Cheng Ji, Jie Ma, Lingjun Sun, Xu Sun, Lijuan Liu, Lijun Wang, Weihong Ge, Yan Bi
{"title":"2 型糖尿病患者骨质矿物质密度低的预测模型:一项观察性横断面研究。","authors":"Cheng Ji, Jie Ma, Lingjun Sun, Xu Sun, Lijuan Liu, Lijun Wang, Weihong Ge, Yan Bi","doi":"10.1007/s12020-023-03500-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients.</p><p><strong>Methods: </strong>This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed.</p><p><strong>Results: </strong>Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated.</p><p><strong>Conclusions: </strong>The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.</p>","PeriodicalId":49211,"journal":{"name":"Endocrine","volume":" ","pages":"369-379"},"PeriodicalIF":3.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction model for low bone mass mineral density in type 2 diabetes: an observational cross-sectional study.\",\"authors\":\"Cheng Ji, Jie Ma, Lingjun Sun, Xu Sun, Lijuan Liu, Lijun Wang, Weihong Ge, Yan Bi\",\"doi\":\"10.1007/s12020-023-03500-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients.</p><p><strong>Methods: </strong>This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed.</p><p><strong>Results: </strong>Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated.</p><p><strong>Conclusions: </strong>The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.</p>\",\"PeriodicalId\":49211,\"journal\":{\"name\":\"Endocrine\",\"volume\":\" \",\"pages\":\"369-379\"},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Endocrine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12020-023-03500-w\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Endocrine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12020-023-03500-w","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Prediction model for low bone mass mineral density in type 2 diabetes: an observational cross-sectional study.
Purpose: Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients.
Methods: This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed.
Results: Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated.
Conclusions: The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.
期刊介绍:
Well-established as a major journal in today’s rapidly advancing experimental and clinical research areas, Endocrine publishes original articles devoted to basic (including molecular, cellular and physiological studies), translational and clinical research in all the different fields of endocrinology and metabolism. Articles will be accepted based on peer-reviews, priority, and editorial decision. Invited reviews, mini-reviews and viewpoints on relevant pathophysiological and clinical topics, as well as Editorials on articles appearing in the Journal, are published. Unsolicited Editorials will be evaluated by the editorial team. Outcomes of scientific meetings, as well as guidelines and position statements, may be submitted. The Journal also considers special feature articles in the field of endocrine genetics and epigenetics, as well as articles devoted to novel methods and techniques in endocrinology.
Endocrine covers controversial, clinical endocrine issues. Meta-analyses on endocrine and metabolic topics are also accepted. Descriptions of single clinical cases and/or small patients studies are not published unless of exceptional interest. However, reports of novel imaging studies and endocrine side effects in single patients may be considered. Research letters and letters to the editor related or unrelated to recently published articles can be submitted.
Endocrine covers leading topics in endocrinology such as neuroendocrinology, pituitary and hypothalamic peptides, thyroid physiological and clinical aspects, bone and mineral metabolism and osteoporosis, obesity, lipid and energy metabolism and food intake control, insulin, Type 1 and Type 2 diabetes, hormones of male and female reproduction, adrenal diseases pediatric and geriatric endocrinology, endocrine hypertension and endocrine oncology.