探索饮食分类及其对炎症性肠病爆发预测的影响,使用Sparse Grouped Least Absolute Shrinkage和Selection Operator方法
Corien L Stevens1, Greetje M C Adriaans2, Corinne E G M Spooren2
1Department of Gastroenterology and Hepatology, University Medical Centre Groningen, University of Groningen, Groningen, the Netherlands.
Clinical nutrition (Edinburgh, Scotland)
|March 6, 2025
概括
这项研究探讨了饮食模式及其预测炎症性肠病 (IBD) 爆发的能力,使用稀疏分组 LASSO (SGL) 方法. 结果表明,虽然最初确定了红肉和蔬菜等某些食物,但女性性别和能量摄入等因素是IBD爆发的更强有力的预测因素.
科学领域:
- 胃肠病学和营养科学 胃肠病学和营养科学
- 在健康研究中的统计建模.
- 炎症性肠病 (IBD) 病因 病因学 炎症性肠病 (IBD) 病因学
背景情况:
- 饮食是影响炎症性肠病 (IBD) 发病和进展的重要环境因素.
- 分析饮食和IBD爆发之间的复杂关系是具有挑战性的.
- 稀疏分组LASSO (SGL) 方法提供了一种新的方法来研究饮食模式及其在IBD中的预测能力.
研究的目的:
- 评估LASSO (SGL) 方法在预测炎症性肠病 (IBD) 爆发方面的有效性.
- 评估食物组的不同分类如何影响IBD爆发的预测.
- 确定关键的饮食成分和其他可能预测IBD患者疾病爆发的因素.
主要方法:
- 利用了两个不同的患者队伍的基线饮食摄入量和24个月纵向疾病过程数据.
- 将食品分为22组,并进一步将其分为三个不同的饮食模式:植物/动物/混合,健康/不健康/中性,超加工/不超加工.
- 应用了SGL方法,利用其参数"lambda"来识别重要的食物组和组内的变量来预测IBD爆发.
主要成果:
- 在基线缓解的427名患者中,106人 (24.8%) 经历了发作;这些患者的红肉和蔬菜原料摄入量较高.
- 预测爆发的预测模型在不同的分类中显示了中等的性能 (AUC范围为0.425-0.612).
- 虽然最初选择了红肉,豆类和蔬菜,但女性的性别和能量摄入量在所有模型中显示出最高的预测值.
结论:
- 食物组分类的方式显著影响SGL方法对IBD爆发的预测能力.
- 这项研究表明,当通过各种分类分析饮食因素时,可能不是炎症性肠病爆发的主要预测因素.
- 需要进一步的研究来完善饮食模式分析,并确定IBD爆发的最强有力的预测因素.
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