Modification of the gut microbiome in an attempt to reduce the risk of child disease: Clinical data from prenatal interventions

3095 Background: The gut microbiome (GM) plays an important role in shaping systemic immune responses. Preclinical and clinical data suggest that GM influences anti-PD-1/PD-L1 or -CTLA-4 Antibody (Ab)-mediated anti-cancer responses. Furthermore, there is strong evidence that antibiotics (ATB) worsen clinical outcomes based on multiple retrospective and one prospective studies using immune checkpoint inhibitor (ICI). However, whether GM profiling, at baseline or post-ATB, could represent a biomarker of response in advanced non-small cell lung cancer (NSCLC) during ICI therapy remains unknown. Methods: We prospectively collected baseline (pre-ICI) fecal samples and clinical data Japanese patients (pts) with NSCLC treated with anti-PD-1/PD-L1 Abs in first or second-line therapy. We performed a 16S rRNA V3-V4 sequencing of gene amplicons of fecal microbes. Amplicon sequence variants were generated with dada2 R package. Diversity analysis was performed with phyloseq R. Differential abundance analysis was performed with both LEfSe and DESeq2 methods. Clinical endpoints were progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and immune-related adverse events (irAE). Results: 70 fecal samples were analyzed. Median OS and PFS in all patients were 16.1 and 5.2 months, respectively. 16 pts (23%) were exposed to ATB 1 month prior to ICI initiation. Pts on ATB had lower α-diversity at baseline and underrepresentation of Clostridiales and Ruminococcaceae UCG 13. When analyzing ATB-free pts, lower α-diversity was observed in non-responders. In addition, Ruminococcaceae UCG 13 was enriched in patients with OS > 12 months, favorable ORR, and PFS > 6 months. Clostridiales order was also enriched in patients with OS > 12 months. Compositional GM differences were also observed between the patients who experienced clinically significant (≥grade 2) irAE; Lactobacillaceae and Raoultella were enriched in pts who had no significant irAE. Conclusions: We demonstrated the negative influence of ATB on GM composition and identified differential bacteria repertoire in pts experiencing favorable clinical outcomes or low grade irAE. Our data pave the way to the development of diagnosis tools aimed at identifying gut dysbiosis to predict resistance or irAE during ICI for NSCLC.

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Bacterial Taxa and Functions Are Predictive of Sustained Remission Following Exclusive Enteral Nutrition in Pediatric Crohn’s Disease

Inflammatory Bowel Diseases ◽

10.1093/ibd/izaa001 ◽

2020 ◽

Vol 26 (7) ◽

pp. 1026-1037 ◽

Cited By ~ 6

Author(s):

Casey M A Jones ◽

Jessica Connors ◽

Katherine A Dunn ◽

Joseph P Bielawski ◽

André M Comeau ◽

...

Keyword(s):

Machine Learning ◽

Crohn’S Disease ◽

Species Richness ◽

Crohn's Disease ◽

Enteral Nutrition ◽

Treatment Response ◽

Clinical Data ◽

Gut Microbiome ◽

Sustained Remission ◽

Exclusive Enteral Nutrition

Abstract Background The gut microbiome is extensively involved in induction of remission in pediatric Crohn’s disease (CD) patients by exclusive enteral nutrition (EEN). In this follow-up study of pediatric CD patients undergoing treatment with EEN, we employ machine learning models trained on baseline gut microbiome data to distinguish patients who achieved and sustained remission (SR) from those who did not achieve remission nor relapse (non-SR) by 24 weeks. Methods A total of 139 fecal samples were obtained from 22 patients (8–15 years of age) for up to 96 weeks. Gut microbiome taxonomy was assessed by 16S rRNA gene sequencing, and functional capacity was assessed by metagenomic sequencing. We used standard metrics of diversity and taxonomy to quantify differences between SR and non-SR patients and to associate gut microbial shifts with fecal calprotectin (FCP), and disease severity as defined by weighted Pediatric Crohn’s Disease Activity Index. We used microbial data sets in addition to clinical metadata in random forests (RFs) models to classify treatment response and predict FCP levels. Results Microbial diversity did not change after EEN, but species richness was lower in low-FCP samples (<250 µg/g). An RF model using microbial abundances, species richness, and Paris disease classification was the best at classifying treatment response (area under the curve [AUC] = 0.9). KEGG Pathways also significantly classified treatment response with the addition of the same clinical data (AUC = 0.8). Top features of the RF model are consistent with previously identified IBD taxa, such as Ruminococcaceae and Ruminococcus gnavus. Conclusions Our machine learning approach is able to distinguish SR and non-SR samples using baseline microbiome and clinical data.

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Microbiome in Multiple Sclerosis: Where Are We, What We Know and Do Not Know

Brain Sciences ◽

10.3390/brainsci10040234 ◽

2020 ◽

Vol 10 (4) ◽

pp. 234

Author(s):

Marina Kleopatra Boziki ◽

Evangelia Kesidou ◽

Paschalis Theotokis ◽

Alexios-Fotios A. Mentis ◽

Eleni Karafoulidou ◽

...

Keyword(s):

Multiple Sclerosis ◽

Central Nervous System ◽

Nervous System ◽

Environmental Factors ◽

Clinical Data ◽

Gut Microbiome ◽

Review Article ◽

The Central Nervous System ◽

Disease Modifying ◽

Research Studies

An increase of multiple sclerosis (MS) incidence has been reported during the last decade, and this may be connected to environmental factors. This review article aims to encapsulate the current advances targeting the study of the gut–brain axis, which mediates the communication between the central nervous system and the gut microbiome. Clinical data arising from many research studies, which have assessed the effects of administered disease-modifying treatments in MS patients to the gut microbiome, are also recapitulated.

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119 The influence of some clinical data in patients with acute myocardial infarction on heart rate turbulence parameters

EP Europace ◽

10.1016/s1099-5129(05)80178-3 ◽

2005 ◽

Vol 7 ◽

pp. 61-61

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Heart Rate ◽

Clinical Data ◽

Heart Rate Turbulence ◽

Turbulence Parameters

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Abstract # P-13: Characteristics of Korean T2DM Patients with Diabetic Retinopathy and Macular Edema: A Study Based on a Standardized Clinical Data

Endocrine Practice ◽

10.1016/s1530-891x(20)43826-1 ◽

2016 ◽

Vol 22 ◽

pp. 19-20

Author(s):

Sang Youl Rhee ◽

Sejeong Park ◽

Ki Young Kim ◽

Suk Chon ◽

Seung-Young Yu ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Macular Edema ◽

Clinical Data

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Folate and vitamin B-12 deficiencies additively impaired memory function and disturbed the gut microbiota in amyloid-β infused rats

International Journal for Vitamin and Nutrition Research ◽

10.1024/0300-9831/a000624 ◽

2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Sunmin Park ◽

Sunna Kang ◽

Da Sol Kim

Keyword(s):

Insulin Resistance ◽

Gut Microbiota ◽

Insulin Signaling ◽

Gut Microbiome ◽

Metabolic Diseases ◽

Memory Function ◽

Amyloid Β ◽

Impaired Memory ◽

Ca1 Region ◽

Folate And Vitamin B12

Abstract. Folate and vitamin B12(V-B12) deficiencies are associated with metabolic diseases that may impair memory function. We hypothesized that folate and V-B12 may differently alter mild cognitive impairment, glucose metabolism, and inflammation by modulating the gut microbiome in rats with Alzheimer’s disease (AD)-like dementia. The hypothesis was examined in hippocampal amyloid-β infused rats, and its mechanism was explored. Rats that received an amyloid-β(25–35) infusion into the CA1 region of the hippocampus were fed either control(2.5 mg folate plus 25 μg V-B12/kg diet; AD-CON, n = 10), no folate(0 folate plus 25 μg V-B12/kg diet; AD-FA, n = 10), no V-B12(2.5 mg folate plus 0 μg V-B12/kg diet; AD-V-B12, n = 10), or no folate plus no V-B12(0 mg folate plus 0 μg V-B12/kg diet; AD-FAB12, n = 10) in high-fat diets for 8 weeks. AD-FA and AD-VB12 exacerbated bone mineral loss in the lumbar spine and femur whereas AD-FA lowered lean body mass in the hip compared to AD-CON(P < 0.05). Only AD-FAB12 exacerbated memory impairment by 1.3 and 1.4 folds, respectively, as measured by passive avoidance and water maze tests, compared to AD-CON(P < 0.01). Hippocampal insulin signaling and neuroinflammation were attenuated in AD-CON compared to Non-AD-CON. AD-FAB12 impaired the signaling (pAkt→pGSK-3β) and serum TNF-α and IL-1β levels the most among all groups. AD-CON decreased glucose tolerance by increasing insulin resistance compared to Non-AD-CON. AD-VB12 and AD-FAB12 increased insulin resistance by 1.2 and 1.3 folds, respectively, compared to the AD-CON. AD-CON and Non-AD-CON had a separate communities of gut microbiota. The relative counts of Bacteroidia were lower and those of Clostridia were higher in AD-CON than Non-AD-CON. AD-FA, but not V-B12, separated the gut microbiome community compared to AD-CON and AD-VB12(P = 0.009). In conclusion, folate and B-12 deficiencies impaired memory function by impairing hippocampal insulin signaling and gut microbiota in AD rats.

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