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中国大学生维生素D缺乏预测模型的建立与验证(一种预测中国大学生维生素D缺乏的动态在线列线图)

Development and validation of prediction model for vitamin D deficiency in Chinese college students (a dynamic online nomogram predicting vitamin D deficiency for Chinese college students).

作者信息

Luo Yingyi, Qu Chunbo, Li Guyanan, Di Qiannan, Ding Shangzhen, Jiang Ruoyou, Wang Ruotong, Wang Siyuan, Na Lixin

机构信息

Medical Technology College, Shanghai University of Medicine and Health Sciences, Shanghai, China.

Public Health College, Shanghai University of Medicine and Health Sciences, Shanghai, China.

出版信息

J Health Popul Nutr. 2025 Apr 21;44(1):129. doi: 10.1186/s41043-025-00871-w.

Abstract

OBJECTIVE

This study aims to develop a model for predicting vitamin D deficiency in Chinese college students using easily accessible clinical characteristics.

METHODS

Data were derived from a cross-section study of the Vitamin D status in Chinese college students in September, 2020. Totally 1,667 freshmen from 26 provinces, autonomous districts or municipalities were analyzed. A LASSO regression model was used to select predictors and the significant factors were used to construct the logistic regression model expression and the nomogram. The prediction model was subjected to100 bootstrap resamples for internal validation to assess its predictive accuracy. Calibration and discrimination were used to assess the performance of the model. A dynamic online nomogram was conducted to make the model easy to use. The clinical use was evaluated by a decision curve analysis.

RESULTS

Gender, region of original residence, milk and yogurt intake, puffed foods intake, outdoor activity duration, UV protection index and "taken calcium or vitamin D supplements within 3 months" were identified as significant predictors of vitamin D deficiency among Chinese college students. The model demonstrated good calibration with a 100 bootstraps analysis. The C-index was 0.677 and the bias-adjusted C-index was 0.668 in internal validation with 100 bootstrap resamples. The decision curve analysis showed a threshold probability between 0.5 and 0.8, using the model added more benefit than considering all patients are deficient or not deficient.

CONCLUSIONS

The performance of this vitamin D deficiency prediction model is commendable, and the dynamic online nomogram was proved to be a user-friendly screening tool for identifying high-risk subjects among Chinese college students. However, external validation is imperative to ensure the model's generalizability.

摘要

目的

本研究旨在利用易于获取的临床特征建立一个预测中国大学生维生素D缺乏的模型。

方法

数据来源于2020年9月对中国大学生维生素D状况的横断面研究。共分析了来自26个省、自治区或直辖市的1667名新生。采用LASSO回归模型选择预测因子,并将显著因素用于构建逻辑回归模型表达式和列线图。对预测模型进行100次自助重抽样以进行内部验证,评估其预测准确性。采用校准和区分度来评估模型的性能。制作了动态在线列线图以使模型易于使用。通过决策曲线分析评估临床实用性。

结果

性别、原居住地、牛奶和酸奶摄入量、膨化食品摄入量、户外活动时长、紫外线防护指数以及“在3个月内服用过钙或维生素D补充剂”被确定为中国大学生维生素D缺乏的显著预测因子。通过100次自助抽样分析,该模型显示出良好的校准。在100次自助重抽样的内部验证中,C指数为0.677,偏差调整后的C指数为0.668。决策曲线分析表明,在阈值概率为0.5至0.8之间时,使用该模型比考虑所有患者是否缺乏能带来更多益处。

结论

该维生素D缺乏预测模型的性能值得称赞,动态在线列线图被证明是一种便于用户使用的筛查工具,可用于识别中国大学生中的高危人群。然而,必须进行外部验证以确保模型的通用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44eb/12010568/58c4b1384bc2/41043_2025_871_Fig1_HTML.jpg

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