收藏切换
Establishment of a nomogram prediction model for overweight and obesity in middle school students based on Lasso regression
收藏切换
PDF
Jun HU1, Feng HONG1, Ya-jun GUO2, Na WANG1, Da-fei REN1, 2
Modern Preventive Medicine | 2025, 52(11) : 2003 - 2008
Less
收藏切换
Modern Preventive Medicine | 2025, 52(11): 2003-2008
Child and Adolescent Health, Maternal and Child Health
Establishment of a nomogram prediction model for overweight and obesity in middle school students based on Lasso regression
Full
Jun HU1, Feng HONG1, Ya-jun GUO2, Na WANG1, Da-fei REN1, 2
Affiliations
  • School of Public Health and Health, Guizhou Medical University, Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, Guiyang, Guizhou 561113, China
Published: 2025-06-10 doi: 10.20043/j.cnki.MPM.202501148
Outline
收藏切换
Objective

To analyze the factors influencing overweight and obesity among middle school students and to establish a nomogram for predicting the risk of overweight and obesity in this population.

Methods

From September to November 2024, a random cluster sampling method was used to select 5 135 middle school students from 20 schools in Tongren city for physical examinations and questionnaire surveys. Lasso regression was employed to identify factors affecting overweight and obesity, and a nomogram prediction model was established and validated.

Results

The prevalence of overweight and obesity among middle school students was 25.02%. Multivariate logistic regression analysis revealed that being female (OR=0.430, 95%CI: 0.371-0.499),boarding at school (OR=0.582, 95%CI: 0.500-0.678), engaging in moderate to high-intensity physical activity for one hour daily (OR=0.730, 95%CI: 0.630-0.847), having non-obese parents (OR=0.466, 95%CI: 0.404-0.538), and external eating triggers (OR=0.945, 95%CI: 0.935-0.956) were protective factors against overweight and obesity. Conversely, consuming fresh fruits and vegetables at every meal (OR=1.308, 95%CI: 1.103-1.549), restrictive eating (OR=1.100, 95%CI: 1.089-1.111), and emotional eating (OR=1.091, 95%CI: 1.079-1.103) were identified as risk factors. The areas under the curve (AUC) for the training and validation sets were 0.790 (95%CI: 0.775-0.806) and 0.765 (95%CI: 0.731-0.799), respectively, indicating good discrimination of the nomogram prediction model. The Brier score was 0.15, and the Hosmer-Leeshawn test suggested good model fit (χ2=10.984, P=0.203).

Conclusion

The nomogram model established in this study effectively predicts the risk of overweight and obesity, providing a reference for screening high-risk students and implementing personalized prevention strategies.

Lasso regression  /  Overweight and obesity  /  Abnormal eating behaviors  /  Nomogram
Jun HU, Feng HONG, Ya-jun GUO, Na WANG, Da-fei REN. Establishment of a nomogram prediction model for overweight and obesity in middle school students based on Lasso regression[J]. Modern Preventive Medicine, 2025 , 52 (11) : 2003 -2008 . DOI: 10.20043/j.cnki.MPM.202501148
Year 2025 volume 52 Issue 11
PDF
55
25
Cite this Article
BibTeX
Article Info
doi: 10.20043/j.cnki.MPM.202501148
  • Receive Date:2025-01-10
  • Online Date:2026-03-18
  • Published:2025-06-10
Article Data
Affiliations
History
  • Received:2025-01-10
Affiliations
    School of Public Health and Health, Guizhou Medical University, Key Laboratory of Environmental Pollution and Disease Monitoring, Ministry of Education, Guiyang, Guizhou 561113, China
References
Share
https://castjournals.cast.org.cn/joweb/xdyfyx/EN/10.20043/j.cnki.MPM.202501148
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT