RESUMEN
Background: Osteocalcin plays a role in glucose metabolism in mice, but its relevance in human energetic metabolism is controversial. Its relationship with markers of energetic metabolism in the pediatric population has not been systematically addressed in infants and adolescents. Objective: This study aims to assess the mean differences between tOC, ucOC, and cOC among healthy children and children with type 1 or type 2 diabetes (T1D or T2D) and the correlation of these bone molecules with metabolic markers. Methods: A systematic review and metanalysis were performed following PRISMA criteria to identify relevant observational studies published in English and Spanish using PubMed, Scopus, EBSCO, and Web of Science databases. The risk of bias was assessed using New Castle-Ottawa scale. Effect size measures comprised standardized mean difference (SMD) and Pearson correlations. Heterogeneity and meta-regressions were performed. Results: The 20 studies included were of high quality and comprised 3,000 pediatric patients who underwent tOC, cOC, or ucOC measurements. Among healthy subjects, there was a positive correlation of ucOC with WC and weight, a positive correlation of tOC with FPG, HDL-c, WC, height, and weight, and a negative correlation between tOC and HbA1c. Among diabetic subjects, a negative correlation of ucOC with HbA1c and glycemia in both T1D and T2D was found and a negative correlation between tOC and HbA1c in T1D but not in T2D. The ucOC concentrations were lower in T2D, T1D, and patients with abnormal glucose status than among controls. The serum concentrations of tOC concentrations were lower among T1D than in controls. The patient's age, altitude, and HbA1c influenced the levels of serum tOC. Conclusion: Osteocalcin is involved in energy metabolism in pediatric subjects because it is consistently related to metabolic and anthropometric parameters. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42019138283.
RESUMEN
Degradation tests are especially difficult to conduct for items with high reliability. Test costs, caused mainly by prolonged item duration and item destruction costs, establish the necessity of sequential degradation test designs. We propose a methodology that sequentially selects the optimal observation times to measure the degradation, using a convenient rule that maximizes the inference precision and minimizes test costs. In particular our objective is to estimate a quantile of the time to failure distribution, where the degradation process is modelled as a linear model using Bayesian inference. The proposed sequential analysis is based on an index that measures the expected discrepancy between the estimated quantile and its corresponding prediction, using Monte Carlo methods. The procedure was successfully implemented for simulated and real data.