{"title":"开发并验证用于个体化腹型肥胖症骨质疏松症风险的提名图模型","authors":"Gangjie Wu , Chun Lei , Xiaobing Gong","doi":"10.1016/j.jocd.2024.101469","DOIUrl":null,"url":null,"abstract":"<div><p><strong>Objective:</strong> This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.</p><p><strong>Methods:</strong> Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013–2014 and 2017–2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).</p><p><strong>Results</strong> Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689–0.738) and 0.701 (95 % CI: 0.662–0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.</p><p><strong>Conclusion:</strong> Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.</p></div>","PeriodicalId":50240,"journal":{"name":"Journal of Clinical Densitometry","volume":"27 2","pages":"Article 101469"},"PeriodicalIF":1.7000,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a nomogram model for individualizing the risk of osteopenia in abdominal obesity\",\"authors\":\"Gangjie Wu , Chun Lei , Xiaobing Gong\",\"doi\":\"10.1016/j.jocd.2024.101469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p><strong>Objective:</strong> This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.</p><p><strong>Methods:</strong> Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013–2014 and 2017–2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).</p><p><strong>Results</strong> Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689–0.738) and 0.701 (95 % CI: 0.662–0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.</p><p><strong>Conclusion:</strong> Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.</p></div>\",\"PeriodicalId\":50240,\"journal\":{\"name\":\"Journal of Clinical Densitometry\",\"volume\":\"27 2\",\"pages\":\"Article 101469\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-01-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Densitometry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1094695024000040\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Densitometry","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1094695024000040","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Development and validation of a nomogram model for individualizing the risk of osteopenia in abdominal obesity
Objective: This study was aimed to create and validate a risk prediction model for the incidence of osteopenia in individuals with abdominal obesity.
Methods: Survey data from the National Health and Nutrition Examination Survey (NHANES) database for the years 2013–2014 and 2017–2018 was selected and included those with waist circumferences ≥102 m in men and ≥88 cm in women, which were defined as abdominal obesity. A multifactor logistic regression model was constructed using LASSO regression analysis to identify the best predictor variables, followed by the creation of a nomogram model. The model was then verified and evaluated using the consistency index (C-index), area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA).
Results Screening based on LASSO regression analysis revealed that sex, age, race, body mass index (BMI), alkaline phosphatase (ALP) and Triglycerides (TG) were significant predictors of osteopenia development in individuals with abdominal obesity (P < 0.05). These six variables were included in the nomogram. In the training and validation sets, the C indices were 0.714 (95 % CI: 0.689–0.738) and 0.701 (95 % CI: 0.662–0.739), respectively, with corresponding AUCs of 0.714 and 0.701. The nomogram model exhibited good consistency with actual observations, as demonstrated by the calibration curve. The DCA nomogram showed that early intervention for at-risk populations has a net positive impact.
Conclusion: Sex, age, race, BMI, ALP and TG are predictive factors for osteopenia in individuals with abdominal obesity. The constructed nomogram model can be utilized to predict the clinical risk of osteopenia in the population with abdominal obesity.
期刊介绍:
The Journal is committed to serving ISCD''s mission - the education of heterogenous physician specialties and technologists who are involved in the clinical assessment of skeletal health. The focus of JCD is bone mass measurement, including epidemiology of bone mass, how drugs and diseases alter bone mass, new techniques and quality assurance in bone mass imaging technologies, and bone mass health/economics.
Combining high quality research and review articles with sound, practice-oriented advice, JCD meets the diverse diagnostic and management needs of radiologists, endocrinologists, nephrologists, rheumatologists, gynecologists, family physicians, internists, and technologists whose patients require diagnostic clinical densitometry for therapeutic management.