to identify metabolites associated with loss of glycemic control in youth-onset type 2 diabetes. RESEARCH DESIGN AND METHODS: We measured 480 metabolites in fasting plasma samples from the TODAY (Treatment Options for Type 2 Diabetes in Adolescents and Youth) study. Participants (N = 393; age 10-17 years) were randomly assigned to metformin, metformin plus rosiglitazone, or metformin plus lifestyle intervention. Additional metabolomic measurements after 36 months were obtained in 304 participants. Cox models were used to assess baseline metabolites, interaction of metabolites and treatment group, and change in metabolites (0-36 months), with loss of glycemic control adjusted for age, sex, race, treatment group, and BMI. Metabolite prediction models of glycemic failure were generated using elastic net regression and compared with clinical risk factors. RESULTS: Loss of glycemic control (HbA1c ≥8% or insulin therapy) occurred in 179 of 393 participants (mean 12.4 months). Baseline levels of 33 metabolites were associated with loss of glycemic control (q < 0.05). Associations of hexose and xanthurenic acid with treatment failure differed by treatment randomization; youths with higher baseline levels of these two compounds had a lower risk of treatment failure with metformin alone. For three metabolites, changes from 0 to 36 months were associated with loss of glycemic control (q < 0.05). Changes in d-gluconic acid and 1,5-AG/1-deoxyglucose, but not baseline levels of measured metabolites, predicted treatment failure better than changes in HbA1c or measures of ß-cell function. CONCLUSIONS: Metabolomics provides insight into circulating small molecules associated with loss of glycemic control and may highlight metabolic pathways contributing to treatment failure in youth-onset diabetes.
ABSTRACT
Activating brown adipose tissue (BAT) improves systemic metabolism, making it a promising target for metabolic syndrome. BAT is activated by 12,13-dihydroxy-9Z-octadecenoic acid (12,13-diHOME), which we previously identified to be inversely associated with BMI and which directly improves metabolism in multiple tissues. Here we profile plasma lipidomics from 83 people and test which lipids' association with BMI replicates in a concordant direction using our novel tool ScreenDMT, whose power and validity we demonstrate via mathematical proofs and simulations. We find that the linoleic acid diols 12,13-diHOME and 9,10-diHOME are both replicably inversely associated with BMI and mechanistically activate calcium influx in mouse brown and white adipocytes in vitro, which implicates this signaling pathway and 9,10-diHOME as candidate therapeutic targets. ScreenDMT can be applied to test directional mediation, directional replication, and qualitative interactions, such as identifying biomarkers whose association is shared (replication) or opposite (qualitative interaction) across diverse populations.