Gut Microbiota Composition and Fecal Metabolic Profiling in Patients With Diabetic Retinopathy

Abstract

Recent evidence suggests there is a link between metabolic diseases and gut microbiota. To investigate the gut microbiota composition and fecal metabolic phenotype in diabetic retinopathy (DR) patients. DNA was extracted from 50 fecal samples (21 individuals with type 2 diabetes mellitus-associated retinopathy (DR), 14 with type 2 diabetes mellitus but without retinopathy (DM) and 15 sex- and age-matched healthy controls) and then sequenced by high-throughput 16S rDNA analysis. Liquid chromatography mass spectrometry (LC-MS)-based metabolomics was simultaneously performed on the samples. A significant difference in the gut microbiota composition was observed between the DR and healthy groups and between the DR and DM groups. At the genus level, Faecalibacterium, Roseburia, Lachnospira and Romboutsia were enriched in DR patients compared to healthy individuals, while Akkermansia was depleted. Compared to those in the DM patient group, five genera, including Prevotella, were enriched, and Bacillus, Veillonella, and Pantoea were depleted in DR patients. Fecal metabolites in DR patients significantly differed from those in the healthy population and DM patients. The levels of carnosine, succinate, nicotinic acid and niacinamide were significantly lower in DR patients than in healthy controls. Compared to those in DM patients, nine metabolites were enriched, and six were depleted in DR patients. KEGG annotation revealed 17 pathways with differentially abundant metabolites between DR patients and healthy controls, and only two pathways with differentially abundant metabolites were identified between DR and DM patients, namely, the arginine-proline and α-linolenic acid metabolic pathways. In a correlation analysis, armillaramide was found to be negatively associated with Prevotella and Subdoligranulum and positively associated with Bacillus. Traumatic acid was negatively correlated with Bacillus. Our study identified differential gut microbiota compositions and characteristic fecal metabolic phenotypes in DR patients compared with those in the healthy population and DM patients. Additionally, the gut microbiota composition and fecal metabolic phenotype were relevant. We speculated that the gut microbiota in DR patients may cause alterations in fecal metabolites, which may contribute to disease progression, providing a new direction for understanding DR.

Metagenomic shotgun sequencing and metabolomic profiling identify specific human gut microbiota associated with diabetic retinopathy in patients with type 2 diabetes

“…Analysis of microbiota composition showed a significant decrease in the percentage of Pasteurellaceae in DR patients ( 12 ). At the genus level using high-throughput 16S rDNA analysis, Faecalibacterium , Roseburia , Lachnospira , and Romboutsia were enriched in DR patients ( 16 ). The 16S rRNA gene sequencing and untargeted metabolomics showed that reduced diversity and altered composition of gut microbiota and specific microbe–metabolite interplay were associated with proliferative DR ( 15 ).…”

“…( 11 ) reported that restructuring of the gut microbiome by long-term intermittent fasting prevented the development of DR and prolonged the life span by activation of tauroursodeoxycholate (TUDCA) receptor (TGR5) in a diabetic db/db mouse model. The recent focus on gut microbiome dysbiosis in DR patients has led to new pathophysiological and therapeutic directions ( 12 – 16 ). Most of these microbiome studies regarding DR used 16S rRNA sequencing, which limited their associations in the microbiome to the genus level.…”

Diosgenin Ameliorates Non-alcoholic Fatty Liver Disease by Modulating the Gut Microbiota and Related Lipid/Amino Acid Metabolism in High Fat Diet-Fed Rats

“…The data were preprocessed for further analysis as follows: filtering merged Raw Tags to get high-quality Clean Tags by software Trimmomatic v0.33; identifying and removing chimeric sequences to get Effective Tags by software UCHIME v4.2. The data analysis method was described previously ( Zhou Z. et al, 2021 ; Lin Li et al, 2021 ).…”

A new predictive model for the concurrent risk of diabetic retinopathy in type 2 diabetes patients and the effect of metformin on amino acids

“…Resident bacterial species in the gut can also influence free amino acid distribution in the gastrointestinal tract ( 28 ). In addition, multiple studies have pointed to a causal relationship between gut microbiome dysbiosis and T2D, found that there is dysregulation of the gut microbiota in DR patients with diabetes,and DR patients exhibited enrichment of gut microbiota components at the genus level ( 29 – 31 ).After studying of T2D complications, it is found that the significant differences in gut microbiota composition not only between the DR and healthy groups, but also between the DR and T2D groups ( 29 ). Gut microbiota, not only worked in amino acids metabolism, but also in several other process, one of the most important is the modulation of the sensitivity to peripheral insulin.…”