Literature DB >> 33833065

Six-month follow-up of gut microbiota richness in patients with COVID-19.

Yanfei Chen1, Silan Gu1, Yunbo Chen1, Haifeng Lu1, Ding Shi1, Jing Guo1, Wen-Rui Wu1, Ya Yang1, Yongtao Li1, Kai-Jin Xu1, Cheng Ding1, Rui Luo1, Chenjie Huang1, Ling Yu1, Min Xu1, Ping Yi2, Jun Liu3, Jing-Jing Tao4, Hua Zhang1, Longxian Lv4, Baohong Wang1, Jifang Sheng1, Lanjuan Li5.   

Abstract

Entities:  

Keywords:  COVID-19; intestinal microbiology

Mesh:

Year:  2021        PMID: 33833065      PMCID: PMC8666823          DOI: 10.1136/gutjnl-2021-324090

Source DB:  PubMed          Journal:  Gut        ISSN: 0017-5749            Impact factor:   23.059


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We read with great interest the recent article published in Gut in which Yeoh et al demonstrated that gut microbiota composition of recovered patients with COVID-19 remained significantly distinct from uninfected controls.1 Persisting symptoms, also known as ‘long COVID-19’, have been reported in a significant proportion of patients following hospital discharge.2 3 Gut dysbiosis might link to long COVID-19 risks.1 Few studies have focused on the recovery process of gut microbiota following SARS-CoV-2 infection. Here, we conducted a prospective study to longitudinally monitor alterations of gut microbiota in patients with COVID-19 using 16S rDNA sequencing (detailed methods in online supplementary materials). Faecal microbiota was monitored at three timepoints, acute phase (from illness onset to viral clearance), convalescence (from viral clearance to 2 weeks after hospital discharge), postconvalescence (6 months after hospital discharge). The gut microbiota richness, measured by Chao 1 index, was obviously lower (p<0.01, Wilcoxon rank-sum test; figure 1A) in the acute phase of COVID-19 (median 217, IQR 164–266) as compared with uninfected controls (median 432, IQR 332–468). There was a non-significant increase of the Chao 1 index from the acute phase (median 217, IQR 164–266) to the convalescence (median 241, IQR 202–279) and postconvalescence (median 259, IQR 193–302). A Bray-Curtis based principal coordinated analysis revealed that the overall microbial composition of patients with COVID-19 deviated from the uninfected controls (analysis of similarities, R = – 0.20, p=0.001, figure 1B). There was a tendency of the gut microbiota composition moving toward the controls from the acute phase to recovery phase along the first principal coordinate. Notably, the species richness as estimated by Chao 1 index, can explain the differences along the first principal coordinate (figure 1C).
Figure 1

Changes of faecal microbial communities in different stages (acute, convalescence, postconvalescence) of patients with COVID-19 (n=30), compared with uninfected controls (n=30). (A) α-Diversity, illustrated by microbiota richness (Chao 1 index), was reduced in COVID-19 (p<0.01, Wilcoxon rank-sum test). Boxes represent the 25th–75th percentile of the distribution; the median is shown as a thick line in the middle of the box; whiskers extend to values with 1.5 times the difference between the 25th and 75th percentiles. ***P<0.001. (B) Principal coordinate analysis (PCoA) of Bray-Curtis distance analysis demonstrated that the overall microbial composition of patients with COVID-19 deviated from the uninfected controls (analysis of similarities, R = – 0.201, p=0.001). (C) The same PCoA plot as (B), coloured by α-diversity measured by Chao 1 index.

Changes of faecal microbial communities in different stages (acute, convalescence, postconvalescence) of patients with COVID-19 (n=30), compared with uninfected controls (n=30). (A) α-Diversity, illustrated by microbiota richness (Chao 1 index), was reduced in COVID-19 (p<0.01, Wilcoxon rank-sum test). Boxes represent the 25th–75th percentile of the distribution; the median is shown as a thick line in the middle of the box; whiskers extend to values with 1.5 times the difference between the 25th and 75th percentiles. ***P<0.001. (B) Principal coordinate analysis (PCoA) of Bray-Curtis distance analysis demonstrated that the overall microbial composition of patients with COVID-19 deviated from the uninfected controls (analysis of similarities, R = – 0.201, p=0.001). (C) The same PCoA plot as (B), coloured by α-diversity measured by Chao 1 index. The median Chao 1 index in postconvalescence was 259. Patients were further divided into two subgroups according to their Chao 1 index in postconvalescence: low (≤259, n=15) and high (>259, n=15) (table 1). Patients with reduced postconvalescence richness had higher level of CRP (p=0.01), as well as higher occurrence of intensive care unit admission (p=0.03) and high flow nasal catheter oxygen therapy therapy (p=0.03) during the acute phase. In postconvalescence, low richness was associated with reduced pulmonary function of forced vital capacity (p=0.03), forced expiratory volume in the first 1 s of expiration (p=0.02), inspiratory vital capacity (p=0.05) and total lung capacity (p=0.05).
Table 1

Comparison of clinical characteristics between patients with high or low microbial richness in the recovery phase

All (n=30)Low (n=15)High (n=15)P value
Age, years53.5 (39.75, 59)53 (40, 57.5)53 (37, 59.75)0.72
Male, n (%)19 (63.3%)10 (66.7%)9 (60%)0.70
BMI of acute phase, kg/m2 24.2 (21.6, 25.2)24.9 (22.3, 25.8)23.8 (21.3, 25.0)0.36
BMI of postconvalescence, kg/m2 24.1 (21.1, 26.6)24.7 (22.2, 27.2)23.1 (21.0, 25.3)0.41
Severe illness during hospitalisation, n (%)10 (33.3%)7 (46.7%)3 (20.0%)0.12
White cell count, ×109/L *5.7 (4.1, 8.9)5.7 (4.3, 9.9)6.5 (4.1, 8.0)0.79
Haemoglobin, g/L *138 (128, 149)146 (128, 152)137 (129, 141)0.28
Platelet count, ×109/L *187 (158, 231)188 (168, 238)182 (139, 225)0.53
Neutrophil count, ×109/L *3.9 (2.6, 7.1)4.3 (2.9, 8.7)3.9 (2.5, 6.7)0.75
Lymphocyte count, ×109/L *0.8 (0.6, 1.2)0.7 (0.5, 1.2)0.9 (0.6, 1.1)0.59
D-dimer, mg/L *236 (170, 467)407 (175, 913)199 (170, 320)0.08
CRP, mg/L *10.8 (5.9, 21.5)15.4 (10.5, 45.7)7.5 (2.2, 11.4)‡ 0.01
HFNC during hospitalisation7 (23.3%)6 (40.0%)1 (6.7%)‡ 0.03
ICU admission4 (13.3%)4 (26.7%)0 (0%)‡ 0.03
Duration from illness onset to hospital admission, d6 (4, 9.7)6 (4.5, 10.5)6 (1.75, 7.5)0.55
Duration of viral shedding in respiratory tract, d17.5 (14, 23.7)18 (14, 22.5)17.5 (14.5, 26.25)0.98
Days of hospitalisation, d17 (14.2, 23.7)17 (15, 22)19.5 (13.75, 25.75)0.65
PFTs
FVC95.5 (89, 105)93 (84, 96)101.5 (94.7, 107.2)‡ 0.03
FEV195.5 (86.2, 107)91 (82.5, 97)103 (90.5, 112)‡ 0.02
PEF82 (71.2, 101)77 (71.5, 90)95.5 (75.2, 101)0.41
FEV1/FVC ratio80.25 (74.4, 88.0)80.7 (76.4, 87.9)80.25 (73.8, 88.0)0.84
FEF25%–75%87.5 (65, 120.2)87 (69.5, 101)87 (65, 135)0.63
MEF 75%86 (72.7, 111)80 (74.5, 89.5)104 (77, 111.7)0.55
MEF 50%80.5 (70, 102.7)81 (70, 91)79.5 (71.7, 118.7)0.88
MEF 25%74.5 (57.2, 114)74 (50, 100)73 (58.7, 127.2)0.48
MVV88 (67.2, 103.7)75 (64, 95.5)96 (78.7, 109.2)0.08
DLCO88.5 (78.5, 95)86 (78, 92)94.5 (79.5, 99)0.07
DLCO/VA73 (67, 79.7)73 (67, 83.5)73 (68.2, 79.2)0.82
IVC83.5 (77.2, 92)82 (70.5, 86.5)88.5 (82.5, 93.5)‡ 0.05
TLC96 (91.2, 105.7)92 (84.5, 98.5)98 (96, 106)‡ 0.05
RV120 (106.2, 130.7)123 (102.5, 130.5)120 (106.7, 129)0.90
RV/TLC124.5 (111.2, 142.7)132 (113.5, 150)120.5 (110.7, 133.5)0.23
Exercise capacity
Pre-6WMT heart rate84 (75.7, 91)85 (81.5, 96)82 (74.5, 86.5)0.15
Pre-6WMT systolic blood pressure130.5 (116.2, 142.7)133 (114, 156.5)123 (115.5, 136.2)0.40
Pre-6WMT diastolic blood pressure77.5 (69, 90.7)71 (66, 99)77.5 (70.5, 86.2)0.85
Pre-6WMT O2 saturation, %98 (97, 99)98 (98, 99)98 (97, 99)0.88
6WMT distance, m600 (540, 640)620 (575, 640)560 (515, 654)0.77
Post-6WMT heart rate103.5 (98.2, 113.7)106 (100, 115)101.5 (97.5, 106.75)0.20
Post-6WMT systolic blood pressure129 (122, 142.5)140 (124, 158.5)127.5 (121.2, 132.2)0.06
Post-6WMT diastolic blood pressure80 (70.7, 86)81 (69, 92.5)78.5 (73.7, 84)0.71
Post-6WMT O2 saturation, %98 (97, 98)98 (97, 98)98 (97, 98)0.71

The quantitative data are shown as median data and IQR data in brackets.

The occurrence data are shown as no. (%). Values indicate no. of positive results/total no. of patients with available assay results.

Between-group comparisons of continuous variable in patients with low and high richness were tested by Kruskal-Wallis test. For categorical variable, χ² test test was used for comparison between groups.

Statistically significance with a p value ≤0.05 was marked as bold.

*The results of laboratory test in the acute phase were compared, usually the first day after hospital admission.

†Pulmonary function tests were expressed as per cent of the predicted value.

‡ A p value ≤0.05 was denoted as statistically significant.

MEF 25%, mean expiratory flow at 25%; MEF 50%, mean expiratory flow at 50%; MEF 75%, mean expiratory flow at 75%; BMI, body mass index; CRP, C reactive protein; DLCO, diffusing capacity of the lung for carbon monoxide; DLCO/VA, diffusing capacity divided by the alveolar volume; FEF25%–75%, forced expiratory flow at 25%–75%; FEV1, forced expiratory volume in the first 1 s of expiration; FVC, forced vital capacity; HFNC, high flow nasal catheter oxygen therapy; ICU, intensive care unit; IVC, inspiratory vital capacity; MVV, maximal voluntary ventilation; PEF, peak expiratory flow; PFTs, pulmonary function tests; RV, residual volume; RV/TLC, residual volume divided by the total lung capacity; TLC, total lung capacity; 6WMT, 6 min walk tests.

Comparison of clinical characteristics between patients with high or low microbial richness in the recovery phase The quantitative data are shown as median data and IQR data in brackets. The occurrence data are shown as no. (%). Values indicate no. of positive results/total no. of patients with available assay results. Between-group comparisons of continuous variable in patients with low and high richness were tested by Kruskal-Wallis test. For categorical variable, χ² test test was used for comparison between groups. Statistically significance with a p value ≤0.05 was marked as bold. *The results of laboratory test in the acute phase were compared, usually the first day after hospital admission. †Pulmonary function tests were expressed as per cent of the predicted value. ‡ A p value ≤0.05 was denoted as statistically significant. MEF 25%, mean expiratory flow at 25%; MEF 50%, mean expiratory flow at 50%; MEF 75%, mean expiratory flow at 75%; BMI, body mass index; CRP, C reactive protein; DLCO, diffusing capacity of the lung for carbon monoxide; DLCO/VA, diffusing capacity divided by the alveolar volume; FEF25%–75%, forced expiratory flow at 25%–75%; FEV1, forced expiratory volume in the first 1 s of expiration; FVC, forced vital capacity; HFNC, high flow nasal catheter oxygen therapy; ICU, intensive care unit; IVC, inspiratory vital capacity; MVV, maximal voluntary ventilation; PEF, peak expiratory flow; PFTs, pulmonary function tests; RV, residual volume; RV/TLC, residual volume divided by the total lung capacity; TLC, total lung capacity; 6WMT, 6 min walk tests. The present study found that microbiota richness was not restored to normal levels after 6-month recovery. Patients with lower postconvalescence richness showed higher level of CRP and illness severity during the acute phase, suggesting close correlations between inflammatory response and gut dysbiosis in COVID-19, as illustrated in previous studies.1 4 Microbial diversity is a critical determinant of microbial ecosystem stability.5 Stable ecosystems provide colonisation resistance to opportunistic pathogens.6 Therefore, the persistent reduction of gut microbiota richness may have long-term biological influence during the COVID-19 pandemic.7 Follow-up studies of 3 months and 6 months have shown pulmonary function impairment along with cardiac abnormalities in patients with COVID-19.2 8 The results here indicated that postconvalescence patients with lower microbial richness had worse pulmonary functions. Gut microbiota is implicated in the pathogenesis of acute lung injury via several potential mechanisms, including direct translocation of bacteria from gut to the lung and immune modulation effects of microbes related metabolites.9 10 Our study corroborates the growing evidence that gut dysbiosis is associated with the recovery process of COVID-19. Due to the relatively small sample size, our results need to be confirmed in further studies with larger sample size and more techniques. Targeted manipulation to promote the microbial diversity could be an important strategy to treat long COVID-19 and speed up recovery.
  10 in total

1.  Alterations of the Gut Microbiota in Patients With Coronavirus Disease 2019 or H1N1 Influenza.

Authors:  Silan Gu; Yanfei Chen; Zhengjie Wu; Yunbo Chen; Hainv Gao; Longxian Lv; Feifei Guo; Xuewu Zhang; Rui Luo; Chenjie Huang; Haifeng Lu; Beiwen Zheng; Jiaying Zhang; Ren Yan; Hua Zhang; Huiyong Jiang; Qiaomai Xu; Jing Guo; Yiwen Gong; Lingling Tang; Lanjuan Li
Journal:  Clin Infect Dis       Date:  2020-12-17       Impact factor: 9.079

2.  The hygiene hypothesis, the COVID pandemic, and consequences for the human microbiome.

Authors:  B Brett Finlay; Katherine R Amato; Meghan Azad; Martin J Blaser; Thomas C G Bosch; Hiutung Chu; Maria Gloria Dominguez-Bello; Stanislav Dusko Ehrlich; Eran Elinav; Naama Geva-Zatorsky; Philippe Gros; Karen Guillemin; Frédéric Keck; Tal Korem; Margaret J McFall-Ngai; Melissa K Melby; Mark Nichter; Sven Pettersson; Hendrik Poinar; Tobias Rees; Carolina Tropini; Liping Zhao; Tamara Giles-Vernick
Journal:  Proc Natl Acad Sci U S A       Date:  2021-02-09       Impact factor: 11.205

Review 3.  Microbiota-mediated colonization resistance against intestinal pathogens.

Authors:  Charlie G Buffie; Eric G Pamer
Journal:  Nat Rev Immunol       Date:  2013-10-07       Impact factor: 53.106

4.  Enrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome.

Authors:  Robert P Dickson; Benjamin H Singer; Michael W Newstead; Nicole R Falkowski; John R Erb-Downward; Theodore J Standiford; Gary B Huffnagle
Journal:  Nat Microbiol       Date:  2016-07-18       Impact factor: 17.745

5.  Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19.

Authors:  Yun Kit Yeoh; Tao Zuo; Chun Kwok Wong; Grace Chung-Yan Lui; Fen Zhang; Qin Liu; Amy Yl Li; Arthur Ck Chung; Chun Pan Cheung; Eugene Yk Tso; Kitty Sc Fung; Veronica Chan; Lowell Ling; Gavin Joynt; David Shu-Cheong Hui; Kai Ming Chow; Susanna So Shan Ng; Timothy Chun-Man Li; Rita Wy Ng; Terry Cf Yip; Grace Lai-Hung Wong; Francis Kl Chan; Paul Ks Chan; Siew C Ng
Journal:  Gut       Date:  2021-01-11       Impact factor: 23.059

6.  6-month consequences of COVID-19 in patients discharged from hospital: a cohort study.

Authors:  Chaolin Huang; Lixue Huang; Yeming Wang; Xia Li; Lili Ren; Xiaoying Gu; Liang Kang; Li Guo; Min Liu; Xing Zhou; Jianfeng Luo; Zhenghui Huang; Shengjin Tu; Yue Zhao; Li Chen; Decui Xu; Yanping Li; Caihong Li; Lu Peng; Yong Li; Wuxiang Xie; Dan Cui; Lianhan Shang; Guohui Fan; Jiuyang Xu; Geng Wang; Ying Wang; Jingchuan Zhong; Chen Wang; Jianwei Wang; Dingyu Zhang; Bin Cao
Journal:  Lancet       Date:  2021-01-08       Impact factor: 79.321

7.  Cardiopulmonary recovery after COVID-19: an observational prospective multicentre trial.

Authors:  Thomas Sonnweber; Sabina Sahanic; Alex Pizzini; Anna Luger; Christoph Schwabl; Bettina Sonnweber; Katharina Kurz; Sabine Koppelstätter; David Haschka; Verena Petzer; Anna Boehm; Magdalena Aichner; Piotr Tymoszuk; Daniela Lener; Markus Theurl; Almut Lorsbach-Köhler; Amra Tancevski; Anna Schapfl; Marc Schaber; Richard Hilbe; Manfred Nairz; Bernhard Puchner; Doris Hüttenberger; Christoph Tschurtschenthaler; Malte Aßhoff; Andreas Peer; Frank Hartig; Romuald Bellmann; Michael Joannidis; Can Gollmann-Tepeköylü; Johannes Holfeld; Gudrun Feuchtner; Alexander Egger; Gregor Hoermann; Andrea Schroll; Gernot Fritsche; Sophie Wildner; Rosa Bellmann-Weiler; Rudolf Kirchmair; Raimund Helbok; Helmut Prosch; Dietmar Rieder; Zlatko Trajanoski; Florian Kronenberg; Ewald Wöll; Günter Weiss; Gerlig Widmann; Judith Löffler-Ragg; Ivan Tancevski
Journal:  Eur Respir J       Date:  2021-04-29       Impact factor: 16.671

8.  Tipping elements in the human intestinal ecosystem.

Authors:  Leo Lahti; Jarkko Salojärvi; Anne Salonen; Marten Scheffer; Willem M de Vos
Journal:  Nat Commun       Date:  2014-07-08       Impact factor: 14.919

9.  Acetate Downregulates the Activation of NLRP3 Inflammasomes and Attenuates Lung Injury in Neonatal Mice With Bronchopulmonary Dysplasia.

Authors:  Qian Zhang; Xiao Ran; Yu He; Qing Ai; Yuan Shi
Journal:  Front Pediatr       Date:  2021-02-04       Impact factor: 3.418

  10 in total
  39 in total

Review 1.  Altered gut microbiota patterns in COVID-19: Markers for inflammation and disease severity.

Authors:  Chiranjib Chakraborty; Ashish Ranjan Sharma; Manojit Bhattacharya; Kuldeep Dhama; Sang-Soo Lee
Journal:  World J Gastroenterol       Date:  2022-07-07       Impact factor: 5.374

Review 2.  Hallmarks of Severe COVID-19 Pathogenesis: A Pas de Deux Between Viral and Host Factors.

Authors:  Roberta Rovito; Matteo Augello; Assaf Ben-Haim; Valeria Bono; Antonella d'Arminio Monforte; Giulia Marchetti
Journal:  Front Immunol       Date:  2022-06-10       Impact factor: 8.786

3.  Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up.

Authors:  Guang-Ying Cui; Ben-Chen Rao; Zhao-Hai Zeng; Xue-Mei Wang; Tong Ren; Hai-Yu Wang; Hong Luo; Hong-Yan Ren; Chao Liu; Su-Ying Ding; Jun-Jie Tan; Zhen-Guo Liu; Ya-Wen Zou; Zhi-Gang Ren; Zu-Jiang Yu
Journal:  Mil Med Res       Date:  2022-06-17

Review 4.  Gut microbiota in COVID-19: key microbial changes, potential mechanisms and clinical applications.

Authors:  Raphaela I Lau; Fen Zhang; Qin Liu; Qi Su; Francis K L Chan; Siew C Ng
Journal:  Nat Rev Gastroenterol Hepatol       Date:  2022-10-21       Impact factor: 73.082

5.  Respiratory tract infections and gut microbiome modifications: A systematic review.

Authors:  Claire A Woodall; Luke J McGeoch; Alastair D Hay; Ashley Hammond
Journal:  PLoS One       Date:  2022-01-13       Impact factor: 3.240

6.  Alteration of the gut microbiota following SARS-CoV-2 infection correlates with disease severity in hamsters.

Authors:  Valentin Sencio; Arnaud Machelart; Cyril Robil; Nicolas Benech; Eik Hoffmann; Chloé Galbert; Lucie Deryuter; Séverine Heumel; Aline Hantute-Ghesquier; Anne Flourens; Priscille Brodin; Fabrice Infanti; Virgile Richard; Jean Dubuisson; Corinne Grangette; Thierry Sulpice; Isabelle Wolowczuk; Florence Pinet; Vincent Prévot; Sandrine Belouzard; François Briand; Martine Duterque-Coquillaud; Harry Sokol; François Trottein
Journal:  Gut Microbes       Date:  2022 Jan-Dec

Review 7.  The Neurological Manifestations of Post-Acute Sequelae of SARS-CoV-2 infection.

Authors:  Narges Moghimi; Mario Di Napoli; José Biller; James E Siegler; Rahul Shekhar; Louise D McCullough; Michelle S Harkins; Emily Hong; Danielle A Alaouieh; Gelsomina Mansueto; Afshin A Divani
Journal:  Curr Neurol Neurosci Rep       Date:  2021-06-28       Impact factor: 5.081

8.  The Gut Microbiota of Critically Ill Patients With COVID-19.

Authors:  Paolo Gaibani; Federica D'Amico; Michele Bartoletti; Donatella Lombardo; Simone Rampelli; Giacomo Fornaro; Simona Coladonato; Antonio Siniscalchi; Maria Carla Re; Pierluigi Viale; Patrizia Brigidi; Silvia Turroni; Maddalena Giannella
Journal:  Front Cell Infect Microbiol       Date:  2021-06-29       Impact factor: 5.293

Review 9.  Probiotics as Adjunctive Treatment for Patients Contracted COVID-19: Current Understanding and Future Needs.

Authors:  Jiangying Peng; Meng Zhang; Guoqiang Yao; Lai-Yu Kwok; Wenyi Zhang
Journal:  Front Nutr       Date:  2021-06-10

Review 10.  Role of Probiotics in the Management of COVID-19: A Computational Perspective.

Authors:  Quang Vo Nguyen; Li Chuin Chong; Yan-Yan Hor; Lee-Ching Lew; Irfan A Rather; Sy-Bing Choi
Journal:  Nutrients       Date:  2022-01-10       Impact factor: 6.706

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