Frontiers in Endocrinology最新文献

{"title":"Correlation between the thyrotropin-thyroxine resistance index and insulin resistance in polycystic ovary syndrome patients with phlegm-dampness syndrome.","authors":"Ying Liu, Ming Zhang, Ying Wang, Xueyan Lv, Shuang Ma, Jinglu Yu, Hongying Kuang, Lihui Hou, Qingxia Song","doi":"10.3389/fendo.2026.1940904","DOIUrl":"https://doi.org/10.3389/fendo.2026.1940904","url":null,"abstract":"<p><strong>Introduction: </strong>Polycystic ovary syndrome (PCOS) patients classified as the phlegm-dampness syndrome typically suffer from metabolic dysfunction mainly driven by insulin resistance (IR). Indices evaluating thyroid hormone responsiveness have emerged as promising biomarkers for metabolic disease research. However, limited research has investigated the thyrotropin-thyroxine resistance index (TT4RI), a marker quantifying central pituitary thyroid sensitivity, in this distinct PCOS subgroup. This study aimed to explore the association between TT4RI and IR in PCOS patients with phlegm-dampness syndrome.</p><p><strong>Methods: </strong>We enrolled 486 PCOS patients with complete baseline data in the present study. Among them, 264 patients were diagnosed with PCOS accompanied by phlegm-dampness syndrome. The 264 patients were further subdivided into two subgroups: with IR (n=208) and without IR (n=56), and clinical indicators were compared between the two subgroups. We evenly stratified the 264 phlegm-dampness syndrome PCOS patients into three groups to perform intergroup comparisons. Spearman's correlation and multivariate linear regression analyses were used to investigate the association. ROC curves were also employed to compare the discriminatory performance of TT4RI, thyroid-stimulating hormone index(TSHI), and FT3/FT4 in identifying insulin resistance in these patients(n=264). The sole primary outcome of this study was HOMA-IR.</p><p><strong>Results: </strong>Relative to the subgroup without IR (n=56), the subgroup with IR (n=208) patients displayed significantly higher blood pressure, obesity-related anthropometric values, triglycerides (TG), fasting plasma glucose (FPG), fasting insulin (FINS), homeostasis model assessment of insulin resistance (HOMA-IR),free triiodothyronine (FT3), thyroid-stimulating hormone (TSH) and TT4RI (P<0.05).Patients in the high TT4RI tertile group exhibited a relatively more prominent phenotype of IR. TT4RI exhibited positive correlations with weight, body mass index (BMI), FPG, FINS, HOMA-IR, free thyroxine (FT4), and TSH (P<0.05).In the multiple linear regression model, after adjusting for age, BMI, systolic blood pressure(SBP) and SHBG, only a marginally significant association was observed between HTT4RI group and IR. ROC analysis indicates that TT4RI exhibits limited discriminatory performance for identifying IR among PCOS patients with phlegm-dampness syndrome.</p><p><strong>Conclusions: </strong>Among phlegm-dampness syndrome PCOS women, diminished central pituitary thyroid hormone sensitivity is associated with increased insulin resistance. Further prospective research is needed to confirm these findings and explore clinical relevance.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1940904"},"PeriodicalIF":5.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13650464/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149165498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Case Report: Atypical femoral fractures in adult-onset hypophosphatasia treated with low-dose asfotase alfa and adjunctive teriparatide therapy.","authors":"Yoon-Sok Chung, Kwang-Kyoun Kim, Gokul B S, Ji-Seong Park","doi":"10.3389/fendo.2026.1909920","DOIUrl":"https://doi.org/10.3389/fendo.2026.1909920","url":null,"abstract":"<p><p>Hypophosphatasia is a rare inherited metabolic bone disease caused by low activity of tissue-nonspecific alkaline phosphatase. Adult-onset hypophosphatasia is increasingly recognized, but fracture patterns and optimal management remain unclear. We report a 52-year-old woman who presented with progressive pain and bilateral atypical femoral fractures. She was diagnosed with hypophosphatasia based on low alkaline phosphatase, markedly elevated pyridoxal 5'-phosphate, a history of metatarsal fracture, premature tooth loss, and pathogenic ALPL variants. Low-dose asfotase alfa was started because standard-dose therapy was not feasible. After 6 months, callus formation was observed, and pain improved. However, 6 months later, the pain recurred, and radiographs showed a progressive fracture, suggesting insufficient fracture healing. Staged intramedullary fixation was performed for both femurs. Two perioperative femoral fractures occurred during the course of surgical management. Therefore, adjunctive teriparatide (20 µg daily for six months) was initiated while continuing low-dose asfotase alfa. At 5-year follow-up, radiographic union of all femoral fractures was achieved, and the patient remained ambulatory without assistance and had no fracture-related pain. This case shows that adult-onset hypophosphatasia can cause severe skeletal fragility and may require individualized multidisciplinary management, including biochemical diagnosis, long-term radiographic surveillance, cautious surgical planning, and selective use of medication.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1909920"},"PeriodicalIF":5.7,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13660733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149180500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of intensive insulin therapy combined with non-insulin antihyperglycemic agents on glycemic control, variability, and metabolic outcomes in patients with type 2 diabetes mellitus: a systematic review and meta-analysis.","authors":"Zixuan Shen, Jiawei Li, Ziyun Zhao, Ziang Chen, Xiaosen Ma, Jing Zhou, Lijing Jiao, Jing Wang","doi":"10.3389/fendo.2026.1948361","DOIUrl":"https://doi.org/10.3389/fendo.2026.1948361","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Intensive insulin therapy (IIT) induces clinical remission in newly diagnosed or poorly controlled type 2 diabetes mellitus (T2DM), but is limited by hypoglycemia risk and weight gain. Combining IIT with non-insulin antihyperglycemic agents may mitigate these drawbacks while improving glycemic variability.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This review was registered with PROSPERO (CRD42024582974). We searched PubMed, Embase, Cochrane, Web of Science, ClinicalTrials.gov, and Chinese databases (CNKI, VIP, Wanfang, and Chinese Medical Association journals) for randomized controlled trials (RCTs) comparing intensive insulin therapy (IIT) alone with IIT plus metformin, dipeptidyl peptidase-4 inhibitors (DPP-4i), sodium-glucose cotransporter-2 inhibitors (SGLT2i), or glucagon-like peptide-1 receptor agonists (GLP-1RA) in adults with type 2 diabetes mellitus (T2DM). The primary outcomes were glycemic variability indices, including mean amplitude of glycemic excursions (MAGE), standard deviation of blood glucose (SDBG), and largest amplitude of glycemic excursions (LAGE). Secondary outcomes included fasting plasma glucose (FPG), 2-h postprandial glucose (PPG-2h), mean blood glucose (MBG), glycated hemoglobin (HbA1c), homeostasis model assessment of β-cell function (HOMA-β), homeostasis model assessment of insulin resistance (HOMA-IR), and insulin requirements, including total daily insulin dose (TDD), basal insulin dose, and bolus insulin dose. Time in range (TIR), body weight, hypoglycemia, and gastrointestinal adverse events (GI-AEs) were analyzed as &lt;i&gt;post hoc&lt;/i&gt; exploratory outcomes.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Thirty-nine RCTs were included. For the primary glycemic-variability outcomes, GRADE assessments indicated that IIT plus DPP-4i likely reduced MAGE (MD -1.39 mmol/L, 95% CI -1.77 to -1.01) and LAGE (MD -1.20 mmol/L, 95% CI -1.66 to -0.75), while IIT plus SGLT2i likely reduced SDBG (MD -0.28 mmol/L, 95% CI -0.44 to -0.11). Evidence was very uncertain for the effects of IIT plus GLP-1RA on MAGE and IIT plus DPP-4i on SDBG. Among secondary outcomes, IIT plus DPP-4i likely reduced FPG (MD -0.44 mmol/L, 95% CI -0.67 to -0.21), while IIT plus GLP-1RA likely reduced HbA1c (MD -0.73%, 95% CI -1.01 to -0.45). IIT plus GLP-1RA, metformin, or SGLT2i may reduce FPG; IIT plus DPP-4i, GLP-1RA, or metformin may reduce PPG-2h; and IIT plus SGLT2i may reduce HbA1c. IIT plus DPP-4i and IIT plus GLP-1RA may also modestly reduce total daily insulin dose. Evidence for the effect of IIT plus SGLT2i on PPG-2h was very uncertain. Among &lt;i&gt;post hoc&lt;/i&gt; exploratory outcomes, IIT plus SGLT2i likely increased TIR (MD 8.23 percentage points, 95% CI 4.13 to 12.32), while IIT plus DPP-4i may reduce hypoglycemia risk (RR 0.53, 95% CI 0.31 to 0.91) and IIT plus GLP-1RA may increase GI-AEs (RR 5.28, 95% CI 1.81 to 15.40). Longer-duration IIT plus GLP-1RA may be associated with greater weight loss (MD -3.63 kg, 95% CI -4.94 to -2.32) in a &lt;i&gt;","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1948361"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cardiovascular risk factors in autoimmune thyroiditis: role of adiponectin, resistin, inflammation and auto-immunity - a cross-sectional study.","authors":"Celestino Neves, João Sérgio Neves, Oksana Sokhatska, Luís Delgado","doi":"10.3389/fendo.2026.1865915","DOIUrl":"https://doi.org/10.3389/fendo.2026.1865915","url":null,"abstract":"<p><strong>Objective: </strong>Autoimmune thyroiditis (AIT) has been associated with an increased cardiovascular risk. Whether adipokines, inflammation and auto-immunity contribute to this increased risk is uncertain. Our aim was to evaluate the interrelationships of adipokines, inflammation, auto-immunity, and thyroid function with cardiovascular risk in autoimmune thyroiditis.</p><p><strong>Methods: </strong>Sixty-two patients with AIT, with normal free T4 (FT4) and free T3 (FT3), without other comorbidities were studied. Patients were divided into two groups: AIT with thyroid-stimulating hormone (TSH) ≤2.5 mU/L and AIT with TSH >2.5 mU/L. Parameters of thyroid function tests, serum lipid, high sensitivity C-reactive protein (hs-CRP) and homocysteine levels, anti-thyroglobulin antibodies, anti-thyroid peroxidase (TPO) antibodies, adiponectin and resistin were measured.</p><p><strong>Results: </strong>Patients with TSH >2.5 mU/L had significantly higher body mass index, higher low density lipoprotein (LDL)-cholesterol levels, and lower lipoprotein(a) levels. No differences in triglycerides, apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), adiponectin, or resistin levels were observed between groups. In the overall cohort, adiponectin levels were positively correlated with high density lipoprotein (HDL)-cholesterol and lipoprotein(a), whereas resistin was positively correlated with hs-CRP and triglyceride levels. Hs-CRP levels were positively correlated with anti-thyroglobulin antibody titers, and homocysteine levels were negatively correlated with FT3. In the subgroup with TSH >2.5 mU/L, FT4 was positively correlated with ApoA1 and HDL-cholesterol and negatively correlated with lipoprotein(a), while resistin was positively correlated with TSH. In patients with TSH ≤2.5 mU/L, adiponectin levels were negatively correlated with TSH.</p><p><strong>Conclusion: </strong>Our study highlights a complex interrelationship between cardiovascular risk factors in patients with AIT. Patients with AIT and TSH >2.5 mU/L may have a more atherogenic lipid profile.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1865915"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A primary care-oriented framework for osteoporosis prediction: integrating multi-algorithm feature selection and machine learning model benchmarking.","authors":"Xiaofang Guan, Xiaoqi Wang, Wenjing Zhang, Juanjuan Li, Zhili Hou, Zhaoting Hu, Pei Sun","doi":"10.3389/fendo.2026.1917386","DOIUrl":"https://doi.org/10.3389/fendo.2026.1917386","url":null,"abstract":"<p><strong>Summary: </strong>Large-scale screening and primary care diagnosis of osteoporosis are challenging due to the limited availability of dual-energy X-ray absorptiometry (DXA). Therefore, predictive models for osteoporosis that do not rely on bone mineral density measurements are essential. Using routine health examination data and applying a consensus feature selection strategy, we constructed machine learning (ML)-based models and compared their performance across multiple dimensions.</p><p><strong>Purpose: </strong>To develop a robust, low-cost, and interpretable framework for osteoporosis prediction based on readily available information, which can be easily integrated into electronic health systems to inform DXA referral in resource-limited settings.</p><p><strong>Methods: </strong>Variables covered by the basic health examination package were obtained from participants who underwent health examinations between January 2024 and January 2025. We implemented a strategy in which only features selected by at least four out of the seven feature selection methods were retained. Six ML algorithms were employed to develop models, and their prediction performances were compared across discrimination, goodness-of-fit, and clinical utility.</p><p><strong>Results: </strong>A total of 2,260 participants were included, with an osteoporosis prevalence of 7.1%. Eight features: age, sex, body mass index, alkaline phosphatase, hematocrit, high-density lipoprotein, urea, and platelet count, were selected for model development. In the validation set, the random forest and artificial neural network (ANN) models demonstrated strongest screening performance, with AUCs of 0.895 (95% CI 0.867-0.923) and 0.890 (95% CI 0.862-0.919), respectively. The ANN model additionally exhibited superior calibration. Shapley additive explanations were applied to interpret the ANN model for osteoporosis prediction.</p><p><strong>Conclusion: </strong>This study presents an easily integrable model for osteoporosis prediction based on routine health examination data. The ANN model provides robust prediction, offering a potential tool for pre-DXA triage and early identification of high-risk individuals in primary care and resource-limited settings.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1917386"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary: Selpercatinib versus multi-kinase inhibitors for advanced medullary thyroid cancer: a network meta-analysis of RET-targeted therapies.","authors":"Arnau Domenech, Martí Hopmans, Erin Barker, Joe W E Moss, Montserrat Carrasco","doi":"10.3389/fendo.2026.1979615","DOIUrl":"https://doi.org/10.3389/fendo.2026.1979615","url":null,"abstract":"","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1979615"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Sociodemographic and clinical predictors of malignancy in thyroid nodules: a systematic review and meta-analysis.","authors":"Vanessa Neto, Catarina Leitão, Cláudia Martins, Margarida Fardilha, Maria Teresa Herdeiro, Alexandra Nunes","doi":"10.3389/fendo.2026.1981738","DOIUrl":"https://doi.org/10.3389/fendo.2026.1981738","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fendo.2026.1885823.].</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1981738"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678730/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early prediction of gestational diabetes mellitus using deep learning applied to routinely collected clinical data.","authors":"Gabriel Cubillos, Max Monckeberg, Sebastian E Illanes, Claudio A Perez","doi":"10.3389/fendo.2026.1941799","DOIUrl":"https://doi.org/10.3389/fendo.2026.1941799","url":null,"abstract":"<p><strong>Background: </strong>Prediction of gestational diabetes mellitus (GDM) in early pregnancy enables timely, targeted interventions to reduce risks for both the mother and fetus. We aimed to improve early GDM prediction and clinical risk stratification using a novel deep learning (DL) approach that transforms tabular clinical data into images using a genetic algorithm.</p><p><strong>Methods: </strong>A dataset of 1,611 pregnancies was used. The dataset contained routinely collected early-pregnancy data, including clinical measurements, blood tests, and medical history. A novel method to transform tabular data into images was applied to enable the use of deep learning models for GDM prediction. A genetic algorithm optimized the spatial arrangement of variables during image generation by determining their spatial positions. Interpretability was assessed using gradient-weighted class activation mapping (Grad-CAM) to identify variables the model preferentially attended to for positive and negative predictions.</p><p><strong>Results: </strong>Multiple model configurations were evaluated by varying the spatial arrangement of variables within the transformation. Prioritizing high sensitivity, one model achieved an accuracy of 0.86, sensitivity of 0.82, specificity of 0.87, and an AUC-ROC of 0.83. Another model achieved an accuracy of 0.79, sensitivity of 0.92, specificity of 0.77, and an AUC-ROC of 0.85. Compared with previously published machine learning models using the same dataset, our approach demonstrated improved performance. Our best models increased specificity by an average of 13.93 percentage points compared with traditional ML models at matched sensitivity levels, thereby reducing false-positive classifications that may lead to additional clinical evaluation. Grad-CAM analysis revealed that attention patterns of the model aligned with established clinical risk factors for GDM, with first trimester fasting glycemia and parity preferentially attended for positive predictions and maternal weight for negative predictions.</p><p><strong>Conclusions: </strong>This study demonstrates that transforming tabular clinical data into image representations, optimized using a genetic algorithm, improves early GDM prediction. Representing tabular variables as images enables more effective application of DL models, which have demonstrated enhanced pattern recognition in image-based tasks compared with traditional machine learning methods. Performance improvements over state-of-the-art approaches were achieved. Accurate DL models can be developed using a limited number of input variables, supporting early identification of women at increased risk of GDM and facilitating timely clinical management.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1941799"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Endocrine-disrupting chemicals in male reproductive health: exposure risks, reproductive toxicity and emerging perspectives.","authors":"Dongyue Ma, Qingjiang Yang, Jun Guo, Wenguang Zhou, Chen Huang, Xiangping Sun","doi":"10.3389/fendo.2026.1956000","DOIUrl":"https://doi.org/10.3389/fendo.2026.1956000","url":null,"abstract":"<p><p>Male reproductive health represents a growing global health challenge, and increasing evidence highlights the contribution of environmental exposures to declining male reproductive function. Endocrine-disrupting chemicals are widespread environmental contaminants that interfere with endocrine regulation and have been associated with impaired semen quality, hormonal imbalance and testicular dysfunction. Here, we summarize current advances in understanding EDC-induced male reproductive toxicity, focusing on major chemical classes, including bisphenols, phthalates, pesticides, per- and polyfluoroalkyl substances and heavy metals. We discuss the interconnected mechanisms underlying reproductive impairment, including disruption of the hypothalamic-pituitary-gonadal axis, altered steroidogenesis, oxidative stress, mitochondrial dysfunction, blood-testis barrier disruption, immune dysregulation and epigenetic reprogramming. Importantly, emerging evidence from developmental origins of health and disease perspectives indicates that exposure during critical developmental windows may establish persistent molecular alterations, influencing reproductive outcomes across the life course. In addition, the metabolic effects of certain EDCs, including obesity and metabolic dysfunction, may further exacerbate male reproductive impairment. Given that human exposure occurs predominantly in the form of complex chemical mixtures, future research should integrate exposomics, multi-omics approaches and longitudinal cohorts to clarify causal relationships and identify susceptible populations. Such advances will be essential for developing precision environmental health strategies to preserve male reproductive function.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1956000"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13678584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term clinical outcomes in females with precocious puberty treated with gonadotropin-releasing hormone analogues.","authors":"Nahla Khawaja, Khalid Karaki, Mohammad K Ruba'i, Yousef Khader, Arwa Kinani, Oraib Farahid, Mohammed El-Khateeb, Omar Ali, Abdulrahman Abujbara, Laith Hyasat, Kamel Ajlouni","doi":"10.3389/fendo.2026.1911743","DOIUrl":"https://doi.org/10.3389/fendo.2026.1911743","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate long-term anthropometric, metabolic, reproductive, and bone mineral density (BMD) outcomes in girls with central precocious puberty (CPP) treated with Gonadotropin-Releasing Hormone analogues (GnRHa).</p><p><strong>Methods: </strong>In this retrospective cohort with cross-sectional follow-up assessment, 82 of 265 eligible females (30.9%) with central precocious puberty or central hypothalamic-pituitary-gonadal axis activation who had received GnRHa participated in long-term evaluation. Anthropometric outcomes, insulin sensitivity indices (HOMA-IR and QUICKI), hormonal profile, BMD, and reproductive outcomes including polycystic ovary syndrome (PCOS) were assessed. Analyses used available cases for each outcome; missing values were not imputed.</p><p><strong>Results: </strong>The mean age at final evaluation was 17.61 ± 4.3 years, and mean GnRHa treatment duration was 3.14 ± 1.9 years. Mean height at final follow-up was 159.85 ± 6.5 cm; the mean difference from baseline predicted adult height was 1.98 cm (95% CI, -0.37 to 4.34; P = 0.097). The distribution of BMI categories did not change significantly from baseline to final follow-up. Mean HOMA-IR and QUICKI were 3.76 ± 1.82 and 0.319 ± 0.025, respectively; among adults, 14/28 (50.0%) met each prespecified adult-derived threshold for reduced insulin sensitivity. PCOS was identified in 15/67 participants (22.4%). Mean lumbar and femoral BMD Z-scores were -0.25 ± 1.23 and -0.55 ± 1.03, respectively.</p><p><strong>Conclusions: </strong>Among participants who completed long-term follow-up, attained height was comparable at the group level with predicted adult and mid-parental height, and mean BMD Z-scores were within age- and sex-referenced ranges. PCOS and PCOS-related features were observed, while insulin sensitivity indices were associated with adiposity. Because only 30.9% of eligible patients participated and there was no untreated comparison group, these findings describe the evaluated cohort and should not be interpreted as causal effects of GnRHa treatment.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"17 ","pages":"1911743"},"PeriodicalIF":5.7,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149223734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}