通过隐性类分析确定与心血管疾病风险相关的饮食模式:德黑兰脂质和葡萄糖研究
Zahra Gaeini1, Sahar Mirzaee1, Parvin Mirmiran2
1Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Journal of health, population, and nutrition
|July 31, 2025
概括
隐性类分析在德黑兰成年人中确定了四种饮食模式,但在十年内发现这些模式与心血管疾病 (CVD) 风险之间没有显著联系. 这表明LCA可能在这个人群中对心血管疾病具有有限的预测价值.
科学领域:
- 营养科学 营养科学
- 流行病学 流行病学
- 公共卫生 公共卫生
背景情况:
- 饮食模式对于心血管疾病 (CVD) 风险至关重要.
- 为了分析饮食模式,存在各种数据驱动的方法,包括隐性类分析 (LCA).
- 了解德黑兰成年人等特定人口的饮食模式对于公共健康至关重要.
研究的目的:
- 通过隐性类分析 (LCA) 识别德黑兰成年人的主要饮食模式.
- 调查这些已识别的饮食模式与患心血管疾病 (CVD) 的风险之间的关联.
主要方法:
- 这项研究包括来自德黑兰脂质和葡萄糖研究 (TLGS) 的1849名成年人 (≥30岁).
- 饮食摄入量使用经过验证的食物频率调查问卷进行评估.
- 隐性类分析 (LCA) 用于推导饮食模式,并使用考克斯比例危险模型来评估心血管疾病风险.
主要成果:
- 他们发现了四种不同的饮食模式:"混合模式"",健康模式"",加工食品模式"和"替代类".
- 在调整了混因素后,在LCA衍生的饮食模式和心血管疾病发病率之间没有发现统计学上显著的关联.
- 危险比率 (HR) 和95%置信区间 (CI) 没有表明任何特定模式对心血管疾病风险具有保护性或有害影响.
结论:
- 在10年的随访期间,在德黑兰成年人中使用LCA获得的饮食模式没有显示出与心血管疾病风险的显著关联.
- 这些发现表明,基于LCA的饮食分类可能在预测这种特定人群中的心血管疾病方面具有有限的实用性.
- 未来的研究应该探索将LCA与其他方法结合起来,使用重复的饮食评估,并考虑特定人口的饮食行为,以更好地阐明饮食与疾病的关系.
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