No abstract

Background The consensus on how to choose a reference gene for serum or plasma miRNA expression qPCR studies has not been reached and none of the potential candidates have yet been convincingly validated. We proposed a new in silico approach of finding a suitable reference for human, circulating miRNAs and identified a new set of endogenous reference miRNA based on miRNA profiling experiments from Gene Expression Omnibus. We used 3 known normalization algorithms (NormFinder, BestKeeper, GeNorm) to calculate a new normalization score. We searched for a universal set of endogenous miRNAs and validated our findings on 2 new datasets using our approach. Results We discovered and validated a set of 13 miRNAs (miR-222, miR-92a, miR-27a, miR-17, miR-24, miR-320a, miR-25, miR-126, miR-19b, miR-199a-3p, miR-30b, miR-30c, miR-374a) that can be used to create a reliable reference combination of 3 miRNAs. We showed that on average the mean of 3 miRNAs (p = 0.0002) and 2 miRNAs (p = 0.0031) were a better reference than single miRNA. The arithmetic means of 3 miRNAs: miR-24, miR-222 and miR-27a was shown to be the most stable combination of 3 miRNAs in validation sets. Conclusions No single miRNA was suitable as a universal reference in serum miRNA qPCR profiling, but it was possible to designate a set of miRNAs, which consistently contributed to most stable combinations.

Objective: We aimed to compare glycemic control and variability parameters obtained from paired records of real-time continuous glucose monitoring (RT-CGM) and flash glucose monitoring (FGM). Methods: Ten Polish boys and 11 girls aged 15.3 ± 2.1 years with type 1 diabetes for 7.7 ± 4.5 years and glycated hemoglobin 7.35 ± 0.7% (57 ± 5 mmol/mol) were recruited between August 2017 and June 2018 and equipped with devices for RT-CGM (iPro2 system with Enlite electrodes) and FGM (FreeStyle Libre) for 1 week. Afterwards, Glyculator 2.0 software was used to calculate and compare key metrics of glycemic control listed in the International Consensus on Use of Continuous Glucose Monitoring, with distinction into all record/night-time/day-time blocks when appropriate. Results: Agreement between the two systems' measurements across patients ranged from poor (R 2 = .39) to nearly perfect (R 2 = .97). Significant differences between RT-CGM and FGM were observed in five important metrics: coefficient of variation (median difference: −4.12% [25%-75%: −7.50% to −2.96%], P = .0001), low blood glucose index (−0.88 [−1.88 to −0.18], P = .0004), % of time below 70 mg/dL (3.9 mmol/L) (−4.77 [−8.39 to −1.19], P = .0015) and 54 mg/dL (3 mmol/L) (−1.33 [−4.07 to 0.00], P = .0033) and primary time in range (TIR) 70-180 mg/dL (8.58 [1.31 to 12.66], P = .0006). Conclusions: RT-CGM and FGM differ in their estimates of clinically important indices of glycemic control. Therefore, such metrics cannot be directly compared between people using different systems. Our result necessitates system-specific guidelines and targets if TIR and glycemic variability are to be used as an endpoint in clinical trials.