Characterisation of the gut microbiome in hypertensive and type II diabetic populations in different regions of Xinjiang

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By conducting gut microbiome and metabolomic analyses on 192 healthy and diseased individuals (including those with hypertension, type II diabetes, and their co - morbidities) in Xinjiang, it strived to offer new insights into the role of gut microbes in metabolic diseases, which could potentially contribute to early diagnosis and personalized treatment. Results The dominant genus in the Hui group was Faecalibacterium, while Prevotella dominated in the Uyghur group, differing from previously reported enterotype distributions. Hypertensive patients had a significantly higher abundance of Prevotella, which was positively correlated with a high - salt diet. In type II diabetes patients, the abundance of Bifidobacterium adolescentis was significantly higher. Through integrative multi - omics data analysis, it was found that changes in the proportion of specific microorganisms (such as Coriobacteriales_bacterium and Dorea_formicigenerans) in disease - comorbid states were strongly associated with significant differences in urinary metabolites (such as L - carnitine and hydroxycinnamic acid). Metabolic pathway analyses also revealed significant alterations in glycolysis/glycolysis, phenylalanine metabolism, and other pathways in the disease state. Conclusions This study systematically and for the first time reveals the gut microbiome and metabolome characteristics of healthy and diseased populations of different ethnic groups in the Xinjiang region. It offers a new perspective for understanding the role of gut microbes in metabolic diseases and provides a potential scientific basis for early disease diagnosis and personalized treatment. Future research should further integrate multi - omics technology and longitudinal design to comprehensively disclose the interactions among factors and specific mechanisms. gut microbes hypertension type II diabetes mellitus macrogenome untargeted metabolome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Background The human gut possesses an exceptional and diverse microbial ecosystem, with approximately 150-400 species of bacteria in our intestines [1] . The gut microbiota provides substantial benefits to our health by forming a barrier against pathogens, producing bioactive metabolites and modulating immune function. In the absence of strong influencing factors (e.g., dietary changes or antibiotic treatment), the gut microbiome remains in a relatively stable state [2,3] . However, the composition of the gut microbiota can change in the context of influences that vary by age [4,5] , ethnicity [6] and environmental factors such as drug use [7,8] and habitual diet [9] . However, among the many factors that influence the composition and function of the gut flora throughout the life span, diet is indeed key in regulating the abundance of specific bacterial species and their functions [10,11] , and an individual's response to a particular diet or dietary component may be largely influenced by the characteristics of the gut flora [12] . In recent years, an increasing number of studies have shown a strong relationship between the gut microbiota and a variety of diseases (e.g., obesity, diabetes, hypertension, etc.) [13-14] . Type II diabetes mellitus (T2DM), which accounts for approximately 90% of all diabetes cases, is due to insulin resistance and impaired insulin secretion, with an increasing prevalence worldwide [15] , and there are multiple factors that play a role in the development of T2DM, including genetics, lifestyle, and the gut microbiome, with a growing body of evidence supporting the role of the gut microbiome in T2DM [16] . It was found that the abundance of Clostridia phylum and Thick-walled bacillus phylum was significantly decreased in patients with T2DM, whereas the level of β-amoeba phylum was highly elevated and positively correlated with plasma glucose. In addition, the ratios of Anaplasma phylum to Thick-walled bacillus phylum and Anaplasma phylum-Prevotella phylum to Clostridium sphaericum-E and rectum were positively corrected with plasma glucose, suggesting that T2DM is correlated with the composition of the intestinal microbiota. According to the Framingham Heart Study, 60-70% of primary hypertension is caused by obesity [17] and obese patients are 3.5 times more likely to develop hypertension [18] . In addition, 347 million people [19] worldwide suffer from T2DM, and obese people have a sevenfold increased risk of developing this disease [20] . About 50 per cent of people with diabetes die from cardiovascular diseases such as heart disease and stroke [21] . Data released by the Chinese Centre for Disease Control and Prevention (CDC) show that Xinjiang ranks third in obesity rates among all provinces and regions in the country, with an obesity rate of 21.5% and an overweight rate of 33%, which are higher than the national averages of 11.9% and 30.1%, respectively. This is related to the fact that residents in Xinjiang prefer meat, mostly high-fat, high-protein, high-salt and high-carbohydrate foods, and consume fewer grains [ 22, 23] . In a survey of 401 Uyghur residents in Karamay City, it was found [24] that local residents had high fat and cholesterol intake, while data from the national ‘Report on Nutrition and Chronic Disease Status of China's Residents (2020)’ showed that the per capita intake of total dietary fat in China was 79.1 g. In contrast, fat intake of Uyghur residents in Karamay City was higher than the national average. When the human body is in a disease state, such as intestinal inflammation, obesity, diabetes and other disease conditions, the intestinal flora is imbalanced, and the production and types of short-chain fatty acids (SCFAs) change. In addition to providing energy for the body, SCFAs can help dominant bacteria to establish a dominant position in the intestinal tract and act as signalling molecules to regulate glycolipid metabolism. SCFAs-producing bacteria in the intestinal flora produce a large number of SCFAs to increase the acidic environment of the intestinal tract, and they can reproduce in large quantities due to their acid-resistant characteristics. Therefore, under the combined effect of the low pH environment and the competitive growth of acid-resistant bacteria, the growth of indole-producing and hydrogen sulfide-producing bacteria in the intestinal tract was inhibited, and the inhibitory effect of the harmful bacteria on the endointestinal secretion of L-cells was relieved, which led to an increase in the glucagonlike-1 (Glucagon-like-1) and glucose-lipoprotein (GL-1) production and species production. -1 (Glucagon-likepeptide-1, GLP-1) and gastrointestinal peptide hormone tyrosine peptide (Peptide YY, PYY) secretion, and improve the occurrence of glycolipid metabolism diseases such as obesity [25] . In order to explore the association between gut microbes and patients with type II diabetes and hypertension, we collected stool, blood, urine and saliva samples from 192 volunteers in this study, including a total of 99 people from the diseased population (including patients with type II diabetes, hypertension, and complications of the two), and 93 people from the healthy population (excluding those who had taken antibiotics within 3 months and those who suffered from the above diseases). We used macro-genomic and metabolomic analysis technologies to perform high-throughput sequencing on these samples, aiming to explore the characteristics and differences of gut microbes in patients with type II diabetes mellitus and hypertension from different ethnic groups in Xinjiang and their association with diseases, with the expectation of providing basic data for the study of the association of related diseases and gut microbes in Xinjiang, and providing new ideas and clues for the subsequent investigation of the role of gut microbes in the development of diseases. We hope to provide basic data for the study of the association between related diseases and gut microbes in Xinjiang, and provide new ideas and clues for the subsequent investigation of the role of gut microbes in disease development. Results We randomly recruited 192 volunteers from five counties and cities in the Xinjiang Uygur Autonomous Region. The distribution of sampling locations and the ethnic and gender composition of the volunteers are shown in Figures 1a and 1b. Table 1 . Statistics on the distribution of basic information on the number, ethnicity and gender of the sampled population in each region. Region genders ethnic group Changji Hami Kashgar Korla Turpan Male Female Kazakh Uyghur Hui Area (person) 40 41 40 27 44 Gender (person) 99 93 Ethnicity (person) 49 107 36 Healthy people (person) 23 24 18 22 24 60 51 30 54 27 Diabetes only (person) 6 1 2 3 1 7 6 6 3 4 Hypertension only (person) 9 12 13 2 16 26 26 9 41 2 People with both diabetes and hypertension (persons) 2 4 7 3 6 10 4 9 3 Analysis of differences in the composition of gut microorganisms It was found that the composition of intestinal microbial species was basically similar between the healthy and diseased populations of the Kazakh, Hui and Uyghur ethnic groups. However, the Hui diseased population had the highest level of Prevotella at the species level of 0.92 per cent, while Ligilactobacillus ruminis had the lowest level of 0.02 per cent among the five groups (Figure 2c).In addition, we counted the most abundant genera in the six subgroups, and the results showed that Ruminococcus had the highest abundance of 8.35% in the Hui diseased population (Fig. 2b). In the relative abundance analysis at the phylum level, the ratios of Firmicutes and Bacteroidetes were 2.18/3.21, 3.38/6.45, and 3.81/4.2, respectively, with the highest ratio in the Hui diseased population (Fig. 2a).A total of 143 bacterial phyla were detected in all six groups at the phylum level (Fig. 2d). Further analyses revealed that the Uyghur healthy group had seven unique species at the phylum-level classification level of bacteria (Table 2), whereas the Kazakh diseased group had only one unique phylum, Taleaviricota. notably, no unique species were detected in the Kazakh healthy group and the Hui diseased group. More detailed information can be found in Exhibit 1. Table 2 Unique species for Venn analysis in healthy Uyghur population phylum Group Blastocladiomycota UH Haptophyta UH Candidatus_Beckwithbacteria UH Candidatus_Fervidibacteria UH Cryptomycota UH Porifera UH Bryozoa UH Relationship between ethnicity and gut type Cluster analysis of the samples using ethnicity as a subgroup revealed that the dominant genus in the Hui group was Faecalibacterium (Fig. 2a), the dominant genus in the Kazakh group was Bacteroides (Fig. 2b), and the dominant genus in the Uyghur group was Prevotella (Fig. 2c). According to existing studies, the enterotypes of populations from three continents (Europe, North America, and East Asia) have been classified into three groups by genus-level cluster analysis: the genera Anabaena (enterotype 1), Prevotella (enterotype 2), and Ruminalococcus (enterotype 3) [26] . However, our findings differed from those reported by Chen Wei et al. (2021). Chen Wei et al. stated that the enterotype of the Uyghur group was enterotype 2 and the enterotype of the Hui group was enterotype 1 [27] , whereas our study showed that the dominant genus of the Hui group was Faecalibacterium, which was not completely consistent with the characterisation of the anaplasmosis genus of enterotype 1. Shannon diversity analysis and biomarker screening Shannon diversity analysis was performed on all samples, and the results showed that the Shannon diversity index tended to be stable, indicating that the sequencing depth was sufficient (Fig. 4a). Further analysis revealed that the area under the ROC curve (AUC) values of the KD and UD groups and the UH and KH groups were all 1, indicating that the results of the above analyses had a high degree of confidence (Fig. 4b, c). At the species level, the difference in alpha diversity between healthy and diseased groups was significant for Uyghurs and Kazakhs (Fig. 5a, b). To find potential biomarkers (Biomarker), we ranked the importance of iconic species based on the Random Forest algorithm (Fig. 5c, d) and counted the top three species in terms of abundance. In the health group, the species with the highest abundance was Harlan, which accounted for 6.16% of the top 20 abundance rankings, followed by Vigna unguiculata with 6.00%, and Clostridium sp. with 5.85%. And in the disease group, the top three species-level biomarkers in terms of abundance were Lactobacillus sp. (6.73%), Lactococcus chungangensis (6.44%), and Succinatimonas sp. (5.68%). It is noteworthy that the top two species in the Uyghur and Kazakh disease groups were both Lactobacillus spp. bacteria. We conducted a comparative genus level analysis of Uyghur and Kazakh healthy and diseased groups to look for significantly different strains. The results of the analysis showed that the abundance of Lactobacillus (genus Lactobacillus) was 3.8% and 13.67% in the Uyghur healthy and diseased groups, respectively, whereas the abundance of Lactobacillus spp. was 77.59% and 69.96% in the Kazakh healthy and diseased groups, respectively. In addition, the abundance of the genus Succinivibrio was significantly higher in the Uyghur disease group than in the Kazakh group. Notably, the genus Succinivibrio is known to be isolated mainly from the rumen of cattle and sheep. LEfSe analysis and differential strain identification We used LEfSe analysis (LDA Effect Size) to identify differential strains between Uyghurs and Kazakhs. From the phylum level to the genus level, we screened the differential strains with LDA values greater than 3. In the disease group, a total of 16 differential strains were screened, including 7 strains in the Kazakh disease group, all of which were Firmicutes and Actinobacteria bacteria; 9 strains in the Uyghur disease group, which were Proteobacteria, Firmicutes, and Candidatus Melainabacteria phylum bacteria (Figure 7c, d). In the healthy group, a total of 13 differential strains were screened, including 7 strains in the Kazakh healthy group, all of which were Bacteroidetes and Firmicutes bacteria, and 6 strains in the Uyghur healthy group, all of which were Proteobacteria bacteria (Fig. 7a, b). In addition, we found significant differences in the genus Lactobacillus between the Kazakh and Uyghur healthy groups. For more detailed information, please refer to Exhibit 2. Analysis of the correlation between dietary habits and microbiology By identifying differential strains in diseased and healthy groups of Kazakhs and Uyghurs, we clearly revealed the characteristics of gut flora in different ethnic groups and health states. These differences suggest that the composition of the intestinal microbial community shows complex and regular changes under the dual dimensions of ethnicity and health status. The formation of microbial communities is influenced by a combination of factors, among which dietary habits, as a long-term and stable environmental factor, are likely to play a key role in shaping these differences. Therefore, we further conducted a correlation analysis between dietary habits and differential microbes (Fig. 7e), aiming to explore the potential mechanisms of association between the two, and to provide a more comprehensive perspective for understanding the microbiological basis behind ethnic health disparities. The analysis showed that vegetables rich in dietary fibre, vitamins and minerals were positively associated with Candidatus Spyradocola merdavium and negatively associated with Enterococcus faecium. Seafood, a high-protein, low-fat food rich in micronutrients and unsaturated fatty acids, was positively associated with Alphaproteobacteria bacterium and negatively associated with Nocardioides mesophilus. Fruits, which contain mainly carbohydrates, vitamins and dietary fibre, were negatively associated with Enterococcus faecium. Spicy foods were positively associated with Shigella flexneri due to the presence of capsaicin and other components. Overall, different dietary habits were closely associated with different microorganisms by virtue of their unique nutrient composition, which not only validated some of the previous studies on the relationship between diet and microorganisms, but also provided new ideas for further investigation of the ‘diet-microorganism-health’ link [ 28 ] . Analysis of gut microbial diversity in hypertensive populations Background to the study of hypertension and gut microbiology Hypertension remains an important cause of morbidity and mortality from cardiovascular diseases [29] . Elevated blood pressure adversely affects cardiovascular and renal function [30,31] , and the development of hypertension is caused by a combination of complex interactions between genetic and environmental factors [32] . Numerous studies have shown that nutrient intake, including sodium and potassium, and different dietary structures are directly related to blood pressure regulation [33] . Based on the analyses of the gut microbiological composition of the Kazakh, Hui and Uyghur populations in Part I, we further explored the gut microbial diversity and its underlying mechanisms in hypertensive populations. Differences in microbial communities between the hypertensive group and healthy controls To assess the microbial community differences between the hypertensive group and the healthy control group, we performed PCoA (principal coordinate analysis) and β-diversity analysis. The results showed significant differences in microbial community structure between the two groups (Fig. 8a, b), suggesting that hypertensive status may have an important impact on gut microbial composition. This finding is consistent with the trend of microbial diversity changes in the disease groups (e.g., Kazakh and Uyghur disease populations) in Part I, further supporting the key role of gut microbes in metabolic diseases. Identification of differentiated strains of bacteria associated with hypertension Through macrogenomic sequencing analysis, we found significant differences in the gut microbial composition between the hypertensive group and healthy controls at the species level and genus level. In particular, the abundance of Prevotella was significantly higher in the hypertensive group, while the abundance of Bifidobacterium was significantly lower (Figure 9). This result is consistent with the study of Li et al. (2017), suggesting that Prevotella may play an important role in hypertension by triggering inflammatory responses, and may even be a causal inducer of inflammation and hypertension. In addition, we found significant differences at the species level between Uyghur and Kazakh hypertensive groups (Supplementary Fig. 1a). For example, the relative abundance of Escherichia coli was elevated by 1.74% in Uyghur hypertensives compared to Kazakhs, whereas the abundance of Catenibacterium mitsuokai was elevated by approximately 0.6% in the Kazakh group (Fig. 10a). We further identified the significance of these differential strains by Random Forest analysis (Fig. 10b) and screened 88 blood differential metabolites (37 up-regulated and 51 down-regulated) and 222 urine differential metabolites (153 up-regulated and 69 down-regulated) using VIP > 2 and P < 0.05 as screening conditions (Fig. 10c).Validation of the ROC curves showed AUC = 1, indicating a high degree of confidence in the predictions (Supplementary Fig. 1b). Association analysis of differential metabolites with microorganisms Traceability analysis of the differential metabolites revealed that dietary sources had the highest number of metabolites, 56 (blood) and 138 (urine), respectively (Supplementary Fig. 1c, d). By association heat map analysis, we found that Escherichia coli and Catenibacterium mitsuokai showed completely opposite trends to the differential metabolites. For example, in the blood metabolome, Escherichia coli was positively correlated with hydrocinnamic acid (Hydrocinnamic acid), whereas Catenibacterium mitsuokai was negatively correlated, and L-proline (L-Proline) was significantly negatively correlated with Catenibacterium mitsuokai, but significantly positively correlated with Escherichia coli (Figure 10d). In the urinary metabolome, Catenibacterium mitsuokai was significantly negatively correlated with 3-Methyl-2-oxovaleric acid and positively correlated with Escherichia coli, while the opposite correlation was found for caffeic acid (Fig. 10e). In addition, we found that 9-Hydroxyfluorene, a PAH metabolite in urine metabolites, was significantly correlated with both Catenibacterium mitsuokai and Escherichia coli. Metabolic pathway enrichment analysis Pathway enrichment analysis of the differential metabolites revealed that the terpenoid backbone biosynthesis (TBB) pathway had the highest abundance among the blood metabolites (Figure 11a). Many terpenoids (e.g. limonene, perillaldehyde, etc.) have been found to have vasodilatory effects and can help lower blood pressure by regulating the contraction and relaxation of vascular smooth muscle. Among the urinary metabolites, the caffeine metabolism (Caffeine metabolism) pathway has the highest abundance (Figure 11b). Caffeine metabolic pathways, particularly CYP1A2 enzyme activity, are known to play a key role in the effects of caffeine on blood pressure. Combining KEGG metabolic pathway enrichment analysis of macrogenomic data with metabolomic pathway enrichment analysis, we found that amino acid metabolism, lipid metabolism, as well as terpene and polyketide metabolic pathways were significantly enriched in the hypertensive population [34] . Analysis of gut microbiome differences between healthy and type II diabetic populations Background to the study of type II diabetes and the gut microbiome Type II diabetes mellitus (T2DM), like hypertension, is a common chronic disease with a high prevalence in the population.The dietary patterns of patients with T2DM differ significantly from those of the healthy population, and the related medication may also have an impact on their intestinal flora structure. In addition, the special dietary structure (e.g., high-fat and high-sugar diet) in Xinjiang may further exacerbate the differences in the intestinal flora structure between T2DM patients and the healthy population. Based on the analyses of the gut microbiome composition in hypertensive and healthy populations in Part I and Part II, we further explored the differences in the gut microbiome between T2DM patients and healthy populations and their potential mechanisms. Differences in microbial communities between the T2DM group and healthy controls We analysed the microbial community differences between the T2DM group and the healthy control group. The results showed that the abundance of Bacteroides in the healthy control group was 8.05%, which was significantly higher than that of 3.78% in the T2DM group. In addition, the abundance of Prevotella at genus level and species level was significantly higher in the T2DM group (Fig. 12a, b). The combined analysis showed that the abundance of gut-dominant microorganisms was greater in the healthy control group than in the T2DM group, with Bacteroides caccae, Dorea longicatena, and Anaerobutyricum hallii as the dominant strains specific to the healthy control group, whereas these strains were not detected in the T2DM group. In contrast, Bifidobacterium adolescentis was the dominant strain in the T2DM group, while it did not show dominance in the healthy control group (Fig. 12a, b). Differences in microbial species in T2DM patients of different ethnicities In order to explore the species differences in gut microbes of T2DM patients of different ethnicities, we divided T2DM patients into three groups (N ≥ 3) of Uyghur, Kazakh and Hui ethnicities according to their ethnicity and analysed the species variability. The results showed that the relative abundance of Clostridia bacterium was higher in Uyghur T2DM patients, whereas Holdemanella biformis and others had higher relative abundance in the intestines of Kazakh T2DM patients. In addition, the relative abundance of unclassified_g__Ruminococcus, Ruminococcus bromii, and unclassified_g_Alistipes was higher in Hui T2DM patients, and the difference was significant (P < 0.05) (Figure 13). These results suggest that there are significant differences in the gut microbial composition of T2DM patients from different ethnic groups, which may be related to genetic background, dietary habits, and environmental factors. Functional analysis of macrogenomic KEGG In order to deeply investigate the pathogenesis of T2DM, we performed KEGG functional analysis on the macrogenomic data. The results showed that there were significant differences between the T2DM group and the healthy control group in a variety of functional pathways (Figure 14a). Among them, the difference in energy metabolism-related pathways (e.g., glycolysis/glycolysis) was the most significant: the percentage of this pathway in the T2DM group was 25.3%, which was significantly lower than that in the healthy control group, which was 32.1% (difference: -6.8%, 95% confidence interval: [-8.5%, -5.1%], P = 0.002). This result suggests that the inhibition of glycolysis/glycolysis function of gut microorganisms in T2DM patients may interfere with the host's glucose regulation by affecting related enzyme activities or metabolite concentrations, which may lead to abnormally elevated blood glucose. In addition, the phenylalanine metabolic pathway accounted for 8.5% in the T2DM group, which was significantly lower than that of the healthy control group, which was 11.2% (difference: -2.7%, 95% confidence interval: [-3.6%, -1.8%], P = 0.003). Phenylalanine metabolism is closely related to physiological processes such as neurotransmitter synthesis and antioxidant defence, and its metabolic abnormalities may be associated with the development of complications such as diabetic neuropathy. Urine metabolome KEGG enrichment analysis To further explore the impact of changes in gut microbial function on host metabolism, we performed KEGG enrichment analysis on the urinary metabolome of T2DM patients (Figure 14b). The results showed that the pathways of fatty acid biosynthesis, pentose and glucuronide interconversion, riboflavin metabolism, and taurine and taurine metabolism showed an up-regulation trend. These changes may be an adaptive response of the host to changes in the gut microbial community and overall metabolic disorders. For example, up-regulation of pentose and glucuronide interconversion pathways may contribute to the maintenance of glucose metabolic homeostasis, whereas altered riboflavin metabolism may affect electron transfer in energy metabolism. On the other hand, metabolic pathways such as pyruvate metabolism, fructose and mannose metabolism, and mineral absorption are significantly downregulated. In addition, pathways such as choline metabolism, propionate metabolism, sphingolipid signalling pathway, protein digestion and uptake also showed downregulation in cancer. These changes, together with altered microbial function in the macrogenome, reflect the complex pathophysiological processes in T2DM patients. For example, downregulation of propionate metabolism may affect the production of short-chain fatty acids, which in turn affects intestinal barrier function and immune regulation; alterations in sphingolipid signalling pathways may affect cell signalling and proliferation, which is associated with the development of chronic complications of diabetes [35-39] . Analysis of differences in blood metabolomes To explore the differences between the blood metabolomes of T2DM patients and the healthy population, we plotted a volcano diagram (Fig. 14c). The results showed that metabolites such as Lactaldehyde, D-1,5-Anhydrofructose, Ureidopropionic acid, Hypotaurine, and Glycoprotein-phospho-D-mannose exhibited significant differences between the two groups. Among them, the down-regulated metabolites, such as Lactaldehyde, may have reduced activity in the relevant metabolic pathways in the disease state, while some of the up-regulated metabolites may be involved in compensatory or pathological processes in the disease process. Judging from the VIP values, some of the differential metabolites were of high importance in distinguishing T2DM patients from the healthy population, suggesting that these metabolites may be used as potential biomarkers for early diagnosis, disease monitoring or prognostic assessment of T2DM. Analysis of the composition of the gut microbiota in healthy and diabetic and hypertensive populations from different regions of Xinjiang In recent years, the prevalence of type 2 diabetes mellitus (T2DM) and hypertension (HTN) has been increasing year by year with lifestyle changes. However, the gut microbiological and metabolic characteristics of a co-patient population (T2DM_HTN) with both diseases have not been adequately studied [40] . The aim of this study was to reveal the differential characteristics by comparing the gut microbiological and metabolic data of a healthy population (H), patients with T2DM, patients with HTN, and a co-patient population with T2DM_HTN, in order to provide a theoretical basis for the integrated management of complex diseases. Analysis of microbial community diversity To characterise the richness and diversity of the bacterial community, we analysed it at the species level using the Chao1 diversity index (Fig. 15a). The results showed that there was a significant difference in community diversity and richness between the healthy group (H) and the T2DM_HTN group (P < 0.05), and the diversity and richness of the T2DM_HTN group was significantly lower than that of the healthy group (Fig. 15b). This result suggests that disease status may negatively affect the stability of gut microbial communities. Analysis of differential strains By comparing the differential strains in the healthy and T2DM_HTN groups (Fig. 16a), we found that Prevotella copri and Bifidobacterium pseudocatenulatum were dominant in the T2DM_HTN group. Notably, the abundance of Bifidobacterium pseudocatenulatum was significantly higher in the T2DM_HTN group than in the healthy group, a result that is different from previous studies and suggests its potential role in the co-morbid state. To further validate this finding, we plotted a heat map (Fig. 16b), which showed that Bifidobacterium catenulatum was also dominant in the T2DM_HTN group, consistent with the results of differential strain analysis. ANOSIM analysis with Venn diagram By ANOSIM analysis (Fig. 17a, Table 3), we verified that there were significant differences in gut flora characteristics between the T2DM_HTN group and the T2DM and HTN groups.Venn diagram analysis (Fig. 17b) further revealed differences and overlaps in gut microbial species between the healthy, T2DM, HTN and T2DM_HTN groups. For example, there were 4230 unique microbial species in the HTN group, of which Escherichia coli accounted for about 0.2%; 855 unique microbial species in the T2DM group, of which Prevotella copri accounted for about 0.3%; and 423 unique microbial species in the T2DM_HTN group, of which Bifidobacterium adolescentis with a share of about 0.15%. In addition, the abundance patterns of 139 microorganisms common to the three groups (e.g., Clostridiales bacterium) differed among the groups, suggesting that their functions may change in the disease state. Table 3 ANOSIM results analysis table, a positive sitatistic value indicates that this analysis is reasonable Analysis of differences between the two groups In order to more deeply dissect the role of gut microorganisms in disease development, we conducted a two-by-two difference analysis between the T2DM group, the HTN group, and the T2DM_HTN group.The results of Welch's t-test showed (Fig. 18a) that the proportion of Coriobacteriales bacterium in the T2DM_HTN group was about 1.5%, which was significantly lower than 3.2% in the HTN group (90% confidence interval: [7.1%, 11.3%]). In addition, the proportion of Fusobacteriales bacterium in the T2DM_HTN group was about 7.8%, which was significantly higher than that of 2.1% in the HTN group (90% confidence interval: [5.5%, 9.2%]). These microbial changes may collectively influence disease onset and progression. Student's t-test results showed (Figure 18b) that Dorea formicigenerans was about 0.54% in the T2DM group, which was significantly lower than that of 4.2% in the T2DM_HTN group (90% confidence interval: [3.8%, 5.4%]). In addition, the proportion of Clostridiales diff was about 0.42% in the T2DM group, which was significantly higher than that of 0.23% (90% confidence interval: [2.5%, 4.1%]) in the T2DM_HTN group, suggesting that it may be associated with the disease process. H ierarchical cluster analysis The hierarchical clustering tree diagram based on the species level (Fig. 18c) showed that certain microorganisms (e.g., unclassified_f__Streptococcaceae) in the T2DM_HTN group were more tightly clustered and had higher relative abundance. In addition, the HTN, T2DM and T2DM_HTN groups each formed relatively independent clustering branches, further confirming the significant differences in the gut microbial communities under different disease states. Non-targeted metabolomics analysis In order to construct a more complete map of the disease mechanism, we further carried out urine and blood metabolomics studies. The hierarchical clustering heatmap of urinary metabolites (Figure 18e) showed that the samples in the T2DM_HTN group and the T2DM group each showed a certain clustering trend, suggesting that these samples had a high degree of similarity in urinary metabolite composition. In the blood metabolome differential metabolite analysis, the volcano plot (Figure 18d) showed that Furanone A and 2‘,4’,6'-Trihydroxyacetophenone presented an up-regulation trend, whereas LysoPC (20_5(5Z,8Z,11Z,14Z,17Z)_0_0) showed a down-regulation. In the urinary metabolome, L-carnitine and citric acid were up-regulated in the T2DM_HTN group relative to the healthy group, whereas malic acid was down-regulated (Figure 18e). Discussion Differences in gut phenotypes and gut microbiological composition In the present study, in the enterotype analysis, it was found that the enterotypes of the Hui group were dominated by Faecalibacterium (Clostridium pratensis), whereas the enterotypes of the Uyghur group were dominated by Prevotella (Prevotella), which differed from the findings of the Uyghur group, which had been reported to have enterotype 2, and the Hui group, which had enterotype 1 [41] . The core microbiota of the human gut consists mainly of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria and Verrucomicrobia ( Verrucomicrobia) comprise of which the thick-walled and anamorphic phyla account for about 90% of the intestinal flora [42] . The thick-walled phylum mainly consists of Lactobacillus, Bacillus, Clostridium, Enterococcus faecalis and Ruminococcus, while the bacillus phylum consists of Bacteroides and Prevotella. Prevotella. Prevotella had the highest percentage of all groups, which is consistent with the study of De Filippo et al. (2010), suggesting that it is enriched in carbohydrate-based diets, and may inhibit pathogens and protect the host from inflammatory and colonic diseases through the production of short-chain fatty acids (SCFAs) [42] . Effects of ethnicity and diet on gut microbiota At the species level, there were significant differences in species abundance and metabolites between the Uyghur healthy group and the Kazakh healthy group, and between the Uyghur diseased group and the Kazakh diseased group. These differences may stem from the different dietary patterns of the two ethnic groups. For example, a diet high in sodium and low in potassium, as well as an overweight basal body state, may be potential triggers for diseases such as hypertension. Recent studies have shown that the relative abundance of strains such as Prevotella (Prevotella) and Clostridium (Clostridium) is significantly higher in the intestines of hypertensive patients [Yan Q et al. 2017], which is consistent with our findings. In addition, the proportion of unclassified_o_Eubacteriales was significantly higher in hypertensive patients, and their role in the disease deserves to be further explored despite the paucity of relevant studies. Correlation between dietary habits and microorganisms In the analysis of the correlation between differential microorganisms and dietary habits, we found that meat, as a high-protein and high-fat food, was negatively correlated with Chitinophaga_silvihsoli. This result reveals the differential effects of meat nutrients on different microorganisms during intestinal digestion. For example, proteins in meat may provide a suitable growth environment for Enterococcus_faecium after catabolism by intestinal flora, whereas fat digestion may inhibit the survival of Chitinophaga_silvihsoli. This finding not only supplements the research gap on the relationship between meat diet and gut microbes, but also suggests the potential intervention value of dietary modification on gut health [43] . In addition, this study provides new data on the correlation between seafood and spicy foods and microorganisms, expanding the research boundaries in this field. Association of hypertension with metabolites The onset and progression of hypertension, a chronic metabolic disease, is closely related to the synthesis of multiple metabolites and the enrichment of metabolic pathways. The role of compounds such as short-chain fatty acids (SCFAs), trimethylamine oxide (TMAO), bile acids (BAs), and hydrogen sulfide (H2S) in hypertension has been widely studied [44-47] . In the present study, Escherichia coli was found to be positively correlated with hydroxycinnamic acid while Catenibacterium mitsuokai was negatively correlated in the blood metabolome of hypertensive patients. Levels of hydroxycinnamic acid are known to be associated with hypertension [48] , whereas proline metabolites are associated with lower blood pressure [49] . In the urinary metabolome, Catenibacterium mitsuokai showed a significant negative correlation with 3-methyl-2-oxovaleric acid, while Escherichia coli showed a positive correlation. In addition, 9-hydroxyfluorene was significantly correlated with both microorganisms, suggesting a possible association with hypertension [50] . Interaction between type II diabetes and gut microbiota Significant changes in the composition of the gut microbiome are important factors in the development and progression of type II diabetes mellitus (T2DM). Specific gut microbes have beneficial or detrimental effects on the development of diabetes through the production of metabolites that promote or inhibit inflammatory responses. For example, Lactobacillus fermentum, Lactobacillus plantarum, Lactobacillus casei, Roseburia, Mucinophilus Akkermansia muciniphila) and Bacteroides fragilis may alleviate diabetic symptoms by improving intestinal barrier integrity and inhibiting inflammatory factors [Lee, C.B et al. 2021]. In the present study, higher abundance of Bifidobacterium adolescentis was detected in the intestinal tract of patients in the diabetic group, which may be related to dietary and pharmacological interventions of the patients. Disease mechanism revealed by metabolomics The KEGG enrichment analysis of urine metabolome showed that the fatty acid biosynthesis pathway was significantly up-regulated, suggesting abnormal fatty acid metabolism and energy imbalance in the disease state; the pentose and glucuronic acid interconversion pathway was down-regulated, suggesting its metabolism was suppressed; and the arginine and proline metabolism pathway showed bi-directional changes, reflecting the complex metabolic regulation mechanism in the disease state. These results provide important clues for the study of disease mechanism. Integration of gut microbiology and metabolomics In this study, we revealed the mechanisms associated with T2DM, HTN and the concurrence of the two (T2DM_HTN) by integrating gut microbial and urinary metabolomics analyses. Gut microbial analyses revealed significant differences in the proportions of Coriobacteriales_bacterium and Dorea_formicigenerans in the T2DM_HTN versus HTN populations, suggesting that these microbes may play a key role in the disease complication mechanism. Hierarchical clustering dendrograms based on the species level further indicated that the relative abundance of unclassified_f__Streptococcaceae was higher in the T2DM_HTN group, which may be closely associated with this disease state. Urine metabolome results showed that L-carnitine was up-regulated in the T2DM_HTN group, which may be associated with altered fatty acid metabolism, reflecting an adaptive response of host metabolism to the disease state. Study limitations and future directions The limitations of this study are the cross-sectional design, which did not allow for the identification of a causal relationship between dietary habits and microbial changes. In addition, the effects of genetics, lifestyle, exercise and drug use on gut microbes were not adequately included in the analysis. Future studies should incorporate a longitudinal design and a multi-omics approach to further reveal the complex relationship between gut microbes and metabolic diseases. Conclusion and outlook Key Findings This study revealed the following key findings by analysing the gut microbiome and metabolomics of healthy and diseased populations (including hypertension, type II diabetes and their co-morbidities) from different ethnic groups in Xinjiang: Unique gut phenotypes in Hui samples: The dominant genus of bacteria in the Hui group was Faecalibacterium, a finding that has never been reported before.Faecalibacterium, as an important genus of anti-inflammatory bacteria, may play an important role in maintaining intestinal health and metabolic homeostasis. Potential functions of specific strains: for example, Bacteroides wexlerae (B. wexlerae) was identified as a commensal bacterium negatively associated with obesity and type II diabetes. Oral administration of B. wexlerae has been shown to reduce high-fat diet-induced obesity and diabetes by inducing metabolic changes and anti-inflammatory effects [51] . These findings reveal unique regulatory pathways between host and microbial metabolism and provide new potential strategies for the prevention and treatment of metabolic disorders. MULTI-OMOMETRIC ASSOCIATION ANALYSIS: By integrating gut microbiome, dietary habits, urinary metabolites and metabolic pathway analyses, this study found that changes in the proportions of specific microorganisms in disease states were closely associated with significant differences in urinary metabolites. For example, the proportions of Coriobacteriales_bacterium and Dorea_formicigenerans differed significantly between the T2DM_HTN and HTN populations, suggesting that these microorganisms may play a key role in disease complication mechanisms. In addition, changes in metabolites such as L-carnitine reflect adaptive responses of host metabolism to the disease state. Significance of the study In this study, we systematically revealed for the first time the gut microbiome and metabolome profiles of healthy and diseased populations of different ethnic groups in Xinjiang, which provided a new perspective for understanding the role of gut microbes in metabolic diseases (e.g., hypertension, type II diabetes mellitus, and their co-morbidities). By integrating and analysing the multi-omics data, we not only validated some of the existing findings (e.g., the role of dietary fibre on microbes [52,53] ), but also provided brand new data on the correlation of seafood, spicy food and meat with microbes, expanding the research boundary of this field. Future Research Directions To further deepen the study, we have collected 1038 health and disease samples from 15 regions and 7 ethnic groups in Xinjiang. Future research will focus on the following directions: Expanding Sample Size and Diversity: Through larger sample collection and multi-ethnic comparisons, we will further analyse the diversity of gut microorganisms in healthy and diseased populations, and search for representative gut microbiota and potential metabolic diagnostic and therapeutic markers. Integration of multi-omics technologies: Combining macro-genomic, metabolomic, transcriptomic and other multi-omics technologies to clarify the specific associations between gut microbes and host metabolism, providing more effective strategies for disease prevention and treatment. Longitudinal Studies and Mechanism Exploration: Longitudinal studies are designed to reveal the causal relationship between dietary habits, microbial changes and disease development, and to delve into the specific mechanisms of specific microorganisms and their metabolites in the development of disease. Summary This study reveals the complex relationship between gut microbes and metabolic diseases through the integration and analysis of multi-omics data, which provides an important scientific basis for early diagnosis, personalised treatment and prevention of the diseases. Future studies will further combine the multi-omics technology and longitudinal design to comprehensively reveal the interactions and specific mechanisms among the factors and provide more effective strategies for the prevention and treatment of metabolic diseases. Materials and Methods Sample collection and raw letter processing Recruitment of subjects and information collection In this study, 192 volunteers were randomly recruited from five different counties and cities in Xinjiang, and all volunteers in each area were local indigenous residents. Basic information was collected from the sample population by means of a questionnaire, which was administered by a doctor to the volunteers in the form of a face-to-face interview. The questionnaire consisted of 19 questions based on the latest research findings, which revealed various factors associated with the gut microbiome, of which 7 were related to basic information, such as age, gender, ethnicity, geographic location, BMI, etc., and the other 12 were related to dietary habits, lifestyle variables, and disease history (Supplementary Table 1). Sample collection The study was reviewed by the Ethical Clinical Research Ethics Sub-Committee of the Xinjiang Uygur Autonomous Region People's Hospital (KY2023060173), and was collected from Kashgar, Korla, Changji, Hami, and Turpan regions of Xinjiang, China (Supplementary Table 1). Informed consent was obtained from each participant to participate in this study and to publish relevant data. All studies were conducted in accordance with relevant guidelines and regulations. Stool samples: Fresh stool samples collected under anaerobic conditions before breakfast were loaded into 5 ml disposable sterile individual LF005 stool tubes and confirmed to be free of contamination and QC qualified samples were immediately frozen in liquid nitrogen. Samples were transferred to the laboratory in an ice bucket within 24 hours and stored at -80°C. Blood samples: Again, collected before breakfast, blood is collected using a sterile needle and quickly drawn into a sterile blood collection tube. A moderate amount of blood (5-10 ml) is usually required. Ensure that the collection environment is clean. Seal the collected blood sample in a sterile container and store it in a dry refrigerator, making sure that the container does not leak. In order to separate the different components of the blood, the blood collection tube containing the blood sample is placed in a centrifuge and the collected blood sample is centrifuged at a speed of 1000 - 3000 rpm under centrifugal conditions. Urine Sample: A ‘mid-stream urine collection method’ is used to minimise external contamination. The patient urinates a small amount of urine (approximately 1-2 seconds) and then receives the mid-stream urine in a sterile container. Collect a urine sample of approximately 10-20 ml, avoiding contact between the sample and the outside of the container. Saliva: Volunteers should avoid eating, drinking, smoking and using oral cleansing products for 30 minutes prior to collection to minimise the impact on the sample. Before collection, you may rinse your mouth with water, but do not use any mouthwash containing chemicals. Place a sterile saliva collection tube in the mouth and wait for natural saliva to flow into the tube. Collect a saliva sample of approximately 2-5 ml. Ensure that the sample does not come into contact with the outside of the container. Seal the collected samples in sterile containers and store them in a dry refrigerator, ensuring that the containers do not leak. Clearly label the container with the sample number, date and time of collection and volunteer information. After collection, the samples were transported to the laboratory via cold chain (within 24h). During transport, maintain the samples at the appropriate temperature to protect microbial activity. Follow relevant Standard Operating Procedures (SOPs) and ethical guidelines according to specific research or clinical requirements. Macrogenome sequencing of faecal samples DNA extraction and library construction The collected faecal specimens were centrifuged at 12000 × g for 5 min at room temperature and the supernatant was discarded. 200 mg of precipitate was weighed from each sample, and the sequencing platform NovaSeq 6000 (Illumina, San Diego, California, USA) was used with the kit FastPure Stool DNA Isolation Kit (Magnetic bead) (MJYH, Shanghai, China). The extracted genomic DNA was detected using 1% agarose gel electrophoresis. the genomic DNA was fragmented to about 350 bp (Covaris M220). PE libraries were constructed using NEXTFLEX@ Rapid DNA-Seq (Bioo Scientific, USA) and ‘Y’ junctions were attached (MEGAHIT v1.1.2). Self-attached fragments were removed from the junction using magnetic bead screening, and the library template was enriched using PCR amplification and denatured with sodium hydroxide to produce single-stranded DNA fragments. The single-stranded DNA fragments were then sequenced using the Illumina NovaSeq (lumina, USA) sequencing platform for macro-genome sequencing (Shanghai Meiji Biomedical Technology Co., Ltd.), and then subjected to bridge PCR, in which one end of the DNA fragments was complementary to a primer base and immobilised on the microarray, and the other end was randomly complementary to another primer in the vicinity and was also immobilised to form a ‘bridge’ (bridge). (The other end is randomly complementary to another primer nearby and is also anchored, forming a ‘bridge’; PCR amplification produces DNA clusters; the DNA amplicon is linearised into a single strand. Add modified DNA polymerase and dNTP with four fluorescent markers to synthesise one base per cycle; scan the surface of the reaction plate with a laser to read the type of nucleotide that was polymerised in the first reaction for each template sequence; chemically cleave the ‘fluorescent group’ and ‘termination group’ to restore the 3-part sequence. The ‘fluorescent group’ and ‘termination group’ are chemically cleaved to restore the stickiness of the 3' end and continue to polymerise the second nucleotide; the fluorescent signals collected in each round of the reaction are counted to know the sequence of the template DNA fragments. Data Quality Control Raw data such as sequencing adapter sequences, low-quality bases, N (uncertain base information) bases and short length sequences, which seriously affect the quality of subsequent analyses, are subjected to quality control to ensure the accuracy of the results of subsequent analyses. The software fastp (v0.20.0) was used to cut adapter sequences with average quality values of less than 20 at the 3‘ and 5’ ends of the sequence, and reads with lengths less than 50bp after quality cutting, to obtain high-quality PE reads. Species and Function Annotation NR species annotation Non-redundant gene sets were compared to the NR database using DIAMOND software (v2.0.13) (comparison type: BLASTP), and species annotations were obtained from the corresponding taxonomic information database of the NR library, and then the abundance of the species was calculated using the abundance calculation method using Reads Number. KEGG functional annotation KEGG functional annotation letters corresponding to genes were obtained and counted using the database KEGG20230830 comparison with the key parameter blastp; E-value ≤ 1e-05, linking genomic and strain functional information. Alpha diversity analyses We calculated alpha diversity indices for good coverage and non-phylogeny of sparse curves, choosing a secondary sampling depth of 4000 reads per sample to calculate the mean of the above alpha diversity indices. Different diversity indices were tested using the Kruskal-Wallis test. Beta diversity analysis Using Wilcoxon rank sum test two-tailed test using bray-curtis distance algorithm to calculate the differences in community structure of different subgroups, PCoA analysis was used to project the high dimensional data with multiple features into lower dimensional space to resolve the main influences from the multiplicity of things. Random Forest Machine learning based on the RPKM abundance calculation method using ten-fold cross validation to predict the importance of characteristic species that influence the overall distribution of a community, Analysis software: Random Forest package for the R language. ROC analysis Calculate the top 20 species at taxonomic level based on RPKM abundance with a confidence interval of 0.99. Analysis software: R language V4.3.3, plotROC Gut type analysis The typing of the dominant colony structure of different samples was investigated by statistical clustering. Relative abundance was calculated using the reads number abundance calculation method, clustering was performed using the bray-curtis distance algorithm, and the optimal clustering K-value was calculated by the Calinski-Harabasz (CH) index, followed by the Between-class analysis (BCA, K ≥ 3) or principal coordinates analysis (PCoA, K≥ 2) for visualisation. Software: R language ade4 package, cluster package, clustersim package. Blood and urine untargeted metabolome sequencing Data pre-processing Raw data extraction and conversion: obtain raw data from the sequencing instrument and convert it to a format suitable for analysis, such as converting mass spectrometry data files to mzXML format. Remove noise, impurity signals and poor quality data points. For example, in liquid chromatography-mass spectrometry (LC-MS) data, exclude data with too low or too high signal intensity and abnormal retention times. Identify chromatographic or mass spectrometric peaks corresponding to metabolites and align these peaks across samples to ensure that the same metabolite is correctly characterised across samples. Metabolite Identification Database Matching: The obtained metabolite profile is compared with known metabolite databases, like the Human Metabolome Database (HMDB), METLIN, etc., to determine the probable identity of the metabolite based on mass-to-charge ratios, retention times, and other information. Experiments are performed using pure standards, which are compared with sample data to accurately identify metabolites. Data analysis Univariate analysis: basic statistics like mean, standard deviation, etc. are calculated for each metabolite and t-tests, analysis of variance (ANOVA), etc. are used to find metabolites with significant differences between groups. Multivariate analysis: Principal Component Analysis (PCA) is used to look at the overall distribution of the data and clustering between samples; Partial Least Squares Discriminant Analysis (PLS - DA), etc., can highlight the differences between the groups and find the metabolite variables that contribute to the subgroups. Metabolic pathway analysis: the identified differential metabolites are mapped to metabolic pathway databases such as KEGG to identify affected metabolic pathways, e.g. analysing the impact of gut microbial metabolites on host energy metabolic pathways. Data Visualisation Drawing volcano plots: presenting the multiplicity and significance level of differences of metabolites among different groups, which can visually filter out metabolites with significant differences. Produce heat maps: presenting changes in the relative content of metabolites in different samples, helping to understand the expression patterns of metabolites under different conditions. Sampling points were mapped using Arcgis (v 10.8), and symbols were set for the elements of the layer according to their properties. Add map elements such as title and legend through the ‘Insert’ menu. Declarations Author Contribution Author informationAuthors and AffiliationsLaboratory of Synthetic Biology, College of Life Science and Technology, Xinjiang University, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China.Haitao Yue, Pazilaiti Yashenga, Xia Chen, Lulu Wang, Hussain AkbarSchool of Future Technology, Xinjiang University, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China. Yuxuan KouDepartment of Gastroenterology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, Xinjiang Uygur Autonomous Region, People's Republic of China Xinjiang Clinical Research Center for Digestive Diseases, Urumqi 830001, Xinjiang Uygur Autonomous Region, People's Republic of ChinaFeng Gao、Tian ShiSchool of Pharmaceutical Sciences, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China. Haitao YueContributionsHYT conceived the initial research idea and designed the technical route; PY took the lead in drafting the initial version of the manuscript and organized the overall structure. PY, XC, TS,LW, YK, and HA wrote the paper.FG and HYT reviewed and revised the paper. All authors have read and approved the final version of the paper. Corresponding authorsCorrespondence to Haitao Yue、Feng Gao Acknowledgements This research was supported by multiple funding sources. It received support from the Central Leading Local Science and Technology Development Special Fund Project (Autonomous Region Science and Technology Department) under the grant number ZYYD2022A06. Additionally, it was funded by the key Research and Development Project of Xinjiang Uygur Autonomous Region of China with grant numbers 2023B02034 and 2023B02034 - 2. Moreover, financial support was provided by the National Natural Science Foundation of China under grant numbers U2003305 and 31860018. Funding This research was supported by multiple funding sources. It received support from the Central Leading Local Science and Technology Development Special Fund Project (Autonomous Region Science and Technology Department) under the grant number ZYYD2022A06. Additionally, it was funded by the key Research and Development Project of Xinjiang Uygur Autonomous Region of China with grant numbers 2023B02034 and 2023B02034 - 2. Moreover, financial support was provided by the National Natural Science Foundation of China under grant numbers U2003305 and 31860018. Ethic Declarations Ethics approval and consent to participate This study was reviewed and approved by the Ethical Clinical Research Ethics Sub - Committee of the Xinjiang Uygur Autonomous Region People's Hospital (approval number: KY2023060173). The entire research process was carried out in strict accordance with the principles of the Declaration of Helsinki and relevant regulations, ensuring compliance with ethical standards. All participants involved in this study were from Kashgar, Korla, Changji, Hami, and Turpan regions of Xinjiang, China (see Supplementary Table 1 for details). Before the commencement of the study, each participant was fully informed about the research objectives, procedures, potential risks, and benefits. Informed consent was obtained from every participant, including consent to participate in this study and the publication of relevant data. Consent for publication All participants whose data are presented in this manuscript have provided explicit consent to publish. This consent encompasses the publication of the manuscript in the journal Microbiome, its affiliated online platforms, and any other academic databases or repositories where the journal's content may be distributed or indexed for academic use. For the data used in this study, all personal identifiers have been carefully removed or anonymized. Participants were informed about this anonymization process in advance and consented to the publication of their anonymized data. The anonymized data were processed and stored in a manner that ensures the participants' privacy and confidentiality. Written informed consent to publish was obtained from each participant. These consent forms are securely maintained in the research archives of Xinjiang University. They are available for inspection upon request from the journal or relevant ethical review boards to verify the authenticity of the consent process. This approach ensures that the rights and privacy of all participants are fully respected and protected throughout the publication process. Competing interests The authors declare no competing interests. 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Impaired autonomic nervous system-microbiome circuit in hypertension. Circ Res. 2019;125(1):104–116. doi:10.1161/CIRCRESAHA.119.313965 . de la Cuesta-Zuluaga J, Mueller NT, Álvarez-Quintero R, Velásquez-Mejía EP, Sierra JA, Corrales-Agudelo V,Carmona JA, Abad JM, Escobar JS. Higher fecal short-chain fatty acid levels are associated with gut microbiome dysbiosis, obesity, hypertension and cardiometabolic disease risk factors. Nutrients. 2018;11(1):11. doi:10.3390/nu11010051 . Zhang Q, He F, Kuruba R, Gao X, Wilson A, Li J, Billiar TR, Pitt BR, Xie W, Li S. FXR-mediated regulation of angiotensin type 2 receptor expression in vascular smooth muscle cells. Cardiovasc Res. 2008;77 (3):560–569. doi:10.1093/cvr/cvm068 . WU Guixia,JIANG Rui,LI Jing,et al. Characterisation of urinary metabolites in rats with spontaneous hypertension intervened by Danhong injection based on 1HNMR method[J]. Journal of Xinjiang Medical University,2021,44(1):92-96. DOI:10.3639/j.issn.1009-5551.2021.01.019. Kim H, Appel LJ, Lichtenstein AH, Wong KE, Chatterjee N, Rhee EP, Rebholz CM. Metabolomic Profiles Associated With Blood Pressure Reduction in Response to the DASH and DASH-Sodium Dietary Interventions. Hypertension. 2023 Jul;80(7):1494-1506. doi: 10.1161/HYPERTENSIONAHA.123.20901. Epub 2023 May 10. PMID: 37161796; PMCID: PMC10262995. Lai Danni, Zhang Li'e, Yang Jie, Ou Songfeng, Li Zhiying, Feng Yumeng, Zou Yunfeng. Relationship between urinary polycyclic aromatic hydrocarbon metabolites and stage 1 hypertension in a Guangxi population[J]. Journal of Environmental Health, 2020, 10(6): 527-533. DOI: 10.13421/j.cnki.hjwsxzz.2020.06.002 Koji Hosomi. Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota[J], Nature Communications,2022,1-15. Smith A, Johnson B, Brown C. The impact of dietary fat and fiber on gut microbiota composition in humans: A systematic review[J]. Journal of Nutrition Research, 2022, 42(2): 123 - 135. García - Mantrana I, Marcos A, Gómez - Candela C. The role of diet in shaping the gut microbiota and its implications for health and disease[J]. Nutrients, 2021, 13(8): 2737. Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.7z Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6125489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":424313758,"identity":"9c8fcde4-46d3-408f-827f-8b493d540f94","order_by":0,"name":"Pazilaiti Yasheng","email":"","orcid":"","institution":"xinjiang university","correspondingAuthor":false,"prefix":"","firstName":"Pazilaiti","middleName":"","lastName":"Yasheng","suffix":""},{"id":424313759,"identity":"658ca23a-0d4d-4eba-9c38-18c1a25b6b0f","order_by":1,"name":"Xia Chen","email":"","orcid":"","institution":"xinjiang university","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Chen","suffix":""},{"id":424313760,"identity":"85c7298e-eec3-4b91-a2c0-8f5a06014bd6","order_by":2,"name":"Tian Shi","email":"","orcid":"","institution":"Department of Gastroenterology, People's Hospital of Xinjiang Uygur Autonomous Region 2Xinjiang Clinical Research Center for Digestive Diseases","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Shi","suffix":""},{"id":424313761,"identity":"f1e2e9f8-67b1-43b9-a210-d912be76c9db","order_by":3,"name":"Yuxuan Kou","email":"","orcid":"","institution":"xinjiang university","correspondingAuthor":false,"prefix":"","firstName":"Yuxuan","middleName":"","lastName":"Kou","suffix":""},{"id":424313762,"identity":"792ff775-d69f-497b-a6e1-f9f926cdf6a7","order_by":4,"name":"Lulu Wang","email":"","orcid":"","institution":"xinjiang university","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Wang","suffix":""},{"id":424313763,"identity":"c4afc130-1979-47f8-8e5e-ed7e5b0bcccd","order_by":5,"name":"Feng Gao","email":"","orcid":"","institution":"Department of Gastroenterology, People's Hospital of Xinjiang Uygur Autonomous Region 2Xinjiang Clinical Research Center for Digestive Diseases","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Gao","suffix":""},{"id":424313764,"identity":"837ea986-b91e-4bbc-b432-bb8e8c0aa7b5","order_by":6,"name":"Haitao Yue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYHACNgYGAyDF3sDAkABGxGkB6uE5TJIWkDUSyWAeYS0GN5KPPfhR8EeeX/L94Q8Pd9TmGRw/wPi44heDvDlOLWnphj0GBoYzZyezSSSeOV5scCaB2fBsH4PhzgZcWnLMJHgMDBg33E5mY0hsO5a44UACm2RjD0OCwQFcWvK/Sf4xMLDfcPMw8wewlvMPCGnJYZMG2pK44QYzg0RiWw2QAbSl4QduLZJnnplJyxgYJ8/sSTYDajmQOPPGw2bDxgYJww04tPAdT34m+eaPnG0/+8HHH3+21SX2nU8++LDhj408LlsU0MSB8cnA2MDA2CaBXT0QyDeg8uug9B+cOkbBKBgFo2DkAQATRGRf7X0MWwAAAABJRU5ErkJggg==","orcid":"","institution":"Laboratory of Synthetic Biology, Xinjiang University, School of Pharmaceutical Sciences.","correspondingAuthor":true,"prefix":"","firstName":"Haitao","middleName":"","lastName":"Yue","suffix":""}],"badges":[],"createdAt":"2025-02-28 05:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6125489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6125489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78241026,"identity":"22e9ddcd-c4e3-467e-b845-b4f62196e2c3","added_by":"auto","created_at":"2025-03-11 09:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":160272,"visible":true,"origin":"","legend":"\u003cp\u003ea Geographic distribution of sampling sites, ethnicity. Sampling sites were mapped using Arcgis. b Cohort composition on age, gender, ethnicity and urbanisation status.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/b23f85ed8da579a485b67675.png"},{"id":78240180,"identity":"d7ee67fa-4063-497e-b90c-4c973d59f1be","added_by":"auto","created_at":"2025-03-11 08:54:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220789,"visible":true,"origin":"","legend":"\u003cp\u003ea. Sector level community bar chart b. Genus level community bar chart c. Species level community bar chart d. Sector level Wayne's plot.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/ed2b53017055d78ff80306f2.png"},{"id":78241036,"identity":"2f7331f8-0520-45b2-a7ef-e0919b23ded9","added_by":"auto","created_at":"2025-03-11 09:02:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174171,"visible":true,"origin":"","legend":"\u003cp\u003ea Hui bowel pattern analysis b. Kazakh bowel pattern analysis c. Uyghur bowel pattern analysis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/025425f1ddccb7ca45e49c68.png"},{"id":78243254,"identity":"6c6077d5-5107-4f6a-a7ee-69f21e479d71","added_by":"auto","created_at":"2025-03-11 09:10:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138119,"visible":true,"origin":"","legend":"\u003cp\u003ea.Shannon diversity index, horizontal coordinates represent the amount of randomly selected sequencing data; vertical coordinates the number of species observed. b Uyghur group.Roc curves have high accuracy when the AUC is above 0.9; as shown in the figure AUC=0.99 indicates high accuracy c Kazakh group.Roc curves.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/993b1ded36f8e5dc6a5ba316.png"},{"id":78240225,"identity":"d67eaa3d-4df6-49b0-96a4-55043d414cab","added_by":"auto","created_at":"2025-03-11 08:54:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":219732,"visible":true,"origin":"","legend":"\u003cp\u003ea Alpha diversity analysis of health groups by ethnicity b Alpha diversity analysis of disease groups by ethnicity c Random forest analysis of Uyghur and Kazakh health groups top 20 statistics d Random forest analysis of Uyghur and Kazakh disease groups top 20 statistics.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/c71994ba2fe157fd15c12324.png"},{"id":78240223,"identity":"3dc6120d-9280-45cb-bc61-f4ed71f28f75","added_by":"auto","created_at":"2025-03-11 08:54:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":124370,"visible":true,"origin":"","legend":"\u003cp\u003ea Comparison of two groups of Uyghur and Kazakh health group genus levels b Comparison of two groups of Uyghur and Kazakh disease group genus levels.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/a829d78011c166a127475e85.png"},{"id":78240126,"identity":"c34d8083-2e70-46fc-92ad-3ec8782729c3","added_by":"auto","created_at":"2025-03-11 08:54:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":851470,"visible":true,"origin":"","legend":"\u003cp\u003eMultilevel species hierarchical tree diagram a LEfSe species hierarchical diagram for the KH\u0026amp;UH group b LDA discrimination results for the KH\u0026amp;UH group c LEfSe species hierarchical diagram for the KD\u0026amp;UD group d LDA discrimination results for the KD\u0026amp;UD group in (In the diagram, the red nodes denote the bacterial taxa that have important roles in the gut microbiota of the Uyghur subjects, while the green nodes denote the bacterial taxa that have important roles in the gut microbiota of the Kazakh subjects); e Analysis diagram of the correlation between different ethnic groups' different microorganisms and dietary habits. group); e Graph of correlation analysis between different ethnic groups' gut differential microorganisms and dietary habits.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/b9344cba71d19b223f5a8615.png"},{"id":78240137,"identity":"b01801d0-8849-455f-9dce-aa11deb97ac9","added_by":"auto","created_at":"2025-03-11 08:54:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":156486,"visible":true,"origin":"","legend":"\u003cp\u003ea. Box plot of differences between β-diversity groups (H=healthy control group, HTN=hypertensive group) Hypertensive group vs. healthy control group b. PCoA plot (HTN=hypertensive group, H=healthy control group).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/710b5b048daaf26757621531.png"},{"id":78241034,"identity":"e0bae6f3-35f6-4980-8289-e64d9a706b58","added_by":"auto","created_at":"2025-03-11 09:02:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":150815,"visible":true,"origin":"","legend":"\u003cp\u003ea shows the relative abundance distribution of genus-level gut microorganisms in the hypertensive group and healthy control group, and Figure 11 b shows the relative abundance distribution of genus-level gut microorganisms in the hypertensive group and healthy control group, where HTN is the hypertensive group and H is the healthy group.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/3013554175a7ee8a1f1b57ed.png"},{"id":78240174,"identity":"9440f088-34c1-476f-8f35-02521960ebce","added_by":"auto","created_at":"2025-03-11 08:54:07","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":427059,"visible":true,"origin":"","legend":"\u003cp\u003ea Comparison of differential microorganisms in the HTNU group HTNK group at the species level; b Plot of species importance ranking in the HTNU group HTNK group; c Volcano Plot of Blood Metabolites. Title: Volcano Plot of Urinary Metabolites; Horizontal: log2Fold Change (logarithm of fold change in metabolite abundance); Vertical: -log10(P - value) (negative logarithm of P-value); Colours: red - up-regulated metabolites, blue - down-regulated metabolites, grey - metabolites with insignificant differences; Labels. metabolites: e.g. L - carnitine, etc., dot size indicates VIP value; this graph presents the differential expression and significance of urinary metabolites; d Heatmap of the correlation between blood differential metabolites and differential bacterial populations in the HTNU group HTNK group, with differential strains in the horizontal coordinate and differential metabolites in the vertical coordinate, where green is microbial-derived metabolites, while blue is the co-metabolites of humans and microbes; e HTNU group Heatmap of correlation between urinary differential metabolites and differential flora for the HTNK group.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/f6b9070f97c6efcdd6d79b54.png"},{"id":78240148,"identity":"0fe8e6f7-54ca-4df9-9ed9-e1254fd50f4e","added_by":"auto","created_at":"2025-03-11 08:54:06","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":138946,"visible":true,"origin":"","legend":"\u003cp\u003ea KEGG Pathway classification statistics, with the functional name of KEGG Pathway Level2 in the vertical coordinates and its corresponding abundance value in the horizontal coordinates, and bar-colouring of KEGG Pathway Level2 according to the KEGG Pathway Level1 to which it belongs b Blood differential metabolite pathway enrichment analysis c Urine differential metabolite Pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/9d68a49dc9a693c923316479.png"},{"id":78241050,"identity":"0ad59f0c-303f-405c-8fdb-251d4472a02a","added_by":"auto","created_at":"2025-03-11 09:02:10","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":167290,"visible":true,"origin":"","legend":"\u003cp\u003ea shows the distribution of relative abundance of gut microbes at the genus level in the type II diabetes group and the healthy control group b the distribution of relative abundance of gut microbes at the species level in the type II diabetes group and the healthy control group, where T2DM is the type II diabetes group and H is the healthy control group.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/adc1095da135b223ed01477c.png"},{"id":78240133,"identity":"92e6e752-a323-4fd4-b16a-bd32e8909ae1","added_by":"auto","created_at":"2025-03-11 08:54:05","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":124567,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart of the multi-species test of difference for diabetic populations of different ethnic groups, where the T2DMG group is the Hui sample people with type II diabetes only, the T2DMK group is the Kazakh sample people with type II diabetes only, and the T2DMU group is the Uyghur sample people with type II diabetes only.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/206d24157cb75efd52e8f548.png"},{"id":78241024,"identity":"5be4fb94-df1f-49cb-8be3-002db8aaaf61","added_by":"auto","created_at":"2025-03-11 09:02:07","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":244420,"visible":true,"origin":"","legend":"\u003cp\u003ea Functional analysis of KEGG in type II diabetic patients and healthy control macrogenomes. The Wilcoxon rank sum test was used to demonstrate the difference in proportions between the two groups in different KEGG pathways. The vertical axis is KEGG pathways and the horizontal axis is the proportion (%) of pathways. Different coloured columns represent type II diabetes mellitus (T2DM) versus healthy control, the numbers on the columns are the difference in proportions, the error lines are the 95% confidence intervals, and the labeled P - value reflects the significance of the difference to reveal the potential role of gut microorganisms in the pathogenesis of diabetes mellitus;b Results of the analysis of the KEGG enrichment for urinary metabolism. This figure shows the KEGG enrichment analysis of urinary metabolism in type 2 diabetic patients and healthy controls. The vertical axis represents different metabolic pathways and the horizontal axis represents differentially expressed protein (DEP) counts. The red colour of the graph represents pathways up-regulated in the urine of type 2 diabetic patients and the blue colour represents down-regulation; c Blood metabolite volcano plot. Title: Volcano Plot of Urinary Metabolites; Horizontal coordinate: log2Fold Change (logarithm of fold change in metabolite abundance); Vertical coordinate: -log10(P - value) (negative logarithm of P-value); Colour: red - up-regulated metabolites, blue - down-regulated metabolites, grey - metabolites with insignificant differences; Labeling Metabolites: e.g. L-carnitine, etc., dot size indicates VIP value; the graph presents the differential expression and significance of urinary metabolites.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/add0a050ed150f272242227e.png"},{"id":78241028,"identity":"90a0e379-b48b-4806-add7-746156831892","added_by":"auto","created_at":"2025-03-11 09:02:07","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":85747,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha and beta diversity analysis. a. Alpha species diversity indices at the species level obtained based on macrogenomic NR species annotation as well as KEGG functional annotation results, between-group differences in the healthy population (H) and in the population with concurrent type II diabetes mellitus and hypertension (T2DM_HTN), Wilcoxon rank sum test, **p \u0026lt; 0.05. b. Alpha diversity indices in the T2DM_HTN group and in the Beta diversity index statistics of microbiota in group H. Welch t-test, **p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/777573775462684c6cda0276.png"},{"id":78243279,"identity":"c325a3dc-ee2e-41bb-8f14-7bf0cce9954c","added_by":"auto","created_at":"2025-03-11 09:10:10","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":182567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eComparison of the two groups and the differences in the mean relative abundance of the same species between the different groups (H vs. T2DM_HTN). Vertical coordinates indicate species names at different taxonomic levels, and horizontal coordinates indicate the percentage value of abundance of a species in that sample. 0.01\u0026lt;p\u0026lt;=0.05 * .b. Heatmaps visualise the distribution of Top dominant species in all samples across the three groups, exploring the trends of species change in the control and treatment groups. The bottom and right sides of the heat map are the sample names and species names, respectively. Shades of colours in the heatmap represent the level of species abundanc.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/f84dedb0053e59626e2304ac.png"},{"id":78240125,"identity":"7e0584ff-13b4-476b-9cb9-51c37aaf14a0","added_by":"auto","created_at":"2025-03-11 08:54:04","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":136047,"visible":true,"origin":"","legend":"\u003cp\u003ea ANOSIM analysis to test whether the difference between groups (two or more groups) is significantly greater than the within-group difference to determine whether the subgroups are meaningful. statistic value is positive, p \u0026lt; 0.01, demonstrating that the between-group difference is greater than the within-group difference. b Venn diagrams were analysed for the T2DM_HTN, T2DM, and HTN groups.This Venn diagram demonstrates the distribution of gut microbial species in the healthy, type 2 diabetes mellitus (T2DM), and diabetes mellitus with hypertension concurrently (T2DM_HTN) groups.There were 423 unique bacteria in the T2DM_HTN group, 855 species in the T2DM group, and 4,230 in the HTN group, with a total of 139 bacteria in the three groups. There were 139 species of common bacteria. ; c Species-level species composition analysis of gut microbes in the T2DM_HTN, T2DM, and HTN groups, with the relative abundance of a variety of microbes in each group illustrated in the figure, reflecting differences in gut microbial community structure across disease states.\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/3fc82edf0f0961f4d7975535.png"},{"id":78240151,"identity":"70c065df-b8e3-49c6-add3-e0dab7813a51","added_by":"auto","created_at":"2025-03-11 08:54:06","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":987289,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of differences between multiple groups. a, b indicates two-by-two comparisons between the T2DM, HTN group and the T2DM_HTN group, respectively. c Hierarchical clustering tree on Species level. subgroups: HTN, T2DM_HTN, T2DM; taxa: multiple microorganisms, such as unclassified_f__Streptococcaceae, etc. (each colour represents different species, see legend for details); Horizontal coordinate: clustering correlation measure (0 - 0.9); Vertical coordinate: sample number (e.g., UKFV_75, etc.); This figure demonstrates the clustering of different samples on the Species level, which helps to analyse the structure of microbial communities in different disease subgroups. d Hierarchical clustering tree on the Urinary Metabolite Hierarchical clustering heatmap. Title: Hierarchical Clustering Heatmap of Urinary Metabolites; Horizontal coordinate: sample number (e.g. UMRV_4, etc.); Vertical coordinate: urinary metabolites; Colour meanings: red - T2DM_HTN group, blue - T2DM group, heatmap colours indicate metabolite relative abundance; Clustering tree: sample clustering on the top, metabolite clustering on the left. sample clustering, metabolite clustering on the left; this figure demonstrates the relationship between urinary metabolite abundance and sample clustering in different disease groups. e/f Volcano Plot of Urine/Blood Metabolites. Title: Volcano Plot of Urinary Metabolites; Horizontal coordinate: log2Fold Change (logarithm of fold change in metabolite abundance); Vertical coordinate: -log10(P - value) (negative logarithm of P-value); Colour: red - up-regulated metabolites, blue - down-regulated metabolites, grey - metabolites with insignificant differences; Labeling Metabolites: e.g. L-carnitine, etc., dot size indicates VIP value; the graph presents the differential expression and significance of urinary metabolites.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/3b501ff8dd8652017c3eaf10.png"},{"id":81093146,"identity":"68b45e6b-cebd-4260-85fe-136c8f94a7f7","added_by":"auto","created_at":"2025-04-22 07:32:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6760190,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/d330bab8-c80a-4ca9-82ff-6abe5da39615.pdf"},{"id":78240182,"identity":"084be501-ffc4-45d1-983c-9fa8ae4bdb45","added_by":"auto","created_at":"2025-03-11 08:54:08","extension":"7z","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":285255,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.7z","url":"https://assets-eu.researchsquare.com/files/rs-6125489/v1/79334a960e65dc0696ddc269.7z"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterisation of the gut microbiome in hypertensive and type II diabetic populations in different regions of Xinjiang","fulltext":[{"header":"Background","content":"\u003cp\u003eThe human gut possesses an exceptional and diverse microbial ecosystem, with approximately 150-400 species of bacteria in our intestines \u003csup\u003e[1]\u003c/sup\u003e. The gut microbiota provides substantial benefits to our health by forming a barrier against pathogens, producing bioactive metabolites and modulating immune function. In the absence of strong influencing factors (e.g., dietary changes or antibiotic treatment), the gut microbiome remains in a relatively stable state \u003csup\u003e[2,3]\u003c/sup\u003e. However, the composition of the gut microbiota can change in the context of influences that vary by age \u003csup\u003e[4,5]\u003c/sup\u003e, ethnicity \u003csup\u003e[6]\u003c/sup\u003e and environmental factors such as drug use \u003csup\u003e[7,8]\u003c/sup\u003e and habitual diet \u003csup\u003e[9]\u003c/sup\u003e. However, among the many factors that influence the composition and function of the gut flora throughout the life span, diet is indeed key in regulating the abundance of specific bacterial species and their functions\u003csup\u003e\u0026nbsp;[10,11]\u003c/sup\u003e, and an individual\u0026apos;s response to a particular diet or dietary component may be largely influenced by the characteristics of the gut flora\u003csup\u003e\u0026nbsp;[12]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn recent years, an increasing number of studies have shown a strong relationship between the gut microbiota and a variety of diseases (e.g., obesity, diabetes, hypertension, etc.) \u003csup\u003e[13-14]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eType II diabetes mellitus (T2DM), which accounts for approximately 90% of all diabetes cases, is due to insulin resistance and impaired insulin secretion, with an increasing prevalence worldwide\u003csup\u003e\u0026nbsp;[15]\u003c/sup\u003e, and there are multiple factors that play a role in the development of T2DM, including genetics, lifestyle, and the gut microbiome, with a growing body of evidence supporting the role of the gut microbiome in T2DM \u003csup\u003e[16]\u003c/sup\u003e. It was found that the abundance of Clostridia phylum and Thick-walled bacillus phylum was significantly decreased in patients with T2DM, whereas the level of \u0026beta;-amoeba phylum was highly elevated and positively correlated with plasma glucose. In addition, the ratios of Anaplasma phylum to Thick-walled bacillus phylum and Anaplasma phylum-Prevotella phylum to Clostridium sphaericum-E and rectum were positively corrected with plasma glucose, suggesting that T2DM is correlated with the composition of the intestinal microbiota.\u003c/p\u003e\n\u003cp\u003eAccording to the Framingham Heart Study, 60-70% of primary hypertension is caused by obesity \u003csup\u003e[17]\u003c/sup\u003e and obese patients are 3.5 times more likely to develop hypertension \u003csup\u003e[18]\u003c/sup\u003e. In addition, 347 million people \u003csup\u003e[19]\u0026nbsp;\u003c/sup\u003eworldwide suffer from T2DM, and obese people have a sevenfold increased risk of developing this disease \u003csup\u003e[20]\u003c/sup\u003e. About 50 per cent of people with diabetes die from cardiovascular diseases such as heart disease and stroke \u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eData released by the Chinese Centre for Disease Control and Prevention (CDC) show that Xinjiang ranks third in obesity rates among all provinces and regions in the country, with an obesity rate of 21.5% and an overweight rate of 33%, which are higher than the national averages of 11.9% and 30.1%, respectively. This is related to the fact that residents in Xinjiang prefer meat, mostly high-fat, high-protein, high-salt and high-carbohydrate foods, and consume fewer grains [\u003csup\u003e22, 23]\u003c/sup\u003e. In a survey of 401 Uyghur residents in Karamay City, it was found \u003csup\u003e[24]\u003c/sup\u003e that local residents had high fat and cholesterol intake, while data from the national \u0026lsquo;Report on Nutrition and Chronic Disease Status of China\u0026apos;s Residents (2020)\u0026rsquo; showed that the per capita intake of total dietary fat in China was 79.1 g. In contrast, fat intake of Uyghur residents in Karamay City was higher than the national average.\u003c/p\u003e\n\u003cp\u003eWhen the human body is in a disease state, such as intestinal inflammation, obesity, diabetes and other disease conditions, the intestinal flora is imbalanced, and the production and types of short-chain fatty acids (SCFAs) change. In addition to providing energy for the body, SCFAs can help dominant bacteria to establish a dominant position in the intestinal tract and act as signalling molecules to regulate glycolipid metabolism. SCFAs-producing bacteria in the intestinal flora produce a large number of SCFAs to increase the acidic environment of the intestinal tract, and they can reproduce in large quantities due to their acid-resistant characteristics. Therefore, under the combined effect of the low pH environment and the competitive growth of acid-resistant bacteria, the growth of indole-producing and hydrogen sulfide-producing bacteria in the intestinal tract was inhibited, and the inhibitory effect of the harmful bacteria on the endointestinal secretion of L-cells was relieved, which led to an increase in the glucagonlike-1 (Glucagon-like-1) and glucose-lipoprotein (GL-1) production and species production. -1 (Glucagon-likepeptide-1, GLP-1) and gastrointestinal peptide hormone tyrosine peptide (Peptide YY, PYY) secretion, and improve the occurrence of glycolipid metabolism diseases such as obesity \u003csup\u003e[25]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn order to explore the association between gut microbes and patients with type II diabetes and hypertension, we collected stool, blood, urine and saliva samples from 192 volunteers in this study, including a total of 99 people from the diseased population (including patients with type II diabetes, hypertension, and complications of the two), and 93 people from the healthy population (excluding those who had taken antibiotics within 3 months and those who suffered from the above diseases). We used macro-genomic and metabolomic analysis technologies to perform high-throughput sequencing on these samples, aiming to explore the characteristics and differences of gut microbes in patients with type II diabetes mellitus and hypertension from different ethnic groups in Xinjiang and their association with diseases, with the expectation of providing basic data for the study of the association of related diseases and gut microbes in Xinjiang, and providing new ideas and clues for the subsequent investigation of the role of gut microbes in the development of diseases. We hope to provide basic data for the study of the association between related diseases and gut microbes in Xinjiang, and provide new ideas and clues for the subsequent investigation of the role of gut microbes in disease development.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe randomly recruited 192 volunteers from five counties and cities in the Xinjiang Uygur Autonomous Region. The distribution of sampling locations and the ethnic and gender composition of the volunteers are shown in Figures 1a and 1b.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003eStatistics on the distribution of basic information on the number, ethnicity and gender of the sampled population in each region.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"118%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003egenders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eethnic group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChangji\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHami\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKashgar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKorla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurpan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKazakh\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUyghur\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHui\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy people (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes only (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension only (person)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeople with both diabetes and hypertension (persons)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differences in the composition of gut microorganisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIt was found that the composition of intestinal microbial species was basically similar between the healthy and diseased populations of the Kazakh, Hui and Uyghur ethnic groups. However, the Hui diseased population had the highest level of Prevotella at the species level of 0.92 per cent, while Ligilactobacillus ruminis had the lowest level of 0.02 per cent among the five groups (Figure 2c).In addition, we counted the most abundant genera in the six subgroups, and the results showed that Ruminococcus had the highest abundance of 8.35% in the Hui diseased population (Fig. 2b). In the relative abundance analysis at the phylum level, the ratios of Firmicutes and Bacteroidetes were 2.18/3.21, 3.38/6.45, and 3.81/4.2, respectively, with the highest ratio in the Hui diseased population (Fig. 2a).A total of 143 bacterial phyla were detected in all six groups at the phylum level (Fig. 2d). Further analyses revealed that the Uyghur healthy group had seven unique species at the phylum-level classification level of bacteria (Table 2), whereas the Kazakh diseased group had only one unique phylum, Taleaviricota. notably, no unique species were detected in the Kazakh healthy group and the Hui diseased group. More detailed information can be found in Exhibit 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Unique species for Venn analysis in healthy Uyghur population\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"547\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 386px;\"\u003e\n \u003cp\u003ephylum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 160px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBlastocladiomycota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHaptophyta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCandidatus_Beckwithbacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCandidatus_Fervidibacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCryptomycota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePorifera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBryozoa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUH\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eRelationship between ethnicity and gut type\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCluster analysis of the samples using ethnicity as a subgroup revealed that the dominant genus in the Hui group was Faecalibacterium (Fig. 2a), the dominant genus in the Kazakh group was Bacteroides (Fig. 2b), and the dominant genus in the Uyghur group was Prevotella (Fig. 2c). According to existing studies, the enterotypes of populations from three continents (Europe, North America, and East Asia) have been classified into three groups by genus-level cluster analysis: the genera Anabaena (enterotype 1), Prevotella (enterotype 2), and Ruminalococcus (enterotype 3) \u003csup\u003e[26]\u003c/sup\u003e. However, our findings differed from those reported by Chen Wei et al. (2021). Chen Wei et al. stated that the enterotype of the Uyghur group was enterotype 2 and the enterotype of the Hui group was enterotype 1 \u003csup\u003e[27]\u003c/sup\u003e, whereas our study showed that the dominant genus of the Hui group was Faecalibacterium, which was not completely consistent with the characterisation of the anaplasmosis genus of enterotype 1.\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShannon diversity analysis and biomarker screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShannon diversity analysis was performed on all samples, and the results showed that the Shannon diversity index tended to be stable, indicating that the sequencing depth was sufficient (Fig. 4a). Further analysis revealed that the area under the ROC curve (AUC) values of the KD and UD groups and the UH and KH groups were all 1, indicating that the results of the above analyses had a high degree of confidence (Fig. 4b, c). At the species level, the difference in alpha diversity between healthy and diseased groups was significant for Uyghurs and Kazakhs (Fig. 5a, b).\u003c/p\u003e\n\u003cp\u003eTo find potential biomarkers (Biomarker), we ranked the importance of iconic species based on the Random Forest algorithm (Fig. 5c, d) and counted the top three species in terms of abundance. In the health group, the species with the highest abundance was Harlan, which accounted for 6.16% of the top 20 abundance rankings, followed by Vigna unguiculata with 6.00%, and Clostridium sp. with 5.85%. And in the disease group, the top three species-level biomarkers in terms of abundance were Lactobacillus sp. (6.73%), Lactococcus chungangensis (6.44%), and Succinatimonas sp. (5.68%). It is noteworthy that the top two species in the Uyghur and Kazakh disease groups were both Lactobacillus spp. bacteria.\u003c/p\u003e\n\u003cp\u003eWe conducted a comparative genus level analysis of Uyghur and Kazakh healthy and diseased groups to look for significantly different strains. The results of the analysis showed that the abundance of Lactobacillus (genus Lactobacillus) was 3.8% and 13.67% in the Uyghur healthy and diseased groups, respectively, whereas the abundance of Lactobacillus spp. was 77.59% and 69.96% in the Kazakh healthy and diseased groups, respectively. In addition, the abundance of the genus Succinivibrio was significantly higher in the Uyghur disease group than in the Kazakh group. Notably, the genus Succinivibrio is known to be isolated mainly from the rumen of cattle and sheep. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLEfSe analysis and differential strain identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used LEfSe analysis (LDA Effect Size) to identify differential strains between Uyghurs and Kazakhs. From the phylum level to the genus level, we screened the differential strains with LDA values greater than 3. In the disease group, a total of 16 differential strains were screened, including 7 strains in the Kazakh disease group, all of which were Firmicutes and Actinobacteria bacteria; 9 strains in the Uyghur disease group, which were Proteobacteria, Firmicutes, and Candidatus Melainabacteria phylum bacteria (Figure 7c, d). In the healthy group, a total of 13 differential strains were screened, including 7 strains in the Kazakh healthy group, all of which were Bacteroidetes and Firmicutes bacteria, and 6 strains in the Uyghur healthy group, all of which were Proteobacteria bacteria (Fig. 7a, b). In addition, we found significant differences in the genus Lactobacillus between the Kazakh and Uyghur healthy groups. For more detailed information, please refer to Exhibit 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the correlation between dietary habits and microbiology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy identifying differential strains in diseased and healthy groups of Kazakhs and Uyghurs, we clearly revealed the characteristics of gut flora in different ethnic groups and health states. These differences suggest that the composition of the intestinal microbial community shows complex and regular changes under the dual dimensions of ethnicity and health status. The formation of microbial communities is influenced by a combination of factors, among which dietary habits, as a long-term and stable environmental factor, are likely to play a key role in shaping these differences. Therefore, we further conducted a correlation analysis between dietary habits and differential microbes (Fig. 7e), aiming to explore the potential mechanisms of association between the two, and to provide a more comprehensive perspective for understanding the microbiological basis behind ethnic health disparities.\u003c/p\u003e\n\u003cp\u003eThe analysis showed that vegetables rich in dietary fibre, vitamins and minerals were positively associated with Candidatus Spyradocola merdavium and negatively associated with Enterococcus faecium. Seafood, a high-protein, low-fat food rich in micronutrients and unsaturated fatty acids, was positively associated with Alphaproteobacteria bacterium and negatively associated with Nocardioides mesophilus. Fruits, which contain mainly carbohydrates, vitamins and dietary fibre, were negatively associated with Enterococcus faecium. Spicy foods were positively associated with Shigella flexneri due to the presence of capsaicin and other components. Overall, different dietary habits were closely associated with different microorganisms by virtue of their unique nutrient composition, which not only validated some of the previous studies on the relationship between diet and microorganisms, but also provided new ideas for further investigation of the \u0026lsquo;diet-microorganism-health\u0026rsquo; link \u003csup\u003e[\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e\u003csup\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Analysis of gut microbial diversity in hypertensive populations\u003c/h2\u003e\n\u003ch2\u003eBackground to the study of hypertension and gut microbiology\u003c/h2\u003e\n\u003cp\u003eHypertension remains an important cause of morbidity and mortality from cardiovascular diseases \u003csup\u003e[29]\u003c/sup\u003e. Elevated blood pressure adversely affects cardiovascular and renal function\u003csup\u003e[30,31]\u003c/sup\u003e, and the development of hypertension is caused by a combination of complex interactions between genetic and environmental factors \u003csup\u003e[32]\u003c/sup\u003e. Numerous studies have shown that nutrient intake, including sodium and potassium, and different dietary structures are directly related to blood pressure regulation \u003csup\u003e[33]\u003c/sup\u003e. Based on the analyses of the gut microbiological composition of the Kazakh, Hui and Uyghur populations in Part I, we further explored the gut microbial diversity and its underlying mechanisms in hypertensive populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in microbial communities between the hypertensive group and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the microbial community differences between the hypertensive group and the healthy control group, we performed PCoA (principal coordinate analysis) and \u0026beta;-diversity analysis. The results showed significant differences in microbial community structure between the two groups (Fig. 8a, b), suggesting that hypertensive status may have an important impact on gut microbial composition. This finding is consistent with the trend of microbial diversity changes in the disease groups (e.g., Kazakh and Uyghur disease populations) in Part I, further supporting the key role of gut microbes in metabolic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of differentiated strains of bacteria associated with hypertension\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough macrogenomic sequencing analysis, we found significant differences in the gut microbial composition between the hypertensive group and healthy controls at the species level and genus level. In particular, the abundance of Prevotella was significantly higher in the hypertensive group, while the abundance of Bifidobacterium was significantly lower (Figure 9). This result is consistent with the study of Li et al. (2017), suggesting that Prevotella may play an important role in hypertension by triggering inflammatory responses, and may even be a causal inducer of inflammation and hypertension.\u003c/p\u003e\n\u003cp\u003eIn addition, we found significant differences at the species level between Uyghur and Kazakh hypertensive groups (Supplementary Fig. 1a). For example, the relative abundance of Escherichia coli was elevated by 1.74% in Uyghur hypertensives compared to Kazakhs, whereas the abundance of Catenibacterium mitsuokai was elevated by approximately 0.6% in the Kazakh group (Fig. 10a). We further identified the significance of these differential strains by Random Forest analysis (Fig. 10b) and screened 88 blood differential metabolites (37 up-regulated and 51 down-regulated) and 222 urine differential metabolites (153 up-regulated and 69 down-regulated) using VIP \u0026gt; 2 and P \u0026lt; 0.05 as screening conditions (Fig. 10c).Validation of the ROC curves showed AUC = 1, indicating a high degree of confidence in the predictions (Supplementary Fig. 1b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation analysis of differential metabolites with microorganisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTraceability analysis of the differential metabolites revealed that dietary sources had the highest number of metabolites, 56 (blood) and 138 (urine), respectively (Supplementary Fig. 1c, d). By association heat map analysis, we found that Escherichia coli and Catenibacterium mitsuokai showed completely opposite trends to the differential metabolites. For example, in the blood metabolome, Escherichia coli was positively correlated with hydrocinnamic acid (Hydrocinnamic acid), whereas Catenibacterium mitsuokai was negatively correlated, and L-proline (L-Proline) was significantly negatively correlated with Catenibacterium mitsuokai, but significantly positively correlated with Escherichia coli (Figure 10d). In the urinary metabolome, Catenibacterium mitsuokai was significantly negatively correlated with 3-Methyl-2-oxovaleric acid and positively correlated with Escherichia coli, while the opposite correlation was found for caffeic acid (Fig. 10e). In addition, we found that 9-Hydroxyfluorene, a PAH metabolite in urine metabolites, was significantly correlated with both Catenibacterium mitsuokai and Escherichia coli.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic pathway enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePathway enrichment analysis of the differential metabolites revealed that the terpenoid backbone biosynthesis (TBB) pathway had the highest abundance among the blood metabolites (Figure 11a). Many terpenoids (e.g. limonene, perillaldehyde, etc.) have been found to have vasodilatory effects and can help lower blood pressure by regulating the contraction and relaxation of vascular smooth muscle. Among the urinary metabolites, the caffeine metabolism (Caffeine metabolism) pathway has the highest abundance (Figure 11b). Caffeine metabolic pathways, particularly CYP1A2 enzyme activity, are known to play a key role in the effects of caffeine on blood pressure. Combining KEGG metabolic pathway enrichment analysis of macrogenomic data with metabolomic pathway enrichment analysis, we found that amino acid metabolism, lipid metabolism, as well as terpene and polyketide metabolic pathways were significantly enriched in the hypertensive population\u003csup\u003e[34]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch2\u003eAnalysis of gut microbiome differences between healthy and type II diabetic populations\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eBackground to the study of type II diabetes and the gut microbiome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eType II diabetes mellitus (T2DM), like hypertension, is a common chronic disease with a high prevalence in the population.The dietary patterns of patients with T2DM differ significantly from those of the healthy population, and the related medication may also have an impact on their intestinal flora structure. In addition, the special dietary structure (e.g., high-fat and high-sugar diet) in Xinjiang may further exacerbate the differences in the intestinal flora structure between T2DM patients and the healthy population. Based on the analyses of the gut microbiome composition in hypertensive and healthy populations in Part I and Part II, we further explored the differences in the gut microbiome between T2DM patients and healthy populations and their potential mechanisms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in microbial communities between the T2DM group and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed the microbial community differences between the T2DM group and the healthy control group. The results showed that the abundance of Bacteroides in the healthy control group was 8.05%, which was significantly higher than that of 3.78% in the T2DM group. In addition, the abundance of Prevotella at genus level and species level was significantly higher in the T2DM group (Fig. 12a, b). The combined analysis showed that the abundance of gut-dominant microorganisms was greater in the healthy control group than in the T2DM group, with Bacteroides caccae, Dorea longicatena, and Anaerobutyricum hallii as the dominant strains specific to the healthy control group, whereas these strains were not detected in the T2DM group. In contrast, Bifidobacterium adolescentis was the dominant strain in the T2DM group, while it did not show dominance in the healthy control group (Fig. 12a, b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in microbial species in T2DM patients of different ethnicities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to explore the species differences in gut microbes of T2DM patients of different ethnicities, we divided T2DM patients into three groups (N \u0026ge; 3) of Uyghur, Kazakh and Hui ethnicities according to their ethnicity and analysed the species variability. The results showed that the relative abundance of Clostridia bacterium was higher in Uyghur T2DM patients, whereas Holdemanella biformis and others had higher relative abundance in the intestines of Kazakh T2DM patients. In addition, the relative abundance of unclassified_g__Ruminococcus, Ruminococcus bromii, and unclassified_g_Alistipes was higher in Hui T2DM patients, and the difference was significant (P \u0026lt; 0.05) (Figure 13). These results suggest that there are significant differences in the gut microbial composition of T2DM patients from different ethnic groups, which may be related to genetic background, dietary habits, and environmental factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional analysis of macrogenomic KEGG\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to deeply investigate the pathogenesis of T2DM, we performed KEGG functional analysis on the macrogenomic data. The results showed that there were significant differences between the T2DM group and the healthy control group in a variety of functional pathways (Figure 14a). Among them, the difference in energy metabolism-related pathways (e.g., glycolysis/glycolysis) was the most significant: the percentage of this pathway in the T2DM group was 25.3%, which was significantly lower than that in the healthy control group, which was 32.1% (difference: -6.8%, 95% confidence interval: [-8.5%, -5.1%], P = 0.002). This result suggests that the inhibition of glycolysis/glycolysis function of gut microorganisms in T2DM patients may interfere with the host\u0026apos;s glucose regulation by affecting related enzyme activities or metabolite concentrations, which may lead to abnormally elevated blood glucose.\u003c/p\u003e\n\u003cp\u003eIn addition, the phenylalanine metabolic pathway accounted for 8.5% in the T2DM group, which was significantly lower than that of the healthy control group, which was 11.2% (difference: -2.7%, 95% confidence interval: [-3.6%, -1.8%], P = 0.003). Phenylalanine metabolism is closely related to physiological processes such as neurotransmitter synthesis and antioxidant defence, and its metabolic abnormalities may be associated with the development of complications such as diabetic neuropathy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUrine metabolome KEGG enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the impact of changes in gut microbial function on host metabolism, we performed KEGG enrichment analysis on the urinary metabolome of T2DM patients (Figure 14b). The results showed that the pathways of fatty acid biosynthesis, pentose and glucuronide interconversion, riboflavin metabolism, and taurine and taurine metabolism showed an up-regulation trend. These changes may be an adaptive response of the host to changes in the gut microbial community and overall metabolic disorders. For example, up-regulation of pentose and glucuronide interconversion pathways may contribute to the maintenance of glucose metabolic homeostasis, whereas altered riboflavin metabolism may affect electron transfer in energy metabolism.\u003c/p\u003e\n\u003cp\u003eOn the other hand, metabolic pathways such as pyruvate metabolism, fructose and mannose metabolism, and mineral absorption are significantly downregulated. In addition, pathways such as choline metabolism, propionate metabolism, sphingolipid signalling pathway, protein digestion and uptake also showed downregulation in cancer. These changes, together with altered microbial function in the macrogenome, reflect the complex pathophysiological processes in T2DM patients. For example, downregulation of propionate metabolism may affect the production of short-chain fatty acids, which in turn affects intestinal barrier function and immune regulation; alterations in sphingolipid signalling pathways may affect cell signalling and proliferation, which is associated with the development of chronic complications of diabetes \u003csup\u003e[35-39]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differences in blood metabolomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the differences between the blood metabolomes of T2DM patients and the healthy population, we plotted a volcano diagram (Fig. 14c). The results showed that metabolites such as Lactaldehyde, D-1,5-Anhydrofructose, Ureidopropionic acid, Hypotaurine, and Glycoprotein-phospho-D-mannose exhibited significant differences between the two groups. Among them, the down-regulated metabolites, such as Lactaldehyde, may have reduced activity in the relevant metabolic pathways in the disease state, while some of the up-regulated metabolites may be involved in compensatory or pathological processes in the disease process. Judging from the VIP values, some of the differential metabolites were of high importance in distinguishing T2DM patients from the healthy population, suggesting that these metabolites may be used as potential biomarkers for early diagnosis, disease monitoring or prognostic assessment of T2DM.\u003c/p\u003e\n\u003ch2\u003eAnalysis of the composition of the gut microbiota in healthy and diabetic and hypertensive populations from different regions of Xinjiang\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eIn recent years, the prevalence of type 2 diabetes mellitus (T2DM) and hypertension (HTN) has been increasing year by year with lifestyle changes. However, the gut microbiological and metabolic characteristics of a co-patient population (T2DM_HTN) with both diseases have not been adequately studied \u003csup\u003e[40]\u003c/sup\u003e. The aim of this study was to reveal the differential characteristics by comparing the gut microbiological and metabolic data of a healthy population (H), patients with T2DM, patients with HTN, and a co-patient population with T2DM_HTN, in order to provide a theoretical basis for the integrated management of complex diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of microbial community diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterise the richness and diversity of the bacterial community, we analysed it at the species level using the Chao1 diversity index (Fig. 15a). The results showed that there was a significant difference in community diversity and richness between the healthy group (H) and the T2DM_HTN group (P \u0026lt; 0.05), and the diversity and richness of the T2DM_HTN group was significantly lower than that of the healthy group (Fig. 15b). This result suggests that disease status may negatively affect the stability of gut microbial communities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differential strains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy comparing the differential strains in the healthy and T2DM_HTN groups (Fig. 16a), we found that Prevotella copri and Bifidobacterium pseudocatenulatum were dominant in the T2DM_HTN group. Notably, the abundance of Bifidobacterium pseudocatenulatum was significantly higher in the T2DM_HTN group than in the healthy group, a result that is different from previous studies and suggests its potential role in the co-morbid state. To further validate this finding, we plotted a heat map (Fig. 16b), which showed that Bifidobacterium catenulatum was also dominant in the T2DM_HTN group, consistent with the results of differential strain analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eANOSIM analysis with Venn diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBy ANOSIM analysis (Fig. 17a, Table 3), we verified that there were significant differences in gut flora characteristics between the T2DM_HTN group and the T2DM and HTN groups.Venn diagram analysis (Fig. 17b) further revealed differences and overlaps in gut microbial species between the healthy, T2DM, HTN and T2DM_HTN groups. For example, there were 4230 unique microbial species in the HTN group, of which Escherichia coli accounted for about 0.2%; 855 unique microbial species in the T2DM group, of which Prevotella copri accounted for about 0.3%; and 423 unique microbial species in the T2DM_HTN group, of which Bifidobacterium adolescentis with a share of about 0.15%. In addition, the abundance patterns of 139 microorganisms common to the three groups (e.g., Clostridiales bacterium) differed among the groups, suggesting that their functions may change in the disease state.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\" width=\"799\" height=\"214\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eANOSIM results analysis table, a positive sitatistic value indicates that this analysis is reasonable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differences between the two groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to more deeply dissect the role of gut microorganisms in disease development, we conducted a two-by-two difference analysis between the T2DM group, the HTN group, and the T2DM_HTN group.The results of Welch\u0026apos;s t-test showed (Fig. 18a) that the proportion of Coriobacteriales bacterium in the T2DM_HTN group was about 1.5%, which was significantly lower than 3.2% in the HTN group (90% confidence interval: [7.1%, 11.3%]). In addition, the proportion of Fusobacteriales bacterium in the T2DM_HTN group was about 7.8%, which was significantly higher than that of 2.1% in the HTN group (90% confidence interval: [5.5%, 9.2%]). These microbial changes may collectively influence disease onset and progression.\u003c/p\u003e\n\u003cp\u003eStudent\u0026apos;s t-test results showed (Figure 18b) that Dorea formicigenerans was about 0.54% in the T2DM group, which was significantly lower than that of 4.2% in the T2DM_HTN group (90% confidence interval: [3.8%, 5.4%]). In addition, the proportion of Clostridiales diff was about 0.42% in the T2DM group, which was significantly higher than that of 0.23% (90% confidence interval: [2.5%, 4.1%]) in the T2DM_HTN group, suggesting that it may be associated with the disease process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003cstrong\u003eierarchical cluster analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hierarchical clustering tree diagram based on the species level (Fig. 18c) showed that certain microorganisms (e.g., unclassified_f__Streptococcaceae) in the T2DM_HTN group were more tightly clustered and had higher relative abundance. In addition, the HTN, T2DM and T2DM_HTN groups each formed relatively independent clustering branches, further confirming the significant differences in the gut microbial communities under different disease states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-targeted metabolomics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to construct a more complete map of the disease mechanism, we further carried out urine and blood metabolomics studies. The hierarchical clustering heatmap of urinary metabolites (Figure 18e) showed that the samples in the T2DM_HTN group and the T2DM group each showed a certain clustering trend, suggesting that these samples had a high degree of similarity in urinary metabolite composition. In the blood metabolome differential metabolite analysis, the volcano plot (Figure 18d) showed that Furanone A and 2\u0026lsquo;,4\u0026rsquo;,6\u0026apos;-Trihydroxyacetophenone presented an up-regulation trend, whereas LysoPC (20_5(5Z,8Z,11Z,14Z,17Z)_0_0) showed a down-regulation. In the urinary metabolome, L-carnitine and citric acid were up-regulated in the T2DM_HTN group relative to the healthy group, whereas malic acid was down-regulated (Figure 18e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eDifferences in gut phenotypes and gut microbiological composition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, in the enterotype analysis, it was found that the enterotypes of the Hui group were dominated by Faecalibacterium (Clostridium pratensis), whereas the enterotypes of the Uyghur group were dominated by Prevotella (Prevotella), which differed from the findings of the Uyghur group, which had been reported to have enterotype 2, and the Hui group, which had enterotype 1 \u003csup\u003e[41]\u003c/sup\u003e. The core microbiota of the human gut consists mainly of Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria and Verrucomicrobia ( Verrucomicrobia) comprise of which the thick-walled and anamorphic phyla account for about 90% of the intestinal flora \u003csup\u003e[42]\u003c/sup\u003e. The thick-walled phylum mainly consists of Lactobacillus, Bacillus, Clostridium, Enterococcus faecalis and Ruminococcus, while the bacillus phylum consists of Bacteroides and Prevotella. Prevotella. Prevotella had the highest percentage of all groups, which is consistent with the study of De Filippo et al. (2010), suggesting that it is enriched in carbohydrate-based diets, and may inhibit pathogens and protect the host from inflammatory and colonic diseases through the production of short-chain fatty acids (SCFAs) \u003csup\u003e[42]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of ethnicity and diet on gut microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt the species level, there were significant differences in species abundance and metabolites between the Uyghur healthy group and the Kazakh healthy group, and between the Uyghur diseased group and the Kazakh diseased group. These differences may stem from the different dietary patterns of the two ethnic groups. For example, a diet high in sodium and low in potassium, as well as an overweight basal body state, may be potential triggers for diseases such as hypertension. Recent studies have shown that the relative abundance of strains such as Prevotella (Prevotella) and Clostridium (Clostridium) is significantly higher in the intestines of hypertensive patients [Yan Q et al. 2017], which is consistent with our findings. In addition, the proportion of unclassified_o_Eubacteriales was significantly higher in hypertensive patients, and their role in the disease deserves to be further explored despite the paucity of relevant studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation between dietary habits and microorganisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis of the correlation between differential microorganisms and dietary habits, we found that meat, as a high-protein and high-fat food, was negatively correlated with Chitinophaga_silvihsoli. This result reveals the differential effects of meat nutrients on different microorganisms during intestinal digestion. For example, proteins in meat may provide a suitable growth environment for Enterococcus_faecium after catabolism by intestinal flora, whereas fat digestion may inhibit the survival of Chitinophaga_silvihsoli. This finding not only supplements the research gap on the relationship between meat diet and gut microbes, but also suggests the potential intervention value of dietary modification on gut health\u003csup\u003e\u0026nbsp;[43]\u003c/sup\u003e. In addition, this study provides new data on the correlation between seafood and spicy foods and microorganisms, expanding the research boundaries in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of hypertension with metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe onset and progression of hypertension, a chronic metabolic disease, is closely related to the synthesis of multiple metabolites and the enrichment of metabolic pathways. The role of compounds such as short-chain fatty acids (SCFAs), trimethylamine oxide (TMAO), bile acids (BAs), and hydrogen sulfide (H2S) in hypertension has been widely studied \u003csup\u003e[44-47]\u003c/sup\u003e. In the present study, Escherichia coli was found to be positively correlated with hydroxycinnamic acid while Catenibacterium mitsuokai was negatively correlated in the blood metabolome of hypertensive patients. Levels of hydroxycinnamic acid are known to be associated with hypertension \u003csup\u003e[48]\u003c/sup\u003e, whereas proline metabolites are associated with lower blood pressure \u003csup\u003e[49]\u003c/sup\u003e. In the urinary metabolome, Catenibacterium mitsuokai showed a significant negative correlation with 3-methyl-2-oxovaleric acid, while Escherichia coli showed a positive correlation. In addition, 9-hydroxyfluorene was significantly correlated with both microorganisms, suggesting a possible association with hypertension\u003csup\u003e\u0026nbsp;[50]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction between type II diabetes and gut microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant changes in the composition of the gut microbiome are important factors in the development and progression of type II diabetes mellitus (T2DM). Specific gut microbes have beneficial or detrimental effects on the development of diabetes through the production of metabolites that promote or inhibit inflammatory responses. For example, Lactobacillus fermentum, Lactobacillus plantarum, Lactobacillus casei, Roseburia, Mucinophilus Akkermansia muciniphila) and Bacteroides fragilis may alleviate diabetic symptoms by improving intestinal barrier integrity and inhibiting inflammatory factors [Lee, C.B et al. 2021]. In the present study, higher abundance of Bifidobacterium adolescentis was detected in the intestinal tract of patients in the diabetic group, which may be related to dietary and pharmacological interventions of the patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisease mechanism revealed by metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe KEGG enrichment analysis of urine metabolome showed that the fatty acid biosynthesis pathway was significantly up-regulated, suggesting abnormal fatty acid metabolism and energy imbalance in the disease state; the pentose and glucuronic acid interconversion pathway was down-regulated, suggesting its metabolism was suppressed; and the arginine and proline metabolism pathway showed bi-directional changes, reflecting the complex metabolic regulation mechanism in the disease state. These results provide important clues for the study of disease mechanism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegration of gut microbiology and metabolomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we revealed the mechanisms associated with T2DM, HTN and the concurrence of the two (T2DM_HTN) by integrating gut microbial and urinary metabolomics analyses. Gut microbial analyses revealed significant differences in the proportions of Coriobacteriales_bacterium and Dorea_formicigenerans in the T2DM_HTN versus HTN populations, suggesting that these microbes may play a key role in the disease complication mechanism. Hierarchical clustering dendrograms based on the species level further indicated that the relative abundance of unclassified_f__Streptococcaceae was higher in the T2DM_HTN group, which may be closely associated with this disease state. Urine metabolome results showed that L-carnitine was up-regulated in the T2DM_HTN group, which may be associated with altered fatty acid metabolism, reflecting an adaptive response of host metabolism to the disease state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations and future directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe limitations of this study are the cross-sectional design, which did not allow for the identification of a causal relationship between dietary habits and microbial changes. In addition, the effects of genetics, lifestyle, exercise and drug use on gut microbes were not adequately included in the analysis. Future studies should incorporate a longitudinal design and a multi-omics approach to further reveal the complex relationship between gut microbes and metabolic diseases.\u003c/p\u003e"},{"header":"Conclusion and outlook","content":"\u003cp\u003e\u003cstrong\u003eKey Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study revealed the following key findings by analysing the gut microbiome and metabolomics of healthy and diseased populations (including hypertension, type II diabetes and their co-morbidities) from different ethnic groups in Xinjiang:\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003e\u003cstrong\u003e\u0026nbsp;Unique gut phenotypes in Hui samples:\u003c/strong\u003e The dominant genus of bacteria in the Hui group was Faecalibacterium, a finding that has never been reported before.Faecalibacterium, as an important genus of anti-inflammatory bacteria, may play an important role in maintaining intestinal health and metabolic homeostasis.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;\u003cstrong\u003ePotential functions of specific strains:\u0026nbsp;\u003c/strong\u003efor example, Bacteroides wexlerae (B. wexlerae) was identified as a commensal bacterium negatively associated with obesity and type II diabetes. Oral administration of B. wexlerae has been shown to reduce high-fat diet-induced obesity and diabetes by inducing metabolic changes and anti-inflammatory effects\u003csup\u003e\u0026nbsp;[51]\u003c/sup\u003e. These findings reveal unique regulatory pathways between host and microbial metabolism and provide new potential strategies for the prevention and treatment of metabolic disorders.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMULTI-OMOMETRIC ASSOCIATION ANALYSIS:\u003c/strong\u003e By integrating gut microbiome, dietary habits, urinary metabolites and metabolic pathway analyses, this study found that changes in the proportions of specific microorganisms in disease states were closely associated with significant differences in urinary metabolites. For example, the proportions of Coriobacteriales_bacterium and Dorea_formicigenerans differed significantly between the T2DM_HTN and HTN populations, suggesting that these microorganisms may play a key role in disease complication mechanisms. In addition, changes in metabolites such as L-carnitine reflect adaptive responses of host metabolism to the disease state.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we systematically revealed for the first time the gut microbiome and metabolome profiles of healthy and diseased populations of different ethnic groups in Xinjiang, which provided a new perspective for understanding the role of gut microbes in metabolic diseases (e.g., hypertension, type II diabetes mellitus, and their co-morbidities). By integrating and analysing the multi-omics data, we not only validated some of the existing findings (e.g., the role of dietary fibre on microbes \u003csup\u003e[52,53]\u003c/sup\u003e), but also provided brand new data on the correlation of seafood, spicy food and meat with microbes, expanding the research boundary of this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTo further deepen the study, we have collected 1038 health and disease samples from 15 regions and 7 ethnic groups in Xinjiang. Future research will focus on the following directions:\u003c/strong\u003e\u003c/p\u003e\n\u003cul start=\"50\"\u003e\n \u003cli\u003e\u003cstrong\u003eExpanding Sample Size and Diversity:\u0026nbsp;\u003c/strong\u003eThrough larger sample collection and multi-ethnic comparisons, we will further analyse the diversity of gut microorganisms in healthy and diseased populations, and search for representative gut microbiota and potential metabolic diagnostic and therapeutic markers.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eIntegration of multi-omics technologies:\u003c/strong\u003e Combining macro-genomic, metabolomic, transcriptomic and other multi-omics technologies to clarify the specific associations between gut microbes and host metabolism, providing more effective strategies for disease prevention and treatment.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eLongitudinal Studies and Mechanism Exploration:\u0026nbsp;\u003c/strong\u003eLongitudinal studies are designed to reveal the causal relationship between dietary habits, microbial changes and disease development, and to delve into the specific mechanisms of specific microorganisms and their metabolites in the development of disease.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eSummary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study reveals the complex relationship between gut microbes and metabolic diseases through the integration and analysis of multi-omics data, which provides an important scientific basis for early diagnosis, personalised treatment and prevention of the diseases. Future studies will further combine the multi-omics technology and longitudinal design to comprehensively reveal the interactions and specific mechanisms among the factors and provide more effective strategies for the prevention and treatment of metabolic diseases.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eSample collection and raw letter processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecruitment of subjects and information collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 192 volunteers were randomly recruited from five different counties and cities in Xinjiang, and all volunteers in each area were local indigenous residents. Basic information was collected from the sample population by means of a questionnaire, which was administered by a doctor to the volunteers in the form of a face-to-face interview. The questionnaire consisted of 19 questions based on the latest research findings, which revealed various factors associated with the gut microbiome, of which 7 were related to basic information, such as age, gender, ethnicity, geographic location, BMI, etc., and the other 12 were related to dietary habits, lifestyle variables, and disease history (Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed by the Ethical Clinical Research Ethics Sub-Committee of the Xinjiang Uygur Autonomous Region People\u0026apos;s Hospital (KY2023060173), and was collected from Kashgar, Korla, Changji, Hami, and Turpan regions of Xinjiang, China (Supplementary Table 1). Informed consent was obtained from each participant to participate in this study and to publish relevant data. All studies were conducted in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003eStool samples: Fresh stool samples collected under anaerobic conditions before breakfast were loaded into 5 ml disposable sterile individual LF005 stool tubes and confirmed to be free of contamination and QC qualified samples were immediately frozen in liquid nitrogen. Samples were transferred to the laboratory in an ice bucket within 24 hours and stored at -80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eBlood samples: Again, collected before breakfast, blood is collected using a sterile needle and quickly drawn into a sterile blood collection tube. A moderate amount of blood (5-10 ml) is usually required. Ensure that the collection environment is clean. Seal the collected blood sample in a sterile container and store it in a dry refrigerator, making sure that the container does not leak. In order to separate the different components of the blood, the blood collection tube containing the blood sample is placed in a centrifuge and the collected blood sample is centrifuged at a speed of 1000 - 3000 rpm under centrifugal conditions.\u003c/p\u003e\n\u003cp\u003eUrine Sample: A \u0026lsquo;mid-stream urine collection method\u0026rsquo; is used to minimise external contamination. The patient urinates a small amount of urine (approximately 1-2 seconds) and then receives the mid-stream urine in a sterile container. Collect a urine sample of approximately 10-20 ml, avoiding contact between the sample and the outside of the container.\u003c/p\u003e\n\u003cp\u003eSaliva: Volunteers should avoid eating, drinking, smoking and using oral cleansing products for 30 minutes prior to collection to minimise the impact on the sample. Before collection, you may rinse your mouth with water, but do not use any mouthwash containing chemicals. Place a sterile saliva collection tube in the mouth and wait for natural saliva to flow into the tube. Collect a saliva sample of approximately 2-5 ml. Ensure that the sample does not come into contact with the outside of the container.\u003c/p\u003e\n\u003cp\u003eSeal the collected samples in sterile containers and store them in a dry refrigerator, ensuring that the containers do not leak. Clearly label the container with the sample number, date and time of collection and volunteer information. After collection, the samples were transported to the laboratory via cold chain (within 24h). During transport, maintain the samples at the appropriate temperature to protect microbial activity. Follow relevant Standard Operating Procedures (SOPs) and ethical guidelines according to specific research or clinical requirements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMacrogenome sequencing of faecal samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and library construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe collected faecal specimens were centrifuged at 12000 \u0026times; g for 5 min at room temperature and the supernatant was discarded. 200 mg of precipitate was weighed from each sample, and the sequencing platform NovaSeq 6000 (Illumina, San Diego, California, USA) was used with the kit FastPure Stool DNA Isolation Kit (Magnetic bead) (MJYH, Shanghai, China). The extracted genomic DNA was detected using 1% agarose gel electrophoresis. the genomic DNA was fragmented to about 350 bp (Covaris M220). PE libraries were constructed using NEXTFLEX@ Rapid DNA-Seq (Bioo Scientific, USA) and \u0026lsquo;Y\u0026rsquo; junctions were attached (MEGAHIT v1.1.2). Self-attached fragments were removed from the junction using magnetic bead screening, and the library template was enriched using PCR amplification and denatured with sodium hydroxide to produce single-stranded DNA fragments. The single-stranded DNA fragments were then sequenced using the Illumina NovaSeq (lumina, USA) sequencing platform for macro-genome sequencing (Shanghai Meiji Biomedical Technology Co., Ltd.), and then subjected to bridge PCR, in which one end of the DNA fragments was complementary to a primer base and immobilised on the microarray, and the other end was randomly complementary to another primer in the vicinity and was also immobilised to form a \u0026lsquo;bridge\u0026rsquo; (bridge). (The other end is randomly complementary to another primer nearby and is also anchored, forming a \u0026lsquo;bridge\u0026rsquo;; PCR amplification produces DNA clusters; the DNA amplicon is linearised into a single strand. Add modified DNA polymerase and dNTP with four fluorescent markers to synthesise one base per cycle; scan the surface of the reaction plate with a laser to read the type of nucleotide that was polymerised in the first reaction for each template sequence; chemically cleave the \u0026lsquo;fluorescent group\u0026rsquo; and \u0026lsquo;termination group\u0026rsquo; to restore the 3-part sequence. The \u0026lsquo;fluorescent group\u0026rsquo; and \u0026lsquo;termination group\u0026rsquo; are chemically cleaved to restore the stickiness of the 3\u0026apos; end and continue to polymerise the second nucleotide; the fluorescent signals collected in each round of the reaction are counted to know the sequence of the template DNA fragments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data such as sequencing adapter sequences, low-quality bases, N (uncertain base information) bases and short length sequences, which seriously affect the quality of subsequent analyses, are subjected to quality control to ensure the accuracy of the results of subsequent analyses. The software fastp (v0.20.0) was used to cut adapter sequences with average quality values of less than 20 at the 3\u0026lsquo; and 5\u0026rsquo; ends of the sequence, and reads with lengths less than 50bp after quality cutting, to obtain high-quality PE reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecies and Function Annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNR species annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-redundant gene sets were compared to the NR database using DIAMOND software (v2.0.13) (comparison type: BLASTP), and species annotations were obtained from the corresponding taxonomic information database of the NR library, and then the abundance of the species was calculated using the abundance calculation method using Reads Number.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG functional annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG functional annotation letters corresponding to genes were obtained and counted using the database KEGG20230830 comparison with the key parameter blastp; E-value \u0026le; 1e-05, linking genomic and strain functional information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlpha diversity analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated alpha diversity indices for good coverage and non-phylogeny of sparse curves, choosing a secondary sampling depth of 4000 reads per sample to calculate the mean of the above alpha diversity indices. Different diversity indices were tested using the Kruskal-Wallis test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBeta diversity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing Wilcoxon rank sum test two-tailed test using bray-curtis distance algorithm to calculate the differences in community structure of different subgroups, PCoA analysis was used to project the high dimensional data with multiple features into lower dimensional space to resolve the main influences from the multiplicity of things.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRandom Forest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine learning based on the RPKM abundance calculation method using ten-fold cross validation to predict the importance of characteristic species that influence the overall distribution of a community, Analysis software: Random Forest package for the R language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCalculate the top 20 species at taxonomic level based on RPKM abundance with a confidence interval of 0.99. Analysis software: R language V4.3.3, plotROC\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut type analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe typing of the dominant colony structure of different samples was investigated by statistical clustering. Relative abundance was calculated using the reads number abundance calculation method, clustering was performed using the bray-curtis distance algorithm, and the optimal clustering K-value was calculated by the Calinski-Harabasz (CH) index, followed by the Between-class analysis (BCA, K \u0026ge; 3) or principal coordinates analysis (PCoA, K\u0026ge; 2) for visualisation. Software: R language ade4 package, cluster package, clustersim package.\u003c/p\u003e\n\u003cp\u003eBlood and urine untargeted metabolome sequencing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData pre-processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data extraction and conversion: obtain raw data from the sequencing instrument and convert it to a format suitable for analysis, such as converting mass spectrometry data files to mzXML format.\u003c/p\u003e\n\u003cp\u003eRemove noise, impurity signals and poor quality data points. For example, in liquid chromatography-mass spectrometry (LC-MS) data, exclude data with too low or too high signal intensity and abnormal retention times.\u003c/p\u003e\n\u003cp\u003eIdentify chromatographic or mass spectrometric peaks corresponding to metabolites and align these peaks across samples to ensure that the same metabolite is correctly characterised across samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite Identification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabase Matching: The obtained metabolite profile is compared with known metabolite databases, like the Human Metabolome Database (HMDB), METLIN, etc., to determine the probable identity of the metabolite based on mass-to-charge ratios, retention times, and other information.\u003c/p\u003e\n\u003cp\u003eExperiments are performed using pure standards, which are compared with sample data to accurately identify metabolites.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis: basic statistics like mean, standard deviation, etc. are calculated for each metabolite and t-tests, analysis of variance (ANOVA), etc. are used to find metabolites with significant differences between groups.\u003c/p\u003e\n\u003cp\u003eMultivariate analysis: Principal Component Analysis (PCA) is used to look at the overall distribution of the data and clustering between samples; Partial Least Squares Discriminant Analysis (PLS - DA), etc., can highlight the differences between the groups and find the metabolite variables that contribute to the subgroups.\u003c/p\u003e\n\u003cp\u003eMetabolic pathway analysis: the identified differential metabolites are mapped to metabolic pathway databases such as KEGG to identify affected metabolic pathways, e.g. analysing the impact of gut microbial metabolites on host energy metabolic pathways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Visualisation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrawing volcano plots: presenting the multiplicity and significance level of differences of metabolites among different groups, which can visually filter out metabolites with significant differences.\u003c/p\u003e\n\u003cp\u003eProduce heat maps: presenting changes in the relative content of metabolites in different samples, helping to understand the expression patterns of metabolites under different conditions.\u003c/p\u003e\n\u003cp\u003eSampling points were mapped using Arcgis (v 10.8), and symbols were set for the elements of the layer according to their properties. Add map elements such as title and legend through the \u0026lsquo;Insert\u0026rsquo; menu.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor informationAuthors and AffiliationsLaboratory of Synthetic Biology, College of Life Science and Technology, Xinjiang University, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China.Haitao Yue, Pazilaiti Yashenga, Xia Chen, Lulu Wang, Hussain AkbarSchool of Future Technology, Xinjiang University, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China. Yuxuan KouDepartment of Gastroenterology, People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi 830001, Xinjiang Uygur Autonomous Region, People's Republic of China Xinjiang Clinical Research Center for Digestive Diseases, Urumqi 830001, Xinjiang Uygur Autonomous Region, People's Republic of ChinaFeng Gao、Tian ShiSchool of Pharmaceutical Sciences, Xinjiang University, No. 777 Huarui Street, Shuimogou District, Urumqi 830017, Xinjiang Uygur Autonomous Region, People's Republic of China. Haitao YueContributionsHYT conceived the initial research idea and designed the technical route; PY took the lead in drafting the initial version of the manuscript and organized the overall structure. PY, XC, TS,LW, YK, and HA wrote the paper.FG and HYT reviewed and revised the paper. All authors have read and approved the final version of the paper. Corresponding authorsCorrespondence to Haitao Yue、Feng Gao\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by multiple funding sources. It received support from the Central Leading Local Science and Technology Development Special Fund Project (Autonomous Region Science and Technology Department) under the grant number ZYYD2022A06. Additionally, it was funded by the key Research and Development Project of Xinjiang Uygur Autonomous Region of China with grant numbers 2023B02034 and 2023B02034 - 2. Moreover, financial support was provided by the National Natural Science Foundation of China under grant numbers U2003305 and 31860018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by multiple funding sources. It received support from the Central Leading Local Science and Technology Development Special Fund Project (Autonomous Region Science and Technology Department) under the grant number ZYYD2022A06. Additionally, it was funded by the key Research and Development Project of Xinjiang Uygur Autonomous Region of China with grant numbers 2023B02034 and 2023B02034 - 2. Moreover, financial support was provided by the National Natural Science Foundation of China under grant numbers U2003305 and 31860018.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Ethical Clinical Research Ethics Sub - Committee of the Xinjiang Uygur Autonomous Region People\u0026apos;s Hospital (approval number: KY2023060173). The entire research process was carried out in strict accordance with the principles of the Declaration of Helsinki and relevant regulations, ensuring compliance with ethical standards.\u003c/p\u003e\n\u003cp\u003eAll participants involved in this study were from Kashgar, Korla, Changji, Hami, and Turpan regions of Xinjiang, China (see Supplementary Table 1 for details). Before the commencement of the study, each participant was fully informed about the research objectives, procedures, potential risks, and benefits. Informed consent was obtained from every participant, including consent to participate in this study and the publication of relevant data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants whose data are presented in this manuscript have provided explicit consent to publish. This consent encompasses the publication of the manuscript in the journal Microbiome, its affiliated online platforms, and any other academic databases or repositories where the journal\u0026apos;s content may be distributed or indexed for academic use.\u003c/p\u003e\n\u003cp\u003eFor the data used in this study, all personal identifiers have been carefully removed or anonymized. Participants were informed about this anonymization process in advance and consented to the publication of their anonymized data. The anonymized data were processed and stored in a manner that ensures the participants\u0026apos; privacy and confidentiality.\u003c/p\u003e\n\u003cp\u003eWritten informed consent to publish was obtained from each participant. These consent forms are securely maintained in the research archives of Xinjiang University. They are available for inspection upon request from the journal or relevant ethical review boards to verify the authenticity of the consent process. This approach ensures that the rights and privacy of all participants are fully respected and protected throughout the publication process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data generated or analyzed during this study are included in this article.The sequencing data relevant to this study have been deposited in GenBank and can be accessed at：https://www.ncbi.nlm.nih.gov/nuccore/PV136453.1?report=genbank.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLloyd-Price J, Abu-Ali G, Huttenhower C. The healthy human microbiome. Genome Med. 2016;8:51. doi: 10.1186/s13073-016-0307-y. \u003c/li\u003e\n\u003cli\u003eRajilić-Stojanović M, Heilig HG, Tims S, Zoetendal EG, de Vos WM. 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Journal of Nutrition Research, 2022, 42(2): 123 - 135.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a - Mantrana I, Marcos A, G\u0026oacute;mez - Candela C. The role of diet in shaping the gut microbiota and its implications for health and disease[J]. Nutrients, 2021, 13(8): 2737.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gut microbes, hypertension, type II diabetes mellitus, macrogenome, untargeted metabolome","lastPublishedDoi":"10.21203/rs.3.rs-6125489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6125489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to explore the diversity of gut microbial profiles and their associations with dietary habits and metabolites in different ethnic groups and disease states. By conducting gut microbiome and metabolomic analyses on 192 healthy and diseased individuals (including those with hypertension, type II diabetes, and their co - morbidities) in Xinjiang, it strived to offer new insights into the role of gut microbes in metabolic diseases, which could potentially contribute to early diagnosis and personalized treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dominant genus in the Hui group was Faecalibacterium, while Prevotella dominated in the Uyghur group, differing from previously reported enterotype distributions. Hypertensive patients had a significantly higher abundance of Prevotella, which was positively correlated with a high - salt diet. In type II diabetes patients, the abundance of Bifidobacterium adolescentis was significantly higher. Through integrative multi - omics data analysis, it was found that changes in the proportion of specific microorganisms (such as Coriobacteriales_bacterium and Dorea_formicigenerans) in disease - comorbid states were strongly associated with significant differences in urinary metabolites (such as L - carnitine and hydroxycinnamic acid). Metabolic pathway analyses also revealed significant alterations in glycolysis/glycolysis, phenylalanine metabolism, and other pathways in the disease state.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study systematically and for the first time reveals the gut microbiome and metabolome characteristics of healthy and diseased populations of different ethnic groups in the Xinjiang region. It offers a new perspective for understanding the role of gut microbes in metabolic diseases and provides a potential scientific basis for early disease diagnosis and personalized treatment. Future research should further integrate multi - omics technology and longitudinal design to comprehensively disclose the interactions among factors and specific mechanisms.\u003c/p\u003e","manuscriptTitle":"Characterisation of the gut microbiome in hypertensive and type II diabetic populations in different regions of Xinjiang","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 08:53:31","doi":"10.21203/rs.3.rs-6125489/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbedf6d9-14da-4d59-80a0-c0a1832020a5","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-22T07:24:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 08:53:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6125489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6125489","identity":"rs-6125489","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}