Gut microbiota associated with obesity and high carotid intima-media thickness among Chinese children

doi:10.21203/rs.3.rs-1622498/v1

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Gut microbiota associated with obesity and high carotid intima-media thickness among Chinese children

https://doi.org/10.21203/rs.3.rs-1622498/v1

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Background

High carotid intima-media thickness (cIMT) in childhood, usually combined with obesity, is an early predictor of cardiovascular disease (CVD) later in life. However, the association between gut microbiota and high cIMT among children with obesity remains unclear. Therefore, we compared differences in composition, community diversity, and richness of gut microbiota among children with obesity and high cIMT (OB + high-cIMT), obesity but normal cIMT (OB + non-cIMT), and normal weight and normal cIMT (i.e., normal children) to identify differential microbiota biomarkers.

Methods

A total of 24 OB + high-cIMT children, 24 OB + non-cIMT children, and 24 normal children aged 10–11 years from the Huantai Childhood Cardiovascular Health Cohort Study were included. All included fecal samples were tested using 16S rRNA sequencing. α-diversity analysis was performed to estimate the community richness and diversity of gut microbes, and LEfSe analysis was used to reveal the main differential biomarkers among these three groups. Random forest analysis was used to further select differential biomarkers. The receiver operating characteristic (ROC) curve was used to evaluate the ability of biomarkers in identifying OB + non-cIMT and OB + high-cIMT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were assessed by Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) according to the KEGG database.

Results

The community richness and diversity of gut microbiota in OB + high-cIMT children were decreased compared with OB + non-cIMT children and normal children. At the genus level, the relative abundances of Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o__Bacteroidales were reduced in OB + high-cIMT children compared with OB + non-cIMT and normal children. In addition, these four significant biomarkers were ranked in the top 1, 2, 6, and 12 in the random forest analysis. ROC analysis results showed that combined Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o__Bacteroidales performed high ability in identifying OB + high-cIMT (area under the curve [AUC]: 0.91, 95% CI = 0.82–1.00]) and moderate ability in identifying OB + non-cIMT (AUC 0.76, 95% CI = 0.62–0.91). PICRUSt showed unique thiamine metabolism and methane metabolism pathways in gut microbial were decreased in OB + high-cIMT compared with OB + non-cIMT.

Conclusion

Gut microbiota plays a pivotal role in detecting children with obesity combined with high cIMT.

gut microbiota

cardiovascular disease

obese

cIMT

children

Cardiovascular disease (CVD) is a major public health issue, which may result in a decline in life expectancy, and an increase in economic burden and the risks of premature deaths (1–3). High carotid intima-media thickness (cIMT) was determined by the guidelines of the American College of Cardiology Foundation/American Heart Association as a class IIa recommendation for intermediate-risk assessment of asymptomatic CVD (4). A recent meta-analysis of 119 clinical trials showed that reduction in the degree of CVD can be predicted by the intervention on the progress of high cIMT (5). The measurement of cIMT is essential in adults with obesity, which is a main modifiable risk factor of CVD later in life (6). Emerging evidence shows that cIMT was used as one of the promising parameters to measure whether obese patients are qualified for bariatric surgery (7–9). We have previously found that the combination of general obesity and abdominal obesity was strongly associated with high cIMT among children (10). Therefore, early detection of obesity and high cIMT among children is of great significance to track the progress of obesity among children, which can provide guidance for early prevention of CVD in later life.

The gut microbiota has been considered to be a very promising target for the prevention and treatment of CVD in adults (11–13). A large number of clinical and experimental studies have shown that gut microbes play a key role in the occurrence and development of obesity mainly by regulating host energy metabolism, substrate metabolism, and inflammatory pathophysiological mechanisms in adults and children (14–19). However, little is known about the specific gut microbiota associated with the progress of obesity (i.e., obesity and high cIMT [OB + high-cIMT]) in children.

Therefore, in this study, we aimed to compare the composition, relative abundance, and community diversity of the gut microbiota among children with OB + high-cIMT, children with obesity but normal cIMT (OB + non-cIMT), and children with normal weight and normal cIMT, and revealed main gut microbiota biomarkers using 16S rRNA sequencing.

Study population

Data were collected from the Huantai Childhood Cardiovascular Health Cohort Study at baseline conducted in a public elementary school in Huantai County, Zibo City, Shandong Province, China, between November 2017 and January 2018. In the current study, 24 children aged 10–11 years with obesity and high cIMT, 24 children with obesity but normal cIMT, and 24 children with normal weight and normal cIMT matched by age and sex were included. This study was approved by the Ethics Committee of the School of Public Health of Shandong University (approval number: 20160308), and written informed consent was obtained from all the investigated children and their parents or guardians (20).

Anthropometric measurements and variables

The ultrasonic stadiometer (Shengyuan Co. Ltd, HGM-300, Henan, China) was used to measure the height (0.1 cm precision) and weight (0.1 kg precision). Body mass index (BMI) (kg/m²) was calculated by the following equation: weight (kg)/height² (m²). Obesity was defined as BMI higher than the 90th percentile for sex and age (21). Waist circumference (WC, cm) was collected at the end of expiration in children using an inelastic measuring tape. Child-specific validation blood pressure (BP)-measuring devices (Omron HEM-7012) was used to measure BP by trained investigators. A portable ultrasound instrument (L12-4, CX30; Royal Philips, Amsterdam, The Netherlands) was used to measure the thickness of the left and right anterior and posterior walls at the proximal end of the common carotid sinus. cIMT value was calculated as the average of the sum of left anterior wall thickness, right anterior wall thickness, left posterior wall thickness, and right posterior wall thickness. Based on the reference value of cIMT for children aged 6 to 11 in China (22), high cIMT was defined as cIMT higher than the 90th percentile in each sex and age group.

For each participant, an automatic analyzer (Beckman Coulter, AU480, Mishima, Shizuoka, Japan) for triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were determined. Demographic information (such as age and gender) were collected through self-reported structured questionnaires.

Fecal collection and gut microbiome profiling

Fecal samples for all included children who had not received antibiotics within the past three months were collected and stored at -80°C. After gDNA was extracted and detected by 1% agarose gel electrophoresis, TransStart FastPfu DNA Polymerase (TransGen AP221-02) was used to amplify the V3–V4 region of 16S rDNA from gDNA. The primer sequences of the V3–V4 region were as follows: 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') (23). After quality control, detection, and quantification, TruSeqTM DNA Sample Prep Kit was used to construct the Miseq library, fix the generated single-stranded DNA fragments, and perform PCR synthesis and amplification, and then MiSeq Reagent Kit v3-600 cycles (Illumina, California, USA) on the Illumina MiSeq platform was used to sequence the library.

Bases with a quality value lower than 20 in the tail of Paired-end (PE) reads were filtered out to obtain the final optimized sequence. After removing single sequences without repetitions, non-repetitive sequences were extracted (http://drive5.com/usearch/manual/dereplication.html) and compared using the Silva database (Release138 http://www.arb-silva.de) (24). Operational taxonomic units (OTU) clustering was performed according to 97% similar non-repetitive sequences (25). After preprocessing, data were imported into Quantitative Insights into Microbial Ecology (QIIME version 1.9.1) for further analysis (26). The ribosomal database project classifier Bayesian algorithm was used to perform taxonomic analysis on the 97% similar level of OTU representative sequences (27), to obtain the species classification information corresponding to each OTU. The raw sequence reads have been stored in the NCBI Sequence Read Archive (PRJNA811365).

Statistical Analysis

Continuous variables with non-normal distribution were represented as Median (P25-P75), and categorical variables were represented as n (%). Non-parametric tests (Kruskal-Wallis H test) and Chi-square tests were used to compare continuous variables and categorical variables, respectively, among the normal group, OB + non-cIMT group, and OB + high-cIMT group. Two-sided P values < 0.05 indicate a significant difference.

Venn diagrams were used to visualize the number of overlap and unique OTUs in the three groups. A bar chart was used to show the species’ composition. α-diversity analyses including Sobs, Shannon, Simpson, Ace, and Chao indicators were used to estimate the community richness and diversity of gut microbes, and the Wilcoxon rank-sum test was used to compare differences between every two groups. Benjamini-Hochberg was used to calculate P values adjusted for false discovery rate (FDR). The rarefaction curve was used to determine whether the sequencing data are sufficient or not. In β-diversity analyses, principal coordinate analysis (PCoA) and nonmetric multidimensional scaling (NMDS) analysis were used to observe the difference in the composition of the overall gut microbiota between the three groups. The microbial biomarkers between each group were analyzed by linear discriminant analysis effect size (LEfSe). Significantly gut microbial markers were obtained using the non-parametric Kruskal-Wallis H test, and then linear discriminant analysis (LDA) to calculate the effect size of different microbiota (LDA ≥ 2.5). Random forest analysis was used to evaluate the degree of contribution of important biomarkers. The receiver operating characteristic (ROC) was used to evaluate the ability of biomarkers in identifying OB + non-cIMT group and OB + high-cIMT group from normal children. All analyses were performed using R version 3.3.1.

Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis was used to standardize the OTU abundance table, and OTUs information based on the Greengene ID (28). The pathway information was obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) database, and the abundance of each pathway category was performed according to the OTU abundance. The predicted functional profiling of gut microbiota was presented using the extended error bar plot displayed in the STAMP version 2.1.3 with Welch’s t-test for every two groups (29).

Study population

A total of 72 children were included in this study including 24 children identified as OB + high-cIMT, 24 as OB + non-cIMT, and 24 as normal weight with normal cIMT matched by age and sex. The demographic and physical characteristics of the participants included in the study were presented in Table 1. The median age of included children was 10.79 years, of which 66.7% were boys. The BMI (16.55 vs. 23.77 vs. 26.18 kg/m²), WC (60.63 vs. 81.85 vs. 85.85 cm), systolic blood pressure (SBP, 104.33 vs. 113.33 vs. 116.83 mmHg), and TG (0.81 vs. 1.15 vs. 1.18 mmol/L) were highest in the OB + high-cIMT group, followed by the OB + non-cIMT group and normal group. The HDL-C (1.83 vs. 1.43 vs. 1.39 mmol/L) was highest in the normal group, followed ty the OB + non-cIMT group and OB + high-cIMT group. There was no significant difference in age and sex distribution, TC, and LDL-C among these three groups.

Table 1

Characteristics of study participants.
	Overall (N = 72)	Normal (N = 24)	OB + non-cIMT (N = 24)	OB + high-cIMT (N = 24)	P value
Boys, %	48(66.7%)	16(66.7%)	16(66.7%)	16(66.7%)	NS
Age, years	10.8(10.5,11.1)	11.0(10.6,11.2)	10.7(10.5,11.0)	10.7(10.4,11.0)	NS
BMI, kg/m²	23.8(17.5,25.8)	16.6(15.5,17.5)	23.8(23.0,24.8)	26.2(25.0,28.9)	<0.001
WC, cm	80.0(62.3,84.9)	60.6(57.5,62.7)	81.9(75.4,84.8)	85.9(83.0,91.9)	<0.001
SBP, mmHg	111.8(105.1,118.8)	104.3(100.8,109.8)	113.3(108.9,119.5)	116.8(111.8,123.7)	<0.001
TG, mmol/L	1.0(0.8,1.3)	0.8(0.6,0.9)	1.2(0.9,1.5)	1.2(1.0,1.6)	<0.001
TC, mmol/L	4.4(3.9,5.0)	4.2(3.8,4.8)	4.4(4.0,5.0)	4.5(4.0,5.3)	NS
LDL-C, mmol/L	2.4(2.0,3.0)	2.2(2.0,2.7)	2.6(2.0,3.0)	2.8(2.2,3.1)	NS
HDL-C, mmol/L	1.5(1.3,1.8)	1.8(1.5,2.0)	1.4(1.2,1.6)	1.4(1.2,1.6)	<0.001
GLU, mmol/L	5.0(4.7,5.4)	4.9(4.5,5.4)	5.0(4.8,5.4)	4.9(4.6,5.4)	NS
The median (P25-P75) and the percentage (%) were used to represent variables. OB + non-cIMT, obesity with normal carotid intima-media thickness; OB + high-cIMT, obesity with high carotid intima-media thickness; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; GLU, glucose; HDL-C, high-density lipoprotein cholesterol; NS, not significant.

Gut microbial composition

A total of 3,909,618 optimization sequence number, 1,611,082,076 bases with an average length of 412.2 (min length: 208; max length: 529) was generated. A total of 642 OTUs were overlapped in these three groups, with 95 unique OTUs in the normal group, 68 unique OTUs in the OB + non-cIMT group, and 45 unique OTUs in the OB + high-cIMT group in the Venn diagram (Fig. 1A).

As shown in the bar chart, at the phylum level (Fig. 1B), Firmicutes was higher in the normal group (66.03%) than that in the OB + non-cIMT group (63.56%) and OB + high-cIMT group (63.89%), whereas Bacteroidetes was lower in the normal group (19.43%) compared with OB + non-cIMT group (21.63%) and OB + high-cIMT group (22.79%). Furthermore, the Firmicutes/Bacteroidetes (F/B) ratio was higher in the normal group (3.40) compared with the OB + non-cIMT group (2.94) and OB + high-cIMT group (2.80). At the genus level, Faecalibacterium (15.64% vs. 11.99% vs. 13.53%), Bifidobacterium (11.44% vs. 9.80% vs. 8.58%), Subdoligranulum (8.33% vs. 5.94% vs. 5.06%) and Blautia (6.49% vs. 5.53% vs. 4.47%) were enriched in normal group, compared with OB + non-cIMT group and OB + high-cIMT group. Bacteroides (12.72% vs. 17.65% vs. 17.17%), Agathobacter (3.40% vs. 6.86% vs. 7.29%), and Megamonas (0.01% vs. 0.75% vs. 6.47%) were enriched in OB + high-cIMT group, compared to normal group and OB + non-cIMT group (Fig. 1C).

Gut microbial community diversity

α-diversity showed that the richness (Sobs, Ace, and Chao) in the OB + high-cIMT group was significantly lower than that in the normal group and OB + non-cIMT group (Fig. 2A-C), as well as the community diversity (Shannon and Simpson, Fig. 2D-E).

The flattened rarefaction curves of the gut microbes indicated that the sequencing depth was enough to represent the most microbial species (Sobs, Ace, Chao, Shannon, Simpson; Fig. 3A-E). PCoA analysis based on the Bray-Curtis distance algorithm showed that the microbial composition of OTU level in OB + high-cIMT group was significantly separated from the normal group and OB + non-cIMT group (P < 0.01, Fig. 4A). NMDS analysis similarly showed a significant difference between the composition of gut microbiota in these three groups (R = 0.083, P < 0.01, Fig. 4B).

Gut microbial biomarkers and potential value in risk assessment

At the phylum level, Proteobacteria was significantly enriched in the OB + high-cIMT group, followed by OB + non-cIMT and normal groups (Fig. 5A, P_FDR < 0.05). At the genus level, 14 significant genera were showed, such as Alistipes, Christensenellaceae_R-7_group (top 4 significant genera), and unclassified_o__Bacteroidale (top 4 significant genera) enriched in the normal group followed by OB + non-cIMT and OB + high-cIMT groups; Intestinibacter, Ruminococcaceae_UCG-002, Family_XIII_AD3011_group (top 4 significant genera), and UBA1819 (top 4 significant genera) enriched in both normal and OB + non-cIMT groups, followed by OB + high-cIMT group; and Lachnoclostridium and Lachnospiraceae_ND3007_group enriched in the OB + high-cIMT group, follwed by OB + non-cIMT and normal groups (Fig. 5B, P_FDR < 0.05). OTU 408 in genus Christensenellaceae_R-7_group, OTU656 in genus UBA1819, OTU740 in genus unclassified_o__Bacteroidales, and OTU70 in genus Family_XIII_AD3011_group were significantly enriched in normal or both normal and OB + non-cIMT groups (Fig. 5C, P_FDR < 0.05). Based on the LDA score (Fig. 5D, LDA ≥ 2.5), LEfSe analysis (Fig. 5E) showed that at the genus level, Alistipes and Christensenellaceae_R-7_group were significantly enriched in the normal group, while Lachnoclostridium was significantly enriched in the OB + high-cIMT group.

Random forest analysis further showed that 2 of the top 4 significant genera, Christensenellaceae_R-7_group and UBA1819 ranked as the two most important biomarkers (Fig. 6A). In addition, the other 2 of the top 4 significant genera, unclassified_o__Bacteroidale and Family_XIII_AD3011_group ranked in the top 6 and 12 in the random forest analysis, respectively. The ROC analysis showed that these four significant biomarkers had a high ability in discriminating OB + high-cIMT group from the normal group (area under the curve [AUC]: 0.91, 95% confidence interval [CI]: 0.82–1.00, Fig. 6B), and had moderate ability in distinguishing children with OB + non-cIMT from normal ones (AUC: 0.76, 95% CI: 0.62–0.91, Fig. 6C).

KEGG pathways contributing to the OB + high-cIMT children

We observed 5 significant KEGG pathways in normal group vs. OB + non-cIMT group (P < 0.05, Effect Size > 0.05, Fig. 7A). The thiamine metabolism and methane metabolism were increased in OB + non-cIMT group than in OB + high-cIMT group (P < 0.05, Effect Size > 0.01, Fig. 7B). There were 14 significant KEGG pathways between OB + high-cIMT group and normal group including the same 5 pathways in normal group vs. OB + non-cIMT group (Fig. 7A), most of which were linked to the hydrolysis of compounds and substrates (P < 0.05, Effect Size > 0.05, Fig. 7C).

In this cross-sectional study, we identified dysbiosis of gut microbiota and microbial biomarkers correlated with the development and progress of obesity among children. The α-diversity of gut microbiota in OB + high-cIMT children and OB + non-cIMT children was decreased compared with the normal group. Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o__Bacteroidales had moderate to high ability in discriminating OB + non-cIMT and OB + high-cIMT group from the normal group.

Emerging evidence among adults has shown that the dysbiosis of gut microbiota was associated with metabolic diseases such as obesity, diabetes, and cardiovascular diseases (30–32). Previous studies showed that the community diversity of gut microbiota was lower among children with type 1 diabetes or combined with other CVD risk factors, such as elevated blood pressure, compared with normal controls (33–35). However, the associations between gut microbiota and subclinical CVD among children have been less reported. To the best of our knowledge, we innovatively found that the overall gut microbial community diversity of gut microbiota was lower among children with OB + high-cIMT compared with OB + non-cIMT and normal controls. Our findings suggest that the high diversity of gut microbiota might be a preventive in the progress of obesity (i.e, OB + high-cIMT).

We identified that the relative abundance of phylum Proteobacteria and genus Lachnoclostridium was the highest among OB + high-cIMT children followed by those with OB + non-cIMT and normal ones, while Alistipes showed an opposite trend. Studies based on animals and adults similarly reported that the relative abundance of Proteobacteria was positively associated with obesity and its related metabolic disorder (36). Consistent with our results among children, Alistipes can be considered as an important protective biomarker for CVD and metabolic syndrome among Chinese adults (37, 38). In contrast, American studies among 54 subjects have suggested that Alistipes were associated with an increased BP (39). The discrepancy may be due to differences in sample size, age and sex distribution, ethnic groups, and different dietary patterns (40). Moreover, a large population-based cohort based on the TwinsUK registered adult twins showed that Lachnoclostridium was positively correlated with visceral fat and increased the risk of cardiometabolic diseases (41, 42). Our findings add to the existing evidence and suggest that Proteobacteria, Alistipes, and Lachnoclostridium might contribute to the development of obesity combined with cardiovascular damage among children.

We found that the relative abundance in genus Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o__Bacteroidales were the top four significantly decreased genera in OB + high-cIMT children compared with normal groups. Our findings were supported by previous studies of related metabolic outcomes that increased relative abundance of Christensenellaceae_R-7_group had a protective effect on BP both in adults and mice (43, 44). Overrepresented UBA1819 could improve the body weight in high-fat-fed mice and rats by reducing adipose tissue inflammation and glucolipid metabolism disorder (45, 46). Although Shi et al. found the abundance of Family_XIII_AD3011_group was increased in high-fat-fed rats (46), a study reported by Lüll et al. among polycystic ovary syndrome women in Finland that Family_XIII_AD3011_group was decreased in females with obesity (47), and our findings among children were consistent with the latter. As reported by metagenomic sequencing on type 2 diabetes mice, the increased abundance of unclassified_o__Bacteroidales was associated with improved glycolipid metabolism (48). Interestingly, we firstly found that genus Christensenellaceae_R-7_group, UBA1819, unclassified_o__Bacteroidales, and Family_XIII_AD3011_group had moderate ability in identifying OB + non-cIMT from normal groups and high ability in identifying OB + high-cIMT from normal groups. Our findings suggest that a decreased abundance of genus Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o__Bacteroidales play a vital role in the development of obesity and damage of carotid intima-media. Interventions targeting these biomarkers may be used as one of the non-invasive diagnoses of obese children with or without damage of carotid intima-media.

Moreover, we found that thiamine metabolism and methane metabolism were significantly lower in OB + high-cIMT group vs. OB + non-cIMT group. In addition, hydrolysis of compounds and substrates pathways contributed to differentiate OB + high-cIMT group from normal group. Thiamine was a cofactor for enzymes regulating glucose metabolism (49), and its concentrations were decreased in patients with type 1 and type 2 diabetes mellitus through the reinforcement of hyperglycemic damage (50). Several studies showed that the use of multivitamins that contained thiamine was inversely associated with myocardial infarction and may reduce the risk of CVD in adults (51, 52). Methane can be produced by Methanomassiliicoccales, which involves the progress of adult CVD (53), and plays a protective effect through anti-inflammation, anti-oxidation, and anti-apoptosis (54, 55). In addition, the imbalanced hydrolysis of compounds and substrates could affect intestinal permeability and microbiota density, which could lead to chronic inflammation (56–61). Our findings suggest that the development and progress of obesity might be mediated by gut microbiota through these pathways.

To the best of our knowledge, this is the first study to explore the association between gut microbiota and obesity with high cIMT in children, and we have found several biomarkers that have moderate to high ability in identifying OB + high-cIMT children and OB + non-cIMT children from normal children. However, several limitations should be noted. First, 16S rRNA sequencing generally does not provide a level of species resolution, and more comprehensive technologies, such as in vivo experiments in mice and metagenomic sequencing, are needed to analyze the underlying mechanisms of the gut microbiota in depth. Second, our case-control study cannot be used for causal inference. Third, our sample size is small compared with studies in adults which need to be further verified in large cohort studies. However, substantial changes in microbiota can be easily measured (62). The flattened rarefaction curves indicate that the sample size is sufficient and reasonable, and more samples will only produce a few new features. Fourth, our study is based on a single center, which needs to be validated in multiple centers and other ethnic groups.

In conclusion, we found that dysbiosis of gut microbiota is associated with obesity with or without carotid intima-media damage in children, and Christensenellaceae_R-7_group, UBA1819, Family_XIII_AD3011_group, and unclassified_o_Bacteroidales had the ability identifying obesity and its progress from normal status. Our study provides effective and targeted guidance for the interventions for children with obesity and target organ damage.

Data availability

The raw sequence reads can be found under NCBI accession number PRJNA811365 and also available from the corresponding author Bo Xi (Email: [email protected]) upon request.

Acknowledgment

We are thankful to the National Natural Science Foundation of China for the funding support.

Author contributions

BX and JS designed the study. LY, XM, and MZ were involved in the data collection process, and JS performed data analysis. XJ wrote the first draft of the manuscript. SS was involved in language modification. All authors reviewed the manuscript and approved the final version for publication.

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

The study protocol was approved by the Institutional Ethics Review Board of the School of Public Health, Shandong University.

Funding

This work was supported by the National Natural Science Foundation of China (Grant ID: 81722039, 81673195), China Postdoctoral Science Foundation (Grant ID: 2021M701978), Natural Science Foundation of Shandong (Grant ID: ZR2021QH272).

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Gut microbiota associated with obesity and high carotid intima-media thickness among Chinese children

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Abstract

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Introduction

Methods

Study population

Anthropometric measurements and variables

Fecal collection and gut microbiome profiling

Statistical Analysis

Results

Study population

Gut microbial composition

Gut microbial community diversity

Gut microbial biomarkers and potential value in risk assessment

KEGG pathways contributing to the OB + high-cIMT children

Discussion

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Data availability

Acknowledgment

Author contributions

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References

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