In this study, for the first time, we successfully established a diabetic diagnosis model through applying intestinal microorganisms combined with clinical indicators, and the verification of the diagnosis model revealed that the diagnosis effect of this model was very good. For the differences in the intestinal flora between diabetes, it has recently been reported in several studies. For instance, Awgichew et al. attributed differences between diabetes and health groups to the role of short-chain fatty acid bacteria [21]. In the research, Zhang et al. reported patients at the genus level of bacteria in the diabetes group. They found that the relative abundance of Prevotella and Alloprevotella was significantly higher [14]. In our study, 91 patients were included, and there was no significant statistical difference between the two groups in basic information such as age and gender. In the diversity analysis, significant differences between the two groups were found, which is consistent with existing research reports. The specific differences in bacterial levels are not consistent with recent studies, and it is considered that most of the patients in this study have underlying diseases. Actually, in the common process of clinical diagnosis and treatment, such similar patients are more common, which are more in line with the complexity of patients in the process of clinical practice, and more suitable for clinical diagnosis and treatment. We employed the random forest model to find 12 distinctly different genus-level bacteria in the included sample population, in combination with POD indicators to establish a powerful diagnostic model (UC = 90.8%, P < 0.001). It is important that the C index is close to 1 when this diagnostic model is validated. These results indicate that the intestinal flora is closely related to the occurrence and development of diabetes.
In our findings, the abnormal abundance of the genus parabacteroides (the intestinal microorganism) in diabetic patients seems to have a significant effect on the diagnosis of diabetes. For the relationship of the genus parabacteroides is not the first reported with diabetes. In a recent prospective randomized controlled study in Spain, the relationship between diet, gut microbes and diabetes was explored. The same similar results were obtained in the study results. In the study results, the role of the genus parabacteroides in affecting the occurrence and development of diabetes is confirmed. This effect seems to be related to the enhancement of the metabolic pathways including terpenoid-quinone, lipopolysaccharides and N-glycan biosynthesis [22]. The genus Alistipes is also very important in the diabetic group, and the effect of the genus Alistipes on diabetes has also been reported recently [23]. The genus Alistipes is currently considered to be a bacterium that produces short-chain fatty acids (SCFA), which have a number of potential roles in modulating metabolic health and DM risk factors, such as blood glucose regulation and metabolic regulation, and maintaining the integrity of the intestinal barrier [24]. There are also effects on the genus Faecalibacterium in diabetic patients, and the different bacterial distribution is more obvious in the diabetic group. Currently, more studies have been reported on the genus Faecalibacterium, and the abnormal abundance of the genus Faecalibacterium was also reported by different institutes in experimental groups [25] and [26]. In our study, the abundance of the genus Faecalibacterium was positively associated with the presence of triglycerides, presuming that it was likely to be related to the immune effects of the genus Faecalibacterium. This similar report has also been mentioned in previous studies [27]. In this study, the genus streptococcus was also selected in randomized forest importance in the diabetes group. The genus streptococcus was widely found in nature, human and animal stools, and healthy people's nasopharynx and intestines, which can mainly cause suppurative inflammation, toxin diseases and hypersensitivity reactive diseases. It was also reported in diabetes in previous studies [23]. In our study, there were two prevotella genera in bacterial abundance and clinical indicators, which is the result of the current Greenggenes database. Both prevotella were annotated, which also emphasize the relationship between prevotella and blood glucose. In our study, the abundance of the genus prevotella was high in the diabetes group, and it was positively associated with the clinical biochemical index fasting blood glucose. This conclusion was also mentioned in a 2020 study in the United States. In this study, the researchers attributed this effect to lipopolysaccharide (LPS), which is a component of the Gram-negative bacteria wall. It can activate the local immune response and may cause low-grade systemic inflammation, leading to insulin resistance and affecting the occurrence and development of diabetes [28]. In addition to the genus mentioned above, the genus Dialister, the genus Butyricimonas and the genus Gemmiger have been shown in the random forest model diabetes group. This conclusion has been confirmed in different studies [29], [30], and [31]. In our heat map of differential bacteria and biochemical indicators, it is suggested that the genus Gemmiger is surely negatively correlated with the percentage of neutrophils, suggesting that genus Gemmiger is likely to play a role through immune regulation.
Among the 12 types of bacteria included in the model in this study, the genus Shigella, the genus Ruminococcus, the genus Actinomyces, and the genus Bifidobacterium were relatively important in the control group. It is not the first time that Shigella has been included in diabetes-related diagnostic models. In a recent study on diabetic nephropathy, the study model included 25 genus bacterial to diagnose diabetic nephropathy, and the AUC area of the diagnostic model after drawing the ROC curve was 0.972. In such a model, the genus Shigella was also emphasized. However, the action mechanism of the genus Shigella and diabetes or diabetes-related diseases is currently unclear. The genus Ruminococcus is closely related to the diet structure of the host. This bacterium has also been reported in diabetes. This conclusion is similar to the structure of this study [31]. The genus Actinomyces is widely distributed in nature and has a wide variety of species. It is a member of the normal flora of the human body and can cause endogenous infections. There are few reports of the genus Actinomyces in the diagnosis model of diabetes. In 2019, a Chinese study reported the microbial structure of patients with hyperlipidemia and gestational diabetes. The report revealed that the abundance of this genus was abnormal between the two groups, which can also verify the results in this study [32]. Among these genera with high control abundance, the genus Bifidobacterium should be the most reported microbial bacteria, which has been clearly defined as probiotics. It has been added to dairy products for consumption and also plays a role in the pharmaceutical industry. In our lefse analysis and research results, the genus Bifidobacterium has the highest LDA in the control group. This conclusion does not seem to be a surprise. The improvement of blood sugar by Bifidobacterium was also mentioned in a study. The elderly patients with type 2 diabetes took 200ml of a compound drink containing Bifidobacterium bifidum every day. After taking it for 1 month, the fasting blood glucose level was significantly reduced [33]. However, at present, there are rare reports on the mechanism of Bifidobacterium to improve blood sugar, which should be the future research direction of probiotics to improve disease. In addition, there are also research reports on the negative correlation between the genus Bifidobacterium and creatinine. In a randomized controlled study in Brazil in 2020, the subjects were given drugs containing Bifidobacterium genus regularly. After follow-up, it was found that the blood creatinine level was different between the two groups, and the creatinine level of the experimental group taking the drug decreased significantly [34].
In our study, there are several limitations that may require close attention. First, although we developed a precise diagnostic model through the intestinal flora, the function of these microbiomes is unknown and we have not done the detection of bacterial metabolites. In this way, we have insufficient research on the mechanism. Second, we included less sample sizes, which were not verified in different regions and different time periods. Third, the method we tested is 16S gene sequencing. In the database, there are still many tentative names, unidentified, unidentified and unclassified bacteria. It is suggested to further explore the multiple group methods such as macrogene. In conclusion, for the first time, we have successfully developed a diagnostic model with high diagnostic effect through utilizing intestinal microbes combined with the clinical biochemical indicators. This model reveals that the intestinal flora can significantly improve the diagnostic ability of diabetes, and also shows that the intestinal flora is involved in the occurrence and development of the disease, providing new mentality for exploring metabolic diseases such as diabetes.