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The COVID-19 pandemic caused by SARS-CoV-2 has led to a wide range of clinical presentations, with respiratory symptoms being common. However, emerging evidence suggests that the gastrointestinal (GI) tract is also affected, with angiotensin-converting enzyme 2, a key receptor for SARS-CoV-2, abundantly expressed in the ileum and colon. The virus has been detected in GI tissues and fecal samples, even in cases with negative results of the reverse transcription polymerase chain reaction in the respiratory tract. GI symptoms have been associated with an increased risk of ICU admission and mortality. The gut microbiome, a complex ecosystem of around 40 trillion bacteria, plays a crucial role in immunological and metabolic pathways. Dysbiosis of the gut microbiota, characterized by a loss of beneficial microbes and decreased microbial diversity, has been observed in COVID-19 patients, potentially contributing to disease severity. We conducted a comprehensive gut microbiome study in 204 hospitalized COVID-19 patients using both shallow and deep shotgun sequencing methods. We aimed to track microbiota composition changes induced by hospitalization, link these alterations to clinical procedures (antibiotics administration) and outcomes (ICU referral, survival), and assess the predictive potential of the gut microbiome for COVID-19 prognosis. Shallow shotgun sequencing was evaluated as a cost-effective diagnostic alternative for clinical settings. Our study demonstrated the diverse effects of various combinations of clinical parameters, microbiome profiles, and patient metadata on the precision of outcome prognostication in patients. It indicates that microbiological data possesses greater reliability in forecasting patient outcomes when contrasted with clinical data or metadata. Furthermore, we established that shallow shotgun sequencing presents a viable and cost-effective diagnostic alternative to deep sequencing within clinical environments.

Relationship between drugs and microbiota is bilateral. Proper composition thus function of microbiota is a key to some medications used in modern medicine. However, there is also the other side of the coin. Pharmacotherapeutic agents can modify the microbiota significantly, which consequently affects its function. A recently published study showed that nearly 25% of drugs administered to humans have antimicrobial effects. Multiple antidepressants are antimicrobials,. and antibiotics with proven antidepressant effects do exist. On the other hand, antibiotics (e.g., isoniaside, minocycline) confer mental phenotype changes, and adverse effects caused by some antibiotics include neurological and psychological symptoms which further supports the hypothesis that intestinal microbiota may affect the function of the central nervous system. Here we gathered comprehensively data on drugs used in psychiatry regarding their antimicrobial properties. We believe our data has strong implications for the treatment of psychiatric entities. Nevertheless the study of ours highlights the need for more well-designed trials aimed at analysis of gut microbiota function.

IntroductionMetformin is the first choice drug in the treatment of type 2 diabetes mellitus but its administration may be linked to gastrointestinal adverse events limiting its use.ObjectivesThe objective of this systematic review and meta-analysis was to assess the risk of gastrointestinal adverse events related to metformin use in patients with type 2 diabetes treated with metformin.MethodsPUB MED/CINAHL/Web of Science/Scopus were searched from database inception until 08.11.2020 for articles in English and randomized controlled trials related to patients with type 2 diabetes treated with metformin were included.ResultsFrom 5315 publications, we identified 199 potentially eligible full-text articles. Finally, 71 randomized controlled trials were included in the meta-analysis. In these studies, metformin use was associated with higher risk of abdominal pain, diarrhea and nausea comparing to control. The risks of abdominal pain and nausea were highest comparing to placebo. Bloating risk was only elevated when metformin treatment was compared to DPP4i.ConclusionsThe risk of gastrointestinal adverse events such as abdominal pain, nausea and diarrhea is higher in type 2 diabetes patients treated with metformin compared to other antidiabetic drugs. There is a higher risk of bloating and diarrhea with metformin immediate-release than with metformin extended release formulation.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289975, identifier CRD42021289975.

Short chain fatty acids (SCFAs) are important metabolites of the gut microbiota. It has been shown that the microbiota and its metabolic activity in children are highly influenced by the type of diet and age. Our aim was to analyse the concentration of fecal SCFAs over two years of life and to evaluate the influence of feeding method on the content of these compounds in feces. We searched PubMed/MEDLINE/Embase/Ebsco/Cinahl/Web of Science from the database inception to 02/23/2021 without language restriction for observational studies that included an analysis of the concentration of fecal SCFAs in healthy children up to 3 years of age. The primary outcome measures-mean concentrations-were calculated. We performed a random-effects meta-analysis of outcomes for which ≥2 studies provided data. A subgroup analysis was related to the type of feeding (breast milk vs. formula vs. mixed feeding) and the time of analysis (time after birth). The initial search yielded 536 hits. We reviewed 79 full-text articles and finally included 41 studies (n = 2,457 SCFA analyses) in the meta-analysis. We found that concentrations of propionate and butyrate differed significantly in breastfed infants with respect to time after birth. In infants artificially fed up to 1 month of age, the concentration of propionic acid, butyric acid, and all other SCFAs is higher, and acetic acid is lower. At 1–3 months of age, a higher concentration of only propionic acid was observed. At the age of 3–6 months, artificial feeding leads to a higher concentration of butyric acid and the sum of SCFAs. We concluded that the type of feeding influences the content of SCFAs in feces in the first months of life. However, there is a need for long-term evaluation of the impact of the observed differences on health later in life and for standardization of analytical methods and procedures for the study of SCFAs in young children. These data will be of great help to other researchers in analyzing the relationships between fecal SCFAs and various physiologic and pathologic conditions in early life and possibly their impact on health in adulthood.

Probiotics are known to regulate host metabolism. In randomized controlled trial we aimed to assess whether interventions with probiotic containing following strains: Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Levilactobacillus brevis W63, Lacticaseibacillus casei W56, Ligilactobacillus salivarius W24, Lactococcus lactis W19, and Lactococcus lactis W58 affect gut microbiota to promote metabolic effects. By 16S rRNA sequencing we analyzed the fecal microbiota of 56 obese, postmenopausal women randomized into three groups: (1) probiotic dose 2.5 × 109 CFU/day (n = 18), (2) 1 × 1010 CFU/day (n = 18), or (3) placebo (n = 20). In the set of linear mixed-effects models, the interaction between pre- or post-treatment bacterial abundance and time on cardiometabolic parameters was significantly (FDR-adjusted) modified by type of intervention (26 and 19 three-way interactions for the pre-treatment and post-treatment abundance, respectively), indicating the modification of the bio-physiological role of microbiota by probiotics. For example, the unfavorable effects of Erysipelotrichi, Erysipelotrichales, and Erysipelotrichaceae on BMI might be reversed, but the beneficial effect of Betaproteobacteria on BMI was diminished by probiotic treatment. Proinflammatory effect of Bacteroidaceae was alleviated by probiotic administration. However, probiotics did not affect the microbiota composition, and none of the baseline microbiota-related features could predict therapeutic response as defined by cluster analysis. Conclusions: Probiotic intervention alters the influence of microbiota on biochemical, physiological and immunological parameters, but it does not affect diversity and taxonomic composition. Baseline microbiota is not a predictor of therapeutic response to a multispecies probiotic. Further multi-omic and mechanistic studies performed on the bigger cohort of patients are needed to elucidate the cardiometabolic effect of investigated probiotics in postmenopausal obesity.