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Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors.

ABSTRACT: Lifestyle and physiological variables on human disease risk have been revealed to be mediated by gut microbiota. Low concordance between case-control studies for detecting disease-associated microbe existed due to limited sample size and population-wide bias in lifestyle and physiological variables. To infer gut microbiota-disease associations accurately, we propose to build machine learning models by including both human variables and gut microbiota. When the model's performance with both gut microbiota and human variables is better than the model with just human variables, the independent gut microbiota -disease associations will be confirmed. By building models on the American Gut Project dataset, we found that gut microbiota showed distinct association strengths with different diseases. Adding gut microbiota into human variables enhanced the classification performance of IBD significantly; independent associations between occurrence information of gut microbiota and irritable bowel syndrome, C. difficile infection, and unhealthy status were found; adding gut microbiota showed no improvement on models' performance for diabetes, small intestinal bacterial overgrowth, lactose intolerance, cardiovascular disease. Our results suggested that although gut microbiota was reported to be associated with many diseases, a considerable proportion of these associations may be very weak. We proposed a list of microbes as biomarkers to classify IBD and unhealthy status. Further functional investigations of these microbes will improve understanding of the molecular mechanism of human diseases.

SUBMITTER: Zhu C

PROVIDER: S-EPMC8722223 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceA healthy lifestyle is associated with a reduced risk of cardiovascular disease in adults with obesity. Little is known about the associations between a healthy lifestyle and the risk of other obesity-attributable diseases in this population.ObjectiveTo examine the association between healthy lifestyle factors and the incidence of major obesity-related diseases in adults with obesity compared with those with normal weight.Design, setting, and participantsThis cohort study evaluated UK Biobank participants aged 40 to 73 years and free of major obesity-attributable disease at baseline. Participants were enrolled from 2006 to 2010 and prospectively followed up for disease diagnosis.ExposuresA healthy lifestyle score was constructed using information on not smoking, exercising regularly, no or moderate alcohol consumption, and eating a healthy diet. For each lifestyle factor, participants scored 1 if they met the criterion for a healthy lifestyle and 0 otherwise.Main outcomes and measuresThe risk of outcomes according to the healthy lifestyle score in adults with obesity compared with those with normal weight were examined using multivariable Cox proportional hazards models with Bonferroni correction for multiple testing. The data analysis was performed between December 1, 2021, and October 31, 2022.ResultsA total of 438 583 adult participants in the UK Biobank were evaluated (female, 55.1%; male, 44.9%; mean [SD] age, 56.5 [8.1] years), of whom 107 041 (24.4%) had obesity. During a mean (SD) follow-up of 12.8 (1.7) years, 150 454 participants (34.3%) developed at least 1 of the studied diseases. Compared with adults with obesity and 0 healthy lifestyle factors, individuals with obesity who met all 4 healthy lifestyle factors were at lower risk of hypertension (HR, 0.84; 95% CI, 0.78-0.90), ischemic heart disease (HR, 0.72; 95% CI, 0.65-0.80), arrhythmias (HR, 0.71; 95% 0.61-0.81), heart failure (HR, 0.65; 95% CI, 0.53-0.80), arteriosclerosis (HR, 0.19; 95% CI, 0.07-0.56), kidney failure (HR, 0.73; 95% CI, 0.63-0.85), gout (HR, 0.51; 95% CI, 0.38-0.69), sleep disorders (HR, 0.68; 95% CI, 0.56-0.83), and mood disorders (HR, 0.66; 95% CI, 0.56-0.78). The lifestyle profiles associated with the lowest risks included a healthy diet and at least 1 of the 2 healthy behaviors of physical activity and never smoking. Compared with adults with normal weight, those with obesity were at higher risk of several outcomes, irrespective of the lifestyle score (adjusted HRs ranged from 1.41 [95% CI, 1.27-1.56] for arrhythmias to 7.16 [95% CI, 6.36-8.05] for diabetes for adults with obesity and 4 healthy lifestyle factors).Conclusion and relevanceIn this large cohort study, adherence to a healthy lifestyle was associated with reduced risk of a wide range of obesity-related diseases, but this association was modest in adults with obesity. The findings suggest that although a healthy lifestyle seems to be beneficial, it does not entirely offset the health risks associated with obesity.

Project description:The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Thus, the systemic effects of small differences in the oral microbiota are unclear. In this study, we explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects by analyzing the hepatic gene expression and serum metabolomic profiles. The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Samples were collected five weeks after administration. Gut microbial communities were analyzed by 16S ribosomal RNA gene sequencing. Hepatic gene expression profiles were analyzed using a DNA microarray and quantitative polymerase chain reaction. Serum metabolites were analyzed by capillary electrophoresis time-of-flight mass spectrometry. The gut microbial composition at the genus level was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of Neat1, Mt1, Mt2, and Spindlin1, which are involved in lipid and glucose metabolism. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with Bifidobacterium, Atomobium, Campylobacter, and Haemophilus, which are characteristic taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.

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Determine independent gut microbiota-diseases association by eliminating the effects of human lifestyle factors.

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