Prevalence of Chronic Diseases and Alterations of Gut Microbiome in Ningxia China During Urbanization: An Epidemiological Survey

Background: In the 21st century, the continuous development of urbanization has dramatically changed people's living environment and lifestyle. The urban environment and the lifestyles it formed are accompanied by the risks that would threaten the health of individuals, as shown by the increased prevalence of chronic diseases. However, there is little research on the effect of urbanization on gut microbiome in residents. Results: Here we investigated the relation between living environment and gut microbiota in a homogenous population along an urban-rural gradient in Ningxia China. According to the degree of urbanization, the population is divided into four groups: mountainous rural (MR) represents non-urbanized areas, mountainous urban (MU) and plain rural (PR) represent preliminary urbanization, and plain urban (PU) is a representative of complete urbanization. Studies have found that with the deepening of urbanization, the prevalence of chronic diseases, such as diabetes, dyslipidemia, fatty liver, gallstones, and renal cysts, have gradually increased. The intestinal richness and diversity of the microbial community were signicantly reduced in the PR and the PU groups compared with the MR and the MU groups. At the phylum level, the abundance of Proteobacteria gradually increased, while the abundance of Actinobacteria gradually decreased in the MU and the PR groups compared with the MR group. Based on linear discriminant analysis selection, the signicantly enriched genera Faecalibacterium, Prevotella, and Pseudobutyrivibrio in the MR group gradually decreased in the MU, the PR, and the PU groups. Effect size results revealed that both residence and diet had an effect on intestinal microbiota. Conclusions: Urbanization attenuates gut microbial diversity and richness and might play a role in the pathogenesis of chronic diseases. Environmental exposure during urbanization, such as westernization of diet and pollution have been shown to affect gut microbiota. In this study, the disparate patterns of gut microbiota composition were revealed at different levels of urbanization, providing an opportunity to understand the pathogenesis of chronic diseases and the contribution of the “rural microbiome” in potential protection against the occurrence of chronic diseases.

factor in modulating the host immune system, in uencing susceptibility to autoimmune diseases [9,10].
Urbanization attenuates microbial diversity and might play a role in the pathogenesis of chronic diseases [3]. The effect of urbanization is likely to be re ected in human gut microbiome, as alterations in the gut microbiome have been associated with in ammatory and autoimmune diseases [11]. However, to date, there is no direct evidence on how urbanization affects microbiome composition during urbanization. Some studies have assessed the impact of urbanization on the gut microbiome by comparing the gut microbiome of healthy individuals in rural and urban areas. It has been reported that people living in non-Western and/or rural areas have higher bacterial diversity compared to those in the United States and Europe [12,13,14,15]. Fecal bacteria from children in a rural African village in Burkina Faso have been reported to be similar to the microbiome of early human settlement at the time of the birth of agriculture [15]. In addition, children from Burkina Faso, who consumed predominantly a high-ber diet, had a unique abundance of bacteria of the genera Prevotella and Xylanibacter, which are known to contain a set of bacterial genes for cellulose and xylan hydrolysis [15]. Similar results have been reported for a higher abundance of Bacteroides, Bi dobacterium, Blautia and Dorea in populations from highly urbanized regions of the United States and Europe, while rural populations from Africa and South America are enriched in Prevotella and members belonging to Clostridiaceae family [13,14,16,17,18,19]. Substantial differences were found in a study comparing fecal microbiota composition in African descendants living in rural, semi-urban areas to those living in urban areas, with Prevotella predominating in semi-urban individuals and Bacteroides predominating in African Americans [20]. These studies compare the gut microbiota composition only in terms of geographical diversity. Our understanding of the role of gut bacteria in relation to factors associated with urbanization is still in its infancy. In particular, there are fewer studies comparing rural and urban microbiomes in homogeneous ethnic populations, and this type of study is more meaningful for us to examine the effects of urbanization on chronic diseases and intestinal microbiota.
Here, we performed a comprehensive study integrating multidimensional dataset of gut microbiota, and host characteristics that were based on clinical and questionnaire-derived data, from a large-scale of epidemiological investigation of 538 mountain rural (MR) participants, 529 mountain urban (MU) participants, 765 plain rural (PR) participants and 675 plain urban (PU) participants. Study work ow is described in Fig. 1. Since the gut microbial ecosystem re ects external environmental triggers, the study of the gut microbiota in residents during urbanization could shed light on the pathogenesis of chronic diseases and provide insights into the prevention of chronic diseases.

Methods Geographical Regions and Subject Recruitment
This epidemiological survey was conducted in Ningxia Hui Autonomous Region, China, which lies between 35°14′ and 39°23′ north latitude and 104°17′ to 107°39′ east longitude. The shape of Ningxia is narrow and long, the topography in Ningxia is complex, and the elevation varies greatly. It lies on the mountainous plateau and crosses the plains and mountains (Fig. 1).
Multi-stage sampling was used to select study subjects. By the end of 2018, there were 5 prefecture-level cities (9 municipal districts, 2 county-level cities, and 11 counties) in Ningxia Hui Autonomous Region. According to the economic development level and location distribution, Xingqing District, Litong District, Pengyang County, and Xiji County were selected as the survey sites in this study (Fig. 1). Strati ed in these four counties and districts according to urban-rural differences, ve neighborhoods (or villages) were selected by simple random sampling at each level, 100 households were selected according to systematic sampling within each neighborhood (or village), and one study subject was selected by KISH table from each household for epidemiological investigation and physical examination (Fig. 1).
Participants must have lived at the survey site for more than 6 months and are aged 18-80 years old.
According to statistics in 2019, the resident population of Ningxia was 6,946,600. 4,000 people were sampled in this epidemiological survey, and 2,507 people nally participated in blood routine tests.
According to the resident population, the participants in this survey can be divided into four groups: MR, MU, PR and PU. The topography and economic development status of the population illustrate the degree of urbanization: PU represents the most urbanized part of Ningxia, MR represents the unurbanized part, and MU and PR are in between.

Disease examination and data collection
The classi cation and diagnosis of diabetes were made according to the Medical Care Standard for Diabetes -2019 [21]. The 2016 "Guidelines" recommend the appropriate levels and abnormal cutoff points for the blood lipid components in the Chinese population. The diagnostic criteria for dyslipidemia were: LDL-C > 3.4 mmol/L or TC > 5.2 mmol/L, HDL-C < 1.04 mmol/L or TG > 1.7 mmol/L [22]. The 2018 Chinese hypertension guidelines did not follow suit with the US guidelines and maintained the current cutpoint of ≥140/90 mmHg for hypertension diagnosis. Diffuse fatty liver can be de ned by the presence of at least two of three abnormal ndings on abdominal ultrasonography: diffusely increased liver near eld ultrasound echo ('bright liver'), liver echo greater than kidney; vascular blurring and the gradual attenuation of far eld ultrasound echo. The typical ndings of fatty liver in computed tomography (CT) include a diffuse decrease of liver density. Portal and hepatic vein branches appear prominent in a scan unenhanced with contrast. A -liver-to-spleen CT ratio less than 1 is de ned as fatty liver: a mild degree of fatty liver has a liver: spleen CT ratio of less than 1 but more than 0.7; a moderate degree of fatty liver has a ratio of less than or equal to 0.7 but more than 0.5, and a severe degree of fatty liver has a ratio less than or equal to 0.5 [23].
The epidemiological survey database was compiled by Ningxia Medical University using Epidata software. Relevant persons were trained before the investigation to standardize the operation methods. Data entry and on-site investigation were conducted simultaneously, and real-time veri cation of double entry was used. Each group had on-site management personnel for summarization and error correction.
The questionnaire was checked daily, and the unquali ed questionnaire was returned to the investigator for correction. Five percent of the questionnaires, blood samples, and ultrasonography results were sampled and reviewed. Quali ed data are uploaded to the database and managed by the administrator.
The details of 2507 subjects recruited in the present study are provided in the Table 1 and Table S1. The entire study was reviewed and approved by the Institutional Review Board of the General Hospital of Ningxia Medical University (No. 2017-200). Informed consents on enrolment had been signed and provided by all participants. Demographic and clinical data of the participants were obtained from the hospital's questionnaires and hospital electronic medical records.
In order to study the dietary habits in different regions and the potential effect of diet on intestinal bacteria, we conducted a questionnaire survey on the dietary status of participants. The survey included 20 pre-dispensed food types. For each category of food, participants were asked about their weekly consumption and converted to annual consumption.

DNA extraction and sequencing
Selection criteria for participation in the gut microbiota test: (i) no antibiotics/antifungal/antiviral drugs in the past month, (ii) no chronic intestinal disease (IBD/in ammatory bowel syndrome/chronic diarrhea/abdominal tuberculosis). The fecal samples were kept at -80°C before extraction of genomic DNA. The DNA extraction was conducted as the method described in the literature [24]. The quality and quantity of DNA were assessed using Bio spectrometer (Eppendorf, Germany) and 0.8% agarose gel electrophoresis. The 16S rRNA V3-V4 region was ampli ed by PCR using 341F (5'-CCTACGGGNGGCWGCAG-3') and 805R (5'-GACTACHVGGGTATCTAATCC-3') primers. PCR products were recovered using 2% agarose gels and puri ed using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). The nal 16S rRNA gene amplicon library was sequenced on the MiSeq platform (Illumina) using a 2 × 300 bp paired-end protocol. Illumina MiSeq sequencing was performed by Shanghai Mobio Biomedical Technology Co., Ltd. (China).
Data processing, OTU clustering and taxonomic pro ling Sequencing read pairs were demultiplexed based on the unique molecular barcodes, and reads were merged using USEARCH Version 8.0. Merging allowed 0 mismatches and a minimum overlap of 50 bases. Sequences that cannot be spliced and chimeras were removed, and chimeras were eliminated using UCHIME software. Sequences less than 400 bases in length after splicing were removed. Operational Taxonomy Units (OTUs) were clustered using UPARSE [25] (version 7.1 http://drive5.com/uparse/) software based on 97% similarity. OTUs were determined by mapping the centroids to the SILVA v128 database. Other analyses were performed using the QIIME 1.9 pipeline [26].
Bacterial Diversity and Taxonomic Analysis: By a sampling-based OTUs analysis, bacterial diversity was determined and shown by the Ace index, the Chao 1 index, the Shannon index, and the Simpson index by using the "vegan" package in R [27]. principal coordinate analysis (PCoA) based on Bary-Curtis distance were conducted to display the microbiome space between both the group samples.

Effect size analysis
In this study, we performed "effect size" analysis strategy to determine whether the clinical and dietary datasets can affect gut microbiota. To assess the proportion of variance of gut microbiota that be explained by clinical and dietary datasets, the adonis function of the R package vegan was used to estimate the "one-to-all" effect size (R 2 ) between each variable of the clinical and dietary datasets to the whole gut microbiota dataset. Only variables with signi cant effect on the gut microbiota dataset were considered. To get rid of redundant variables, the spearman correlation coe cient between variables was calculated, and variables with correlation coe cient greater than 0.5 were removed. Functional annotation of 16S rRNA genes based on KEGG Pro le In order to identify the differentially abundant functional processes and pathways across the four groups, predictive functional pro ling was performed on the taxonomic pro les obtained according to the 16S rRNA gene amplicon sequencing using the PICRUSt version 1.0.0 pipeline [28].
Statistical analysis PERMANOVA and ANOSIM were performed respectively using the adonis and the anosim functions implemented in the "vegan" package of R [27]. Spearman Rank correlation test was performed to analyze the correlation between the clinical variables and the bacterial genera in stool. Principal component analysis (PCA) analysis of clinical and dietary variables was performed using the prcomp function in R. We performed PERMANOVA to study the differences in the bacterial community composition among the four groups and used PCoA ordination to visualize the differences graphically. Multivariate logistic regression analysis after adjusting for possible confounding factors is performed using the glm function in R to ascertain the signi cant in uences for chronic diseases and is calculated as adjusted odds ratio (95%, con dence interval [CI]). Linear discriminant analysis (LDA) coupled with the effect size (LEfSe) algorithm was used to identify biomarkers that were characteristics of each group based on the abundance values [29].
Continuous data were expressed as median, and dichotomous variables as counts and proportions. Chisquare test was used to compare categorical data. Group wise comparisons of abundance of various taxa in the different groups were performed using Kruskal-Wallis H test and Wilcoxon rank sum test (for pairwise comparisons). The Benjamini-Hochberg FDR method was used to correct the multiple comparisons [30]. P-values < 0.05 were considered statistically signi cant. The data visualization in this study was mostly conducted in R environment using ggplot2 [31]. All statistical analyses were performed using R 4.0.

Availability of data and materials
The 16S rRNA sequencing data were deposited in the NCBI's Sequence Read Archive (SRA) database under the accession ID PRJNA673060. unique persistent identi er and hyperlink to dataset(s) in https://www.ncbi.nlm.nih.gov/bioproject/673060.

Characteristics and Clinical Information of the Participants
A total of 2507 adult healthy subjects were included in the present study. The MR group consisted of 538 adults (231 male) with a median age of 53 years; the MU group consisted of 529 adults (179 male) with a median age of 49 years; the PR group consisted of 765 adults (274 male) with a median age of 53 years, and the PU group included 675 adults (232 male) with a median age of 55 years (Table 1).
Participant details and clinical baseline information were shown in Table S1. Signi cant shift was observed on the four groups in the over clinical variables as indicated by PCA analysis (Fig. 2A). Consistently, we found that with the deepening of urbanization (MR→MU→PR→PU), the prevalence of chronic diseases had a gradually increasing trend, such as diabetes, dyslipidemia, and fatty liver disease. The highest prevalence among chronic diseases was dyslipidemia, followed by fatty liver disease. Further logistics regression analysis revealed that the risk of chronic diseases was signi cantly higher in the MU, PR, and PU groups compared with the MR group (odds ratio signi cantly greater than 1) after adjusting for gender and age effects ( Fig. 2B and Table S2).

Dietary information and gut microbial diversity analysis
With the development of urbanization, people's living and dietary habits have changed. To detect changes in diet and gut microbiota, we screened the 2,507 participants and obtained stool samples from 1,177 people (1,045 of whom had dietary questionnaire information). Separation of clinical metadata among the four groups (1087 samples) was revealed by PCA ( Fig. 3A and Table S3). Similarly, signi cant shift was also observed on the four groups in the overall composition of the gut microbiota at genus level as indicated by PCoA (Fig. 3B) and PERMANOVA analysis (Table S4). Coherently, the PCA results revealed that the four groups were signi cantly different in dietary habits (Fig. 3C). The MU, the PR and the PU groups consumed more meat, rice, and fewer potatoes than the MR group (Table S5).

Taxonomic composition and diversity of fecal microbiota
In the four groups, the rarefaction analysis showed that the number of OTUs richness nearly approached saturation as the number of samples increased (Fig. 4A). A Venn diagram displaying the overlaps among groups showed that 1812 of the total number of 2793 OTUs were shared in the four groups (Fig. 4A), indicating a high similarity in the core microbiome across the four regions. Overall, the fecal microbiomes across the four groups were dominated by Firmicutes followed by Bacteroidetes, Actinobacteria and Proteobacteria (Fig. 4B). Bacterial members from Verrucomicrobia, Tenericutes and Cyanobacteria were also detected in most of the subjects, although their abundance was observed to be low (< 0.02%) (Table  S6). Firmicutes and Bacteroidetes abundance did not change signi cantly among the four groups, but the abundance of Proteobacteria gradually increased, while the abundance of Actinobacteria gradually decreased in the MU, the PR and the PU groups compared with the MR group (Table S6). Consistently, the microbial community richness indicated by Ace and Chao 1 estimators and community diversity estimated by Shannon and Simpson indices showed signi cant changes among the four groups ( Fig. 4C and Table S7). Namely, the microbial community gut richness and diversity were signi cantly reduced in the PR and PU groups compared with the MR and the MU groups ( Fig. 4C and Table S7).
Variations in gut microbial community across regions Overall, at the genus level, the top ten genera among the four groups were Bacteroides, Faecalibacterium, Ruminococcaceae_uncultured, Prevotella,Lachnospiraceae_incertae_sedis, Subdoligranulum, Escherichia-Shigella, Pseudobutyrivibrio, Ruminococcus, and Blautia (Table S8). The differences in taxonomic composition are also manifested at the genus level. Differential analysis revealed that with the deepening of urbanization (MR→MU→PR→PU), some bacterial genera, such as Faecalibacterium, Prevotella, Pseudobutyrivibrio, Ruminococcus, and Bi dobacterium gradually decreased, while some bacterial genera, such as Bacteroides, Blautia, Klebsiella, Anaerostipes, and Enterobacter gradually increased ( Fig.  5 and Table S8). These results are consistent with PCoA (Fig. 3B) and PERMANOVA analysis (Table S4), indicating that region have signi cant in uence on the microbial community pro les of the gut microbiota.

Effect size and correlation analysis
In order to investigate the effect of external factors on gut microbiota, we performed effect size and correlation analysis. In this study, we showed that residence was an important explanatory variable for fecal microbiota composition along the urban-rural gradient, as it explained 2.3% of the variance (Fig.  6A). In total, clinical metadata and diet explained 18.7% of the variance in the gut microbiota (Fig. 6A). Furthermore, diet explained 2.9% of the variance, while hepatic and renal function explained 5% of the variance and blood routine explained 2.5% of the variance ( Fig. 6A and Table S9). To further explore the links between gut microbiota, and clinical variables, we performed a Spearman correlation analysis. It was found that the bacteria such as Ruminococcaceae_uncultured, Ruminococcaceae_unclassi ed, Christensenellaceae_uncultured, Faecalibacterium and Bi dobacterium, enriched in the MR group were positively correlated with HDL and negatively correlated with UA and Triglyceride, while bacteria enriched in the PU group, such as Lachnospiraceae_incertae_sedis, Lachnospiraceae_uncultured, Escherichia-Shigella, and Enterobacteriaceae_unclassi ed, were negatively correlated with HDL and positively correlated with UA and Triglyceride (Fig. 6B).

Region-speci c signatures in gut microbial composition
To further identify the region-speci c species signatures, investigation was performed at the genus level using LEfSe analysis. A total of 32 genera were identi ed as the sets of region-speci c. Based on LDA selection, the MR group was signi cantly enriched with 11 genera including Faecalibacterium, Prevotella, and Pseudobutyrivibrio, the MU group was signi cantly enriched with 5 genera including Bi dobacterium, Akkermansia, and Alistipes; the PR group was signi cantly enriched with 8 genera including Blautia, Clostridium_sensu_stricto_1, and Enterobacter, and the PU group was signi cantly enriched with 8 genera including Bacteroides, Veillonella, Dialister, and Anaerostipes (Fig. 7A).
Region speci c trends in functional pro le Generally, for healthy individuals, differences at the taxonomic level the gut microbiomes may show remarkably similar pro le in functional repertoire. Therefore, to investigate the region-speci c functional characteristics of the current microbiome dataset, we attempted to investigate region-speci c trends in the functional composition of the gut microbiomes of the four groups. The KEGG orthology (KO) and the KEGG pathway/module pro le were constructed using the PICRUSt version 1.0.0 pipeline and the 16S rRNA marker gene sequences were used to predict the microbial community function pro les. Different groups were observed to have distinct trends in their functional pro les of the gut microbiomes. As shown in Fig. 7B, KEGG pathway analysis found 41 signi cantly enriched pathways related to metabolism and disease in the four groups. Based on the LDA selection, 13 predicted microbial functions including terpenoid backbone biosynthesis, metabolism of terpenoids and polyketides, and thiamine metabolism were remarkably enriched in the MR group, 6 functions including fatty acid elongation in mitochondria and sphingolipid metabolism were remarkably enriched in the MU group, 9 predicted microbial functions including lipopolysaccharide biosynthesis proteins and glycan degradation were remarkably enriched in the PR group, and 13 predicted microbial functions including fatty acid metabolism, lipid metabolism, fructose and mannose metabolism, and propanoate metabolism were remarkably enriched in the PU group (all p < 0.05, Fig. 7B, Table S10).

Discussion
In the 21st century, with the rapid development of the world economy, there is an emerging consensus that urbanization is affecting human health despite its economic importance and contribution to development. Diet, contamination, socioeconomic status, and other environmental factors have long-term effects on human intestinal microbiota that may increase the risk of chronic diseases during urbanization. The adoption of Western lifestyle, diet and exposure to higher levels of pollution might have led to the loss of speci c bacterial species of our ancestral microbiota, which in turn increased the prevalence of chronic diseases [32]. Urbanization may alter the gut microbiome and further affect residents' health, but studies are currently lacking. For the rst time, we studied the increase in the prevalence of chronic diseases during rapid industrialization in Ningxia, China, and signi cant differences in gut microbiota and function between rural and urban areas within populations of homogeneous ethnicity. The disparate patterns of the gut microbiota composition in rural and urban areas offer an opportunity to understand the contribution of a "rural microbiome" in potentially protecting against the development of chronic diseases.
Our results revealed that Firmicutes and Actinobacteria gradually decreased while Proteobacteria gradually increased as urbanization deepened (MR→MU→PR→PU). This is consistent with the literature where the predominant microbial populations in rural populations were from the Firmicutes and Actinobacteria phyla [33]. These bacterial communities are favored by the consumption of starch-rich bread and potatoes, typical foods for rural Russia and some low-income socioeconomic groups [33,34,35]. It was reported that urbanization is associated with an increased proportion of Bacteroides, Blautia, Faecalibacterium, Ruminococcus, and Bi dobacterium, whereas Prevotella is increased in the gut microbiota of individuals residing in non-industrialized societies [13,14,15]. These ndings suggest that the urbanization process has signi cantly altered the gut microbiota composition of genetically similar populations living in different regions, such as rural versus urban. Alterations of the Prevotella : Bacteroides ratio have been associated with certain diet. High intake of amino acids, lipids, and cholesterol has been shown to increase the abundance of Bacteroides, while Prevotella favors the intake of sugars and other complex carbohydrates [13,15,36]. Our ndings showed that with the progress of urbanization, Faecalibacterium, Prevotella gradually decreased while Bacteroides increased. Children from rural African villages with high ber intake have abundant Prevotella, which has the ability to hydrolyze cellulose and xylan [15]. In general, gut microbiota adapt to a polysaccharide-rich diet, enabling the host to extract energy from dietary ber while protecting against in ammation [37]. This may be related to the low prevalence of chronic diseases, as we found a signi cant enrichment of Faecalibacterium in the MR group. Faecalibacterium is one of the most abundant bacterial genera in the adult colon of healthy individuals, accounting for more than 5% of the total bacterial population [38,39]. Faecalibacterium is known for its immunomodulatory properties and is effective in improving intestinal in ammation and intestinal barrier function [40,41,42,43].
Our results demonstrated that urban residents consumed more meat and less whole grains, which were similar to Western dietary habits. Western diet are mainly characterized by consumption of more high saturated fat, red meat and carbohydrates, and less fresh fruits, vegetables and whole grains. In mouse and human studies, Western diet have been shown to in uence the pathogenesis and/or development of many diseases, including diabetes, obesity, hypercholesterolemia, and cardiovascular disease [44,45,46,47,48]. In addition, we also found that with the deepening of urbanization, the diversity of gut microbiota also gradually decreased, which may also be related to diet. Individuals who consume foods based on raw plants or ber have a higher diversity of gut microbiota than westerners on Western diet [12,13,14,15,18]. Similarly, animal experiments have shown that a low-ber diet is associated with depletion of microorganisms in mice, becoming more aggressive and irreversible over several generations [45]. These data suggested that Western diet were associated with irreversible gut microbiota dysbiosis and partially explained the impact of industrialization on microbiome alterations. Furthermore, urban residents are more exposed to food additives, which can also cause changes in gut microbiota. For example, arti cial sweeteners can induce dysglycaemia by altering the gut microbiota, reinforcing the glycan degradation capacity of gut microorganisms and increasing energy harvesting and a number of pathways involved in sphingolipid metabolism and transport [49]. Consistently, our study found that sphingolipid metabolism was enriched in the MU group and glycan degradation was enhanced in the PR group. It should be pointed out that in this study, clinical and dietary metadata explained only 18% of the gut microbiota variance, indicating that there are other factors that affect gut microbiota, such as air quality. Air pollution coincides with urbanization and is believed to have an increasing adverse impact on public health [2], including an increase in cardiovascular and respiratory diseases [50,51], and gastrointestinal disease [52,53].
In summary, consistent with the theory of microbiota disappearance and its association with the emergence of chronic diseases, a loss of gut bacterial richness and diversity during urbanization was observed, which may be associated with the high prevalence of chronic diseases. The limitations of this study are based on 16S rRNA gene sequencing, which may provide incomplete and biased insight into the composition of intestinal microbiota. An in-depth understanding of intestinal bacterial species or strains during urbanization and their function in the pathogenesis of chronic diseases is still needed.

Conclusion
Our ndings suggest that the gut microbial composition and function, which are in uenced by residence and diet, differ according to the level of urbanization. Therefore, the increase in the prevalence of chronic diseases that accompanies urbanization is associated with aberrant in the composition of gut microbiome. The reduction in microbial diversity based on 16S rRNA sequences simply indicated an unbalanced gut ecosystem, but did not provide us with more detailed information about strains (species) or certain microbial functions. Overall, functional studies are also lacking. Since reducing the incidence of chronic diseases and guaranteeing the health of residents are crucial in terms of economic and social development, We propose that it is necessary to establish a focused and novel framework for the study of  Tables   Due to technical limitations, table 1 is only available    Study design and ow diagram. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.

Figure 1
Study design and ow diagram. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors.               The signi cantly different bacterial genera based on Wilcoxon rank-sum test in the four groups. With the deepening of urbanization, the abundance of some bacterial genera gradually decreased (left box) and some gradually increased (right box) as shown by red line delimited.

Figure 5
The signi cantly different bacterial genera based on Wilcoxon rank-sum test in the four groups. With the deepening of urbanization, the abundance of some bacterial genera gradually decreased (left box) and some gradually increased (right box) as shown by red line delimited.

Figure 5
The signi cantly different bacterial genera based on Wilcoxon rank-sum test in the four groups. With the deepening of urbanization, the abundance of some bacterial genera gradually decreased (left box) and some gradually increased (right box) as shown by red line delimited.

Supplementary Files
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