A growing body of evidence suggests interplay between the gut microbiota and the pathogenesis of nonalcoholic fatty liver disease (NAFLD). However, the role of the gut microbiome in early detection of NAFLD is unclear. Prospective studies are necessary for identifying reliable, microbiome markers for early NAFLD. We evaluated 2487 individuals in a community-based cohort who were followed up 4.6 years after initial clinical examination and biospecimen sampling. Metagenomic and metabolomic characterizations using stool and serum samples taken at baseline were performed for 90 participants who progressed to NAFLD and 90 controls who remained NAFLD free at the follow-up visit. Cases and controls were matched for gender, age, body mass index (BMI) at baseline and follow-up, and 4-year BMI change. Machine learning models integrating baseline microbial signatures (14 features) correctly classified participants (auROCs of 0.72 to 0.80) based on their NAFLD status and liver fat accumulation at the 4-year follow up, outperforming other prognostic clinical models (auROCs of 0.58 to 0.60). We confirmed the biological relevance of the microbiome features by testing their diagnostic ability in four external NAFLD case-control cohorts examined by biopsy or magnetic resonance spectroscopy, from Asia, Europe, and the United States. Our findings raise the possibility of using gut microbiota for early clinical warning of NAFLD development.

Context: Accumulating evidence implies that sleep disturbance is involved in metabolic disorders. Objective: We comprehensively evaluated the association between various dimensions of sleep behaviors and the risk for metabolic dysfunction–associated fatty liver disease (MAFLD). Methods: In this cross-sectional study of 5011 participants with self-reported sleep behaviors and radiologically diagnosed MAFLD, a comprehensive healthy sleep score was generated to evaluate the associations between sleep behaviors and MAFLD risk using multivariate logistic regression adjusting for demographics, lifestyles, medication, and metabolic comorbidities. Furthermore, mediation analysis was utilized to assess the extent to which obesity explains the effect of sleep quality on MAFLD risk. Results: Late bedtime, snoring, and daytime napping for over 30 minutes significantly associated with an increased risk of MAFLD, with odds ratios (OR) of 1.37 (95% CI 1.10, 1.70), 1.59 (95% CI 1.33, 1.91), and 1.17 (95% CI 1.02, 1.35), respectively, after full adjustments including obesity. Participants with disturbance in nighttime sleep and prolonged daytime napping showed the highest risk for MAFLD (OR 2.38, 95% CI 1.73, 3.27). Each additional increase of healthy sleep score was associated with a 16% reduction in MAFLD risk. Further stratified analysis revealed that people with a sedentary lifestyle and central obesity experienced more prominent adverse effects from poor sleep quality than others. Moreover, obesity accounted for only 20.77% of the total effect of sleep quality on MAFLD risk. Conclusions: Sleep behaviors, both cumulatively and individually, are associated with MAFLD risk. Public health awareness and strategies should be encouraged to curb MAFLD.

The importance of gut microbes in mediating the benefits of lifestyle intervention is increasingly recognized. However, compared to the bacterial microbiome, the role of intestinal fungi in exercise remains elusive. With our established randomized controlled trial of exercise intervention in Chinese males with prediabetes (n = 39, ClinicalTrials.gov:NCT03240978), we investigated the dynamics of human gut mycobiome and further interrogated their associations with exercise-elicited outcomes using multi-omics approaches. Clinical variations and biological samples were collected before and after training. Fecal fungal composition was analyzed using the internal transcribed spacer 2 (ITS2) sequencing and integrated with paired shotgun metagenomics, untargeted metabolomics, and Olink proteomics. Twelve weeks of exercise training profoundly promoted fungal ecological diversity and intrakingdom connection. We further identified exercise-responsive genera with potential metabolic benefits, including Verticillium, Sarocladium, and Ceratocystis. Using multi-omics approaches, we elucidated comprehensive associations between changes in gut mycobiome and exercise-shaped metabolic phenotypes, bacterial microbiome, and circulating metabolomics and proteomics profiles. Furthermore, a machine-learning algorithm built using baseline microbial signatures and clinical characteristics predicted exercise responsiveness in improvements of insulin sensitivity, with an area under the receiver operating characteristic (AUROC) of 0.91 (95% CI: 0.85–0.97) in the discovery cohort and of 0.79 (95% CI: 0.74–0.86) in the independent validation cohort (n = 30). Our findings suggest that intense exercise training significantly remodels the human fungal microbiome composition. Changes in gut fungal composition are associated with the metabolic benefits of exercise, indicating gut mycobiome is a possible molecular transducer of exercise. Moreover, baseline gut fungal signatures predict exercise responsiveness for diabetes prevention, highlighting that targeting the gut mycobiome emerges as a prospective strategy in tailoring personalized training for diabetes prevention.

Non-high-density lipoprotein cholesterol (non-HDL-c) was a strong risk factor for incident cardiovascular diseases and proved to be a better target of lipid-lowering therapies. Recently, gut microbiota has been implicated in the regulation of host metabolism. However, its causal role in the variation of non-HDL-c remains unclear. Microbial species and metabolic capacities were assessed with fecal metagenomics, and their associations with non-HDL-c were evaluated by Spearman correlation, followed by LASSO and linear regression adjusted for established cardiovascular risk factors. Moreover, integrative analysis with plasma metabolomics were performed to determine the key molecules linking microbial metabolism and variation of non-HDL-c. Furthermore, bi-directional mendelian randomization analysis was performed to determine the potential causal associations of selected species and metabolites with non-HDL-c. Decreased Eubacterium rectale but increased Clostridium sp CAG_299 were causally linked to a higher level of non-HDL-c. A total of 16 microbial capacities were found to be independently associated with non-HDL-c after correcting for age, sex, demographics, lifestyles and comorbidities, with the strongest association observed for tricarboxylic acid (TCA) cycle. Furthermore, decreased 3-indolepropionic acid and N-methyltryptamine, resulting from suppressed capacities for microbial reductive TCA cycle, functioned as major microbial effectors to the elevation of circulating non-HDL-c. Overall, our findings provided insight into the causal effects of gut microbes on non-HDL-c and uncovered a novel link between non-HDL-c and microbial metabolism, highlighting the possibility of regulating non-HDL-c by microbiota-modifying interventions. A full list of funding bodies can be found in the Sources of funding section.