亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

#3953 TOXIC MICROBIOME AND CHRONIC KIDNEY DISEASE: INSIGHTS FROM THE CKD-REIN COHORT STUDY

微生物群 肾脏疾病 医学 队列 透析 肾功能 内科学 血液透析 肠道菌群 生理学 队列研究 免疫学 生物信息学 生物
作者
Sandra Wagner,Laetitia Koppe,Manolo Laiola,Islam Amine Larabi,Florence Thirion,Denis Fouque,Emmanuelle Le Chatelier,Jean‐Claude Alvarez,Ziad A. Massy,Dusko Ehrlich,Bénédicte Stengel
出处
期刊:Nephrology Dialysis Transplantation [Oxford University Press]
卷期号:38 (Supplement_1)
标识
DOI:10.1093/ndt/gfad063c_3953
摘要

Abstract Background and Aims Many uremic toxins (UTs) originate from gut microbiome, and contribute to chronic kidney disease (CKD) progression and cardiovascular morbidity. In order to reduce uremic symptoms and CKD progression, patients have several dietary restrictions, which may influence gut microbiome composition, and impact UTs production. An altered microbiome may contribute to UTs increase in those patients. However, the role of key bacterial taxa in producing UTs and the impact of diet on UTs variance in non-dialyzed patients are not well known. The objectives of this study were, first, to compare microbial features between CKD patients and healthy controls, and, second, to investigate the relation of gut microbiome with uremic toxicity, as well as the potential impact of diet on such relationship. Method Characterization of gut metagenomes, 10 UTs and 3 precursors’ serum concentrations by LC-MS/MS, host characteristics and diet were obtained from 240 non-dialysis CKD patients from the CKD-REIN cohort (mean ± SD): age: 68 ± 11 years, 71% male, estimated glomerular filtration rate (eGFR): 33.2 ± 12.7 ml/min/1.73m². First, to identify microbial biomarkers characterizing the gut microbiome-related toxicity in CKD, we compared microbiome features between 78 CKD patients and 78 age-, sex-, and BMI-matched healthy controls from the Milieu Interieur (MI) cohort: age: 58 ± 10 years, 60% male, eGFR: 89 ± 13. Second, we performed a multiomics’ data integration analysis via a supervised modelling to investigate cross-sectionally the association between host characteristics, gut microbiome, UTs, and diet-related features according to CKD severity (eGFR<30, n = 110 vs eGFR ≥30 mL/min/1.73m², n = 130). Results Compared to healthy controls, CKD patients had a significant reduced gut microbiome health index. Several Metagenomic Species Pan-genomes (MSPs) were significantly contrasted between MI and CKD cohorts: 43 species were enriched in CKD patients vs 24 in controls. Species most enriched in CKD patients included several UTs producers such as Lachnospiraceae spp, Dysosmobacter – Oscillibacter spp, Butyricimonas faecihominis, Victivallis vadensis and Hungatella spp, some of which were positively correlated with the following UTs: 3-Carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF), trimethylamine-N-oxide (TMAO), and indole-3-acetic acid (3-IAA). Moreover, species belonging to Enterocloster and Hungatella genera (both members of Lachnospiraceae family) were found to be negatively correlated with eGFR. Among species associated with CKD severity, species carrying genes for UTs production were observed such as Desuflovibiro fairfieldensis, Bacteroides clarus and Blautia obeum along with increasing alcohol and hot drinks consumption, CRP and several UTs (kynurenic acid, indoxyl sulfate and Phenylacetylglutamine) levels. In contrast, some taxa like Faecalibacterium prausnitzii and Dysosmobacter welbionis were associated with legume intake but not with UTs. Conclusion Our study highlights an alteration of gut microbiome in CKD patients compared to healthy controls, with increased abundance of UTs producer species. The results of the multidimensional data integration modelling suggest a strong interplay between food intake, gut microbiome modifications, UTs accumulation and clinical features. These findings might open to promising therapeutic strategies to reduce microbiome-related toxicity.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
7秒前
大模型应助秋下采纳,获得10
11秒前
飞龙发布了新的文献求助10
17秒前
赘婿应助argon采纳,获得10
22秒前
科研通AI6.2应助清秀面包采纳,获得10
22秒前
bkagyin应助西瓜番茄采纳,获得10
23秒前
可爱的函函应助飞龙采纳,获得10
29秒前
飞龙完成签到,获得积分10
38秒前
42秒前
45秒前
Nian发布了新的文献求助10
47秒前
颜九发布了新的文献求助10
49秒前
LJC完成签到,获得积分10
1分钟前
科研通AI6.3应助俞俊敏采纳,获得10
1分钟前
1分钟前
颜九完成签到,获得积分10
1分钟前
俞俊敏发布了新的文献求助10
1分钟前
科研通AI6.2应助Nian采纳,获得10
1分钟前
orixero应助缥缈采纳,获得10
1分钟前
1分钟前
CodeCraft应助科研通管家采纳,获得10
1分钟前
SciGPT应助科研通管家采纳,获得10
1分钟前
张欢馨应助科研通管家采纳,获得10
1分钟前
大头完成签到 ,获得积分10
1分钟前
2分钟前
跳跃雨寒完成签到 ,获得积分10
2分钟前
2分钟前
123123完成签到 ,获得积分10
2分钟前
鹏虫虫完成签到 ,获得积分10
2分钟前
123完成签到 ,获得积分10
2分钟前
秋下完成签到,获得积分10
2分钟前
凶狠的映易完成签到 ,获得积分10
2分钟前
2分钟前
Nian发布了新的文献求助10
2分钟前
3分钟前
等于零完成签到 ,获得积分10
3分钟前
3分钟前
myiyio发布了新的文献求助10
3分钟前
Orange应助科研通管家采纳,获得10
3分钟前
3分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Scientific Writing and Communication: Papers, Proposals, and Presentations 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6371605
求助须知:如何正确求助?哪些是违规求助? 8185245
关于积分的说明 17271304
捐赠科研通 5426013
什么是DOI,文献DOI怎么找? 2870525
邀请新用户注册赠送积分活动 1847432
关于科研通互助平台的介绍 1694042