Maternal smoking during pregnancy increases the risk of gut microbiome-associated childhood overweight and obesity.

Childhood obesity is linked to maternal smoking during pregnancy. Gut microbiota may partially mediate this association and could be potential targets for intervention; however, its role is understudied. We included 1,592 infants from the Canadian Healthy Infants Longitudinal Development Cohort. Data on environmental exposure and lifestyle factors were collected prenatally and throughout the first three years. Weight outcomes were measured at one and three years of age. Stool samples collected at 3 and 12 months were analyzed by sequencing the V4 region of 16S rRNA to profile microbial compositions and magnetic resonance spectroscopy to quantify the metabolites. We showed that quitting smoking during pregnancy did not lower the risk of offspring being overweight. However, exclusive breastfeeding until the third month of age may alleviate these risks. We also reported that maternal smoking during pregnancy significantly increased Firmicutes abundance and diversity. We further revealed that Firmicutes diversity mediates the elevated risk of childhood overweight and obesity linked to maternal prenatal smoking. This effect possibly occurs through excessive microbial butyrate production. These findings add to the evidence that women should quit smoking before their pregnancies to prevent microbiome-mediated childhood overweight and obesity risk, and indicate the potential obesogenic role of excessive butyrate production in early life.

A synbiotic preparation (SIM01) for post-acute COVID-19 syndrome in Hong Kong (RECOVERY): a randomised, double-blind, placebo-controlled trial.

BACKGROUND: Post-acute COVID-19 syndrome (PACS) affects over 65 million individuals worldwide but treatment options are scarce. We aimed to assess a synbiotic preparation (SIM01) for the alleviation of PACS symptoms. METHODS: In this randomised, double-blind, placebo-controlled trial at a tertiary referral centre in Hong Kong, patients with PACS according to the US Centers for Disease Control and Prevention criteria were randomly assigned (1:1) by random permuted blocks to receive SIM01 (10 billion colony-forming units in sachets twice daily) or placebo orally for 6 months. Inclusion criterion was the presence of at least one of 14 PACS symptoms for 4 weeks or more after confirmed SARS-CoV-2 infection, including fatigue, memory loss, difficulty in concentration, insomnia, mood disturbance, hair loss, shortness of breath, coughing, inability to exercise, chest pain, muscle pain, joint pain, gastrointestinal upset, or general unwellness. Individuals were excluded if they were immunocompromised, were pregnant or breastfeeding, were unable to receive oral fluids, or if they had received gastrointestinal surgery in the 30 days before randomisation. Participants, care providers, and investigators were masked to group assignment. The primary outcome was alleviation of PACS symptoms by 6 months, assessed by an interviewer-administered 14-item questionnaire in the intention-to-treat population. Forward stepwise multivariable logistical regression was performed to identify predictors of symptom alleviation. The trial is registered with ClinicalTrials.gov, NCT04950803. FINDINGS: Between June 25, 2021, and Aug 12, 2022, 463 patients were randomly assigned to receive SIM01 (n=232) or placebo (n=231). At 6 months, significantly higher proportions of the SIM01 group had alleviation of fatigue (OR 2·273, 95% CI 1·520-3·397, p=0·0001), memory loss (1·967, 1·271-3·044, p=0·0024), difficulty in concentration (2·644, 1·687-4·143, p<0·0001), gastrointestinal upset (1·995, 1·304-3·051, p=0·0014), and general unwellness (2·360, 1·428-3·900, p=0·0008) compared with the placebo group. Adverse event rates were similar between groups during treatment (SIM01 22 [10%] of 232 vs placebo 25 [11%] of 231; p=0·63). Treatment with SIM01, infection with omicron variants, vaccination before COVID-19, and mild acute COVID-19, were predictors of symptom alleviation (p<0·0036). INTERPRETATION: Treatment with SIM01 alleviates multiple symptoms of PACS. Our findings have implications on the management of PACS through gut microbiome modulation. Further studies are warranted to explore the beneficial effects of SIM01 in other chronic or post-infection conditions. FUNDING: Health and Medical Research Fund of Hong Kong, Hui Hoy and Chow Sin Lan Charity Fund, and InnoHK of the HKSAR Government. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Faecal microbiome-based machine learning for multi-class disease diagnosis.

Systemic characterisation of the human faecal microbiome provides the opportunity to develop non-invasive approaches in the diagnosis of a major human disease. However, shared microbial signatures across different diseases make accurate diagnosis challenging in single-disease models. Herein, we present a machine-learning multi-class model using faecal metagenomic dataset of 2,320 individuals with nine well-characterised phenotypes, including colorectal cancer, colorectal adenomas, Crohn's disease, ulcerative colitis, irritable bowel syndrome, obesity, cardiovascular disease, post-acute COVID-19 syndrome and healthy individuals. Our processed data covers 325 microbial species derived from 14.3 terabytes of sequence. The trained model achieves an area under the receiver operating characteristic curve (AUROC) of 0.90 to 0.99 (Interquartile range, IQR, 0.91-0.94) in predicting different diseases in the independent test set, with a sensitivity of 0.81 to 0.95 (IQR, 0.87-0.93) at a specificity of 0.76 to 0.98 (IQR 0.83-0.95). Metagenomic analysis from public datasets of 1,597 samples across different populations observes comparable predictions with AUROC of 0.69 to 0.91 (IQR 0.79-0.87). Correlation of the top 50 microbial species with disease phenotypes identifies 363 significant associations (FDR < 0.05). This microbiome-based multi-disease model has potential clinical application in disease diagnostics and treatment response monitoring and warrants further exploration.

Pyrosequencing of the bacteria associated with Platygyra carnosus corals with skeletal growth anomalies reveals differences in bacterial community composition in apparently healthy and diseased tissues.

Corals are rapidly declining globally due to coral diseases. Skeletal growth anomalies (SGA) or "coral tumors" are a group of coral diseases that affect coral reefs worldwide, including Hong Kong waters in the Indo-Pacific region. To better understand how bacterial communities may vary in corals with SGA, for the first time, we examined the bacterial composition associated with the apparently healthy and the diseased tissues of SGA-affected Platgyra carnosus using 16S ribosomal rRNA gene pyrosequencing. Taxonomic analysis revealed Proteobacteria, Bacteroidetes, Cyanobacteria, and Actinobacteria as the main phyla in both the apparently healthy and the diseased tissues. A significant difference in the bacterial community composition was observed between the two conditions at the OTU level. Diseased tissues were associated with higher abundances of Acidobacteria and Gemmatimonadetes, and a lower abundance of Spirochaetes. Several OTUs belonging to Rhodobacteraceae, Rhizobiales, Gammaproteobacteria, and Cytophaga-Flavobacterium-Bacteroidetes (CFB) were strongly associated with the diseased tissues. These groups of bacteria may contain potential pathogens involved with the development of SGA or opportunistic secondary or tertiary colonizers that proliferated upon the health-compromised coral host. We suggest that these bacterial groups to be further studied based on inoculation experiments and testing of Koch's postulates in efforts to understand the etiology and progression of SGA.