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Associations between the gut microbiome and fatigue in cancer patients.

ABSTRACT: Fatigue is the most prevalent symptom of cancer and its treatments. Changes in the intestinal microbiome have been identified in chronic fatigue syndrome and other neuropsychiatric disorders, and cancer patients. However, the association between intestinal microbiome and fatigue in patients with advanced cancers has not been evaluated. Understanding the connection between intestinal microbiome and fatigue will provide interventional and therapeutic opportunities to manipulate the microbiome to improve fatigue and other patients' reported outcomes. In this project, we aimed to identify associations between microbiome composition and fatigue in advanced cancer patients. In this cross-sectional observational study at a tertiary cancer care center, we included 88 patients with advanced, metastatic, unresectable cancers who were in a washout period from chemotherapy. We measured fatigue using the MD Anderson Symptom Inventory-Immunotherapy fatigue score, and used 16srRNA to analyze intestinal microbiome. Using correlation analysis we found that Eubacterium hallii was negatively associated with fatigue severity scores (r?=?-?0.30, p?=?0.005), whereas Cosenzaea was positively associated with fatigue scores (r?=?0.33, p?=?0.0002). We identified microbial species that exhibit distinct composition between high-fatigued and low-fatigued cancer patients. Further studies are warranted to investigate whether modulating the microbiome reduces cancer patients' fatigue severity and improves their quality of life.

SUBMITTER: Hajjar J

PROVIDER: S-EPMC7954807 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Associations between the gut microbiome and fatigue in cancer patients.

Hajjar Joud J Mendoza Tito T Zhang Liangliang L Fu Siqing S Piha-Paul Sarina A SA Hong David S DS Janku Filip F Karp Daniel D DD Ballhausen Alexej A Gong Jing J Zarifa Abdulrazzak A Peterson Christine B CB Meric-Bernstam Funda F Jenq Robert R Naing Aung A

Scientific reports 20210312 1

Fatigue is the most prevalent symptom of cancer and its treatments. Changes in the intestinal microbiome have been identified in chronic fatigue syndrome and other neuropsychiatric disorders, and cancer patients. However, the association between intestinal microbiome and fatigue in patients with advanced cancers has not been evaluated. Understanding the connection between intestinal microbiome and fatigue will provide interventional and therapeutic opportunities to manipulate the microbiome to i ...[more]

PMID: 33712647

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Project description:BackgroundThe infant intestinal microbiome plays an important role in metabolism and immune development with impacts on lifelong health. The linkage between the taxonomic composition of the microbiome and its metabolic phenotype is undefined and complicated by redundancies in the taxon-function relationship within microbial communities. To inform a more mechanistic understanding of the relationship between the microbiome and health, we performed an integrative statistical and machine learning-based analysis of microbe taxonomic structure and metabolic function in order to characterize the taxa-function relationship in early life.ResultsStool samples collected from infants enrolled in the New Hampshire Birth Cohort Study (NHBCS) at approximately 6-weeks (n = 158) and 12-months (n = 282) of age were profiled using targeted and untargeted nuclear magnetic resonance (NMR) spectroscopy as well as DNA sequencing of the V4-V5 hypervariable region from the bacterial 16S rRNA gene. There was significant inter-omic concordance based on Procrustes analysis (6 weeks: p = 0.056; 12 months: p = 0.001), however this association was no longer significant when accounting for phylogenetic relationships using generalized UniFrac distance metric (6 weeks: p = 0.376; 12 months: p = 0.069). Sparse canonical correlation analysis showed significant correlation, as well as identifying sets of microbe/metabolites driving microbiome-metabolome relatedness. Performance of machine learning models varied across different metabolites, with support vector machines (radial basis function kernel) being the consistently top ranked model. However, predictive R2 values demonstrated poor predictive performance across all models assessed (avg: - 5.06% -- 6 weeks; - 3.7% -- 12 months). Conversely, the Spearman correlation metric was higher (avg: 0.344-6 weeks; 0.265-12 months). This demonstrated that taxonomic relative abundance was not predictive of metabolite concentrations.ConclusionsOur results suggest a degree of overall association between taxonomic profiles and metabolite concentrations. However, lack of predictive capacity for stool metabolic signatures reflects, in part, the possible role of functional redundancy in defining the taxa-function relationship in early life as well as the bidirectional nature of the microbiome-metabolome association. Our results provide evidence in favor of a multi-omic approach for microbiome studies, especially those focused on health outcomes.

Project description:BackgroundObservational studies have shown gut microbiomes were associated with cardiovascular diseases (CVDs), but their roles remain controversial, and these associations have not yet been established causally.MethodsTwo-sample Mendelian randomization (MR) was used to investigate whether gut microbiome had a causal effect on the risk of CVDs. To obtain comprehensive results, we performed two sets of MR analyses, one with single nucleotide polymorphisms (SNPs) that smaller than the genome-wide statistical significance threshold (5 × 10-8) as instrumental variables, and the other with SNPs that lower than the locus-wide significance level (1 × 10-5). Summary-level statistics for CVDs, including coronary artery disease (CAD), myocardial infarction, heart failure, atrial fibrillation, stroke and its subtypes were collected. The ME estimation was performed using the inverse-variance weighted and Wald ratio methods. Sensitivity analysis was performed using the weighted median, MR-Egger, leave-one-out analysis, MR pleiotropy residual sum and outlier and MR Steiger.ResultsBased on the locus-wide significance level, genetically predicted genus Oxalobacter was positively associated with the risk of CAD (odds ratio (OR) = 1.06, 95% confidence interval (CI), 1.03 - 1.10, P = 1.67 × 10-4), family Clostridiaceae_1 was negatively correlated with stroke risk (OR = 0.83,95% CI, 0.75-0.93, P = 7.76 × 10-4) and ischemic stroke risk (OR = 0.823,95% CI, 0.74-0.92, P = 4.15 × 10-4). There was no causal relationship between other genetically predicted gut microbiome components and CVDs risk. Based on the genome-wide statistical significance threshold, the results showed that the gut microbiome had no causal relationship with CVDs risk.ConclusionOur findings reveal that there are beneficial or adverse causal effects of gut microbiome components on CVDs risk and provide novel insights into strategies for the prevention and management of CVDs through the gut microbiome.

Project description:The occurrence and development of colorectal cancer (CRC) and advanced adenoma (AA) are closely related to the gut microbiome, and AA has a high cancerization progression rate to CRC. Current studies have revealed that bacteriological analysis cannot identify CRC from AA. The objective was to explore microbial targets that could identify CRC and AA from a microecological perspective and to figure out the best way to identify CRC based on fecal microbes. The metagenomic sequencing data were used to describe the gut microbiome profile and analyze the differences between microbial abundance and microbial single nucleotide polymorphism (SNP) characteristics in AA and CRC patients. It was found that there were no significant differences in the diversity between the two groups. The abundance of bacteria (e.g., Firmicutes, Clostridia, and Blautia), fungi (Hypocreales), archaea (Methanosarcina, Methanoculleus, and Methanolacinia), and viruses (Alphacoronavirus, Sinsheimervirus, and Gammaretrovirus) differed between AA and CRC patients. Multiple machine-learning algorithms were used to establish prediction models, aiming to identify CRC and AA. The accuracy of the random forest (RF) model based on the gut microbiome was 86.54%. Nevertheless, the accuracy of SNP was 92.31% in identifying CRC from AA. In conclusion, using microbial SNP was the best method to identify CRC, it was superior to using the gut microbiome, and it could provide new targets for CRC screening. IMPORTANCE There are differences in characteristic microorganisms between AA and CRC. However, current studies have indicated that bacteriological analysis cannot identify CC from AA, and thus, we wondered if there were some other targets that could be used to identify CRC from AA in the gut microbiome. The differences of SNPs in the gut microbiota of intraindividuals were significantly smaller than those of interindividuals. In addition, compared with intestinal microbes, SNP was less affected by time with certain stability. It was discovered that microbial SNP was better than the gut microbiome for identifying CRC from AA. Therefore, screening characteristic microbial SNP could provide a new research direction for identifying CRC from AA.

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Associations between the gut microbiome and fatigue in cancer patients.

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Associations between the gut microbiome and fatigue in cancer patients.

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