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Objective <p>To examine the associations between dietary patterns and prevalent myopia in school-aged children and to develop a nomogram-based cross-sectional stratification model for prevalent myopia.</p> Methods <p>We analyzed cross-sectional data from 1,316 children (ages 6–10&#xa0;years) enrolled in the 2024 Tianjin Myopia Student Behavioral Health Survey. Cycloplegic refraction and axial length (AL) were measured, and standardized questionnaires were used to collect information on diet, parental myopia, and lifestyle behaviors. Dietary patterns were derived using factor analysis. Associations with AL and myopia were assessed using multivariable regression, with subgroup analyses across behavioral and familial factors. A nomogram was constructed to estimate the cross-sectional probability of prevalent myopia in the study sample, and its calibration was evaluated.</p> Results <p>Three dietary patterns were identified: Pattern 1 (refined carbohydrates and fruits), Pattern 2 (fast food and snacks), and Pattern 3 (whole grains and nuts). Compared with Pattern 1, Pattern 3 was associated with shorter AL (β = − 0.11, 95% CI − 0.21 to 0.00, P = 0.041) and lower odds of prevalent myopia (OR = 0.68, 95% CI 0.48–0.96, P = 0.029). No significant associations were observed for Pattern 2. Exploratory subgroup analyses suggested possible heterogeneity across screen-time and outdoor-activity strata, and these findings should be interpreted cautiously. In multivariable model analyses, parental myopia remained associated with higher odds of prevalent myopia across models, and higher screen-time categories were associated with higher odds in the Pattern 1 and Pattern 2 models. Carrot intake frequency was inversely associated with prevalent myopia only in the Pattern 2 model. Age and sex were also associated with prevalent myopia. The nomogram showed moderate discrimination and acceptable calibration for prevalent myopia in the study sample.</p> Conclusion <p>A dietary pattern characterized by whole grains and nuts was associated with shorter AL and lower odds of prevalent myopia. The nomogram may support cross-sectional stratification of prevalent myopia in the study sample. However, it should not be interpreted as a tool for predicting incident myopia or the onset of future myopia.</p>

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Dietary patterns and behavioral factors in relation to childhood myopia: insight from school-age students in Tianjin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Zixun Wang,
  • Jingtao Yu,
  • Jialiang Wu,
  • Desheng Song,
  • Xueshuo Xie,
  • Chenyang Zhao,
  • Yin Li,
  • Bei Du,
  • Ruihua Wei

摘要

Objective

To examine the associations between dietary patterns and prevalent myopia in school-aged children and to develop a nomogram-based cross-sectional stratification model for prevalent myopia.

Methods

We analyzed cross-sectional data from 1,316 children (ages 6–10 years) enrolled in the 2024 Tianjin Myopia Student Behavioral Health Survey. Cycloplegic refraction and axial length (AL) were measured, and standardized questionnaires were used to collect information on diet, parental myopia, and lifestyle behaviors. Dietary patterns were derived using factor analysis. Associations with AL and myopia were assessed using multivariable regression, with subgroup analyses across behavioral and familial factors. A nomogram was constructed to estimate the cross-sectional probability of prevalent myopia in the study sample, and its calibration was evaluated.

Results

Three dietary patterns were identified: Pattern 1 (refined carbohydrates and fruits), Pattern 2 (fast food and snacks), and Pattern 3 (whole grains and nuts). Compared with Pattern 1, Pattern 3 was associated with shorter AL (β = − 0.11, 95% CI − 0.21 to 0.00, P = 0.041) and lower odds of prevalent myopia (OR = 0.68, 95% CI 0.48–0.96, P = 0.029). No significant associations were observed for Pattern 2. Exploratory subgroup analyses suggested possible heterogeneity across screen-time and outdoor-activity strata, and these findings should be interpreted cautiously. In multivariable model analyses, parental myopia remained associated with higher odds of prevalent myopia across models, and higher screen-time categories were associated with higher odds in the Pattern 1 and Pattern 2 models. Carrot intake frequency was inversely associated with prevalent myopia only in the Pattern 2 model. Age and sex were also associated with prevalent myopia. The nomogram showed moderate discrimination and acceptable calibration for prevalent myopia in the study sample.

Conclusion

A dietary pattern characterized by whole grains and nuts was associated with shorter AL and lower odds of prevalent myopia. The nomogram may support cross-sectional stratification of prevalent myopia in the study sample. However, it should not be interpreted as a tool for predicting incident myopia or the onset of future myopia.