Microbial community with predictive power can be a new non-invasive substitute for clinical and pathological identification of diabetic nephropathy

Abstract

Background Diabetic nephropathy is characterized by increased incidence, deficient diagnostic methods and poor prognosis. New idea about altered gut microbiome associated with diagnosis and development of diabetic nephropathy remains to be verified. The major aim of our study is to relate fecal microbiome to clinically diagnosed diabetic kidney disease (DKD) or pathologically identified diabetic nephropathy (defined as DN) and further evaluate diagnosis potential of microbial markers for DKD/DN. We carried out 16S rRNA sequencing on a discovery cohort consisting of 352 patients (DKD = 120, DM = diabetes mellitus = 92, Con = healthy controls = 140) to identify microbial taxa and construct DKD classifier. Functional relevance and clinic correlation of microbiome changes were performed using PICRUSt and Spearman analysis, respectively. Independent 60 DKDs and 116 non-DKDs (DM = 46, Con = 70) were used to validate the results. The same analysis was performed on DKD pathologic subtypes (DN = 22, MN = membranous nephropathy = 22). Results DKD/DM samples had a distinct microbiome signature with lower alpha-diversity and significantly different microbial composition compared with Con (P < 0.001). Expansion of opportunistic pathogens (Peptostreptococcaceae_incertae_sedis, Clostridium_sensu_stricto_1, Streptococcus, Enterococcus, Erysipelotrichaceae_incertae_sedis), sulphate-reducing bacteria (Desulfovibrio) and depletion of bacteria producing short-chain fatty acids (SCFA) (Bacteroides, Faecalibacterium, Blautia and Roseburia) were major contributors to above differences. Interestingly, mucosa-associated bacteria including Akkermansia and Ruminococcus were also increased in DKD. The combination of 11 microbial markers could separate 120 DKDs from 232 non-DKDs with an area under curve (AUC) of 88.12%. Correspondingly, diagnostic power of microbial biomarkers was evaluated in a validation cohort of 60 patients and 116 non-DKDs (AUC = 79.75%). Besides DKD-related lipid and arginine metabolism, we also observed an increase of metabolism of aromatic amino acid in DM. Additionally, microbial comparison was carried out between pathologic subtypes of DKD, which could be used to distinguish DN from MN with 77.69% AUC. Conclusion Gut microbiome-related changes were associated with pathogenesis of DKD/DN; Microbiota-targeted markers could be an alternative test for DKD diagnosis and a non-invasive choice to differentiate DKD pathologic subtypes.