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Abstract Type 2 diabetes mellitus (T2DM) is globally recognized as a significant health concern, with diabetic foot (DF) identified as a severe long-term complication that can lead to tissue death or amputation. The discovery of the impact of mycobiota, a diverse group of multicellular eukaryotes in the gut microbiome, on the onset of endocrine disorders holds great significance. Therefore, this research aimed to examine variations in fungal mycobiome and identify potential biomarkers for T2DM and T2DM-DF. Fecal and blood samples were collected from 33 individuals with T2DM, 32 individuals with T2DM-DF, and 32 healthy individuals without any health conditions (HC). Blood samples were used for laboratory parameters analysis, while total DNA was extracted from fecal samples and sequenced using Illumina 18s rRNA. Bioinformatics tools were employed to analyze fungal abundance and diversity, revealing differentially expressed fungal species and signature fungi that distinguished between T2DM, T2DM-DF, and HC groups. Firstly, significant alterations in some laboratory parameters were observed among the three groups, which also differed between T2DM and T2DM-DF. The diversity of gut fungi in T2DM and T2DM-DF significantly differed from that of the HC group; however, more pronounced changes were observed in T2DM-DF. Additionally, two significantly altered phyla, Ascomycota and Basidiomycota, were identified with higher Ascomycota abundance but lower Basidiomycota abundance in both the T2DM and T2DM-DF compared to the HC group. Furthermore, the top 15 fungi showing significant changes at the species level included a notable decrease in Rhodotorula_mucilaginosa abundance in patients with T2DM compared to HC and a substantial increase in unclassified_g_Candida abundance specifically seen only among patients with T2DM-DF, but not among those diagnosed with T2DM or HC. Thirdly, KEGG was employed to analyze enzyme expression across the three groups, revealing a more pronounced alteration in gut fungal function within T2DM-DF compared to T2DM. Subsequently, to accurately identify signature fungi in each group, a random forest was utilized to rank the top 15 significant fungi. Notably, 11 fungi were identified as potential biomarkers for distinguishing T2DM or T2DM-DF from HC, while eight fungi could discriminate between T2DM and T2DM-DF. Furthermore, receiver operating characteristic curve (ROC) analysis demonstrated enhanced accuracy of predicted outcomes. These findings suggest that changes in fungal mycobiome are closely associated with the progression and complications of T2DM and DF, offering promising prospects for diagnosis and treatment. |
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