BACKGROUND: Studies on the impact of blood glucose indicators on metabolism remain relatively scarce. The aim of this study was to investigate the associations between blood glucose indicators and metabolic disorders in China. METHODS: Data were from the Thyroid disorders, Iodine status and Diabetes Epidemiological survey (TIDE survey), which randomly selected 31 cities from 31 provinces in the Chinese mainland. A total of 68,383 participants without preexisting diabetes and have complete data on blood glucose, lipids, and blood pressure were included in the analysis. The diabetic population was divided into seven groups based on different types of elevated blood glucose levels, including fasting plasma glucose (FPG), postprandial glucose (PPG), and hemoglobin A1c (HbA1c): FPG ≥7 mmol/L; PPG ≥11.1 mmol/L; HbA1c ≥6.5%; FPG ≥7 mmol/L and PPG ≥11.1 mmol/L; FPG ≥7 mmol/L and HbA1c ≥6.5%; PPG ≥11.1 mmol/L and HbA1c ≥6.5%; FPG ≥7 mmol/L, PPG ≥11.1 mmol/L, and HbA1c ≥6.5%. The effects of each blood glucose indicator on metabolism were investigated separately. Weighted calculation was applied during the analysis, with the weighting coefficient based on the number of people corresponding to the population characteristics of each sample in the 2010 Chinese Census. A logistic regression model with restricted cubic splines (RCS) was employed to characterize the nonlinear associations of age and body mass index (BMI) with the risk of diabetes subtypes defined by distinct blood glucose indicators elevations, as well as the relationships between different blood glucose indicators (FPG, PPG, HbA1c) and the risk of metabolic disorders such as hypertension, hypertriglyceridemia, hypercholesterolemia, high low-density lipoprotein cholesterol (high LDL-C) and low high-density lipoprotein cholesterol (low HDL-C). RESULTS: Among individuals with diabetes, elevated PPG alone was the most common abnormality, affecting 26.96% (1382/5127) of the population. Among the seven groups with only one elevated blood glucose indicator, individuals with elevated PPG alone exhibited the highest mean levels of triglycerides (TG) at 2.11 mmol/L (95% confidence interval [CI]: 1.97-2.25 mmol/L, P = 0.004), total cholesterol (TC) at 5.26 mmol/L (95% CI: 5.18-5.33 mmol/L, P <0.001), and low-density lipoprotein cholesterol (LDL-C) at 3.12 mmol/L, (95% CI: 3.06-3.19 mmol/L, P = 0.001). Individuals with elevated PPG alone showed a high prevalence of hypertension (806/1382, 58.32%), hypertriglyceridemia (676/1382, 48.91%), hypercholesterolemia (694/1382, 50.22%), High LDL-C (525/1382, 37.94%), and Low HDL-C (364/1382, 26.34%). The association of age and BMI with the risk of diabetes revealed that the older the patient, the steeper the RCS curve for the odds ratio (OR) of diabetes with elevated PPG alone (age = 60, OR = 2.79, 95% CI [2.49-3.12], P <0.01). Similarly, as BMI increased, the RCS curve for the OR of diabetes with elevated HbA1c alone also steepened (BMI = 35, OR = 3.75, 95% CI [3.23-4.35], P <0.001). Additionally, the RCS yielded a positive association between blood glucose indicators and metabolic diseases risk. In individuals with diabetes, RCS for both the ORs of metabolic diseases (hypertension, hypertriglyceridemia, hypercholesterolemia, high LDL-C, low HDL-C) and the levels of metabolic indicators (TG, TC, LDL-C, HDL-C) revealed some inflection points within the ranges of FPG 5-6 mmol/L, PPG 6-8 mmol/L, and HbA1c 5.5-6.0%. CONCLUSIONS PPG is more closely related to metabolic disorders than FPG and HbA1c in people with diabetes. For patients with diabetes and metabolic disorders, it may be necessary to monitor blood glucose fluctuations within specific ranges (FPG 5-6 mmol/L, PPG 6-8 mmol/L, and HbA1c 5.5-6.0%).
Humans ; Female ; Cross-Sectional Studies ; Male ; Blood Glucose/metabolism* ; Middle Aged ; Glycated Hemoglobin/metabolism* ; Adult ; Metabolic Diseases/epidemiology* ; Aged ; China ; Diabetes Mellitus/blood* ; East Asian People

Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.

Aim Based on coronary CT-fractional flow reserve(CT-FFR)combined with machine learning methods,a nomogram prediction model for coronary in-stent restenosis(ISR)was developed to assess the risk of ISR.Methods Retrospective analysis was performed on patients who underwent re-examination after PCI at our hospital from January 2022 to January 2025.According to the exclusion criteria,a total of 210 patients were enrolled,including 100 cases of ISR and 110 cases of non-ISR.The dataset was randomly divided into training and test sets at a 7∶3 ratio.Af-ter univariate analysis to screen potential predictors,LASSO regression was applied to identify feature variables with non-ze-ro coefficients.Subsequently,three machine learning(ML)algorithms including random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB)were used to rank the importance of the significant factors.The intersection of the top 10 variables from each algorithm was used as input for bidirectional stepwise multivariate Logistic re-gression.An ISR risk score was then constructed and visualized using a nomogram.Results A total of 14 predictive factors were identified through LASSO regression,including diastolic blood pressure,C-reactive protein,triglycerides(TG),N-terminal pro-brain natriuretic peptide(NT-proBNP),low density lipoprotein cholesterol(LDLC),minimum stent diameter＜3 mm,systolic blood pressure,△CT-FFR,CT-FFR,interleukin-6(IL-6),body mass index,glycosylated hemoglobin(HbA1c),history of hypertension,and high density lipoprotein cholesterol(HDLC).Following stepwise screening using three ML algorithms and Logistic regression,six independent risk factors for ISR were identified:elevated△CT-FFR,IL-6,NT-proBNP,TG and CT-FFR values,and minimum stent diameter＜3 mm.The area under the curve for the training set and test set were 0.995(95％CI:0.989～1.000)and 0.965(95％CI:0.927～1.000),respectively.Decision curve analysis demonstrated high net benefit across threshold probabilities of 0～1.00 in the training set and 0～0.92 in the test set.The nomogram integrating these six predictors exhibited high accuracy and clinical utility.Con-clusion The ISR nomogram prediction model based on LASSO-ML combined with CT-FFR technology has high accuracy and clinical utility for ISR.

Anaplastic thyroid cancer (ATC) is the most malignant thyroid cancer with a low incidence but high mortality. ATC is highly aggressive, rapidly progressing, and has poor prognosis. Current treatment options is not efficacious, so there is an urgent need to investigate its pathogenesis to update the treatment and improve the survival rate. Previous studies have found that most ATC can develop from well-differentiated thyroid cancer, and BRAF and RAS mutations are the key driving factors of ATC. TP53, PI3K pathway, PTEN, TERT, SWI/SNF complex Subunit, NF2 and other mutations also play an important role in the occurrence of ATC. Recent studies have found that single gene mutation is often not sufficient to drive the occurrence of ATC, and ATC is usually developed from the accumulation of multiple mutations in well-differentiated thyroid cancer. Therefore, this paper reviews the role of common combined mutations in ATC, deepens the understanding of the pathogenesis, and provides a basis for finding effective therapeutic targets.

Objective:To investigate the thyroid volume of adults in Lanzhou City, and analyze its influencing factors.Methods:In June 2016, according to the principle of multi-stage stratified cluster sampling, Han residents aged 18 and above in Chengguan, Xigu and Qilihe districts of Lanzhou City who had lived there for more than 5 years were selected as research subjects, and a portable B-ultrasound machine was used for thyroid examination. Morning urine samples of the subjects were collected to test urinary iodine; fasting venous blood samples of the subjects were collected to test serum thyroid stimulating hormone (TSH), thyroid peroxidase antibody (TPOAb), anti-thyroglobulin antibody (TgAb), blood lipids [triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL)] and blood uric acid (Ua) levels. At the same time, body indexes systolic blood pressure (SP), diastolic blood pressure (DP), waist circumference, height and weight were measured, and the body mass index (BMI) was calculated. Multiple linear regression was used to analyze the influencing factors of thyroid volume.Results:A total of 1 009 subjects were included, aged (43.50 ± 15.16) years, and the thyroid volume was (8.74 ± 3.39) ml. Among them, 534 males had a thyroid volume of (9.46 ± 3.43) ml; 475 females had a thyroid volume of (7.93 ± 3.15) ml, the thyroid volume of males was larger than that of females ( t = 7.36, P < 0.01). Thyroid volume was positively correlated with age, height, weight, BMI, SP, waist circumference, LDL, Ua and TgAb ( r = 0.07, 0.23, 0.33, 0.27, 0.10, 0.27, 0.10, 0.08, 0.07, P < 0.05), and it was negatively correlated with thyroid nodules, TPOAb, TSH and urinary iodine ( r = - 0.16, - 0.07, - 0.10, - 0.08, P < 0.05). After multiple linear regression analysis, TSH, TPOAb, TgAb and thyroid nodules were included in the regression equation, and the standardized B values were - 0.135, - 0.065, 0.123 and - 0.197, respectively. Conclusions:The thyroid volume of males is larger than that of females in Lanzhou City. TSH, TPOAb, TgAb and thyroid nodules are influencing factors of thyroid volume.

Thyroid cancer is highly heterogeneous and has three main types of histopathology: low-malignant papillary thyroid cancer, follicular cancer, and high-malignant undifferentiated cancer. It has recently been hypothesized that thyroid cancer stem cell (TCSC) plays a major role in both cancer initiation and metastasis, but the origin of TCSC and underlying mechanism in the development of thyroid cancer are still unclear. In this article, we introduced the origin theory of thyroid cancer from TCSC and the relation of TCSC to immune system and tumor microenvironment as well as TCSC-based treatment strategy. This will provide a new direction for the treatment of thyroid cancer, so as to improve the prognosis of patients with refractory thyroid cancer.

Thyroid hormone receptors are expressed in myocardium and vascular endothelial tissue and are very sensitive to changes in circulating thyroid hormone (TH) levels, especially triiodothyronine (T 3), therefore small changes can adversely affect the cardiovascular system. Evidence from several studies in recent years supports the association between abnormal thyroid function at the time of cardiovascular event and subsequent adverse cardiovascular outcomes. Several clinical trials have found that treatment of subclinical thyroid disease improves cardiovascular risk factors, suggesting the potential benefit of TH therapy in reducing cardiovascular outcome events. This article reviews the relationship between T 3 and cardiovascular disease and its physiological effects on the heart, as well as the therapeutic potential of T 3 in heart failure and acute myocardial infarction.

Hashimoto thyroiditis(HT) is a classic autoimmune thyroiditis (AIT), characterized by diffuse lymphocytic infiltration, destruction of thyroid structure, and positive autoantibodies. The pathogenesis of HT is complex and related to genetic susceptibility, immune system disorders, and environmental factors. The imbalance of T helper cell 1 (Th1)/ T helper cell 2 (Th2) is traditionally believed to be the main mechanism of HT. However, recent studies have shown that T helper cell 17 (Th17) plays an important role in the occurrence and development of HT through non-coding RNA regulation, autophagy-related pathway regulation, the balance with regulatory T cell (Treg). These mechanisms can enhance the release of inflammatory factors and aggravate HT by stimulating the differentiation of Th17, the inflammatory environment of HT also further stimulates the differentiation of Th17 and amplifies the inflammatory response. The regulatory mechanisms of Th17 are complex and have not yet been fully studied. Therefore, this article reviews the related mechanism of Th17 in HT to provide insights for novel therapeutic targets.

Background: Developmental hypothyroidism impairs learning and memory in offspring, which depend on extensive neuronal circuits in the entorhinal cortex, together with the hippocampus and neocortex. The entorhinal-dentate gyrus pathway is the main entrance of memory circuits. We investigated whether developmental hypothyroidism impaired the morphological development of the entorhinal-dentate gyrus pathway. Methods: We examined the structure and function of the entorhinal-dentate gyrus pathway in response to developmental hypothyroidism induced using 2-mercapto-1-methylimidazole. Results: 1,1´-Dioctadecyl-3,3,3´,3´-tetramethylindocarbocyanine perchlorate tract tracing indicated that entorhinal axons showed delayed growth in reaching the outer molecular layer of the dentate gyrus at postnatal days 2 and 4 in hypothyroid conditions. The proportion of fibers in the outer molecular layer was significantly smaller in the hypothyroid group than in the euthyroid group at postnatal day 4. At postnatal day 10, the pathway showed a layer-specific distribution in the outer molecular layer, similar to the euthyroid group. However, the projected area of entorhinal axons was smaller in the hypothyroid group than in the euthyroid group. An electrophysiological examination showed that hypothyroidism impaired the long-term potentiation of the perforant and the cornu ammonis 3–cornu ammonis 1 pathways. Many repulsive axon guidance molecules were involved in the formation of the entorhinaldentate gyrus pathway. The hypothyroid group had higher levels of erythropoietin-producing hepatocyte ligand A3 and semaphorin 3A than the euthyroid group. Conclusion We demonstrated that developmental hypothyroidism might influence the development of the entorhinal-dentate gyrus pathway, contributing to impaired long-term potentiation. These findings improve our understanding of neural mechanisms for memory function.