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<p>Glycemic variability is an emerging marker of metabolic health, yet its behavioral determinants in healthy populations remain unclear. This study investigated whether meal patterns—specifically timing, distribution, and frequency—are associated with glycemic variability. Ninety-six individuals aged 18–27 years wore continuous glucose monitors for 14 consecutive days, providing 1036 complete daily records. Daily glucose profiles were clustered using dynamic time warping, and concurrent meal and sleep logs were analyzed to characterize dietary patterns across clusters. Cluster 2 (765 days) exhibited broader distributions and higher densities of elevated glucose values compared with cluster 1 (271 days), reflecting greater variability (17.7% vs. 14.1%, <i>p</i> = 0.0001). Peak daily glucose was also significantly higher in cluster 2, although average glucose did not differ. Total energy intake was lower in cluster 2 than cluster 1 (1781.1 vs. 1981.4 kcal, <i>p</i> = 0.0003), with no significant differences in macronutrient composition. By contrast, meal patterns differed substantially: cluster 2 was characterized by fewer eating occasions, later breakfast timing, and a greater proportion of daily energy intake at dinner. These findings suggest that meal timing and distribution are important dimensions of dietary recommendations, indicating a shift in focus from nutrient intake alone toward including meal patterns.</p>

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Distinct meal timing and frequency patterns contribute to daily glycemic variability

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Dong-Hwa Jeong,
  • YoonJu Song

摘要

Glycemic variability is an emerging marker of metabolic health, yet its behavioral determinants in healthy populations remain unclear. This study investigated whether meal patterns—specifically timing, distribution, and frequency—are associated with glycemic variability. Ninety-six individuals aged 18–27 years wore continuous glucose monitors for 14 consecutive days, providing 1036 complete daily records. Daily glucose profiles were clustered using dynamic time warping, and concurrent meal and sleep logs were analyzed to characterize dietary patterns across clusters. Cluster 2 (765 days) exhibited broader distributions and higher densities of elevated glucose values compared with cluster 1 (271 days), reflecting greater variability (17.7% vs. 14.1%, p = 0.0001). Peak daily glucose was also significantly higher in cluster 2, although average glucose did not differ. Total energy intake was lower in cluster 2 than cluster 1 (1781.1 vs. 1981.4 kcal, p = 0.0003), with no significant differences in macronutrient composition. By contrast, meal patterns differed substantially: cluster 2 was characterized by fewer eating occasions, later breakfast timing, and a greater proportion of daily energy intake at dinner. These findings suggest that meal timing and distribution are important dimensions of dietary recommendations, indicating a shift in focus from nutrient intake alone toward including meal patterns.