Gut microbiota and metabolic health among overweight and obese individuals

The global prevalence of obesity and related diseases is increasing, with obesity being a major risk factor for metabolic disease. However, some obese individuals remain metabolically healthy (MHO), a phenotype often observed in younger, active individuals with good nutrition and low ectopic/visceral fat. While MHO is sometimes considered a temporary state preceding metabolic syndrome, studies suggest lower all-cause mortality and cardiovascular disease (CVD) risk in MHO compared to metabolically unhealthy obese (MUO) individuals. The underlying protective mechanisms in MHO and the factors driving the development of metabolic abnormalities remain unclear and require further investigation. Obesity and metabolic disease are complex, arising from the interplay of genetic and environmental factors. In recent years, the gut microbiota has emerged as a significant contributor to obesity and metabolic disease development. Obesity is associated with reduced gut microbiota diversity and altered composition, leading to decreased metabolic energy consumption compared to lean individuals. Gut microbiota influence metabolic health through interactions with the host immune system and responses to environmental factors, including diet. The microbiota has been linked to obesity-related comorbidities like type 2 diabetes (T2D), hypertension, and dyslipidemia. Modulating the microbiota can alleviate metabolic syndrome disorders, further supporting the association between specific microbial composition and metabolic phenotype. This study addresses the gap in knowledge by evaluating differences in gut microbiota between metabolically healthy (MH) and metabolically unhealthy (MU) overweight and obese individuals, aiming to shed light on the mechanisms underlying MHO and the potential for gut microbiome modulation in preventing metabolic abnormalities.

Previous research has established associations between gut microbiota, obesity, and metabolic syndrome. However, this study uniquely focuses on the differences in microbiomes according to metabolic health within the obese population. Studies have shown that decreased gut microbial richness and altered compositions are observed in individuals with hypertension and dyslipidemia compared to their healthy counterparts. Furthermore, a negative correlation between gut microbiome diversity and triglyceride levels, and a positive correlation with high-density lipoprotein cholesterol (HDL-C) levels, have been reported. The existing literature highlights the potential impact of gut microbiota on various metabolic components, but a direct comparison between metabolically healthy and unhealthy obese individuals regarding their gut microbiome has been lacking, prompting this investigation.

This study utilized data from the Kangbuk Samsung Health Study, a cohort study of Korean men and women. Fecal samples were collected from 1463 participants (aged 23-78 years) who underwent comprehensive health examinations between June 2014 and September 2014. After applying exclusion criteria (missing data, BMI <23, antibiotic/probiotic use within recent weeks, history of cardiovascular disease or malignancy, and samples with <2000 sequences), 747 participants were included in the final analysis. Overweight and obese individuals were classified into metabolically healthy (MH) and metabolically unhealthy (MU) groups based on Asian-specific BMI criteria and the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria for metabolic abnormalities. Fecal DNA was extracted using the MOBio PowerSoil DNA Isolation Kit, and 16S rRNA gene sequencing (V3-V4 regions) was performed on the Illumina MiSeq platform. Sequence data were processed using QIIME2 with DADA2 for quality control, chimera removal, and amplicon sequence variant (ASV) construction. Taxonomy was assigned to ASVs using a pre-trained Naive Bayes classifier against the Greengenes 16S rRNA sequence database. Alpha diversity (observed ASVs, Shannon index, Pielou's evenness, Faith's PD) and beta diversity (Bray-Curtis, Jaccard, unweighted and weighted UniFrac) were analyzed to assess differences in microbiota composition and structure between MH and MU groups. Statistical significance was determined using the Kruskal-Wallis test for alpha diversity and PERMANOVA for beta diversity. To investigate associations between gut microbial composition and metabolic health status, generalized linear models (MaAsLin) were used, adjusting for age, sex, and BMI. Linear discriminant analysis (LDA) effect size (LEfSe) analysis was employed to identify taxa that best explained differences between MH and MU groups. Functional profiling of the microbial communities was performed using PICRUSt2 to predict MetaCyc pathways and identify differences in functional attributes between the two groups. Statistical analyses were conducted using SPSS, R, and online tools (Galaxy, STAMP).

The study revealed significant differences in gut microbiota composition and diversity between metabolically healthy (MH) and unhealthy (MU) overweight and obese individuals. The MU group exhibited significantly lower alpha diversity (observed ASVs, Faith's PD, Shannon's index) compared to the MH group, indicating reduced richness and evenness of bacterial communities. Beta diversity analysis using various distance metrics also confirmed significant differences in microbial community composition between the two groups. Principal Coordinate Analysis (PCOA) plots showed some separation between MH and MU groups, though not complete separation, highlighting inter-individual variation. Analysis of bacterial taxa abundance revealed significant differences between MH and MU groups. The genus *Oscillospira* (family Ruminococcaceae) and the family Coriobacteriaceae were significantly more abundant in the MH group, while the genus *Fusobacterium* (phylum Fusobacteria) was significantly more abundant in the MU group. These findings remained significant after adjusting for age, sex, and BMI. LEfSe analysis corroborated these results, confirming the enrichment of *Oscillospira*, *Clostridium*, and Ruminococcaceae in the MH group and *Fusobacterium* and Fusobacteriaceae in the MU group. Interestingly, the family Leuconostocaceae was also more abundant in the MH group. Functional prediction using PICRUSt2 indicated differences in metabolic pathways between the groups. Pathways related to vitamin biosynthesis (cob(II)yrinate a,c-diamide biosynthesis I, preQ0 biosynthesis, 6-hydroxymethyl-dihydropterin diphosphate biosynthesis III, thiamin diphosphate biosynthesis I) and nucleotide biosynthesis (pyrimidine and purine pathways) were enriched in the MU group. In contrast, pathways for L-lysine biosynthesis and glycogen biosynthesis I were upregulated in the MH group. There was no significant difference observed in the Firmicutes/Bacteroidetes ratio between MH and MU groups.

This study provides evidence for a strong association between gut microbiota composition and metabolic health in overweight and obese individuals. The lower alpha diversity and altered composition observed in the MU group align with previous findings linking reduced gut microbial richness to metabolic abnormalities. The enrichment of *Oscillospira* and Coriobacteriaceae in the MH group is noteworthy, given the potential beneficial effects of SCFAs (produced by some *Oscillospira* species) and Coriobacteriaceae's involvement in bile acid, steroid, and phytoestrogen metabolism. These bacteria likely contribute to improved metabolic health by influencing glucose homeostasis, lipid metabolism, and inflammation. Conversely, the increased abundance of *Fusobacterium* in the MU group supports previous research linking this genus to intestinal inflammation and potentially exacerbating metabolic dysfunction. The findings related to metabolic pathways highlight potential mechanisms by which gut microbiota influences metabolic health. The upregulation of vitamin and nucleotide biosynthesis pathways in the MU group might reflect increased demand or altered metabolic processes associated with metabolic dysfunction. Conversely, the increased L-lysine and glycogen biosynthesis in the MH group align with previous research suggesting their roles in maintaining healthy metabolic parameters. Overall, the results suggest that specific alterations in the gut microbiota contribute to the development of metabolic abnormalities in the obese population, and modulation of the gut microbiome may be a valuable strategy in preventing or mitigating these problems.

This study demonstrated significant differences in gut microbiota diversity and composition between metabolically healthy and unhealthy obese individuals. The findings highlight the potential of specific bacterial taxa, including *Oscillospira*, Coriobacteriaceae, and *Fusobacterium*, as biomarkers and therapeutic targets for managing metabolic health in obesity. Further research is crucial to elucidate the precise mechanisms by which these bacteria impact metabolism and to investigate the potential of gut microbiome modulation as an intervention strategy for preventing metabolic diseases. Future studies should include longitudinal analyses to assess the stability of MHO status and the dynamics of gut microbiota changes, as well as functional studies beyond 16S rRNA sequencing to obtain a more complete understanding of the bacterial genes and metabolic pathways involved.

This study has several limitations. The cross-sectional design prevents causal inferences about the relationship between gut microbiota and metabolic health. The study population was limited to Koreans, potentially affecting the generalizability of the findings to other ethnic groups. The use of 16S rRNA gene sequencing provided information on bacterial taxonomy but limited insights into functional aspects of the microbiome. Future research employing whole-genome sequencing and metabolomic analyses could provide a more comprehensive understanding of the gut microbiome's role in metabolic health.