A meta-analysis of diabetes risk prediction models applied to prediabetes screening.

AIM: To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes. METHODS: The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes. RESULTS: A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information. CONCLUSIONS: Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

Characterization of Developmental Neurobehavioral Toxicity in a Zebrafish MPTP-Induced Model: A Novel Mechanism Involving Anemia.

Zebrafish represent an economical alternative to rodents for developmental neurotoxicity (DNT) testing. Mechanistic understanding is the key to successfully translating zebrafish findings to humans. In the present study, we used a well-known dopaminergic (DA) neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) as a model chemical to uncover the molecular pathways for observed DNT effects. To enhance the specificity of potential molecular targets, we restricted our exposure to a concentration that is nonteratogenic yet exhibits high DNT effects and an exposure window sensitive to MPTP. Our DNT assessment based on a battery of motor and social behavioral tests revealed an effective concentration of 1 µM and a sensitive window of 48-96 h postfertilization (hpf) for MPTP-induced hypoactivity. It is worth noting that this hypoactivity persisted into later larval development until 28 dpf. We observed increased cell apoptosis, oxidative stress, and decreased ATP levels in larvae immediately after exposure at 96 hpf. Significant reductions of DA neurons were found in the retina at 72, 96, and 120 hpf. No visible deformity was found in motoneurons at 72, 96, and 120 hpf. Transcriptome analysis uncovered a novel pathway manifested by significant upregulation of genes enriched with erythropoiesis. Sensitive window exposure of MPTP and other DA neurotoxins rotenone and paraquat exhibited a concentration-dependent effect on transcriptional changes of embryonic hemoglobins and anemia. Given that anemia is a significant risk factor for Parkinson's disease and MPTP is known to cause parkinsonism in humans, we concluded that anemia resulting from dysregulation of primitive erythropoiesis during embryonic development might serve as a common mechanism underlying DA neurotoxin-induced DNT effects between zebrafish and humans.