Gut Microbiota Associations with Metabolic Health and Obesity Status in Older Adults.
Xiaozhong ZhongJanas M HarringtonSeán R MillarIvan J PerryPaul W O'TooleCatherine M PhillipsPublished in: Nutrients (2020)
Emerging evidence links the gut microbiota with several chronic diseases. However, the relationships between metabolic syndrome (MetS), obesity and the gut microbiome are inconsistent. This study aimed to investigate associations between gut microbiota composition and diversity and metabolic health status in older adults (n = 382; median age = 69.91 [± 5 years], male = 50.79%) with and without obesity. Gut microbiome composition was determined by sequencing 16S rRNA gene amplicons. Results showed that alpha diversity and richness, as indicated by the Chao1 index (p = 0.038), phylogenetic diversity (p = 0.003) and observed species (p = 0.038) were higher among the metabolically healthy non-obese (MHNO) individuals compared to their metabolically unhealthy non-obese (MUNO) counterparts. Beta diversity analysis revealed distinct differences between the MHNO and MUNO individuals on the phylogenetic distance scale (R2 = 0.007, p = 0.004). The main genera contributing to the gut composition among the non-obese individuals were Prevotella, unclassified Lachnospiraceae, and unclassified Ruminococcaceae. Prevotella, Blautia, Bacteroides, and unclassified Ruminococcaceae mainly contributed to the variation among the obese individuals. Co-occurrence network analysis displayed different modules pattern among different metabolic groups and revealed groups of microbes significantly correlated with individual metabolic health markers. These findings confirm relationships between metabolic health status and gut microbiota composition particularly, among non-obese older adults.
Keyphrases
- metabolic syndrome
- weight loss
- insulin resistance
- type diabetes
- adipose tissue
- network analysis
- bariatric surgery
- uric acid
- public health
- healthcare
- physical activity
- obese patients
- single cell
- mental health
- cardiovascular risk factors
- weight gain
- health information
- cardiovascular disease
- gene expression
- social media
- transcription factor
- genome wide identification
- genome wide analysis