Fecal, Duodenal and Tumor Microbiota Composition of Esophageal Carcinoma Patients, a Longitudinal Prospective Cohort.

BACKGROUND: The microbiome has been associated with chemotherapy and immune checkpoint inhibitor (ICI) efficacy. How this pertains to resectable esophageal carcinoma (EC) is unknown. Our aim was to identify microbial signatures in resectable EC associated with response to neoadjuvant chemoradiotherapy (nCRT) with or without ICI. METHODS: From two prospectively collected EC cohorts (n = 172 in total) treated with nCRT alone (n = 132) or a combination of nCRT and ICI (n = 40), fecal samples were available at baseline, during treatment, and pre-surgery. Additionally, in the ICI treated patients, tumor and duodenal snap frozen biopsies were collected over time. Fecal, tumor and duodenal DNA were extracted for 16S rRNA sequencing. Associations were investigated between microbiome composition pathological complete response (pCR) and progression-free survival (PFS). RESULTS: There was a significant shift in the microbiota profile of the fecal, tumor and duodenal microbiota over time. In the total cohort, patients with a pCR had a stable fecal alpha diversity, while the diversity of poor responders decreased during treatment, p = 0.036. Pre-surgery, lower alpha diversity (<4.12) was related to worse PFS, log-rank p = 0.025. Baseline tumor biopsies of patients with short PFS had more Fusobacterium. A low baseline duodenal alpha diversity (<3.96) was associated with worse PFS, log-rank p = 0.012. CONCLUSIONS: Lower intestinal alpha diversity was associated with worse response and survival of EC patients. In tumor biopsies Fusobacterium was more abundant in patients with poor PFS. After further mechanistic validation, these findings may aid in response prediction and the design of novel microbiome modulating treatments for EC patients.

Novel Proteome Targets Marking Insulin Resistance in Metabolic Syndrome.

CONTEXT/OBJECTIVE: In order to better understand which metabolic differences are related to insulin resistance in metabolic syndrome (MetSyn), we used hyperinsulinemic-euglycemic (HE) clamps in individuals with MetSyn and related peripheral insulin resistance to circulating biomarkers. DESIGN/METHODS: In this cross-sectional study, HE-clamps were performed in treatment-naive men (n = 97) with MetSyn. Subjects were defined as insulin-resistant based on the rate of disappearance (Rd). Machine learning models and conventional statistics were used to identify biomarkers of insulin resistance. Findings were replicated in a cohort with n = 282 obese men and women with (n = 156) and without (n = 126) MetSyn. In addition to this, the relation between biomarkers and adipose tissue was assessed by nuclear magnetic resonance imaging. RESULTS: Peripheral insulin resistance is marked by changes in proteins related to inflammatory processes such as IL-1 and TNF-receptor and superfamily members. These proteins can distinguish between insulin-resistant and insulin-sensitive individuals (AUC = 0.72 ± 0.10) with MetSyn. These proteins were also associated with IFG, liver fat (rho 0.36, p = 1.79 × 10-9) and visceral adipose tissue (rho = 0.35, p = 6.80 × 10-9). Interestingly, these proteins had the strongest association in the MetSyn subgroup compared to individuals without MetSyn. CONCLUSIONS: MetSyn associated with insulin resistance is characterized by protein changes related to body fat content, insulin signaling and pro-inflammatory processes. These findings provide novel targets for intervention studies and should be the focus of future in vitro and in vivo studies.

Metabolite Profile of Treatment-Naive Metabolic Syndrome Subjects in Relation to Cardiovascular Disease Risk.

Metabolic syndrome (MetSyn) is an important risk factor for type 2 diabetes and cardiovascular diseases (CVD). This study aimed to find distinct plasma metabolite profiles between insulin-resistant and non-insulin resistant subjects with MetSyn and evaluate if MetSyn metabolite profiles are related to CVD risk and lipid fluxes. In a cross-sectional study, untargeted metabolomics of treatment-naive males with MetSyn (n = 132) were analyzed together with clinical parameters. In a subset of MetSyn participants, CVD risk was calculated using the Framingham score (n = 111), and lipolysis (n = 39) was measured by a two-step hyperinsulinemic euglycemic clamp using [1,1,2,3,3-2H5] glycerol to calculate lipolysis suppression rates. Peripheral insulin resistance was related to fatty acid metabolism and glycerolphosphorylcholine. Interestingly, although insulin resistance is considered to be a risk factor for CVD, we observed that there was little correspondence between metabolites associated with insulin resistance and metabolites associated with CVD risk. The latter mainly belonged to the androgenic steroid, fatty acid, phosphatidylethanolamine, and phophatidylcholine pathways. These data provide new insights into metabolic changes in mild MetSyn pathophysiology and MetSyn CVD risk related to lipid metabolism. Prospective studies may focus on the pathophysiological role of the here-identified biomarkers.

Fecal Microbiota Transplantation from Overweight or Obese Donors in Cachectic Patients with Advanced Gastroesophageal Cancer: A Randomized, Double-blind, Placebo-Controlled, Phase II Study.

PURPOSE: Cachexia is a multifactorial syndrome, associated with poor survival in patients with cancer, and is influenced by the gut microbiota. We investigated the effects of fecal microbiota transplantation (FMT) on cachexia and treatment response in patients with advanced gastroesophageal cancer. EXPERIMENTAL DESIGN: In a double-blind randomized placebo-controlled trial performed in the Amsterdam University Medical Center, we assigned 24 cachectic patients with metastatic HER2-negative gastroesophageal cancer to either allogenic FMT (healthy obese donor) or autologous FMT, prior to palliative chemotherapy (capecitabine and oxaliplatin). Primary objective was to assess the effect of allogenic FMT on satiety. Secondary outcomes were other features of cachexia, along with disease control rate (DCR), overall survival (OS), progression-free survival (PFS), and toxicity. Finally, exploratory analyses were performed on the effect of FMT on gut microbiota composition (metagenomic sequencing) and metabolites (untargeted metabolomics). RESULTS: Allogenic FMT did not improve any of the cachexia outcomes. Patients in the allogenic group (n = 12) had a higher DCR at 12 weeks (P = 0.035) compared with the autologous group (n = 12), longer median OS of 365 versus 227 days [HR = 0.38; 95% confidence interval (CI), 0.14-1.05; P = 0.057] and PFS of 204 versus 93 days (HR = 0.50; 95% CI, 0.21-1.20; P = 0.092). Patients in the allogenic group showed a significant shift in fecal microbiota composition after FMT (P = 0.010) indicating proper engraftment of the donor microbiota. CONCLUSIONS: FMT from a healthy obese donor prior to first-line chemotherapy did not affect cachexia, but may have improved response and survival in patients with metastatic gastroesophageal cancer. These results provide a rational for larger FMT trials.

Neoadjuvant Chemoradiotherapy Combined with Atezolizumab for Resectable Esophageal Adenocarcinoma: A Single-arm Phase II Feasibility Trial (PERFECT).

PURPOSE: The CROSS trial established neoadjuvant chemoradiotherapy (nCRT) for patients with resectable esophageal adenocarcinoma (rEAC). In the PERFECT trial, we investigated the feasibility and efficacy of nCRT combined with programmed-death ligand-1 (PD-L1) inhibition for rEAC. PATIENTS AND METHODS: Patients with rEAC received nCRT according to the CROSS regimen combined with five cycles of atezolizumab (1,200 mg). The primary endpoint was the feasibility of administering five cycles of atezolizumab in ≥75% patients. A propensity score-matched nCRT cohort was used to compare pathologic response, overall survival, and progression-free survival. Exploratory biomarker analysis was performed on repeated tumor biopsies. RESULTS: We enrolled 40 patients of whom 85% received all cycles of atezolizumab. Immune-related adverse events of any grade were observed in 6 patients. In total, 83% proceeded to surgery. Reasons for not undergoing surgery were progression (n = 4), patient choice (n = 2), and death (n = 1). The pathologic complete response rate was 25% (10/40). No statistically significant difference in response or survival was found between the PERFECT and the nCRT cohort. Baseline expression of an established IFNγ signature was higher in responders compared with nonresponders (P = 0.043). On-treatment nonresponders showed either a high number of cytotoxic lymphocytes (CTL) with a transcriptional signature consistent with expression of immune checkpoints, or a low number of CTLs. CONCLUSIONS: Combining nCRT with atezolizumab is feasible in patients with rEAC. On the basis of our exploratory biomarker study, future studies are necessary to elucidate the potential of neoadjuvant immunotherapy in patient subgroups.See related commentary by Catenacci, p. 3269.

Plasma Metabolites Related to Peripheral and Hepatic Insulin Sensitivity Are Not Directly Linked to Gut Microbiota Composition.

Plasma metabolites affect a range of metabolic functions in humans, including insulin sensitivity (IS). A subset of these plasma metabolites is modified by the gut microbiota. To identify potential microbial-metabolite pathways involved in IS, we investigated the link between plasma metabolites, gut microbiota composition, and IS, using the gold-standard for peripheral and hepatic IS measurement in a group of participants with metabolic syndrome (MetSyn). In a cross-sectional study with 115 MetSyn participants, fasting plasma samples were collected for untargeted metabolomics analysis and fecal samples for 16S rRNA gene amplicon sequencing. A two-step hyperinsulinemic euglycemic clamp was performed to assess peripheral and hepatic IS. Collected data were integrated and potential interdependence between metabolites, gut microbiota, and IS was analyzed using machine learning prediction models. Plasma metabolites explained 13.2% and 16.7% of variance in peripheral and hepatic IS, respectively. Fecal microbiota composition explained 4.2% of variance in peripheral IS and was not related to hepatic IS. Although metabolites could partially explain the variances in IS, the top metabolites related to peripheral and hepatic IS did not significantly correlate with gut microbiota composition (both on taxonomical level and alpha-diversity). However, all plasma metabolites could explain 18.5% of the variance in microbial alpha-diversity (Shannon); the top 20 metabolites could even explain 44.5% of gut microbial alpha-diversity. In conclusion, plasma metabolites could partially explain the variance in peripheral and hepatic IS; however, these metabolites were not directly linked to the gut microbiota composition, underscoring the intricate relation between plasma metabolites, the gut microbiota, and IS in MetSyn.

Oral vancomycin treatment does not alter markers of postprandial inflammation in lean and obese subjects.

Intake of a high-fat meal induces a systemic inflammatory response in the postprandial which is augmented in obese subjects. However, the underlying mechanisms of this response have not been fully elucidated. We aimed to assess the effect of gut microbiota modulation on postprandial inflammatory response in lean and obese subjects. Ten lean and ten obese subjects with metabolic syndrome received oral vancomycin 500 mg four times per day for 7 days. Oral high-fat meal tests (50 g fat/m2 body surface area) were performed before and after vancomycin intervention. Gut microbiota composition, leukocyte counts, plasma lipopolysaccharides (LPS), LPS-binding protein (LBP), IL-6 and MCP-1 concentrations and monocyte CCR2 and cytokine expression were determined before and after the high-fat meal. Oral vancomycin treatment resulted in profound changes in gut microbiota composition and significantly decreased bacterial diversity in both groups (phylogenetic diversity pre- versus post-intervention: lean, 56.9 ± 7.8 vs. 21.4 ± 6.6, P < 0.001; obese, 53.9 ± 7.8 vs. 21.0 ± 5.9, P < 0.001). After intervention, fasting plasma LPS significantly increased (lean, median [IQR] 0.81 [0.63-1.45] EU/mL vs. 2.23 [1.33-3.83] EU/mL, P = 0.017; obese, median [IQR] 0.76 [0.45-1.03] EU/mL vs. 1.44 [1.11-4.24], P = 0.014). However, postprandial increases in leukocytes and plasma LPS were unaffected by vancomycin in both groups. Moreover, we found no changes in plasma LBP, IL-6 and MCP-1 or in monocyte CCR2 expression. Despite major vancomycin-induced disruption of the gut microbiota and increased fasting plasma LPS, the postprandial inflammatory phenotype in lean and obese subjects was unaffected in this study.

Iloprost infusion prevents the insulin-induced reduction in skeletal muscle microvascular blood volume but does not enhance peripheral glucose uptake in type 2 diabetic patients.

AIMS: In type 2 diabetes impaired insulin-induced muscle perfusion is thought to contribute to reduced whole-body glucose uptake. In this study, we examined the effects of iloprost, a stable prostacyclin analogue, on insulin-induced muscle capillary recruitment and whole-body glucose uptake. MATERIALS AND METHODS: In a randomized cross-over design, 12 type 2 diabetes patients (age, 55 [46-69] years; BMI, 33.1 [31.0-39] kg/m2 ) underwent two hyperinsulinaemic-euglycaemic clamps, one with and one without simultaneous low-dose iloprost infusion. Contrast-enhanced ultrasonography of the vastus lateralis muscle was performed before and during the clamp. Muscle capillary recruitment was calculated as percentage change in microvascular blood volume (MBV) before and during the clamp. RESULTS: Insulin infusion reduced skeletal muscle MBV by ~50% compared to the fasting state (fasting, 1.77·10-4 [1.54·10-5 -2.44·10-3 ] arbitrary units (AU); hyperinsulinaemia, 6.69·10-5 [2.68·10-6 -5.72·10-4 ] AU; P = 0.050). Infusion of iloprost prevented this insulin-induced skeletal muscle capillary derecruitment, from (-49.5 [-89.5 to 55.3] %) to (8.0 [-68.8 to 306.6] %), for conditions without and with iloprost, respectively. The rate of glucose disappearance (Rd ) did not change significantly during iloprost infusion (17.3 [10.0-40.8] µmol/kg/min) compared with insulin infusion alone (17.6 [9.9-68.7] µmol/kg/min). CONCLUSIONS: Our data suggest that acute improvement in insulin-stimulated muscle perfusion is not an attractive therapeutic approach to bypass cellular resistance to glucose uptake in type 2 diabetes. Whether long-term improvements in insulin-induced muscle perfusion may prove beneficial for glucose disposal remains to be determined.

Gut microbiota, metabolism and psychopathology: A critical review and novel perspectives.

Psychiatric disorders are often associated with metabolic comorbidities. However, the mechanisms through which metabolic and psychiatric disorders are connected remain unclear. Pre-clinical studies in rodents indicate that the bidirectional signaling between the intestine and the brain, the so-called microbiome-gut-brain axis, plays an important role in the regulation of both metabolism and behavior. The gut microbiome produces a vast number of metabolites that may be transported into the host and play a part in homeostatic control of metabolism as well as brain function. In addition to short chain fatty acids, many of these metabolites have been identified in recent years. To what extent both microbiota and their products control human metabolism and behavior is a subject of intense investigation. In this review, we will discuss the most recent findings concerning alterations in the gut microbiota as a possible pathophysiological factor for the co-occurrence of metabolic comorbidities in psychiatric disorders.

Gut Microbiota and the Gut-Brain Axis: New Insights in the Pathophysiology of Metabolic Syndrome.

OBJECTIVE: Emerging preclinical evidence has shown that the bidirectional signaling between the gastrointestinal (GI) tract and the brain, the so-called gut-brain axis, plays an important role in both host metabolism and behavior. In this review, we discuss the potential mechanisms of the brain-gut axis in relation to the pathophysiology of metabolic syndrome. METHODS: A selective literature review was conducted to evaluate GI and brain interactions. RESULTS: Evidence suggests reduced microbial diversity in obesity and metabolic dysregulation. However, findings of microbiota composition in obese individuals are inconsistent, and the investigation of causality between gut microbiota and energy homeostasis is complex because multiple variables contribute to the gut microbiota composition. The microbial metabolites short chain fatty acids are found to exert numerous physiologic effects, including energy homeostasis through the regulation of GI hormones such as cholecystokinin, glucagon-like peptide 1, peptide tyrosine-tyrosine, and leptin. Preclinical studies show that modifying rodents' microbiota through fecal transplantation results in alterations of these GI hormones and subsequently an altered metabolism and behavior. However, whether and to what extent preclinical findings translate to human metabolism is unclear. CONCLUSIONS: One of the major limitations and challenges in this field of research is interindividual variability of the microbiome. Future research needs to combine recent insights gained into tracking the dynamics of the microbiome as well as the metabolic responses. Furthermore, advanced mapping of the human microbiome is required to investigate the metabolic implications of the gut-brain axis to develop targeted interventions for obesity and metabolic syndrome.

The intestinal microbiota, energy balance, and malnutrition: emphasis on the role of short-chain fatty acids.

INTRODUCTION: Malnutrition refers to both over- and undernutrition and results from a disruption in energy balance. It affects one in three people worldwide and is associated with increased morbidity and mortality. The intestinal microbiota represents a newly identified factor that might contribute to the development of malnutrition, as it harbors traits that complement the human metabolic and endocrine capabilities, thereby influencing energy balance. Areas covered: In the current review, we aim to give a comprehensive overview on the microbiota, its development and its possible influence on energy balance, with emphasis the role of short-chain fatty acids. We also consider microbial characteristics associated with obesity and undernutrition and evaluate microbial manipulating strategies. The PubMed database was searched using the terms: 'gastrointestinal microbiota', 'volatile fatty acids', 'malnutrition', 'undernutrition', 'obesity', 'insulin resistance', 'prebiotics', 'probiotics', 'antibiotics' and 'fecal microbiota transplantation'. Expert commentary: Microbiota make important contributions to the regulation of energy balance, whereas microbial disturbances might predispose to malnutrition. If we manage to manipulate the microbiota to our benefit, it could lead to preventive or therapeutic strategies targeting malnutrition.

Gut Microbiota in Obesity and Undernutrition.

Malnutrition is the result of an inadequate balance between energy intake and energy expenditure that ultimately leads to either obesity or undernutrition. Several factors are associated with the onset and preservation of malnutrition. One of these factors is the gut microbiota, which has been recognized as an important pathophysiologic factor in the development and sustainment of malnutrition. However, to our knowledge, the extent to which the microbiota influences malnutrition has yet to be elucidated. In this review, we summarize the mechanisms via which the gut microbiota may influence energy homeostasis in relation to malnutrition. In addition, we discuss potential therapeutic modalities to ameliorate obesity or undernutrition.