Gut Microbiome Group, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Barcelona Hospital Campus, Passeig Vall d'Hebron 119-129, 08035 Barcelona, Spain.
Departament de Medicina, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.
Nutrients. 2021 Aug 27;13(9):2978. doi: 10.3390/nu13092978.
Diet is recognised as the main driver of changes in gut microbiota. However, linking habitual dietary intake to microbiome composition and activity remains a challenge, leaving most microbiome studies with little or no dietary information. To fill this knowledge gap, we conducted two consecutive studies ( = 84: a first pilot study ( = 40) to build a web-based, semi-quantitative simplified FFQ (sFFQ) based on three 24-h dietary recalls (24HRs); a second study ( = 44) served to validate the newly developed sFFQ using three 24HRs as reference method and to relate gut microbiome profiling (16S rRNA gene) with the extracted dietary and lifestyle data. Relative validation analysis provided acceptable classification and agreement for 13 out of 24 (54%) food groups and 20 out of 29 nutrients (69%) based on intraclass correlation coefficient, cross-classification, Spearman's correlation, Wilcoxon test, and Bland-Altman. Microbiome analysis showed that higher diversity was positively associated with age, vaginal birth, and intake of fruit. In contrast, microbial diversity was negatively associated with BMI, processed meats, ready-to-eat meals, sodium, and saturated fat. Our analysis also revealed a correlation between food groups or nutrients and microbial composition. Overall, we provide the first dietary assessment tool to be validated and correlated with microbiome data for population studies.
饮食被认为是肠道微生物群变化的主要驱动因素。然而,将习惯性饮食摄入与微生物组组成和活性联系起来仍然是一个挑战,这使得大多数微生物组研究几乎没有或没有饮食信息。为了填补这一知识空白,我们进行了两项连续的研究(n=84:第一项初步研究(n=40),基于三份 24 小时膳食回忆(24HR),构建了一个基于网络的半定量简化 FFQ(sFFQ);第二项研究(n=44)用于使用三份 24HR 作为参考方法验证新开发的 sFFQ,并将肠道微生物组分析(16S rRNA 基因)与提取的饮食和生活方式数据相关联。基于内类相关系数、交叉分类、斯皮尔曼相关性、威尔科克森检验和 Bland-Altman,相对验证分析为 24 个食品组中的 13 个(54%)和 29 个营养素中的 20 个(69%)提供了可接受的分类和一致性。微生物组分析表明,更高的多样性与年龄、阴道分娩和水果摄入呈正相关。相比之下,微生物多样性与 BMI、加工肉类、即食餐、钠和饱和脂肪呈负相关。我们的分析还揭示了食物组或营养素与微生物组成之间的相关性。总的来说,我们提供了第一个经过验证并与人群研究微生物组数据相关联的饮食评估工具。