Polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) have attracted considerable attention owing to their environmental persistence, bioaccumulation, and high toxicity. This study aimed to investigate changes in serum metabolites following exposure to PCDD/Fs and to reveal a novel pathogenesis of PCDD/Fs. Serum samples were collected from 75 residents living near a municipal solid waste incinerator in China to analyse the relationship between PCDD/Fs and serum metabolic components. The serum level in the low-exposure group [19.07 (13.44-23.89) pg-TEQ/L] was significantly lower than that in the high-exposure group [115.60 (52.28-592.65) pg-TEQ/L]. Non-targeted metabolomic studies based on liquid chromatography-high resolution mass spectrometry have been applied to the metabolomic analysis of serum. Thirty-seven metabolites with significant differences among the different groups were identified as biomarkers. Pathway analysis revealed that high dioxin exposure perturbed various biological processes, including glycerol phospholipid metabolism and the interconversion of pentose and glucuronate. The results of a population health survey showed that the serum dioxin concentration in patients with diabetes was significantly higher than that in the control population. These findings suggest that dioxin exposure is associated with several potential adverse health risks, including inflammation, diabetes, and cardiovascular disease, through metabolic changes.

BACKGROUND: Limited information is available on the effect of ideal cardiovascular health (CVH) and abnormal glucose metabolism in elderly people. We aimed to analyze the prevalence of CVH behaviors, abnormal glucose metabolism, and their correlation in 65 and older people.
METHODS: In this study, randomized cluster sampling, multivariate logistic regression, and mediating effects analysis were used. Recruiting was carried out between January 2020 and December 2020, and 1984 participants aged 65 years or older completed the study.
RESULTS: The prevalence of abnormal glucose metabolism in this group was 26.7% (n = 529), among which the prevalence of impaired fasting glucose (IFG) was 9.5% (male vs. female: 8.7% vs 10.1%, P = 0.338), and the prevalence of type 2 diabetes mellitus (T2DM) was 19.0% (male vs. female: 17.8 vs. 19.8%, P = 0.256). The ideal CVH rate (number of ideal CVH metrics ≥ 5) was only 21.0%. The risk of IFG and T2DM decreased by 23% and 20% with each increase in one ideal CVH metrics, with OR (95%CI) of 0.77(0.65-0.92) and 0.80(0.71-0.90), respectively (P -trend < 0.001). TyG fully mediated the ideal CVH and the incidence of T2DM, and its mediating effect OR (95%CI) was 0.88(0.84-0.91).
CONCLUSIONS: Each increase in an ideal CVH measure may effectively reduce the risk of abnormal glucose metabolism by more than 20%.

The atherogenic index of plasma (AIP) and cardiovascular disease (CVD) in participants with abnormal glucose metabolism have been linked in previous studies. However, it was unclear whether AIP control level affects the further CVD incidence among with diabetes and pre-diabetes. Therefore, our study aimed to investigate the association between AIP control level with risk of CVD in individuals with abnormal glucose metabolism.
Participants with abnormal glucose metabolism were included from the China Health and Retirement Longitudinal Study. CVD was defined as self-reporting heart disease and/or stroke. Using k-means clustering analysis, AIP control level, which was the log-transformed ratio of triglyceride to high-density lipoprotein cholesterol in molar concentration, was divided into five classes. The association between AIP control level and incident CVD among individuals with abnormal glucose metabolism was investigated multivariable logistic regression analysis and application of restricted cubic spline analysis.
398 (14.97%) of 2,659 participants eventually progressed to CVD within 3 years. After adjusting for various confounding factors, comparing to class 1 with the best control of the AIP, the OR for class 2 with good control was 1.31 (95% CI, 0.90-1.90), the OR for class 3 with moderate control was 1.38 (95% CI, 0.99-1.93), the OR for class 4 with worse control was 1.46 (95% CI, 1.01-2.10), and the OR for class 5 with consistently high levels was 1.56 (95% CI, 1.03-2.37). In restricted cubic spline regression, the relationship between cumulative AIP index and CVD is linear. Further subgroup analysis demonstrated that the similar results were observed in the individuals with agricultural Hukou, history of smoking, diastolic blood pressure ≥ 80mmHg, and normal body mass index. In addition, there was no interaction between the AIP control level and the subgroup variables.
In middle-aged and elderly participants with abnormal glucose metabolism, constant higher AIP with worst control may have a higher incidence of CVD. Monitoring long-term AIP change will contribute to early identification of high risk of CVD among individuals with abnormal glucose metabolism.

OBJECTIVE: 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.

OBJECTIVE: To examine the association of serum connecting peptide (C-peptide) concentrations with gestational diabetes mellitus (GDM) risk among Chinese women.
METHODS: A nested case-control study was conducted on 436 reproductive-aged women, involving 218 GDM cases and 218 controls matched at 1:1 by maternal age, in Beijing, China between January 2016 and December 2017. Fasting serum C-peptide were successively determined at 10-14 and 15-20 weeks of gestation. Restricted cubic spline and logistic regression analyses were utilized, and receiver operating characteristic (ROC) curves were generated to evaluate the predictive capacity of C-peptide for GDM.
RESULTS: Fasting serum C-peptide concentrations exhibited a significant decrease from the initial to the subsequent trimester in females with normal glucose tolerance (NGT). For each 1 log ng/mL increase of fasting serum C-peptide during the first and second trimesters, GDM risk increased by 2.38-fold [odds ratio (OR): 2.38, 95% confidence intervals (95%CI): 1.33-4.40] and 3.07-fold (OR: 3.07, 95%CI: 1.49-6.62), respectively. The areas under the ROC curves for the first- and second-trimester C-peptide were 80.4% and 82.4%.
CONCLUSIONS: Our findings revealed a positive correlation between fasting serum C-peptide during the first and second trimesters and the risk of GDM or its subtypes, underscoring the potential of C-peptide as a predictor for GDM development.

Previous studies primarily focused on the single metal exposure and one-sided glucose metabolism disordered states, leading to conflicting results. Herein, we combined diabetes and prediabetes as abnormal glucose metabolism (AGM) to describe the effect of metal mixture exposure on it. Eligible data were obtained from the National Health and Nutrition Examination Survey (NHANES) 2015-2016. In the generalized linear model (GLM), Cd (OR: 1.060, 95 %CI: 1.032-1.089, P value < 0.001) and Tl (OR: 1.039, 95 %CI: 1.004-1.075, P value = 0.031) exposure were positively associated with AGM. In the weighted quantile sum (WQS) regression model, the positive index was obviously associated with AGM (OR: 1.358, 95 %CI: 1.007-1.832, P value = 0.045). In the least absolute shrinkage and selection operator (LASSO) regression model, Cd and Tl were selected as the most contributors. In the Bayesian kernel machine regression (BKMR) model, the effect of co-exposure to metal mixture was associated with AGM, and Cd exposure showed a significantly positive trend. In conclusion, Cd and Tl exposure exhibited independent positive effects on AGM among metal mixture exposure, consistent with their effects on prediabetes.

Antipsychotic medications increase the risk of abnormal glucose metabolism. However, in clinical practice, it is difficult to predict this risk because it is affected by medication-related and background factors. This study aimed to identify the risk factors for abnormal glucose metabolism during antipsychotic treatment. We conducted a multicenter, prospective, cohort study in patients with schizophrenia, schizoaffective disorder, or bipolar disorder. Of these patients, those with prediabetes or possible diabetes were excluded. Finally, 706 patients were included in the analysis. The hazard ratio (HR) for each factor was calculated for events of progression to hyperglycemia using time-dependent Cox regression analysis stratified according to facility type and adjusted for available background and drug-related factors. Treatments with olanzapine (HR = 2.06, 95% confidence interval [CI] = 1.05-4.05), clozapine (HR = 4.25, 95% CI = 1.56-11.60), and chlorpromazine (HR = 4.48, 95% CI = 1.21-16.57), overweight and obesity (HR = 1.57, 95% CI = 1.02-2.41), and hypertriglyceridemia (HR = 1.72, 95% CI = 1.02-2.88) were associated with a significantly higher occurrence of hyperglycemic progression. The number and daily dose of antipsychotics were not associated with their occurrence. Our study demonstrated that more careful monitoring is necessary during olanzapine, clozapine, and chlorpromazine treatment because of the higher occurrence of abnormalities in glucose metabolism. Furthermore, patients with obesity or hypertriglyceridemia warrant monitoring for the occurrence of abnormal glucose metabolism, regardless of the type of antipsychotic medication.

[This corrects the article DOI: 10.3389/fneur.2023.1103026.].

The high co-morbidity of abnormal glucose metabolism in depressed patients has been extensively studied, but few studies have explored abnormal glucose metabolism in young patients with major depressive disorder (MDD). This study aimed to examine the prevalence and clinical correlates of abnormal glucose metabolism in young patients with first-episode medication-naïve (FEMN) MDD.
A cross-sectional study was conducted on 1289 young Chinese outpatients with FEMN MDD. All subjects were assessed on the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale (HAMA), Positive and Negative Syndrome Scale, and their sociodemographic information was collected, and blood pressure, blood glucose, lipid and thyroid hormone levels were measured.
The prevalence of abnormal glucose metabolism was 12.57% in young FEMN MDD outpatients. Thyroid stimulating hormone (TSH) levels and HAMA scale scores were associated with fasting blood glucose levels in patients with FEMN MDD (P<0.05), and TSH could differentiate patients with abnormal normal glucose metabolism from those without abnormal glucose metabolism (Area Under Curve of 0.774).
Our study showed a high prevalence of comorbid glucose metabolism abnormalities in young FEMN MDD outpatients. TSH may be a promising biomarker of abnormal glucose metabolism in young patients with FEMN MDD.

UNASSIGNED: We aimed to determine a method to identify normal cerebrospinal fluid (CSF) glucose levels by examining the correlation between blood and CSF glucose levels in patients with normal and abnormal glucose metabolism.
UNASSIGNED: One hundred ninety-five patients were divided into two groups according to their glucose metabolism. The glucose levels were obtained from CSF and fingertip blood at 6, 5, 4, 3, 2, 1, and 0  h before lumbar puncture. SPSS 22.0 software was used for the statistical analysis.
UNASSIGNED: In both the normal and abnormal glucose metabolism groups, CSF glucose levels increased with blood glucose levels at 6, 5, 4, 3, 2, 1, and 0  h before lumbar puncture. In the normal glucose metabolism group, the CSF/blood glucose ratio range was 0.35-0.95 at 0-6  h before lumbar puncture, and the CSF/average blood glucose ratio range was 0.43-0.74. In the abnormal glucose metabolism group, the CSF/blood glucose ratio range was 0.25-1.2 at 0-6  h before lumbar puncture, and the CSF/average blood glucose ratio range was 0.33-0.78.
UNASSIGNED: The CSF glucose level is influenced by the blood glucose level 6  h before lumbar puncture. In patients with normal glucose metabolism, direct measurement of the CSF glucose level can be used to determine whether the CSF level is normal. However, in patients with abnormal or unclear glucose metabolism, the CSF/average blood glucose ratio should be used to determine whether the CSF glucose level is normal.