Aims: Glycated albumin (GA) is a biomarker for short-term (2-3 weeks) glycaemic control. However, the predictive utility of GA for diabetes and prediabetes is largely uncharacterised. We aimed to investigate the relationships of baseline serum GA levels with incident diabetes and prediabetes. Methods:This was a longitudinal cohort study involving 516 subjects without diabetes or prediabetes at baseline. Blood glucose levels were observed during followup. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using COX proportional hazard models. Receiver operating characteristic curves and areas under the curves (AUCs) were used to evaluate the discriminating abilities of glycaemic biomarkers and prediction models.Results: During a 9-year follow-up, 51 individuals (9.88%) developed diabetes and 92 (17.83%) prediabetes. Unadjusted HRs (95% CI) for both diabetes and prediabetes increased proportionally with increasing GA levels in a dose-response manner. Multivariable-adjusted HRs (95% CI) for diabetes were significantly elevated from 1.0 (reference) to 5.58 (1.86-16.74). However, the trend was no longer significant for prediabetes after multivariable adjustment. AUCs for GA, fasting blood glucose (FBG) and 2-h postprandial blood glucose (2h-PBG) for predicting diabetes were 0.698, 0.655 and 0.725, respectively. The AUCs for GA had no significant differences compared with those for FBG (p = 0.376) and 2h-PBG (p = 0.552). Replacing FBG or 2h-PBG or both with GA in diabetes prediction models made no significant changes to the AUCs of the models.Conclusions: GA is of good prognostic utility in predicting diabetes. However, GA may not be a useful biomarker for predicting prediabetes. K E Y W O R D S biomarker, cohort study, glycated albumin, type 2 diabetes Yuanyuan Bai and Yujie Fang are co-first authors.

DACH1 is an important component of the retinal determinate gene network (RDGN), which regulates the expression of target genes by directly binding or interacting with other factors. DACH1 shows inhibitory effects in most tumors, but its role in papillary thyroid carcinoma is unclear and warrants further investigation. We assessed the expression of DACH1 in different tissues and correlation with immune infiltration by The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMMER2.0 databases). The effects of DACH1 on the proliferation and migration of TPC‐1 and Bcpap cells were assessed by cell viability assay, colony formation assay, wound healing assay, transwell migration assay, and flow cytometry. Finally, the effects of DACH1 on CXCL8, CXCL10, and CXCL12 expression in Nthy‐ori‐3‐1, TPC‐1 and Bcpap cells were assessed by enzyme‐linked immunosorbent assay kit and real‐time polymerase chain reaction, respectively. The results showed that DACH1 was differentially expressed in different tumors and tissues. Basal expression of DACH1 was lower in thyroid and papillary thyroid carcinoma than in other normal tissues and corresponding tumors, and positively correlated with CD8+ T cell infiltration. In Nthy‐ori‐3‐1, TPC‐1 and Bcpap cells, overexpression of DACH1 inhibited cell migration and proliferation, and the opposite results was obtained by knocking down DACH1 using small interfering RNA. We also demonstrated that DACH1 regulated chemokines CXCL8, CXCL10, and CXCL12, thereby modulating tumor immunity.

Purpose Metabolic syndrome (Mets) is a pathological condition that includes many abnormal metabolic components and requires a simple detection method for rapid use in a large population. The aim of the study was to develop a diagnostic model for Mets in a Chinese population with noninvasive anthropometric and demographic predictors. Patients and methods Least absolute shrinkage and selection operator (LASSO) regression was used to screen predictors. A large sample from the China National Diabetes and Metabolic Disorders Survey (CNDMDS) was used to develop the model with logistic regression, and internal, internal-external and external validation were conducted to evaluate the model performance. A score calculator was developed to display the final model. Results We evaluated the discrimination and calibration of the model by receiver operator characteristic (ROC) curves and calibration curve analysis. The area under the ROC curves (AUCs) and the Brier score of the original model were 0.88 and 0.122, respectively. The mean AUCs and the mean Brier score of 10-fold cross validation were 0.879 and 0.122, respectively. The mean AUCs and the mean Brier score of internal–external validation were 0.878 and 0.121, respectively. The AUCs and Brier score of external validation were 0.862 and 0.133, respectively. Conclusions The model developed in this study has good discrimination and calibration performance. Its stability was proved by internal validation, external validation and internal-external validation. Then, this model has been displayed by a calculator which can exhibit the specific predictive probability for easy use in Chinese population.

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