Development and validation of a nomogram model for individualizing the risk of osteopenia in abdominal obesity

IF 1.7 4区 医学 Q4 ENDOCRINOLOGY & METABOLISM
Gangjie Wu , Chun Lei , Xiaobing Gong
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Abstract

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.

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
方法选取2013-2014年和2017-2018年美国国家健康与营养调查(NHANES)数据库中的调查数据,将男性腰围≥102米、女性腰围≥88厘米者定义为腹型肥胖。利用 LASSO 回归分析法构建了一个多因素逻辑回归模型,以确定最佳预测变量,随后创建了一个提名图模型。结果基于 LASSO 回归分析的筛选显示,性别、年龄、种族、体重指数 (BMI)、碱性磷酸酶 (ALP) 和甘油三酯 (TG) 是腹型肥胖症患者骨质疏松症发生的重要预测因素(P<0.05)。这六个变量被纳入了提名图。在训练集和验证集中,C 指数分别为 0.714(95% CI:0.689-0.738)和 0.701(95% CI:0.662-0.739),相应的 AUC 分别为 0.714 和 0.701。正如校准曲线所示,提名图模型与实际观察结果具有良好的一致性。结论性别、年龄、种族、体重指数(BMI)、谷丙转氨酶(ALP)和谷草转氨酶(TG)是腹型肥胖患者骨质疏松的预测因素。所构建的提名图模型可用于预测腹型肥胖人群骨质增生的临床风险。
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来源期刊
Journal of Clinical Densitometry
Journal of Clinical Densitometry 医学-内分泌学与代谢
CiteScore
4.90
自引率
8.00%
发文量
92
审稿时长
90 days
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.
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