PDF(Pubmed)
Abstract：
Predicting response to exclusive enteral nutrition (EEN) in active Crohn\'s disease (CD) could lead to therapy personalization and pretreatment optimization.
This study aimed to explore the ability of pretreatment parameters to predict fecal calprotectin (FCal) levels at EEN completion in a prospective study in children with CD.
In children with active CD, clinical parameters, dietary intake, cytokines, inflammation-related blood proteomics, and diet-related metabolites, metabolomics and microbiota in feces, were measured before initiation of 8 wk of EEN. Prediction of FCal levels at EEN completion was performed using machine learning. Data are presented with medians (IQR).
Of 37 patients recruited, 15 responded (FCal < 250 μg/g) to EEN (responders) and 22 did not (nonresponders). Clinical and immunological parameters were not associated with response to EEN. Responders had lesser (μmol/g) butyrate [responders: 13.2 (8.63-18.4) compared with nonresponders: 22.3 (12.0-32.0); P = 0.03], acetate [responders: 49.9 (46.4-68.4) compared with nonresponders: 70.4 (57.0-95.5); P = 0.027], phenylacetate [responders: 0.175 (0.013-0.611) compared with nonresponders: 0.943 (0.438-1.35); P = 0.021], and a higher microbiota richness [315 (269-347) compared with nonresponders: 243 (205-297); P = 0.015] in feces than nonresponders. Responders consumed (portions/1000 kcal/d) more confectionery products [responders: 0.55 (0.38-0.72) compared with nonresponders: 0.19 (0.01-0.38); P = 0.045]. A multicomponent model using fecal parameters, dietary data, and clinical and immunological parameters predicted response to EEN with 78% accuracy (sensitivity: 80%; specificity: 77%; positive predictive value: 71%; negative predictive value: 85%). Higher taxon abundance from Ruminococcaceae, Lachnospiraceae, and Bacteroides and phenylacetate, butyrate, and acetate were the most influential variables in predicting lack of response to EEN.
We identify microbial signals and diet-related metabolites in feces, which could comprise targets for pretreatment optimization and personalized nutritional therapy in pediatric CD.
This study aimed to explore the ability of pretreatment parameters to predict fecal calprotectin (FCal) levels at EEN completion in a prospective study in children with CD.
In children with active CD, clinical parameters, dietary intake, cytokines, inflammation-related blood proteomics, and diet-related metabolites, metabolomics and microbiota in feces, were measured before initiation of 8 wk of EEN. Prediction of FCal levels at EEN completion was performed using machine learning. Data are presented with medians (IQR).
Of 37 patients recruited, 15 responded (FCal < 250 μg/g) to EEN (responders) and 22 did not (nonresponders). Clinical and immunological parameters were not associated with response to EEN. Responders had lesser (μmol/g) butyrate [responders: 13.2 (8.63-18.4) compared with nonresponders: 22.3 (12.0-32.0); P = 0.03], acetate [responders: 49.9 (46.4-68.4) compared with nonresponders: 70.4 (57.0-95.5); P = 0.027], phenylacetate [responders: 0.175 (0.013-0.611) compared with nonresponders: 0.943 (0.438-1.35); P = 0.021], and a higher microbiota richness [315 (269-347) compared with nonresponders: 243 (205-297); P = 0.015] in feces than nonresponders. Responders consumed (portions/1000 kcal/d) more confectionery products [responders: 0.55 (0.38-0.72) compared with nonresponders: 0.19 (0.01-0.38); P = 0.045]. A multicomponent model using fecal parameters, dietary data, and clinical and immunological parameters predicted response to EEN with 78% accuracy (sensitivity: 80%; specificity: 77%; positive predictive value: 71%; negative predictive value: 85%). Higher taxon abundance from Ruminococcaceae, Lachnospiraceae, and Bacteroides and phenylacetate, butyrate, and acetate were the most influential variables in predicting lack of response to EEN.
We identify microbial signals and diet-related metabolites in feces, which could comprise targets for pretreatment optimization and personalized nutritional therapy in pediatric CD.
摘要：
背景：预测活动性克罗恩病（CD）对专有肠内营养（EEN）的反应可能会导致治疗个性化和预处理优化。
目的：本研究旨在探讨在CD患儿的一项前瞻性研究中，预处理参数预测EEN完成时粪便钙卫蛋白（FCal）水平的能力。
方法：在患有活跃CD的儿童中，临床参数,饮食摄入量，细胞因子,炎症相关血液蛋白质组学，和饮食相关的代谢产物，代谢组学和粪便中的微生物群，在开始8周的EEN之前测量。使用机器学习进行EEN完成时FCal水平的预测。数据以中位数(IQR)呈现。
结果：在招募的37名患者中，15人对EEN（应答者）有反应（FCal<250μg/g），22人没有（非应答者）。临床和免疫学参数与对EEN的反应无关。响应者的丁酸酯含量较低（μmol/g）[响应者：13.2（8.63-18.4），而非响应者：22.3（12.0-32.0）；P=0.03]，醋酸盐[反应者：49.9（46.4-68.4），无反应者：70.4（57.0-95.5）；P=0.027]，苯乙酸酯[应答者：0.175（0.013-0.611）与无应答者相比：0.943（0.438-1.35）；P=0.021]，与无反应者相比，粪便中的微生物群丰富度[315（269-347）：243（205-297）；P=0.015]。响应者消耗（部分/1000千卡/天）更多的糖果产品[响应者：0.55（0.38-0.72），而非响应者：0.19（0.01-0.38）;P=0.045]。使用粪便参数的多组分模型，饮食数据，临床和免疫学参数以78%的准确度预测EEN应答(灵敏度:80%;特异性:77%;阳性预测值:71%;阴性预测值:85%)。来自Ruminococycaceae的分类单元丰度较高，落叶松科,拟杆菌和苯乙酸盐，丁酸盐,和乙酸盐是预测对EEN缺乏反应的最有影响的变量。
结论：我们鉴定了粪便中的微生物信号和饮食相关代谢产物，其中可能包括儿科CD的预处理优化和个性化营养治疗的目标。
目的：本研究旨在探讨在CD患儿的一项前瞻性研究中，预处理参数预测EEN完成时粪便钙卫蛋白（FCal）水平的能力。
方法：在患有活跃CD的儿童中，临床参数,饮食摄入量，细胞因子,炎症相关血液蛋白质组学，和饮食相关的代谢产物，代谢组学和粪便中的微生物群，在开始8周的EEN之前测量。使用机器学习进行EEN完成时FCal水平的预测。数据以中位数(IQR)呈现。
结果：在招募的37名患者中，15人对EEN（应答者）有反应（FCal<250μg/g），22人没有（非应答者）。临床和免疫学参数与对EEN的反应无关。响应者的丁酸酯含量较低（μmol/g）[响应者：13.2（8.63-18.4），而非响应者：22.3（12.0-32.0）；P=0.03]，醋酸盐[反应者：49.9（46.4-68.4），无反应者：70.4（57.0-95.5）；P=0.027]，苯乙酸酯[应答者：0.175（0.013-0.611）与无应答者相比：0.943（0.438-1.35）；P=0.021]，与无反应者相比，粪便中的微生物群丰富度[315（269-347）：243（205-297）；P=0.015]。响应者消耗（部分/1000千卡/天）更多的糖果产品[响应者：0.55（0.38-0.72），而非响应者：0.19（0.01-0.38）;P=0.045]。使用粪便参数的多组分模型，饮食数据，临床和免疫学参数以78%的准确度预测EEN应答(灵敏度:80%;特异性:77%;阳性预测值:71%;阴性预测值:85%)。来自Ruminococycaceae的分类单元丰度较高，落叶松科,拟杆菌和苯乙酸盐，丁酸盐,和乙酸盐是预测对EEN缺乏反应的最有影响的变量。
结论：我们鉴定了粪便中的微生物信号和饮食相关代谢产物，其中可能包括儿科CD的预处理优化和个性化营养治疗的目标。
