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抗TNF治疗对溃疡性结肠炎患者粪便代谢和脂质组学特征的影响及临床缓解的预测

Changes in fecal metabolic and lipidomic features by anti-TNF treatment and prediction of clinical remission in patients with ulcerative colitis.

作者信息

Kim Seok-Young, Shin Seung Yong, Park Soo Jung, Im Jong Pil, Kim Hyo Jong, Lee Kang-Moon, Kim Ji Won, Jung Sung-Ae, Lee Jun, Kang Sang-Bum, Shin Sung Jae, Kim Eun Sun, Kim You Sun, Kim Tae Oh, Kim Hyun-Soo, Park Dong Il, Kim Hyung Kil, Kim Eun Soo, Kim Young-Ho, Teng Dennis, Kim Jong-Hwa, Kim Wonyong, Saeed Maham, Moon Jung Min, Kim Kisung, Choi Chang Hwan, Choi Hyung-Kyoon

机构信息

College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea.

Department of Internal Medicine, Chung-Ang University College of Medicine, Seoul, Republic of Korea.

出版信息

Therap Adv Gastroenterol. 2023 May 2;16:17562848231168199. doi: 10.1177/17562848231168199. eCollection 2023.

DOI:10.1177/17562848231168199
PMID:37153496
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10161336/
Abstract

BACKGROUND

Therapeutic targets for ulcerative colitis (UC) and prediction models of antitumor necrosis factor (TNF) therapy outcomes have not been fully reported.

OBJECTIVE

Investigate the characteristic metabolite and lipid profiles of fecal samples of UC patients before and after adalimumab treatment and develop a prediction model of clinical remission following adalimumab treatment.

DESIGN

Prospective, observational, multicenter study was conducted on moderate-to-severe UC patients ( = 116).

METHODS

Fecal samples were collected from UC patients at 8 and 56 weeks of adalimumab treatment and from healthy controls (HC,  = 37). Clinical remission was assessed using the Mayo score. Metabolomic and lipidomic analyses were performed using gas chromatography mass spectrometry and nano electrospray ionization mass spectrometry, respectively. Orthogonal partial least squares discriminant analysis was performed to establish a remission prediction model.

RESULTS

Fecal metabolites in UC patients markedly differed from those in HC at baseline and were changed similarly to those in HC during treatment; however, lipid profiles did not show these patterns. After treatment, the fecal characteristics of remitters (RM) were closer to those of HC than to those of non-remitters (NRM). At 8 and 56 weeks, amino acid levels in RM were lower than those in NRM and similar to those in HC. After 56 weeks, levels of 3-hydroxybutyrate, lysine, and phenethylamine decreased, and dodecanoate level increased in RM similarly to those in HC. The prediction model of long-term remission in male patients based on lipid biomarkers showed a higher performance than clinical markers.

CONCLUSION

Fecal metabolites in UC patients markedly differ from those in HC, and the levels in RM are changed similarly to those in HC after anti-TNF therapy. Moreover, 3-hydroxybutyrate, lysine, phenethylamine, and dodecanoate are suggested as potential therapeutic targets for UC. A prediction model of long-term remission based on lipid biomarkers may help implement personalized treatment.

摘要

背景

溃疡性结肠炎(UC)的治疗靶点以及抗肿瘤坏死因子(TNF)治疗效果的预测模型尚未得到充分报道。

目的

研究阿达木单抗治疗前后UC患者粪便样本的特征性代谢物和脂质谱,并建立阿达木单抗治疗后临床缓解的预测模型。

设计

对中重度UC患者(n = 116)进行前瞻性、观察性、多中心研究。

方法

在阿达木单抗治疗第8周和第56周从UC患者以及健康对照(HC,n = 37)中收集粪便样本。使用梅奥评分评估临床缓解情况。分别采用气相色谱质谱法和纳米电喷雾电离质谱法进行代谢组学和脂质组学分析。进行正交偏最小二乘判别分析以建立缓解预测模型。

结果

UC患者粪便代谢物在基线时与HC患者显著不同,且在治疗期间变化与HC患者相似;然而,脂质谱未呈现这些模式。治疗后,缓解者(RM)的粪便特征比未缓解者(NRM)更接近HC患者。在第8周和第56周,RM中氨基酸水平低于NRM,且与HC患者相似。56周后,RM中3-羟基丁酸、赖氨酸和苯乙胺水平降低,十二烷酸水平升高,与HC患者情况相似。基于脂质生物标志物的男性患者长期缓解预测模型表现优于临床标志物。

结论

UC患者粪便代谢物与HC患者显著不同,抗TNF治疗后RM中的水平变化与HC患者相似。此外,3-羟基丁酸、赖氨酸、苯乙胺和十二烷酸被认为是UC的潜在治疗靶点。基于脂质生物标志物的长期缓解预测模型可能有助于实施个性化治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/bdd1a95717d9/10.1177_17562848231168199-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/5a31187f5fd5/10.1177_17562848231168199-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/dcdb4740884d/10.1177_17562848231168199-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/5dbc08025ad3/10.1177_17562848231168199-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/23a2e0611249/10.1177_17562848231168199-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/eaa800a32f20/10.1177_17562848231168199-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/bdd1a95717d9/10.1177_17562848231168199-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/5a31187f5fd5/10.1177_17562848231168199-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/dcdb4740884d/10.1177_17562848231168199-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/5dbc08025ad3/10.1177_17562848231168199-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/23a2e0611249/10.1177_17562848231168199-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/eaa800a32f20/10.1177_17562848231168199-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f09/10161336/bdd1a95717d9/10.1177_17562848231168199-fig6.jpg

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