Frontiers in Endocrinology最新文献

Background: The accurate preoperative prediction of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) poses a significant clinical challenge. Although clinicopathological features are commonly utilized, their predictive accuracy remains limited, and the role of multi-gene co-mutations is not fully understood.

Objective: This study aimed to develop and validate an integrated risk model that combines next-generation sequencing (NGS) data with clinicopathologic features for the preoperative prediction of LNM in PTC.

Methods: We retrospectively analyzed 521 patients with PTC. Gene mutations were analyzed using NGS. Independent risk factors for central (CLNM) and lateral (LLNM) lymph node metastasis were identified through univariate and multivariate logistic regression analyses.

Results: The BRAF V600E mutation was the most prevalent (82.15%). Notably, high-risk multi-gene co-mutations -specifically, BRAF V600E co-occurring with TERT, PIK3CA, and/or TP53)-were identified as the strongest independent risk factor for CLNM (odds ratio [OR] = 6.319, 95% confidence interval [CI]: 1.738-22.976, P = 0.005). Other significant risk factors included male sex, age <45 years, bilateral lesions, tumor size >1 cm, lymphovascular invasion (LVI), and extrathyroidal extension,with gross ETE demonstrating the highest ORs (> 21).

Conclusion: Preoperative NGS profiling, particularly the detection of high-risk multi-gene co-mutations, provides a powerful tool for refined risk assessment. This molecularly guided strategy has the potential to inform personalized surgical planning directly, optimizing the extent of lymph node dissection to improve oncologic outcomes while minimizing unnecessary morbidity.

[This corrects the article DOI: 10.3389/fendo.2025.1592290.].

Appetite regulation in fish relies on complex neuroendocrine pathways within the brain-gut axis, with ghrelin (Ghrl) and cholecystokinin (Cck) as central players. However, their spatial distribution along the gastrointestinal tract (GIT) and responses to feeding status remain poorly understood in teleosts. This study investigated: i) the baseline distribution of Ghrl and Cck levels along the GIT of juvenile Sparus aurata fed a commercial diet; ii) their temporal dynamics during short-term fasting and refeeding; and iii) the influence of diet composition on their spatiotemporal profiles. Juveniles were fed for 92 days with: i) a control diet containing 20% fishmeal (CT); ii) a plant protein diet replacing 60% of fishmeal with hydrolyzed plant protein (PP); and iii) the PP diet supplemented with 2% LB-Green_Grape functional additive (GG). Fish were sampled at 2, 6, and 24 h post-feeding (Cf), after 7 days of fasting (Ft), and at 2, 6, and 24 h post-refeeding (Rf). Hormone levels were quantified across five GIT segments, including the stomach (S1) and four equal intestinal segments (S2-S5). Baseline characterization revealed elevated Ghrl content in S3 and S5, whereas Cck levels were highest in S5. During fasting, Ghrl levels declined, while Cck increased in S1, S2, and S5 with distinct temporal patterns. After refeeding, gastric Ghrl levels (S1) decreased within 24 h, potentially reflecting secretion into plasma and involvement in hunger signaling, although plasma levels were not measured. In contrast, Cck levels in the anterior intestine (S2) rose sharply 24 h after refeeding, suggesting an anticipatory response to refeeding, possibly related to a dual role involving both rapid satiety signaling and preparatory modulation of digestive activity. The PP and GG diets maintained high gastric Ghrl (S1) and lowered intestinal Cck (S2) levels after feeding, especially in the PP diet. This pattern may either prolong satiety and reduce feed intake or reflect changes in hormone release due to lower caloric intake, with the PP diet lowering growth and feed efficiency, partially offset by the functional additive. The study maps Ghrl and Cck in the S. aurata GIT, showing spatial, temporal, and dietary regulation, with implications for aquaculture nutrition.

Background: Approximately 40% of patients with diabetes develop diabetic kidney disease (DKD), necessitating renal replacement therapy such as dialysis or transplantation. Furthermore, DKD significantly elevates the risk of cardiovascular events and all-cause mortality, while imposing a substantial economic burden on healthcare systems. This study investigates the association between surrogate markers of insulin resistance (IR)-the triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), and the triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C)-and DKD in individuals with type 2 diabetes mellitus (T2DM), evaluating their predictive utility for DKD progression.

Methods: We enrolled 311 patients diagnosed with T2DM between January 2024 and April 2025. Baseline clinical characteristics and variations in IR markers across proteinuria stages were analyzed. Multivariable logistic regression assessed the association between these markers and DKD, while receiver operating characteristic (ROC) curve analysis evaluated their predictive performance. Restricted cubic spline (RCS) models explored dose-response relationships, supplemented by subgroup and interaction analyses.

Results: Higher quartiles of TyG, TyG-BMI, and TG/HDL-C were significantly associated with increased DKD risk (trend P < 0.001). ROC analysis revealed moderate-to-strong predictive accuracy for all three markers (AUC > 0.7). RCS modeling indicated a linear relationship between TyG and DKD risk (nonlinearity P = 0.378), whereas TyG-BMI and TG/HDL-C exhibited nonlinear associations (nonlinearity P < 0.05). Subgroup analysis identified a significant gender interaction for TG/HDL-C, with a stronger association in males (interaction P < 0.05). Age did not significantly modify these relationships. The biomarkers' association with DKD was more pronounced in patients with HbA1c ≥ 7%, while the TyG-BMI-DKD link was weaker in those with HbA1c < 7%. Stratified analysis by BMI showed a significant interaction (interaction P < 0.05).

Conclusion: TyG, TyG-BMI, and TG/HDL-C are positively correlated with DKD risk in T2DM patients and demonstrate substantial predictive value, supporting their potential as accessible, cost-effective indicators for DKD risk assessment.

Epilepsy can cause metabolic disorders, and metabolic abnormalities can also trigger epilepsy, forming a bidirectional pathological cycle. Over the past century, from the earliest use of ketogenic diets to treat epilepsy, it has been confirmed that metabolic intervention can control seizures. Subsequent studies have gradually revealed that metabolic disorders such as glucose abnormality and vitamin B6 deficiency can directly induce epilepsy, while epileptic seizures themselves can cause lactic acidosis, electrolyte imbalance and other internal environment disorders. With the breakthroughs in metabolomics technology, the research on epilepsy and metabolism has entered a systematic stage, and their relationship has attracted increasing attention. However, current reviews mostly focus on the isolated analysis of a single metabolic element (such as iron, vitamin D), lacking a systematic integration of multiple metabolic elements. This review for the first time integrates the changes of seven major metabolic elements (glucose, lipids, vitamins, minerals, water, adenosine triphosphate, uric acid) in the onset, progression and treatment of epilepsy; summarizes the clinical associations between metabolic diseases (diabetes mellitus, alcoholism, uremia) and epilepsy; reveals the specific metabolic changes in childhood epilepsy; and emphasizes the importance of epilepsy metabolomics data. It provides a reference for basic research and a metabolic monitoring framework for clinicians.

Background: The global incidence of thyroid nodules is rising substantially. The correlation between excessive iodine intake and an elevated risk of thyroid nodules remains a subject of debate.

Objective: To evaluate the categorical and quantitative dose-response associations between iodine status levels and the risk of thyroid nodules.

Methods: We systematically searched PubMed, Web of science, Embase, Cochrane Library and Scopus for studies published before December 2025 that investigated the association between iodine status and thyroid nodules, without language restrictions. The categorical association was assessed by pooling odds ratios (ORs) for thyroid nodules across different iodine status categories, using the adequate category as the reference. The continuous dose-response association was evaluated using random-effects generalized least squares spline models. The primary outcome was the prevalence of thyroid nodules at different iodine status statuses.

Results: This meta-analysis included 25 cross-sectional studies comprising 54621 participants (57.7% women) and 13569 thyroid nodule events. In categorical analyses, participants in the iodine deficiency (median urinary iodine concentration [UIC]: 50 μg/L) showed higher odds of thyroid nodules (OR = 1.28, 95%CI=1.09-1.50) compared to the adequate iodine category (median UIC, 150 μg/L). No significant association was found for both the more-than-adequate iodine (median UIC, 250 μg/L) category and excessive iodine (median UIC, 350 μg/L) categories (OR = 1.02, 95% CI = 0.93-1.11; OR = 1.13, 95%CI=0.98-1.30, respectively). Nine studies reporting continuous UIC outcomes, the mean difference (MD) between participants with and without nodules was 4.11 (95% CI, 2.51 to 5.71, P < 0.01). Continuous dose-response analysis revealed a significant U-shaped nonlinear correlation (P for nonlinearity < 0.001), with increased risks at both deficient and excessive iodine levels. These associations remained consistent in analyses by unadjusted variables of sex, age and BMI. Further analysis stratified by geographical factors also revealed similar correlation tendency.

Conclusions: The relationship between iodine intake and thyroid nodule risk follows a U-shaped, nonlinear pattern, where both deficiency and excess are associated with increased risks. More than adequate iodine intake showed a trend toward a lower prevalence of thyroid nodules. However, this observation should be interpreted with caution due to wide confidence intervals, heterogeneity, and potential residual confounding.

Systematic review registration: PROSPERO, https://www.crd.york.ac.uk/PROSPERO/view/CRD420251236621, identifier CRD420251236621.

Objective: Radioactive iodine (RAI) therapy is a cornerstone treatment for Graves' hyperthyroidism (GH), yet failure rates remain significant due to the complexity of individual patient responses. Traditional fixed-dose or simple calculated-dose methods often fail to account for non-linear interactions among clinical features.

Methods: We retrospectively analyzed data from 1,292 GH patients who received initial RAI therapy between June 2018 and July 2024. Comprehensive pre-treatment clinical, laboratory, and imaging data, including age, gender, FT4, 3-hour radioactive iodine uptake (RAIU 3h), thyroid weight, and thyroid receptor antibodies (TRAb), were collected. Stepwise regression with the Akaike Information Criterion (AIC) was employed for feature selection, identifying nine optimal predictors. Six machine learning algorithms were compared, with performance evaluated using AUC, Brier score, and Decision Curve Analysis (DCA). SHapley Additive exPlanations (SHAP) analysis provided model interpretability.

Results: The final cohort, comprising 1,292 patients (61.3% female, median age 37 years), achieved a 75.8% remission rate. Nine significant variables were identified as optimal predictors: gender, age, history of antithyroid drug use, disease course over 2 years, total iodine dose (TID), free thyroxine (FT4), RAIU 3h, thyroid weight, and TRAb. Among the algorithms tested, the Random Forest (RF) model demonstrated superior performance, achieving an AUC of 0.950 on the independent test set and a Brier score of 0.067, indicating excellent discrimination and calibration. SHAP analysis confirmed RAIU 3h, FT4, age, and thyroid weight as the most influential features, providing clinical transparency.

Conclusion: The developed interpretable machine learning framework offers a precise, personalized tool for predicting RAI outcomes, potentially guiding optimizing dosing strategies to reduce treatment failure.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder characterized by a high incidence rate and multiple complications, posing significant threats to women's health and quality of life. The etiology of PCOS involves a complex interplay of genetic, metabolic, hormonal, immunological and environmental factors, though its precise mechanisms remain incompletely understood. This review explores the roles of oxidative stress, autophagy, ferroptosis, epigenetic modifications, post-translational modifications, chronic low-grade inflammation, and gut microbiota in the pathogenesis of PCOS. Current therapeutic strategies often combine lifestyle modifications with pharmacological interventions to address the multifaceted symptoms of PCOS. Drawing on the latest research, this review highlights advanced glycation end products (AGEs), sex hormone-binding globulin (SHBG), and microRNAs (miRNAs) as promising targets for PCOS prevention and treatment. Future research should focus on developing targeted drugs for these molecular pathways, offering new avenues for managing PCOS. This review will provide a scientific foundation for advancing PCOS treatment strategies.

Background: Evidence on the efficacy and safety of radioligand therapy (RLT) in lung neuroendocrine tumors (LNETs) remains scarce. The limited data available, derived mainly from retrospective analyses are based on small patient cohorts and heterogeneous treatment protocols. The objective of this study was to assess the efficacy and safety of RLT in patients with SSTR-positive LNETs treated with either [¹⁷⁷Lu]Lu-DOTA-TATE or tandem therapy with [⁹⁰Y]Y-DOTA-TATE/[¹⁷⁷Lu]Lu-DOTA-TATE at Polish ENETS Center of Excellence.

Methods: We conducted a retrospective analysis of 22 LNET patients who received RLT and had complete follow-up data. Treatment response and survival outcomes were evaluated. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. Prognostic associations with PFS and OS were explored using univariate and multivariable Cox proportional hazards models, treatment-related AE were graded according to CTCAE.

Results: A total of 22 patients with LNETs (Med. 61 years; 68.2% male) were included. Histology comprised 31.8% typical carcinoid, 54.5% atypical carcinoid, and 13.6% LNET G3. 14 patients received [¹⁷⁷Lu]Lu-DOTA-TATE and 8 tandem [⁹⁰Y]Y/[¹⁷⁷Lu]Lu-DOTA-TATE. At a median follow-up of 54 months, median PFS and OS were 16.0 months (95% CI: 11.2-20.8) and 62.0 months (95% CI: 30.7-93.3), respectively. PFS was longer in patients with high SSTR uptake (34vs16 months; p=0.021) and, in unadjusted exploratory analyses, in those treated with tandem therapy (34vs16 months; p=0.037). OS differed significantly by histology and by prior chemotherapy, while FDG-avid disease was associated with shorter PFS and OS. However, these subgroup comparisons are based on a very small sample and should be regarded as exploratory and interpreted with caution. Treatment was generally well tolerated, with hematologic toxicity being the most common.

Conclusions: RLT demonstrated signals of clinically meaningful activity and an acceptable safety profile in patients with advanced LNETs in this small retrospective cohort. Outcomes were numerically more favorable in individuals with high SSTR uptake and in those treated with tandem therapy, but the study was not designed to compare treatment regimens. These exploratory findings should be regarded as hypothesis-generating only and do not provide evidence of comparative efficacy.