Frontiers in Endocrinology最新文献

{"title":"The metabolic syndrome-insulin resistance index: a tool for identifying dyslipidemia across varied glucose metabolic score in patients with cardiovascular disease.","authors":"Lu Yu, Yutong Liu, Ruiying Guo, Tong Yang, Guangwei Pan, Yuanyuan He, Shan Gao, Rongrong Yang, Zhu Li, Lin Li, Chunquan Yu","doi":"10.3389/fendo.2025.1473308","DOIUrl":"https://doi.org/10.3389/fendo.2025.1473308","url":null,"abstract":"<p><strong>Purpose: </strong>The METS-IR index, a non-insulin-based metabolic score, represents a new marker closely linked to insulin resistance. This study aimed to evaluate the relationship between the METS-IR index and dyslipidemia in individuals diagnosed with Cardiovascular disease (CVD), as well as to delve deeper into how varying glucose metabolic conditions influence this relationship.</p><p><strong>Methods: </strong>This multicenter retrospective investigation encompassed 214,717 individuals diagnosed with CVD across China, spanning from September 1, 2014, to June 1, 2022, ultimately incorporating 17,632 cases in the conclusive analysis. All cases were grouped according to quartiles of METS-IR. The American College of Cardiology classifies dyslipidemia into four distinct categories: hyper-triglyceridemia (hyper-TG), hyper-cholesterolemia (hyper-TC), hypo-high-density lipoprotein cholesterolemia (hypo-HDL), and hyper-low-density lipoprotein cholesterolemia (hyper-LDL). Dyslipidemia is diagnosed when any one of these conditions is present. Logistic regression analysis was performed to estimate the odds ratio (OR) and 95% confidence interval (CI), assessing the relationship between the METS-IR index and dyslipidemia risk in patients with CVD. To evaluate the precision of the METS-IR index in identifying dyslipidemia, receiver operating characteristic (ROC) curve was produced.</p><p><strong>Results: </strong>The results of the baseline analysis showed that 11,934 cases had dyslipidemia, with notable variations observed in the clinical and biological attributes among CVD cases (<i>P</i> < 0.05 to < 0.001). Logistic regression analysis showed that the METS-IR index was significantly associated with the risk of dyslipidemia (odds ratio [OR]: 1.14; 95% confidence interval [CI] 1.13-1.15; <i>P</i> < 0.001). The OR for dyslipidemia in Q4 of the METS-IR index was 11.94 (95% CI 10.60-13.45; <i>p</i> < 0.001) compared to Q1. ROC analysis revealing an area under the curve (AUC) of 0.747 (95% CI 0.739-0.754; <i>P</i> < 0.001). The association between the METS-IR index and dyslipidemia proved significant across all glycemic status groups, with the highest OR observed in the Q4 subgroup of cases with NGR (OR: 15.43; 95% CI 12.21-19.49).</p><p><strong>Conclusion: </strong>The risk of developing dyslipidemia is positively associated with heightened METS-IR levels in individuals afflicted with CVD, and these relationships hold significance across all glycemic metabolic conditions. METS-IR could potentially aid in forecasting the risk of dyslipidemia development in individuals diagnosed with CVD.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1473308"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150040","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":"The rising burden of diabetes and state-wise variations in India: insights from the Global Burden of Disease Study 1990-2021 and projections to 2031.","authors":"Shubham Chauhan, Mahalaqua Nazli Khatib, Suhas Ballal, Pooja Bansal, Kiran Bhopte, Abhay M Gaidhane, Balvir S Tomar, Ayash Ashraf, M Ravi Kumar, Ashish Singh Chauhan, Muhammed Shabil, Diptismita Jena, Ganesh Bushi, Prakasini Satapathy, Lara Jain, Vaibhav Jaiswal, Manvi Pant","doi":"10.3389/fendo.2025.1505143","DOIUrl":"https://doi.org/10.3389/fendo.2025.1505143","url":null,"abstract":"<p><strong>Background: </strong>Diabetes is a major public health concern in India, contributing significantly to morbidity and mortality. With variations in disease burden across states, a detailed understanding of trends in incidence, prevalence, and Disability Adjusted Life Years (DALYs) is essential for targeted interventions.</p><p><strong>Methods: </strong>This study utilized Global Burden of Disease (GBD) data from 1990 to 2021 to examine trends in diabetes across Indian states. Age-standardized incidence, prevalence, mortality, and DALYs were analyzed using Join point regression to estimate Annual Percentage Change (APC). Autoregressive Integrated Moving Average (ARIMA) models were employed to project diabetes trends up to 2031.While the GBD data provide robust national and regional estimates, their modeled nature may not capture the full spectrum of local epidemiological variations.</p><p><strong>Results: </strong>Diabetes incidence increased from 162.74 to 264.53 per 100,000 between 1990 and 2021, with an APC of 0.63%. Joinpoint analysis identified episodic surges in incidence, with APCs of 2.25% during 1996-1999 and 2.07% during 2005-2011, suggesting intervals of accelerated increase relative to the gradual progression typically observed in chronic conditions. Mortality rose from 23.09 to 31.12 per 100,000 (APC: 0.12%). Southern and Western states, such as Tamil Nadu and Goa, exhibited the highest prevalence and DALYs. Forecasted trends indicate that by 2031, the prevalence will reach 8585.45 per 100,000, and DALYs will exceed 1241.57 per 100,000.</p><p><strong>Conclusion: </strong>The burden of diabetes in India has risen markedly over the past three decades. These findings underscore the urgent need for health policies that emphasize lifestyle modifications and improved healthcare access. A comprehensive approach that integrates primary prevention through community-based health education, dietary counseling, and initiatives to promote physical activity with secondary prevention measures such as systematic screening and timely clinical management, is essential for effective diabetes control and management in high-burden states.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1505143"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149966","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":"Diabetes and brain: omics approaches to study diabetic encephalopathy.","authors":"Nicoletta Lionetti, Maria Grazia Di Lago, Tania Brescia, Federica Bevilacqua, Antonio Gnoni","doi":"10.3389/fendo.2025.1570585","DOIUrl":"https://doi.org/10.3389/fendo.2025.1570585","url":null,"abstract":"<p><p>Diabetes mellitus (DM) is a complex metabolic disorder associated with many complications, including diabetic encephalopathy (DE). DE is a severe neurological condition characterized by a progressive decline in cognitive and motor functions, significantly impacting patients' quality of life. Despite advancements in understanding DM, the intricate pathogenetic mechanisms underlying DE remain incompletely elucidated. This review comprehensively analyzes the application of omics technologies to decipher the molecular basis of DE and identify potential diagnostic biomarkers and therapeutic targets. Several studies on animal models of DE have revealed specific metabolic signatures and changes in gene expression in key memory brain regions, like the hippocampus, highlighting potential therapeutic targets. We explore how these \"omics\" approaches have provided novel insights into the complex interplay of factors contributing to DE. Recurrent alterations were identified upon evaluation of analysis from human tissues and <i>in vitro</i> models of DE. Findings indicate that this pathological condition is characterized by impaired energy metabolism, oxidative stress, neuroinflammation, neuroendocrine dysfunction and the influence of the gut microbiota. A multi-omics approach, integrating data from various models and limited human studies, enhances translational understanding of DE pathogenesis, with new implications for diagnosis and treatment.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1570585"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149838","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":"Evaluating indices of insulin resistance and estimating the prevalence of insulin resistance in a large biobank cohort.","authors":"Usama Aliyu, Salman M Toor, Ibrahem Abdalhakam, Mohamed A Elrayess, Abdul Badi Abou Samra, Omar M E Albagha","doi":"10.3389/fendo.2025.1591677","DOIUrl":"https://doi.org/10.3389/fendo.2025.1591677","url":null,"abstract":"<p><strong>Introduction: </strong>Insulin resistance (IR) is involved in the pathogenesis of various metabolic disorders. Several surrogate indices of IR have been proposed. We assessed the performance of seven clinically relevant indirect measures of IR and estimated the prevalence of IR in a large population-based cohort.</p><p><strong>Methods: </strong>The study was conducted on fasting individuals from the Qatar biobank (QBB) participants (<i>n</i> = 7,875). Individuals were considered insulin sensitive (IS) if lean, not diagnosed with diabetes, no hypertriglyceridemia, and not on lipid-lowering drugs, while individuals with Type 2 diabetes (T2D) were considered insulin resistant (IR). Cut-offs were determined as the top or lowest quartile values in the IS participants. The performance of IR indices was based on area under the curve (AUC), sensitivity and specificity.</p><p><strong>Results: </strong>The cut-off for HOMA-IR was determined at 1.878, HOMA2-IR (insulin); 1.128, HOMA2-IR (C-peptide); 1.307, QUICKI; 0.347, TyG; 8.281, McAi; 7.727 and 1.718 for TG/HDL. All IR indices analyzed yielded AUC values ranging from 0.83 to 0.92. TyG was the most robust measure for IR (AUC = 0.92, Sensitivity = 0.90, Specificity = 0.79). The overall prevalence of IR in Qatar was estimated at ~51 - 65%.</p><p><strong>Conclusions: </strong>TyG index was the most robust index for determining IR in the Qatari population. The proposed cut-offs could serve as a reference in Middle Eastern populations for IR screening.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1591677"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149893","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":"Research on the role and mechanism of the PI3K/Akt/mTOR signalling pathway in osteoporosis.","authors":"Chuanlong Liu, Jianqiang Zhang, Ziyu Ye, Ji Luo, Bing Peng, Zhexiang Wang","doi":"10.3389/fendo.2025.1541714","DOIUrl":"https://doi.org/10.3389/fendo.2025.1541714","url":null,"abstract":"<p><p>Osteoporosis is a systemic metabolic bone disease characterised mainly by reduced bone mass, bone microstructure degradation, and loss of bone mechanical properties. As the world population ages, more than 200 million people worldwide suffer from the pain caused by osteoporosis every year, which severely affects their quality of life. Moreover, the prevalence of osteoporosis continues to increase. The pathogenesis of osteoporosis is highly complex and is closely related to apoptosis, autophagy, oxidative stress, the inflammatory response, and ferroptosis. The PI3K/Akt/mTOR signalling pathway is one of the most crucial intracellular signal transduction pathways. This pathway is not only involved in bone metabolism and bone remodelling but also closely related to the proliferation and differentiation of osteoblasts, osteoclasts, and bone marrow mesenchymal stem cells. Abnormal activation or inhibition of the PI3K/Akt/mTOR signalling pathway can disrupt the balance between osteoblast-mediated bone formation and osteoclast-mediated bone resorption, ultimately leading to the development of osteoporosis. This review summarises the molecular mechanisms by which the PI3K/Akt/mTOR signalling pathway mediates five pathological mechanisms, namely, apoptosis, autophagy, oxidative stress, the inflammatory response, and ferroptosis, in the regulation of osteoporosis, aiming to provide a theoretical basis for the development of novel and effective therapeutic drugs and intervention measures for osteoporosis prevention and treatment.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1541714"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150036","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":"Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.","authors":"Weijun Zhou, Lijuan Li, Xiaowen Hao, Lanying Wu, Lifu Liu, Binyu Zheng, Yangzheng Xia, Yong Liu","doi":"10.3389/fendo.2025.1537386","DOIUrl":"https://doi.org/10.3389/fendo.2025.1537386","url":null,"abstract":"<p><strong>Objective: </strong>To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).</p><p><strong>Methods: </strong>From December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression method, alongside the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm in conjunction with multivariate logistic regression. Eight ML algorithms, namely Decision Tree, Random Forest (RF), K-nearest neighbors, Support vector machine, Extreme Gradient Boosting, Naive Bayes, Logistic regression, and Light Gradient Boosting machine, were developed for the prediction of CLNM. The performance of these models was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 scores. Additionally, the Shapley Additive Explanation (SHAP) algorithm was utilized to clarify the results of the optimal ML model.</p><p><strong>Results: </strong>The results indicated that 32.95% of the patients (234/710) presented with CLNM. Tumor diameter, multifocality, lymph nodes identified via ultrasound (US-LN), and extrathyroidal extension (ETE) were identified as independent predictors of CLNM. The RF model achieved the highest performance in the validation set with an AUC of 0.893(95%CI: 0.846-0.940), accuracy of 0.832, sensitivity of 0.764, specificity of 0.866, PPV of 0.743, NPV of 0.879, and F1-score of 0.753. Furthermore, the DCA demonstrated that the RF model exhibited a superior clinical net benefit.</p><p><strong>Conclusion: </strong>Our model predicted the risk of CLNM in PTMC patients with high accuracy preoperatively.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1537386"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149961","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":"Comparison of the clinical and prognostic characteristics of patients with different pathological types in acromegaly.","authors":"Liye Chong, Yuxing Lou, Xue Chen, Wenji Zhao, Wei Zhang, Ziwei Zhang, Fan Yang, Ping Li","doi":"10.3389/fendo.2025.1571598","DOIUrl":"https://doi.org/10.3389/fendo.2025.1571598","url":null,"abstract":"<p><strong>Context: </strong>Acromegaly is caused by somatotroph tumors. Recently, the WHO recommended the use of transcription factors (TFs) together with pituitary hormones to accurately classify the subtypes.</p><p><strong>Objective: </strong>This study aims to evaluate differences in the clinical and prognostic characteristics of acromegaly patients with different pathological types.</p><p><strong>Methods: </strong>A retrospective study was conducted on 94 acromegaly patients who underwent surgical treatment. Patients were classified into two groups on the basis of TFs expression by IHC. PIT1 tumors were positive only for PIT1, and PIT1/SF1 tumors were positive for both PIT1 and SF1. Additionally, on the basis of the expression of GH and PRL by IHC, PIT1 tumors were further subdivided into GH positive tumors (those positive for only GH) and GH/PRL positive tumors (those positive for both GH and PRL). Differences in clinical and prognostic features among the pathological groups were evaluated.</p><p><strong>Results: </strong>PIT1/SF1 tumors represented 30.9% (n = 29) of the acromegaly patients in this cohort. PIT1/SF1 tumors had a higher baseline IGF-1 index (2.77 ± 0.73 vs. 2.39 ± 0.74, <i>P</i> = 0.024) than PIT1 tumors. Despite the higher proportion of postoperative GH < 1 μg/L, the biochemical remission rate of PIT1/SF1 tumors (30.8% vs. 27.6%, <i>P</i> = 0.812) was similar to that of PIT1 tumors. Compared with those with GH positive tumors, patients with GH/PRL positive tumors were younger at diagnosis (42.50 ± 13.36 vs. 49.05 ± 11.69, <i>P</i> = 0.046), and the proportion of male patients was higher (50.0% vs. 23.3%, <i>P</i> = 0.048). Furthermore, patients with GH/PRL positive tumors had a significantly higher postoperative GH level [7.30 (3.18-11.08) vs. 2.49 (1.57-6.84), <i>P</i> = 0.011] and IGF-1 index (1.82 ± 0.94 vs. 1.31 ± 0.63, <i>P</i> = 0.011) during follow-up. The biochemical remission rate in GH/PRL positive tumors was lower, but the difference was not statistically significant (18.2% vs. 37.2%, <i>P</i> = 0.159).</p><p><strong>Conclusion: </strong>PIT1/SF1 tumors represent approximately 30.0% of acromegaly patients. Despite higher baseline IGF-1 levels, the clinical and prognostic features of patients with PIT1/SF1 tumors are similar to those of patients with PIT1 tumors. GH/PRL positive tumors, characterized by their earlier age at diagnosis and male predominance, tend to exhibit a lower biochemical remission rate compared to GH positive tumors.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1571598"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104045/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149383","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":"Obesity and the accelerated decline in total sleep time increases the self-reported diagnoses of diabetes.","authors":"Lijing Yan, Huanhuan Sun, Yuling Chen, Xiaohui Yu, Jingru Zhang, Peijie Li","doi":"10.3389/fendo.2025.1473892","DOIUrl":"https://doi.org/10.3389/fendo.2025.1473892","url":null,"abstract":"<p><strong>Introduction: </strong>The aim of this study was to investigate the relationship between obesity and the accelerated decline in Total Sleep Time (TST) and its potential impact on the self-reported diagnoses of diabetes.</p><p><strong>Methods: </strong>Our study addresses this gap by analyzing trends in a longitudinal cohort study conducted in China, using data from the China Health and Nutrition Survey (CHNS). Employing a joint model, inter-individual variability and intra-individual variability in TST, and its impact on self-reported diagnoses of diabetes were considered.</p><p><strong>Results: </strong>Our findings reveal that self-reported diagnoses of diabetes prevalence in China rose from 1.10% in 2004 to 3.06% in 2015, accompanied by a decrease in average TST from 8.12 to 7.80. With age, TST decreased by 0.01 per year. Among coffee or tea consumers, it decreased by 0.03, while alcohol users saw a decrease of 0.07. The obese group experienced a decrease of 0.05, the overweight group 0.03, and the normal weight group 0.01. Each 1-hour decrease in TST was associated with a substantial 3.61-fold increase in self-reported diagnoses of diabetes risk (95% CI: 2.92-4.44). Specifically, individuals with a higher baseline TST tend to experience smaller changes over time, whereas those with a lower baseline TST tend to experience larger changes.</p><p><strong>Discussion: </strong>For the obese, TST decreases at an accelerated rate which contributes to the risk of self-reported diagnoses of diabetes. The findings underscore the role of sleep loss in diabetes risk, with implications for public policy. Future research and interventions should emphasise the impact of sleep management, particularly on obesity and metabolic health, to develop more effective prevention and treatment strategies.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1473892"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149957","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":"Editorial: Xenosensors as the targets of endocrine-disrupting chemicals.","authors":"Nasrin Ghassemi-Barghi, Fazlullah Khan, Zahra Bayrami","doi":"10.3389/fendo.2025.1611152","DOIUrl":"https://doi.org/10.3389/fendo.2025.1611152","url":null,"abstract":"","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1611152"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149847","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":"Insulin resistance assessed by estimated glucose disposal rate and the risk of abdominal aortic calcification: findings from a nationwide cohort study.","authors":"Bo Zhao, Zongliang Yu, Fengyan Tang, Zhenqin Feng, Junfeng Wang, Zhaoxiang Wang","doi":"10.3389/fendo.2025.1560577","DOIUrl":"https://doi.org/10.3389/fendo.2025.1560577","url":null,"abstract":"<p><strong>Purpose: </strong>The estimated glucose disposal rate (eGDR) serves as a straightforward and noninvasive indicator of insulin resistance (IR). This study aims to explore the association between eGDR and the risk of abdominal aortic calcification (AAC).</p><p><strong>Methods: </strong>We utilized data from adult participants (≥40 years old, n=3006) from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) database. AAC was measured by dual-energy X-ray absorptiometry and quantified using the Kauppila score. Severe AAC (SAAC) was defined as an AAC score > 6. Logistic regression, restricted cubic spline (RCS), and subgroup analysis were used to analyze the relationship between eGDR and SAAC risk.</p><p><strong>Results: </strong>In fully adjusted models, eGDR was found to be negatively associated with SAAC (OR=0.86, 95%CI:0.79-0.94, <i>P</i><0.001). Compared to participants in the lowest eGDR quantile, those in the highest quantile exhibited a lower risk of SAAC (OR=0.47, 95%CI:0.25-0.91, <i>P</i>=0.026). The RCS analysis indicates a nonlinear relationship between eGDR and SAAC risk, with a turning point at 7.05 mg/kg/min. Subgroup analysis showed that the association between eGDR and SAAC risk was more significant in women.</p><p><strong>Conclusions: </strong>The degree of IR assessed by eGDR is associated with SAAC risk. The eGDR shows promise as an epidemiological tool for evaluating the influence of IR on AAC.</p>","PeriodicalId":12447,"journal":{"name":"Frontiers in Endocrinology","volume":"16 ","pages":"1560577"},"PeriodicalIF":3.9,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149923","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}