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1.
BMC Med ; 18(1): 281, 2020 10 21.
Article in English | MEDLINE | ID: mdl-33081767

ABSTRACT

BACKGROUND: Adjuvant chemotherapy induces weight gain, glucose intolerance, and hypertension in about a third of women. The mechanisms underlying these events have not been defined. This study assessed the association between the microbiome and weight gain in patients treated with adjuvant chemotherapy for breast and gynecological cancers. METHODS: Patients were recruited before starting adjuvant therapy. Weight and height were measured before treatment and 4-6 weeks after treatment completion. Weight gain was defined as an increase of 3% or more in body weight. A stool sample was collected before treatment, and 16S rRNA gene sequencing was performed. Data regarding oncological therapy, menopausal status, and antibiotic use was prospectively collected. Patients were excluded if they were treated by antibiotics during the study. Fecal transplant experiments from patients were conducted using Swiss Webster germ-free mice. RESULTS: Thirty-three patients were recruited; of them, 9 gained 3.5-10.6% of baseline weight. The pretreatment microbiome of women who gained weight following treatment was significantly different in diversity and taxonomy from that of control women. Fecal microbiota transplantation from pretreatment samples of patients that gained weight induced metabolic changes in germ-free mice compared to mice transplanted with pretreatment fecal samples from the control women. CONCLUSION: The microbiome composition is predictive of weight gain following adjuvant chemotherapy and induces adverse metabolic changes in germ-free mice, suggesting it contributes to adverse metabolic changes seen in patients. Confirmation of these results in a larger patient cohort is warranted.


Subject(s)
Breast Neoplasms/complications , Chemotherapy, Adjuvant/adverse effects , Gastrointestinal Microbiome/genetics , Genital Neoplasms, Female/complications , Weight Gain/drug effects , Adolescent , Adult , Aged , Animals , Breast Neoplasms/drug therapy , Cohort Studies , Female , Genital Neoplasms, Female/drug therapy , Humans , Mice , Middle Aged , Young Adult
2.
Genome Med ; 12(1): 92, 2020 10 27.
Article in English | MEDLINE | ID: mdl-33109272

ABSTRACT

BACKGROUND: Multiple studies suggest a key role for gut microbiota in IgE-mediated food allergy (FA) development, but to date, none has studied it in the persistent state. METHODS: To characterize the gut microbiota composition and short-chain fatty acid (SCFAs) profiles associated with major food allergy groups, we recruited 233 patients with FA including milk (N = 66), sesame (N = 38), peanut (N = 71), and tree nuts (N = 58), and non-allergic controls (N = 58). DNA was isolated from fecal samples, and 16S rRNA gene sequences were analyzed. SCFAs in stool were analyzed from patients with a single allergy (N = 84) and controls (N = 31). RESULTS: The gut microbiota composition of allergic patients was significantly different compared to age-matched controls both in α-diversity and ß-diversity. Distinct microbial signatures were noted for FA to different foods. Prevotella copri (P. copri) was the most overrepresented species in non-allergic controls. SCFAs levels were significantly higher in the non-allergic compared to the FA groups, whereas P. copri significantly correlated with all three SCFAs. We used these microbial differences to distinguish between FA patients and non-allergic healthy controls with an area under the curve of 0.90, and for the classification of FA patients according to their FA types using a supervised learning algorithm. Bacteroides and P. copri were identified as taxa potentially contributing to KEGG acetate-related pathways enriched in non-allergic compared to FA. In addition, overall pathway dissimilarities were found among different FAs. CONCLUSIONS: Our results demonstrate a link between IgE-mediated FA and the composition and metabolic activity of the gut microbiota.


Subject(s)
Disease Susceptibility , Food Hypersensitivity/etiology , Immunoglobulin E/immunology , Microbiota , Aged , Aged, 80 and over , Biomarkers , Fatty Acids, Volatile/metabolism , Female , Food Hypersensitivity/metabolism , Gastrointestinal Microbiome , Humans , Machine Learning , Male , Microbiota/immunology , Middle Aged , Probiotics , RNA, Ribosomal, 16S/genetics
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