Supplementary data for: Circulating miRNAs detect high versus low visceral adipose tissue inflammation in patients living with obesity.

Abstract: Background: The severity of visceral adipose tissue (VAT) inflammation in individuals with obesity is thought to signify obesity sub-phenotype(s) associated with higher cardiometabolic risk. Yet, this tissue is not accessible for direct sampling in the non-surgical patient. We hypothesized that circulating miRNAs (circ-miRs) could serve as biomarkers to distinguish human obesity subgroups with high or low extent of VAT-inflammation. Methods: Discovery and validation cohorts of patients living with obesity undergoing bariatric surgery (n=35 and 51, respectively) were included. VAT inflammation was classified into low/high based on an expression score derived from the mRNA levels of TNFA, IL6 and CCL2 (determined by rtPCR). Differentially-expressed circ-miRs were identified, and their discriminative power to detect low/high VAT inflammation was assessed by ROC-AUC analysis. Results: Fifty three out of 263 circ-miRs (20%) were associated with high VAT inflammation according to Mann-Whitney analysis in the discovery cohort. Of those, 12 (12/53=23%) were differentially expressed according to Deseq2, and 6 significantly discriminated between high and low VAT inflammation with ROC-AUC>0.8. Of the resulting 5 circ-miRs that were differentially abundant in all three statistical approaches, 3 were unaffected by hemolysis and validated in an independent cohort. Circ-miRs 181b-5p, 1306-3p, and 3138 combined with HOMA-IR exhibited ROC-AUC of 0.951 (95%CI:0.865-1) and 0.808 (95%CI:0.654-0.963) in the discovery and validation cohorts, respectively, providing strong discriminative power between participants with low versus high VAT inflammation. Predicted target genes of these miRNAs are enriched in pathways of insulin and inflammatory signaling, circadian entrainment, and cellular senescence. Conclusions: Circ-miRs that identify patients with low versus high VAT inflammation constitute a putative tool to improve personalized care of patients with obesity