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Plasma metabolomic and lipidomic profiling highlights metabolic changes in keloid‐prone individuals

Keloidal scarring represents a common and disfiguring skin problem. To date, however, keloid pathobiology is poorly understood, and treatments remain suboptimal. Previous transcriptome investigation of serial skin biopsies during keloid formation has revealed key metabolic pathways that may contribute to the disease.1 Therefore, to characterize further the metabolic alterations between healthy and keloidprone individuals, we undertook a casecontrol metabolic and lipidomic exploration in plasma. EDTA plasma samples were collected from 37 keloidprone subjects (18 females, 19 males; median age 40; mean BMI 24.8) and 37 healthy control individuals (17 females, 20 males; median age 38; mean BMI 24.1). All subjects were of Taiwanese descent. Metabolomic profiling was performed using ultrahighperformance liquid chromatography– tandem mass spectrometry (UPLC– MS/ MS), whereas lipid extracts underwent infusionMS analysis by Metabolon (Morrisville, NC, USA). Based on the global metabolite platform, a total of 1,114 biochemicals were detected, of which 864 were compounds of known structural identity and the remaining 250 were unnamed (Table S1). In addition, complex lipid panel profiling detected 994 biochemicals. Statistical analysis (Welch's ttest) between the keloidprone and control individuals identified 129 biochemicals of statistically significant (p < 0.05) differences within the global metabolomics platform (Table S2), with most changes driven mainly by the male cohort, and 21 within the complex lipid panel. Random Forest (RF) analysis resulted in a predictive accuracy of 83% in discriminating keloidprone individuals from controls based on the global metabolomics platform (Figure 1A,B and Table S3), and 56% based on the complex lipid panel (data not illustrated). Of the characterized differentially expressed metabolites, some individual metabolites are known to be involved in fibrosing processes in skin or other tissues. For instance, taurodeoxycholate has been shown to result in fibrosis within the subcutis after its injection into rat tails.2 Metabolic pathway analysis indicated changes in systemic amino acid levels in all keloid subjects, the aetiology of which is uncertain, but which may relate to increased collagen catabolism. Also, altered in all keloidprone individuals was haem oxygenase activity: This family of enzymes execute antiinflammatory, antiapoptotic, and antiproliferative functions through effector molecules generated by haem catabolism, with HO1 recently identified as a biomarker for fibrosis in interstitial lung disease.3 In male keloid subjects, changes in primary and secondary bile acid levels were noted. How such changes relate to keloid biology is uncertain although bile acid alterations have been noted in liver and renal fibrosis. Male subject plasma also revealed with changes in metabolites associated with energy metabolism and collagen remodelling that may be directly related to keloid pathology (Figure 1C). Lower levels of collagen breakdown markers such as proline, glycine and trans4hydroxyproline observed in males could reflect changes in collagen turnover (increased deposition) or remodelling in peripheral scar tissue. Larger cohorts of male and female individuals warrant further characterization to see if these sex differences are biologically relevant. Metabolic dysregulation has already been shown to underlie fibrotic disorders, including keloids.4 Moreover, glycolytic metabolism in keloid fibroblasts has previously been observed, suggesting that dysregulation of metabolic pathways such as glucose metabolism may contribute to keloid formation.5 In contrast to the discriminatory findings for the metabolomics data, lipidomics showed minimal changes between keloidprone and control subjects, suggesting similar lipid profiles between all individuals. Although it was not possible to characterize several unknown metabolites in our dataset, including the 5 most differentially expressed metabolites, and it is not feasible to determine whether the characterized metabolite differences are drivers or consequences of the keloid process, our data provide possible new insights and targets in keloid pathobiology that warrant further validation and analysis in the quest to determine which individuals might be prone to developing keloids as well discovering better treatments.