Frontiers in Endocrinology最新文献

Objective: Ischemic stroke-associated pneumonia (iSAP) affects about 10% of acute ischemic stroke patients during hospitalization. Current prediction scales for iSAP are insufficient. Identifying early biomarkers for stroke-associated pneumonia is crucial for improving patient outcomes. This study aimed to investigate the predictive value of euthyroid sick syndrome (ESS) for iSAP in acute-stage of ischemic stroke patients.

Methods: We studied 1767 acute ischemic stroke patients within one week of symptom onset, categorizing them into an infection group (iSAP, n=376) and control group (control, n=1391). COX regression analysis was used to identify the potential risk and protected factors. Kaplan-Meier time-event curves and Log-Rank tests were performed to differentiate infection time in patients with ESS or normal T3 group.

Results: The iSAP group had higher rates of risk factors like older age, atrial fibrillation, COPD, and ESS, along with elevated levels of WBC, CRP,and FT4 levels (all P < 0.001). Conversely, iSAP patients had lower GCS scores, eGFR, TSH, T3, FT3 (all P < 0.001) and T4 levels (P = 0.005) upon admission. No significant differences were observed in sex, smoking history, hypertension, diabetes, or LDL-C levels (P > 0.05). COX regression analysis identified age, KWST scores, leukocyte count, CRP, and ESS (all P < 0.001) as significantly correlated with iSAP. ROC analysis revealed ESS as a predictor with sensitivity of 35.64% and specificity of 87.92% for SAP prediction, like atrial fibrillation and higher than COPD and eGFR.

Conclusion: ESS at admission predicts a higher risk of stroke-associated pneumonia in acute-stage of ischemic stroke.

Background: The mechanisms associated between diabetic peripheral neuropathy (DPN) and various brain function abnormalities in patients remains unclear. This study attempted to indirectly evaluate the effect of DPN on brain function in patients with type 2 diabetes mellitus (T2DM) by characterizing the resting-state functional connectivity (FC) of the lower limb sensorimotor cortex (LSM).

Methods: Forty-four T2DM patients with diabetic peripheral neuropathy (DPN), 39 T2DM patients without diabetic peripheral neuropathy (ND), and 43 healthy controls (HCs) underwent a neuropsychological assessment and resting-state functional magnetic resonance imaging examinations to examine the differences in FC between the LSM and the whole brain. The relationships of FC with clinical/cognitive variables were examined.

Results: In comparison with the HCs group, the ND group showed reduced FC of the LSM with the right lateral occipitotemporal cortex (LOTC) and increased FC with the medial superior frontal gyrus (SFGmed), while the DPN group showed reduced FC of the LSM with the right cerebellar lobule VI, the right LOTC, the rostral prefrontal cortex (rPFC), and the anterior cingulate gyrus (ACC). Moreover, in comparison with the ND group, the DPN group showed reduced FC of the LSM with the ACC, SFGmed, and rPFC. In the DPN group, the FC between the LSM and right cerebellar lobule VI was significantly correlated with fasting blood glucose levels (r = -0.490, p = 0.001), and that between the LSM and ACC was significantly correlated with the Montreal Cognitive Assessment score (r = 0.479, p = 0.001).

Conclusions: Patients with T2DM may show abnormal motion-related visual perceptual function before the appearance of DPN. Importantly, DPN can influence the brain regions that maintain motion and motor control, and this effect is not limited to motor function, which may be the central neuropathological basis for diabetic peripheral neuropathy.

Diabetic cardiomyopathy (DCM) is defined as structural and functional cardiac abnormalities in diabetes, and cardiomyocyte death is the terminal event of DCM. Ferroptosis is iron-dependent oxidative cell death. Evidence has indicated that iron overload and ferroptosis play important roles in the pathogenesis of DCM. Mitochondria, an important organelle in iron homeostasis and ROS production, play a crucial role in cardiomyocyte ferroptosis in diabetes. Studies have shown some anti-diabetic medicines, plant extracts, and ferroptosis inhibitors might improve DCM by alleviating ferroptosis. In this review, we systematically reviewed the evidence of ferroptosis in DCM. Anti-ferroptosis might be a promising therapeutic strategy for the treatment of DCM.

Aims: Our study, employing a multi-omics approach, aimed to delineate the distinct gut microbiota and metabolic characteristics in individuals under 30 with unclassified diabetes, thus shedding light on the underlying pathophysiological mechanisms.

Methods: This age- and sex-matched case-control study involved 18 patients with unclassified diabetes, 18 patients with classic type 1 diabetes, 13 patients with type 2 diabetes, and 18 healthy individuals. Metagenomics facilitated the profiling of the gut microbiota, while untargeted liquid chromatography-mass spectrometry was used to quantify the serum lipids and metabolites.

Results: Our findings revealed a unique gut microbiota composition in unclassified diabetes patients, marked by a depletion of Butyrivibrio proteoclasticus and Clostridium and an increase in Ruminococcus torques and Lachnospiraceae bacterium 8_1_57FAA. Comparative analysis identified the combined marker panel of five bacterial species, seven serum biomarkers, and three clinical parameters could differentiate patients with UDM from HCs with an AUC of 0.94 (95% CI 0.85-1). Notably, the gut microbiota structure of patients with unclassified diabetes resembled that of type 2 diabetes patients, especially regarding disrupted lipid and branched-chain amino acid metabolism.

Conclusions: Despite sharing certain metabolic features with type 2 diabetes, unclassified diabetes presents unique features. The distinct microbiota and metabolites in unclassified diabetes patients suggest a significant role in modulating glucose, lipid, and amino acid metabolism, potentially influencing disease progression. Further longitudinal studies are essential to explore therapeutic strategies targeting the gut microbiota and metabolites to modify the disease trajectory.

Gasless endoscopic thyroidectomy obviates the necessity for carbon dioxide insufflation to establish a surgical workspace, thus mitigating the potential complications associated with this practice. This technique presents several benefits, such as the maintenance of neck functionality, minimal scarring, and enhanced visibility of the surgical field, which contribute to its extensive adoption in clinical settings. The objective of this study is to synthesize the current methodologies of gasless endoscopic thyroidectomy and to evaluate the advantages and disadvantages inherent to each technique. It aims to offer theoretical insights to assist surgeons in determining the most suitable approach for gasless endoscopic thyroidectomy in their clinical practice.

Objective: This study aimed to compare the effects of high-intensity interval training (HIIT) combined with resistance training (RT) versus HIIT alone on body composition, cardiorespiratory fitness and glycolipid metabolism in young women with overweight/obesity.

Methods: This randomized controlled trial divided 40 subjects into an experimental group (HIIT+RT) and a control group (HIIT). Both groups underwent training three times per week for eight weeks. Body composition, cardiorespiratory fitness and glycolipid levels were assessed before and after the intervention.

Results: The results revealed that compared to baseline, both the experimental and control groups showed significant improvements in body weight, body mass index (BMI), Body fat, waist circumference (WC), waist-hip ratio (WHR), peak oxygen uptake (VO₂peak), vital capacity (VC), oxygen pulse (VO₂/HR), minute ventilation, resting heart rate, blood oxygen saturation, blood pressure, fasting blood glucose, triglycerides and high-density lipoprotein cholesterol (p<0.05). Additionally, a significant increase in muscle mass and a significant reduction in 2-hour postprandial glucose were observed in the experimental group (p<0.05). Muscle mass (mean difference: 2.75%), VO₂peak (mean difference: 1.61 mL/min/kg), VC (mean difference: 334mL), and VO₂/HR (mean difference: 0.51mL/beat) showed greater improvement in the HIIT+RT group compared to the HIIT group (p<0.05).

Conclusion: In conclusion, an 8-week regimen of either combined HIIT and RT or HIIT significantly improves body composition, cardiorespiratory fitness and glycolipid metabolism in women with overweight/obesity. However, the combined training appears to offer more benefits than HIIT alone. Further research is needed to evaluate the long-term effects and feasibility of combined training.

Trial registration: https://www.chictr.org.cn/, identifier ChiCTR2300075961.

Introduction: Forkhead box E1 (FOXE1) is a transcription factor with a crucial role in thyroid morphogenesis and differentiation. Promoter hypermethylation downregulates FOXE1 expression in different tumor types; nevertheless, its expression and relationship with methylation status in differentiated thyroid cancer (DTC) remain unclear.

Methods: A total of 33 pairs of matched samples of PTC tumors and non-tumors were included. Tumor cell cultures were treated with either 5-Aza-2'-deoxycytidine demethylating agent or dimethyl sulfoxide (DMSO). A real-time polymerase chain reaction (RT-PCR) and Western blotting were performed to assess FOXE1 expression. The methylation status was quantified using bisulfite sequencing. A luciferase gene assay was used to determine CpG-island functionality. Gene expression and promoter methylation of FOXE1 and FOXE1-regulated genes were also analyzed with data from The Cancer Genome Atlas (TCGA) thyroid samples.

Results: After demethylating treatment, increased FOXE1 mRNA was observed concomitantly with reduced promoter methylation of CpGisland2. A negative correlation between mRNA downregulation and an increased methylation level of CpGisland2 was observed in tumors. Diminished protein expression was also detected in some DTC cell lines and in some tumor samples, suggesting the involvement of post-transcriptional regulatory mechanisms. CPGisland2 was proved to be an enhancer. TCGA data analysis showed low FOXE1 mRNA expression in tumors with a negative correlation with methylation status and a positive correlation with the expression of most of its target genes. Reduced FOXE1 expression, accompanied by a high methylation level, was associated with PTC aggressiveness (tall cell variant, advanced extra thyroid extension, T4 American Joint Committee on Cancer (AJCC) classification), age at diagnosis (over 45 years old), and presence of a BRAFV600E mutation.

Conclusion: FOXE1 mRNA was downregulated in DTC compared with non-tumors, followed by high CpGisland methylation. A coupling of low mRNA expression and high methylation status was related to characteristics of aggressiveness in DTC tumors.

Post-translational modification (PTM) plays a crucial role in adaptation of mammals to environmental changes, enabling them to survive in stressful situations. One such PTM is SUMO modification, which is evolutionarily conserved. It involves the covalent and reversible attachment of a small ubiquitin-like modifier (SUMO) to lysine (Lys) residues in the target protein. SUMOylation regulates various functions, including cell proliferation, differentiation, apoptosis, senescence, and maintenance of specific cellular activities. It achieves this by influencing protein-protein interactions, subcellular localization, protein stability, and DNA binding activity. Mounting evidence suggests that SUMOylation is implicated in the pathogenesis of metabolic disorders such as obesity, insulin resistance, and fatty liver. This review aims to provide an overview of the role of SUMOylation in regulating tissue adaptation to metabolic stress. Recent advancements in spectroscopic techniques have shed light on potential targets of SUMOylation and the underlying regulatory mechanisms have been elucidated, laying the theoretical foundation for the development of targeted SUMOylation interventions for metabolic syndrome while minimizing side effects.

Background: Type 2 diabetes mellitus (T2DM) is a global health problem characterized by insulin resistance and hyperglycemia. Early detection and accurate prediction of T2DM is crucial for effective management and prevention. This study explores the integration of machine learning (ML) and explainable artificial intelligence (XAI) approaches based on metabolomics panel data to identify biomarkers and develop predictive models for T2DM.

Methods: Metabolomics data from T2DM (n = 31) and healthy controls (n = 34) were analyzed for biomarker discovery (mostly amino acids, fatty acids, and purines) and T2DM prediction. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to enhance the model's accuracy and interpretability. Advanced three tree-based ML algorithms (KTBoost: Kernel-Tree Boosting; XGBoost: eXtreme Gradient Boosting; NGBoost: Natural Gradient Boosting) were employed to predict T2DM using these biomarkers. The SHapley Additive exPlanations (SHAP) method was used to explain the effects of metabolomics biomarkers on the prediction of the model.

Results: The study identified multiple metabolites associated with T2DM, where LASSO feature selection highlighted important biomarkers. KTBoost [Accuracy: 0.938; CI: (0.880-0.997), Sensitivity: 0.971; CI: (0.847-0.999), Area under the Curve (AUC): 0.965; CI: (0.937-0.994)] demonstrated its effectiveness in using complex metabolomics data for T2DM prediction and achieved better performance than other models. According to KTBoost's SHAP, high levels of phenylactate (pla) and taurine metabolites, as well as low concentrations of cysteine, laspartate, and lcysteate, are strongly associated with the presence of T2DM.

Conclusion: The integration of metabolomics profiling and XAI offers a promising approach to predicting T2DM. The use of tree-based algorithms, in particular KTBoost, provides a robust framework for analyzing complex datasets and improves the prediction accuracy of T2DM onset. Future research should focus on validating these biomarkers and models in larger, more diverse populations to solidify their clinical utility.