Risk predictors of glycaemic control in children and adolescents with type 1 diabetes: A systematic review and meta-analysis

OBJECTIVES

To conduct systematic evaluation of the risk predictors of glycaemic control in children and adolescents with type 1 diabetes mellitus.

METHODS

Cohort studies on risk predictors of glycaemic control in children and adolescents with type 1 diabetes were retrieved from CNKI, PubMed, Web of Science, Embase databases, etc. from the construction of the repository to 3 February 2023. Literature screening was conducted according to inclusion and exclusion criteria, then data extraction of region, sample size, age, follow-up time, risk predictors, outcome indicators, etc., and quality evaluation of The Newcastle-Ottawa Scale were conducted by two researchers while the third researcher makes decisions if there are disagreements. Finally, Revman5.4 and StataMP17 were used for meta-analysis.

RESULTS

A total of 29 studies were included, and the results showed that insulin pump [Weighed mean difference (WMD) = -.48, 95% CI (-.73, -.24), p < .01], high-frequency sensor monitoring, early use of insulin pumps, prospective follow-up male, white race, large body mass index-standardised scoring, conscientiousness, agreeableness of mothers, eicosapentaenoic acid, leucine and protein (p < .05) were beneficial for reducing HbA1c levels in children and adolescents with diabetes. Ketoacidosis [WMD = .39, 95% CI (.28, .50), p < .01], selective admission, higher HbA1c level at one time (p < .01), higher glutamate decarboxylase antibody at 1 month after diagnosis, lower socio-economic status, non-living with biological parents, non-two-parent family, family disorder, family history of diabetes and high carbohydrate intake (p < .05) increased HbA1c levels in children and adolescents with diabetes.

CONCLUSION

For children and adolescents with type 1 diabetes mellitus, the use of insulin pump, high-frequency sensor monitoring, prospective follow-up, good family support and reasonable diet are conducive to blood glucose control, while selective admission and DKA are not. Disease characteristics and demographic characteristics of children are closely related to subsequent blood glucose control, and the relationship between diagnosis age and blood glucose control needs to be further explored.

Integration of single-cell sequencing and bulk expression data reveals chemokine signaling pathway in proliferating cells is associated with the survival outcome of osteosarcoma

BACKGROUND

Osteosarcoma, as the most common primary bone malignancy, is urgent to be well-studied on the biomarkers and therapeutic targets to improve the five-year survival rate. Transcriptomic analysis using single-cell RNA or bulk RNA sequencing has been developed to detect biomarkers in various cancer types.

METHODS AND RESULTS

We applied Scissor to combine single-cell RNA-seq data and bulk transcriptome data of osteosarcoma, providing cell-level information and sample phenotypes to identify the survival-associated cell subpopulations. By investigating the differences between the survival-associated cell subpopulations, we identified CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2 in the proliferating cells that are significantly associated with osteosarcoma patient outcome. Then we assigned the risk score for each sample based on the cell proportion-normalized gene expression and validated it in the public dataset.

CONCLUSIONS

This study provides the clinical insight that chemokine signaling pathway genes (CCL21, CCL22, CCL24, CXCL11, CXCL12, CXCL13, GNAI2, and RAC2) in proliferating cells might be the potential biomarkers for treatment of osteosarcoma.