{"title":"肢端肥大症中与人际功能障碍相关的代谢因素:中国一项全国性的横断面研究。","authors":"Shashi Kiran Tagilapalli, Zongming Wang, Yuechu Lucinda Lu, Guofeng Zhang, Weijie Su, Zhentian Wu, Jiaming Wang, Qing Rao, Haijun Wang, Dongsheng He, Yonggao Mou, Shun Yao, Yanmei Tie, Wenli Chen","doi":"10.1111/cns.70607","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>To identify the clinical manifestations, metabolic factors, and comorbidities independently associated with interpersonal dysfunction in Chinese acromegaly patients.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We analyzed clinical, cognitive, and comorbidity data from 585 acromegaly patients across 112 tertiary hospitals in China (July 1995–December 2018). Interpersonal difficulties were quantified using the Inventory of Interpersonal Problems-Distress (IIP-D) and dichotomized into low (< 17) and high (≥ 17) groups. Group differences were tested with nonparametric tests. Supervised machine-learning models were developed to predict features associated with the high IIP-D group, with performance evaluated via five-fold cross-validation. The top-performing model was further validated on the held-out data set and the feature importance analysis identified the key predictors. Exploratory hierarchical clustering (Ward's method) was used to explore symptom groupings, though sampling adequacy was limited.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Patients with high interpersonal distress exhibited significantly higher preoperative growth hormone (GH), frontal bossing, palpitations, and cognitive impairment (all <i>p</i> < 0.05). Among machine-learning models, extreme gradient boosting (XGBoost) demonstrated the highest performance, area under the curve (AUC = 0.868 in across-validation), and maintained strong accuracy in final testing (AUC = 0.873). Key independent predictors included frontal bossing, palpitations, cardiomyopathy, disease duration, preoperative GH, acral enlargement, arrhythmia, and atrial fibrillation.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>Physical disfigurement, palpitations, cardiac comorbidities, and elevated GH levels independently predict high IIP-D in acromegaly. Integrating systematic psychosocial screening into neuroendocrine care, alongside referral to psychoneuroendocrine teams, may help mitigate social disability and improve quality of life.</p>\n </section>\n </div>","PeriodicalId":154,"journal":{"name":"CNS Neuroscience & Therapeutics","volume":"31 9","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436684/pdf/","citationCount":"0","resultStr":"{\"title\":\"Metabolic Factors Related to Interpersonal Dysfunction in Acromegaly: A Nationwide Cross-Sectional Study in China\",\"authors\":\"Shashi Kiran Tagilapalli, Zongming Wang, Yuechu Lucinda Lu, Guofeng Zhang, Weijie Su, Zhentian Wu, Jiaming Wang, Qing Rao, Haijun Wang, Dongsheng He, Yonggao Mou, Shun Yao, Yanmei Tie, Wenli Chen\",\"doi\":\"10.1111/cns.70607\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To identify the clinical manifestations, metabolic factors, and comorbidities independently associated with interpersonal dysfunction in Chinese acromegaly patients.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We analyzed clinical, cognitive, and comorbidity data from 585 acromegaly patients across 112 tertiary hospitals in China (July 1995–December 2018). Interpersonal difficulties were quantified using the Inventory of Interpersonal Problems-Distress (IIP-D) and dichotomized into low (< 17) and high (≥ 17) groups. Group differences were tested with nonparametric tests. Supervised machine-learning models were developed to predict features associated with the high IIP-D group, with performance evaluated via five-fold cross-validation. The top-performing model was further validated on the held-out data set and the feature importance analysis identified the key predictors. Exploratory hierarchical clustering (Ward's method) was used to explore symptom groupings, though sampling adequacy was limited.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Patients with high interpersonal distress exhibited significantly higher preoperative growth hormone (GH), frontal bossing, palpitations, and cognitive impairment (all <i>p</i> < 0.05). Among machine-learning models, extreme gradient boosting (XGBoost) demonstrated the highest performance, area under the curve (AUC = 0.868 in across-validation), and maintained strong accuracy in final testing (AUC = 0.873). Key independent predictors included frontal bossing, palpitations, cardiomyopathy, disease duration, preoperative GH, acral enlargement, arrhythmia, and atrial fibrillation.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>Physical disfigurement, palpitations, cardiac comorbidities, and elevated GH levels independently predict high IIP-D in acromegaly. Integrating systematic psychosocial screening into neuroendocrine care, alongside referral to psychoneuroendocrine teams, may help mitigate social disability and improve quality of life.</p>\\n </section>\\n </div>\",\"PeriodicalId\":154,\"journal\":{\"name\":\"CNS Neuroscience & Therapeutics\",\"volume\":\"31 9\",\"pages\":\"\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2025-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436684/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"CNS Neuroscience & Therapeutics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/cns.70607\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"CNS Neuroscience & Therapeutics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cns.70607","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Metabolic Factors Related to Interpersonal Dysfunction in Acromegaly: A Nationwide Cross-Sectional Study in China
Objective
To identify the clinical manifestations, metabolic factors, and comorbidities independently associated with interpersonal dysfunction in Chinese acromegaly patients.
Methods
We analyzed clinical, cognitive, and comorbidity data from 585 acromegaly patients across 112 tertiary hospitals in China (July 1995–December 2018). Interpersonal difficulties were quantified using the Inventory of Interpersonal Problems-Distress (IIP-D) and dichotomized into low (< 17) and high (≥ 17) groups. Group differences were tested with nonparametric tests. Supervised machine-learning models were developed to predict features associated with the high IIP-D group, with performance evaluated via five-fold cross-validation. The top-performing model was further validated on the held-out data set and the feature importance analysis identified the key predictors. Exploratory hierarchical clustering (Ward's method) was used to explore symptom groupings, though sampling adequacy was limited.
Results
Patients with high interpersonal distress exhibited significantly higher preoperative growth hormone (GH), frontal bossing, palpitations, and cognitive impairment (all p < 0.05). Among machine-learning models, extreme gradient boosting (XGBoost) demonstrated the highest performance, area under the curve (AUC = 0.868 in across-validation), and maintained strong accuracy in final testing (AUC = 0.873). Key independent predictors included frontal bossing, palpitations, cardiomyopathy, disease duration, preoperative GH, acral enlargement, arrhythmia, and atrial fibrillation.
Conclusion
Physical disfigurement, palpitations, cardiac comorbidities, and elevated GH levels independently predict high IIP-D in acromegaly. Integrating systematic psychosocial screening into neuroendocrine care, alongside referral to psychoneuroendocrine teams, may help mitigate social disability and improve quality of life.
期刊介绍:
CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews and reports of novel findings of therapeutic relevance to the central nervous system, as well as papers related to clinical pharmacology, drug development and novel methodologies for drug evaluation. The journal focuses on neurological and psychiatric diseases such as stroke, Parkinson’s disease, Alzheimer’s disease, depression, schizophrenia, epilepsy, and drug abuse.