Changes of gut microbiota between different weight reduction programs

Accepted Manuscript

Changes of gut microbiota between different weight reduction programs Belle Yanyu Lin , Wei- De Lin , Chih-Kun Huang , Ming-Che Hsin , Wen-Yuan Lin , Aurora D. Pryor PII: DOI: Reference:

S1550-7289(18)30582-3 https://doi.org/10.1016/j.soard.2019.01.026 SOARD 3644

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Surgery for Obesity and Related Diseases

Received date: Revised date: Accepted date:

9 September 2018 20 December 2018 28 January 2019

Please cite this article as: Belle Yanyu Lin , Wei- De Lin , Chih-Kun Huang , Ming-Che Hsin , Wen-Yuan Lin , Aurora D. Pryor , Changes of gut microbiota between different weight reduction programs, Surgery for Obesity and Related Diseases (2019), doi: https://doi.org/10.1016/j.soard.2019.01.026

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ACCEPTED MANUSCRIPT Changes of gut microbiota between different weight reduction programs

Belle Yanyu Lin1,2, Wei-De Lin3, Chih-Kun Huang6,8, Ming-Che Hsin6,8, Wen-Yuan Lin4,5,7,*, Aurora D. Pryor9,* Syosset High School, Syosset, New York, USA 2

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Cornell University, Ithaca, New York, USA

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School of Post-Baccalaureate Chinese Medicine and 3Department of Social Medicine, 4

Family Medicine, and 5Surgery, College of Medicine, China Medical University, Taichung, Taiwan

Department of Family Medicine and 7Body Sciences & Metabolic Disorders

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International Medical Center, China Medical University Hospital, Taichung, Taiwan 8

Department of Surgery, Stony Brook University Medical Center, Stony Brook, New York.

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Short running title: Gut microbiota change between weight reduction programs *: equal contribution as corresponded author

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Correspondence and reprint request to:

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Wen-Yuan Lin, MD, MS, PhD

Department of Family Medicine, China Medical University Hospital,

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2, Yuh-Der Rd, Taichung, Taiwan 404 Tel: +886-4-22052121 ext 4709, Fax: +886-4-22361803,

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E-mail: [email protected] And

Aurora D. Pryor, MD Division of Bariatric, Foregut, and Advanced Gastrointestinal Surgery Department of Surgery, Health Sciences Center T18-040

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ACCEPTED MANUSCRIPT Stony Brook Medicine Stony Brook, NY 11794-8191 Tel: +1-631-444-2274; Fax: +1-631-444-6176 E-mail: [email protected]

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Financial Disclosure: None reported.

Funding/Support: This study was financially supported by grants from Ministry of Science and Technology, Taiwan (MOST 106-2314-B-039-031 and 107-2314-B-039 -055 -MY3), China Medical University Hospital (CMUH105-REC1-091 and DMR-107-086) and from the

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Ministry of Health and Welfare, Taiwan (MOHW 107-TDU-B-212-123004)

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Word Count: Abstract: 263 words, main text: 3705 words

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ACCEPTED MANUSCRIPT Abstract Background: Gut microbiota may induce obesity, diabetes, and metabolic syndrome. Different weight reduction programs may induce different changes in gut microbiota. Objectives: To assess the changes in gut microbiota between obese adults who participated in two different weight reduction programs, the dietary counseling group (DC) and sleeve

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gastrectomy group (SG), for three months. Setting: A University Hospital

Methods: Ten obese subjects from each group were matched according to gender, age, and body mass index (BMI). Gut microbiota compositions were determined by metagenomics

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using Next Generation Sequencing before and after treatment. Anthropometric indices,

metabolic factors and gut microbiota were compared between groups and within groups. Results: After three-months of treatment, compared to subjects in DC, subjects in SG

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experienced a greater reduction in body weight, BMI, body fat, waist and hip circumference, diastolic blood pressure, hemoglobin, insulin, insulin resistance, glutamate pyruvate

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transaminase, blood urine nitrogen, and glycated hemoglobin (HbA1c). A total of 8, 17, and

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46 species experienced significant abundance changes in DC, in SG, and between two groups, respectively. Diversity of the gut flora increased in SG and between the two groups after

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treatment. The weight change over the course of the weight loss program was further adjusted and only 4 species, including Peptoniphilus_lacrimalis_315_B, Selenomonas_4 sp.,

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Prevotella_2 sp., and Pseudobutyrivibrio sp., were found to be significantly different between the two weight loss programs. These four species may be the different gut microbiota change between internal and surgical weight reduction programs. Conclusions: There are significant differences not only in anthropometric indices and metabolic factors but also in gut microbiota change between the two programs. Keywords: gut microbiota, sleeve gastrectomy, dietary counseling, weight reduction

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ACCEPTED MANUSCRIPT Introduction

The number of obese individuals has doubled globally since 1980. As of 2014, 52% of adults were overweight or obese [1]. In the United States, the prevalence of adult obesity increased rapidly from 30.5 % in 1999 to 37.7 % in 2013 [2]. World Health Organization

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defines overweight as a body mass index (BMI) greater than or equal to 25 and obesity as a BMI exceeding or equal to 30 [1]. Obesity increases the risk of life-threatening diseases

including type 2 diabetes, hypertension, coronary heart disease, and cancer [3]. Although physical inactivity and poor eating habits contribute to weight gain, studies have been

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conducted to determine the extent to which genes are responsible for obesity [4].

Intestinal microbiota diversity and taxonomy are influenced by obesity and insulin resistance [5-8]. According to the Human Microbiome Project and the MetaHit consortium, the human

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gut hosts approximately 1014 microorganisms which contain two to twenty million microbial genes [6, 9]. Most human gut microbiota include four major bacterial phyla: Bacteroidetes

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and Firmicutes, which collectively constitute about 60 % of the digestive tract bacteria [10,

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11], Proteobacteria and Actionobacteria [9]. In addition, Bifidobacterium, Lactobacillus, Bacteroides, Clostridium, Escherichia, Streptococcus, and Ruminococcus are the most

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common gut bacterial genera in adults. The gut microbiota plays a critical role in human health in that bacteria produce enzymes for carbohydrate metabolism, short-chain fatty acids,

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lipopolysaccharides, and secondary bile acids [6, 9] which enter the circulatory system to influence inflammation, immunity, energy homeostasis, and intestinal transit regulation [11-13].

Animal research has further revealed associations between gut microbiota and obesity, diabetes, and metabolic syndrome [13, 14]. Backhed and others found that when gut microbiota from obese humans was fed to germ-free mice, the rodents became obese. But

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ACCEPTED MANUSCRIPT when gut microbiota from lean humans was consumed, the mice remained lean [15]. Fecal/gut microbiota transplantation has been proposed as an effective treatment for weight control. Ridaura et al. transplanted fecal microbiota from discordant twins who were obese into germ-free mice and concluded that body fat mass and obesity-related metabolic phenotypes are transmissible through fecal microbiota communities [16]. Suárez-Zamorano

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et al. found that the depletion of microbiota, either by means of antibiotic treatment or by the use of germ-free mice, can improve glucose tolerance and insulin sensitivity, and reduce obesity in obese leptin-deficient mice and high-fat diet-fed mice. After being quickly

depleted of gut microbiome, obese mice were found to lose weight and become healthier

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[17].

The ratio of Firmicutes to Bacteroidetes and the abundance of individual microbial species, known as gut microbial richness, have also been found to influence body weight and

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metabolism. Results, from animal and human studies, have shown that obese subjects have a higher Firmicutes to Bacteroidetes ratio than non-obese subjects [18-20], although other

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studies have yielded inconsistent results [6, 21]. Compared to obese subjects, lean subjects

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may have higher microbial richness which contributes to diversity in the gut ecosystem [14, 22, 23].

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Multiple methods have been utilized to attempt to lose weight in humans. The efficacy and effects of different weight control methods may vary depending on the subject. Previous

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human studies have found that gut microbiota may change after intervention. After bariatric surgery, obese subjects may experience a dramatic change in weight as well as a change in gut microbiota diversity, amount, or ratio [13, 24]. For example, Damms-Machado et al. found that Firmicutes to Bacteroidetes ratio decrease after sleeve gastrectomy (SG) [25]. Both Zhang and Furet et al. reported similar results after Roux-en-Y Gastric Bypass (RYGB), but Murphy et al. found opposite results [26, 27]. Due to the inconsistent results on gut

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ACCEPTED MANUSCRIPT microbiota change after bariatric surgery, the topic deserves further study. Medical weight reduction interventions, such as low-calorie diet and exercise, in obese subjects have also resulted in changes in microbial composition [24, 29]. For example, Damms-Machado et al. found that increased Firmicutes to Bacteroidetes ratio after very low-calorie diet [25]. However, Ley et al. reported that Firmicutes to Bacteroidetes ratio decreased after low

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calorie diet control [19]. Studies on the change of gut microbiota after diet control also

revealed inconsistent results [19, 25]. Weight loss results in humans depend on microbiota baseline profiles, dietary macronutrient intake, and metabolic status [12, 25]. However,

sstudies comparing changes in gut microbiota for different weight reduction methods are rare.

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These changes in gut microbiota composition among obese subjects undergoing SG were compared to a control group undergoing dietary counseling (DC).

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Patient Selection

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Materials and Methods

Participants were adult obese Chinese subjects (BMI ≥ 30 kg/m2), recruited from a weight

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management outpatient clinic in a tertiary hospital from October of 2016 to March of 2017.

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All were aged between 20 to 64 years and undergoing surgical intervention (SG) or a DC program.

Subjects were enrolled in the study if they met the inclusion criteria and provided

informed consent. Participants were excluded from the study if they experienced type 1 or 2 diabetes mellitus, severe infection, insufficient renal function with serum creatinine >1.3 mg/dl, insufficient liver function, adrenal or thyroid dysfunction, hypoalbuminemia with

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ACCEPTED MANUSCRIPT albumin < 30 g/L, anemia with hemoglobin <12mg/dL in female or < 14 mg/dL in male, porphyria, cardiovascular disease, untreated or on anti-hypertensive treated severe hypertension, peripheral vascular disease, stroke, gastrointestinal disease, fatal disease, psychiatric or neurological disorders requiring chronic medications, other significant concomitant diseases, history of bulimia, laxative abuse, or substance abuse. In addition,

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participants who were pregnant, breast feeding, with child-bearing potential and not using contraception, unable or unwilling to comply with the protocol requirements, considered by the investigator to be unfit for the study, or had previously participated in controlled diet programs, treatments or clinical trials within 30 days were also excluded. In order to

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minimize variables, the participants in the DC group and those in the SG group were matched by gender, age (± 5 years), and BMI (± 5 kg/m2). The ten most similar subjects from each

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group were then selected.

Dietary counseling program and sleeve gastrectomy description

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Subjects in DC group were provided with a 12-week education program. Each participant

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experienced a group weight reduction therapy, including diet control by registered dietitians and exercise training by trainers, every week for 12 weeks (spent at least 2 hours on every

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visit). The registered dietitians taught the subjects how to reduce their daily calorie consumption by 500 kcal from their initial daily calorie consumption and how to obtain

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balanced diet. Subjects exercised for 1 hour/week during the program. Dietitians also asked for a food record every visit to monitor their progress. Standard SG was performed laparoscopically by qualified bariatric surgeons at the hospital. The technique of SG and post-operation arrangement was described as below. SG was performed laparoscopically. In all 10 patients in SG group, the method of operation was the same. The operation begins from distal portion of stomach. LigasureTM for vessel ligation

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ACCEPTED MANUSCRIPT was used to release greater omentum from stomach. Then, the surgeon proceeds proximally to fundus area of stomach. The adhesion of gastric fundus to diaphragmatic crus is totally released for a complete dissection. The cutting of stomach from 4cm proximal to pylorus was started using a load stapler (60mm, black, Tri-StapleTM , from Medtronic ) with introduction of a Fr. 38 oral gastric tube as stent for first clipping. Then, the sequence of residual cutting is

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to follow with the calibration of the Fr.38 oral gastric tube inside stomach by load stapler (60mm, purple, Tri-StapleTM from Medtronic). In the last cutting, space would be left to maintain 1cm distance to esophagogastric angle to prevent injury to esophagus. After completion of the stapling and gastric tube production, the leak test nor suturing

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reinforcement for staple line was performed, unless persisting oozing from stable line or serosa tear near staple line were noted. The abdominal cavity drainage tube is not

performed. Patients are kept hospitalized until an adequate tolerance of oral fluids, no nausea,

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no pain and normal walking. Proton pump inhibitor was given for 30 days after operation. Vitamin-mineral and protein supplementation would be arranged for one year.

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Revaluation with surgeons, dietitians, and exercise trainer were held every 3 months till the

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first year. Patients consumed a usual diet before surgical intervention, fluid diet for 2 weeks afterwards, soft diet for the third week and solid diet on week 4. Complete normal diet was

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suggested and monitored by registered dietitians after week 12.

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Acquisition of anthropometric index, sociodemographic factors, and lifestyle behaviors

Information on anthropometric index, sociodemographic factors, and lifestyle behaviors of each subject were obtained by trained staff. Height, waist circumference (WC), hip circumference (Hip C) (measured to the nearest 0.1 cm) and weight (measured to the nearest 0.1 kg) were measured to determine the extent of weight loss for each patient. WC was

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ACCEPTED MANUSCRIPT measured at the mid-point between the inferior margin of the last rib and the crest of the ilium in a horizontal plane according to World Health Organization’s definition. BMI was calculated by dividing weight (kg) by height squared (m2). Blood pressure (BP) was measured on the right arm with an adequate cuff using a standard mercury sphygmomanometer. Body composition was measured using “InBody 370 (InBody Co.,

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Seoul, Korea)” bioelectrical impedance analysis. Sociodemographic factors and lifestyle behaviors were obtained to provide baseline characteristics. Age, gender, employment, education, diet, and physical activity frequency were collected by self-administered

questionnaires. Smoking, alcohol consumption, and betel nut chewing history (a local

Acquisition of blood and stool samples

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stimulant), were obtained to account for factors which might influence weight change.

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Each subject provided a blood sample after 12 hours of overnight fasting. Within four hours of collection, the biochemistry of each sample was assessed by registered clinical laboratory

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technologists. The homeostasis model assessment was used to estimate the degree of insulin resistance (IR) [HOMA-IR = fasting insulin x fasting serum glucose / 22.5, where insulin was

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measured in μU/mL and glucose measured in mg/dL]. Fresh stool samples were collected

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from participants in a clean container and stored in a sealed plastic bag. Upon collection, samples were placed in collection tubes and stored in a freezer at -80 °C for two weeks before

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DNA extraction.

Analysis of anthropometric index, blood and stool samples

At the time points of one month and three months after the commencement of each treatment, anthropometric indices such as weight, body fat, waist and hip circumference, and BMI were

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ACCEPTED MANUSCRIPT collected. Metabolic factors including fasting glucose, insulin, glycated hemoglobin (HbA1c), uric acid, liver function, renal function, total cholesterol (TCHOL), triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were obtained through blood samples. The changes in the ratio or abundance of various microbiota, including Firmicutes to Bacteroidetes ratio and diversity, were analyzed

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through stool samples. Next Generation Sequencing (NGS) technology was used to assess differences in gut microbiome. Then, anthropometric indices, metabolic factors, and changes in the ratio or abundance of various microbiota were compared within the SG group, within

Metagenomics analysis of gut microbes

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the DC group, and between groups.

A small piece of fecal material was removed from each frozen sample and DNA was

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extracted from the stool using the Qiagen QIAamp DNA Stool Mini Kit. The DNA concentration of each sample was then determined fluorometrically and all samples were

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normalized to 3.51 ng/μL for Polymerase Chain Reaction (PCR) amplification. Bacterial 16S

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rRNA amplicons were generated by amplifying the V4 hypervariable region of the 16S rRNA gene using single-indexed universal primers (U515F/806R) flanked by Illumina standard

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adapter sequences and the following parameters: 98 °C (3:00) + [98 °C (0:15) +50 °C (0:30) +72 °C (0:30)] × 25 cycles +72 °C (7:00). Amplicons were then pooled for sequencing using

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the Illumina MiSeq platform and V2 chemistry with 2×250 bp paired-end reads. If the returned sample contained more than 10,000 reads, it was considered successful. The sample was then analyzed using NGS, which allowed the DNA of various gut microbes to be identified. The Firmicutes to Bacteroidetes ratio, abundance of different microbiota, and diversity between and within groups were then determined. Alpha diversity (α-diversity) is the mean species diversity in sites or habitats at a local scale. A diversity index is a

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ACCEPTED MANUSCRIPT quantitative measure that reflects how many different types (such as species) there are in a dataset, and takes into account for how evenly the basic entities (such as individuals) are distributed among those types. Two different methods were used for the diversity calculation, Chao1 and ACE. The Chao1 approach uses the numbers of singletons and doubletons to estimate the number of missing species because missing species information is mostly

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concentrated on those low frequency counts. The ACE approach is a non-parametric

estimator proposed by Chao and his colleagues. The observed species are separated as rare and abundant groups; only the rare group is used to estimate the number of missing species. The estimated CV is used to characterize the degree of heterogeneity among species

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discovery probabilities [30, 31].

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Statistical Analysis

The data were presented as means and standard deviations (SD) for continuous variables and

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as percentages for categorical variables. Kolmogorov-Smirnov test was assessed before

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further analyses, and log transformation was used for variables with significant deviation from normal distribution. The differences on anthropometric indices, metabolic factors, and

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the ratio or abundance of various gut microbiota between two different interventions groups were compared at each time point. A Mann-Whitney U test was used to calculate differences

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and changes from baselines for continuous data. A Chi-square (χ2) test was used for categorical data between two groups at each time point. A paired t test was used to calculate the changes within a group at each time point. In addition, the Spearman correlation test was used for variables within same group. All statistical tests were 2-sided at the 0.05 significance level and performed using SPSS statistical software (17th version, SPSS Inc., Chicago, IL, USA).

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Results

Analysis of the change of anthropometric index and metabolic factors between and within

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group

As shown in Table 1, baseline characteristics were measured and compared between the two groups. Physical attributes used to pair the subjects, including age, BMI, WC, were all

statistically similar except for the diastolic BP. In addition, all laboratory assays, except

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insulin levels, GPT, fasting glucose levels, and HOMA-IR, were not statistically significantly different.

Table 2 shows the change of the measured characteristics from baseline to the indicated

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month(s) between DC and SG group and within each group. Although subjects in both groups significantly decreased their body weight and BMI (p < 0.05), subjects who underwent SG

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had significantly greater reductions than subjects who underwent DC both after one and three

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months of treatment (p < 0.05). Over the course of three months, compared to subjects in DC, subjects in the SG experienced a significantly greater reduction in body weight, BMI, body

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fat, WC, hip C, diastolic BP, hemoglobin, insulin, insulin resistance, glutamate pyruvate

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transaminase (GPT), blood urine nitrogen, fasting glucose levels, and HbA1c (p < 0.05).

Analysis of the change of gut microbiota between and within group

The baseline characteristics between two different weight reduction programs are shown in Table 1. The Firmicutes to Bacteroidetes ratio and total bacteria (diversity) abundances have no significant differences at baseline. The change in diversity between the baseline and

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ACCEPTED MANUSCRIPT after 3 months of intervention was significantly different in the SG group and between two groups (Table 2, p < 0.05). Figure 1 (1A, 1B, 1C, 1D) shows each individual’s change in diversity among baseline, after one and three months of intervention in each group. Figure 1A and 1B show the individual α-diversity change using two different methods (Chao 1 and ACE) in the DC group, there was inconsistent change after three months of treatment

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compared to baseline (both p > 0.05). But, in the SG group (Figure 1C and 1D), the diversity increased as time increased (both p < 0.05). Figure 2 shows the change of gut microbiota between baseline and after 3 months of intervention between and within group. A total of 8, 17, and 46 species’ abundance significantly changed in DC, SG, and between two groups (p

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< 0.05), respectively. When the weight loss of each individual was further adjusted for weight loss differences, age, and gender, only four species significantly changed between groups over the course of three months (p < 0.05). These four species were Peptoniphilus lacrimalis,

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Selenomonas_4 sp., Prevotella_2 sp., and Pseudobutyrivibrio sp. Figure 3 further shows the distribution of gut microbiota change between baseline and after three months of intervention

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Discussion

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within DC, SG group and between DC and SG group.

The magnitude of weight loss and changes in anthropometric indices were higher in the SG

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group, consistent with known advantages of bariatric surgery [32]. In both groups, the Firmicutes to Bacteroidetes ratio change was not statistically significant. However, the Firmicutes to Bacteroidetes ratio dropped after the first month and increased afterwards for those who underwent SG. On the other hand, the opposite was found in the DC group. Previous studies have revealed inconsistent results. For example, Damms-Machado et al. found that the Firmicutes to Bacteroidetes ratio increased in the very low-calorie diet group

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ACCEPTED MANUSCRIPT but decreased in the SG group [25]. Ley et al., Santacruz et al. and Remely et al. found that Firmicutes to Bacteroidetes ratio decreased after weight loss for those who underwent low calorie diet control [19, 22, 29]. Murphy et al. compared obese subjects under two surgical interventions (RYGB and SG) and found that only the RYGB group experienced an increase in the Firmicutes to Bacteroidetes ratio [28]. Mbakwa et al. found that Firmicutes to

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Bacteroidetes ratio is not significantly associated with obesity in children [33]. These

inconsistent results may be due to different intervention methods or anatomical changes induced by different surgical interventions. This relationship deserves further study.

In our study, the diversity in gut microbiota increased with bariatric surgery but not with

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dietary intervention. Previous studies were inconsistent in this area as well. While Remely et al. found an increase after four months of dietary intervention [22], Shao et al. found a decrease after surgical intervention [21]. Mbakwa et al. found that the diversity and richness

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were not significantly associated with overweight children [33]. In this study, it was found that the diversity increased over the course of treatment in the SG group, but not the DC

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group. The changes of the gut microbiota in the bariatric surgery (SG) group gradually increased after surgical intervention. In the beginning, patients are instructed to eat according

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to certain post-op dietary guidelines. Since these diets are abnormal, the first month’s data do

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not accurately reflect upon the gut microbiota. Thus, further longitudial study is needed to determine the differences, as patients return to their normal diets.

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When the two groups were compared, 46 species were identified to be significantly

different (all p < 0.05). After weight change (BMI change) was accounted for, the abundance of four species (Peptoniphilus lacrimalis, Selenomonas_4 sp., Prevotella_2 sp., and Pseudobutyrivibrio sp.) were found to be significantly different between the two weight reduction programs.

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ACCEPTED MANUSCRIPT Previous studies had found that the composition of the gut microbiota is entirely different between healthy and unhealthy subjects, such as obese and cancer patients [34, 35]. In particular, Pseudobutyrivibrio sp. has been reported to be related to inflammation diseases and colon cancer [36]. As well, this species has been linked to the production of butyric acid, which was much lower in colon cancer patients. Butyric acid is an essential nutrient for

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normal colon cells. It can increase the apoptosis and decrease the formation of colon cancer cells. Once absorbed, butyrate is mainly used as energy source by the colonic epithelium. On the other hand, Prevotella sp. was found to be completely absent from the colon cancer

patients [36]. Prevotella sp. increases energy harvest from vegetables. Its greater abundance

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in healthy individuals compared to those with colon cancer shows the significant difference in the intake of fiber and other plant compounds. As previous research has shown, obesity increases the incidence of colon cancer [37, 38]. It was also found that profound weight

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reduction reduces the chance of cancer and increases lifespans [39, 40]. This study found that both Pseudobutyrivibrio and Prevotella sp. were significantly different between groups and

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increased in SG group (both p < 0.05). Both of these two species can inhibit the formation of

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colon cancer cells, which is compatible with the ration of previous studies. Both Peptoniphilus lacrimalis and Selenomonas_4 sp. were found to have minor but significant

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changes between two different weight reduction programs. Peters et al. found that Peptoniphilus sp. is significantly different between subjects with colon cancer and those that

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don’t have colon cancer [35]. De Chierico et al. also found that Peptoniphilus sp. is related to nonalcoholic fatty liver disease [41]. These two diseases are closely related to obesity. Therefore, it is reasonable to believe that Peptoniphilus sp. is related to weight change and Selenomonas sp. is related to the synthesis of amino acid, which may be related to different weight reduction programs.

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ACCEPTED MANUSCRIPT Some limitations need to be addressed. First, the sample size is small which may cause some bias. Second, although we have matched the two groups according to age, gender, and BMI, the baseline characteristics between two groups are significantly different on insulin resistance. Since the gut microbiota is closely related to insulin resistance, it may cause some potential effects. However, the baseline characteristics of gut microbiota (Firmicutes,

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Bacteroidetes, Firmicutes/Bacteroidetes ratio, and diversity) between groups are statistically insignificant. The potential effect on the difference of insulin resistance in the baseline

between groups can be minimized. Third, the follow up period in the study is 3 months,

which may be not long enough to reflect the real change on gut microbiota after intervention.

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Based on our current findings and limitations, the following items worth further studies,

include (1) a comparison with a RYGB group of patients; (2) the number of subjects in each group should be increased; (3) the follow up gut microbiota analysis should be lengthened to

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1 year and 2 years post-intervention.

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Conclusions

In conclusion, gut microbiota is closely related to weight change as well as different weight

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reduction programs. After weight loss, the composition of gut microbiota is significantly changed. These gut microbiota changes may further link to other obesity-related diseases,

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such as cancer. As well, gut microbiota might play an important role on weight control and other obesity-induced diseases.

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ACCEPTED MANUSCRIPT Financial Disclosure: None of the authors have anything to disclose. Funding/Support: This study was financially supported by grants from Ministry of Science and Technology, Taiwan (MOST 106-2314-B-039-031 and 107-2314-B-039 -055 -MY3), China Medical University Hospital (CMUH105-REC1-091 and DMR-107-086) and from the Ministry of Health and Welfare, Taiwan (MOHW 107-TDU-B-212-123004)

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Acknowledgments

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We would like to thank Ms. Mei-Gin Huang and Mr. Ryan Lu for data collection assistance.

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Microbiota After Gastric Bypass and Sleeve Gastrectomy Bariatric Surgery Vary According to Diabetes Remission. Obes Surg. 2017;27:917-25.

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ACCEPTED MANUSCRIPT recapture debugging with unequal failure rates. Biometrika 80: 193-2011993. [32] Schauer PR, Kashyap SR, Wolski K, Brethauer SA, Kirwan JP, Pothier CE, et al. Bariatric surgery versus intensive medical therapy in obese patients with diabetes. N Engl J Med. 2012;366:1567-76. [33] Mbakwa, C. A., Hermes, G. D., Penders, J. , Savelkoul, P. H., Thijs, C. , Dagnelie, P. C.,

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health and diseases. Int J Mol Sci. 2015;16:7493-519.

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burden of cancer attributable to high body-mass index in 2012: a population-based study. Lancet Oncol. 2015;16:36-46. [39] Sjostrom L, Narbro K, Sjostrom CD, Karason K, Larsson B, Wedel H, et al. Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med. 2007;357:741-52. [40] Sjostrom L. Review of the key results from the Swedish Obese Subjects (SOS) trial - a prospective controlled intervention study of bariatric surgery. J Intern Med. 2013;273:219-34.

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Table 1. The baseline characteristics between two different weight reduction programs. These characteristics are compared in order to ensure that differences in the future are due to the difference in weight loss program.

Dietary counseling group (n=10)

Sleeve gastrectomy group (n=10)

P value

Men Age (years) Height (cm) Weight (kg) BMI (kg/m2) Body fat (%) Waist circumference (cm) Hip circumference (cm) Systolic BP(mmHg) Diastolic BP(mmHg) TSH (uIU/ml) Homocysteine (umol/L) GPT (IU/L) BUN (mg/dl) Creatine (mg/dl) eGFR (ml/min/1.7) uric acid (mg/dl) TCHOL (mg/dl) Triglycerides (mg/dl) HDL-C (mg/dl) LDL-C (mg/dl) Fasting glucose (mg/dl) A1C (%) Insulin (uIU/ml) HOMA-IR Bacteroidetes Phylum Firmicutes Phylum Firmicutes/Bacteroidetes ratio Diversity (Chao) Diversity (ACE)

4(40%) 38.0±10.5 162.3±8.9 95.5±18.1 36.0±4.55 43.7±5.4 107.4±11.5 116.4±10.3 129.1±14.3 77.7±10.4 1.65±0.63 10.4±2.7 40.6±26.9 10.2±4.0 0.78±0.11 95.3±13.0 6.4±1.8 216.1±40.0 132.3±80.2 45.8±7.8 138.0±35.7 92.6±9.4 5.70±0.37 12.4±7.6 2.87±1.81 0.529±0.148 0.399±0.135 0.868±0.484

4(40%) 36.2±9.9 168.2±8.4 102.4±19.3 35.9±4.0 45.3±3.2 113.8±10.3 118.6±8.2 141.4±17.4 87.0±8.0 1.80±1.46 11.7±3.8 82.7±43.6 9.5±2.4 0.76±0.20 104.6±28.5 6.55±2.62 212.0±38.2 157.5±76.3 45.2±7.8 135.1±31.9 101.3±7.3 5.62±0.21 22.1±3.6 5.52±0.89 0.481±0.122 0.401±0.129 0.958±0.590

0.697 0.146 0.427 0.968 0.436 0.207 0.595 0.102 0.039 0.766 0.421 0.020 0.639 0.734 0.366 0.883 0.817 0.481 0.854 0.847 0.033 0.562 0.003 0.001 0.435 0.964 0.714

247.7±51.0 248.5±49.6

0.282 0.260

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Baseline

219.9±60.8 220.1±58.8

Abbreviations: body mass index, BMI; blood pressure, BP; thyroid-stimulating hormone; glutamate pyruvate transaminase, GPT; blood urea nitrogen, BUN; estimated glomerular filtration rate, eGFR; total cholesterol, TCHOL; high-density-lipoprotein cholesterol, HDL-C;

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ACCEPTED MANUSCRIPT Table 2. Measured characteristics before, after one month, and after three months of the dietary counseling group and the bariatric surgery group. These reveal the significant changes throughout the weight loss program.

Sleeve gastrectomy group

1 month (n=10)1

3 month (n=10)2

Baseline (n=10)

4(40%)

-

-

4(40%)

Age (years)

38.0±10.5

-

-

36.2±9.9

Height (cm)

162.3±8.9

-

-

168.2±8.4

BMI (kg/m2)

36.0±4.55

35.1±4.6**

34.8±4.2*

35.9±4.0

Body weight (kg)

95.5±18.1

93.5±17.6** 92.4±16.7* 102.4±19.3

Body fat (%)

43.7±5.4

43.2±6.0

Waist circumference (cm)

107.4±11.5

107.8±9.4

Hip circumference (cm)

116.4±10.3

Systolic BP(mmHg)

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45.3±3.2

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-

-

-

-

-

-

-

-

-

-

-

32.2±3.5***

29.3±3.3***

91.5±17.0***

83.5±16.2*** <0.001 <0.001

43.5±4.3**

<0.001 <0.001

40.2±4.5**

0.084 <0.001

103.7±12.5 113.8±10.3 103.8±10.0***

94.1±12.6***

0.001 <0.001

115.1±9.9

114.8±10.6

105.4±9.4*** <0.001 <0.001

129.1±14.3

131.0±13.5

126.9±11.1 141.4±17.4

Diastolic BP(mmHg)

77.7±10.4

77.4±7.2

78.1±7.2

Hemoglobin (gm/dl)

13.9±1.4

13.9±1.7

Homocysteine (umol/L)

10.4±2.7

GPT (IU/L)

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P 4 3 P value 1 month (n=10)1 3 month (n=10)2 value

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Dietary counseling group Baseline (n=10)

111.5±9.5***

0.229

0.155

87.0±8.0

79.2±9.5

75.1±8.8*

0.025

0.007

14.2±1.5

14.1±1.5

13.7±1.3*

13.6±1.3**

0.234 <0.001

10.9±4.4

10.5±3.5

11.7±3.8

11.2±4.4

10.5±3.4

0.878

0.943

40.6±26.9

29.8±12.3

29.8±8.9

82.7±43.6

54.4±37.4**

27.7±18.4**

0.045

0.006

BUN (mg/dl)

10.2±4.0

9.9±3.2

10.9±3.6

9.5±2.4

9.0±3.3

7.4±3.4

0.626

0.015

Creatine (mg/dl)

0.78±0.11

0.79±0.09

0.78±0.10

0.76±0.20

0.78±0.20

0.78±0.18

0.480

0.832

eGFR (ml/min/1.7)

95.3±13.0

92.8±9.9

94.9±13.3

104.6±28.5

97.2±20.8

105.2±26.8

0.198

0.881

uric acid (mg/dl)

6.4±1.8

6.5±1.4

6.3±1.3

6.55±2.62

6.76±2.45

6.32±1.62

0.664

0.871

TCHOL (mg/dl)

216.1±40.0

197.8±36.5

200.2±35.9 212.0±38.2

187.2±45.7*

198.4±65.8

0.333

0.837

132.3±80.2

104.1±45.6

109.2±49.9 157.5±76.3

102.8±37.8**

90.0±45.0**

0.352

0.146

45.8±7.8

45.7±7.2

38.1±9.0**

43.0±7.3

0.007

0.355

138.0±35.7

130.6±28.9

126.3±33.2

130.9±52.4

0.890

0.750

Fasting glucose (mg/dl)

92.6±9.4

95.7±7.8

89.2±7.1

101.3±7.3

91.2±7.7**

91.8±6.1**

0.051

0.113

A1C (%)

5.70±0.37

5.52±0.26*

5.67±0.28

5.62±0.21

5.35±0.25**

5.16±0.29**

0.374

0.002

Insulin (uIU/ml)

12.4±7.6

12.3±11.3

10.0±4.4

22.1±3.6

11.2±3.9***

7.04±2.16***

0.001 <0.001

HOMA-IR

2.87±1.81

3.03±3.08

2.18±0.92

5.52±0.89

2.51±0.89***

1.59±0.48***

0.001 <0.001

Triglycerides (mg/dl) HDL-C (mg/dl)

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46.3±6.6

45.2±7.8

130.3±30.6 135.1±31.9

Bacteroidetes Phylum

0.529±0.148

0.509±0.113 0.575±0.101 0.481±0.122

0.486±0.140

0.455±0.097

0.725

0.396

Firmicutes Phylum

0.399±0.135

0.425±0.092 0.363±0.096 0.401±0.129

0.303±0.092

0.385±0.144

0.049

0.819

Firmicutes/Bacteroidetes ratio 0.868±0.484

0.926±0.464 0.671±0.280 0.958±0.590

0.680±0.284

0.956±0.667

0.219

0.595

Diversity (Chao)

219.9±60.8

228.5±51.5

227.2±43.4 247.7±51.0

287.0±63.0

341.1±101.0** 0.427

0.014

Diversity (ACE)

220.1±58.8

233.2±47.3

229.6±54.9 248.5±49.6

281.3±63.1

339.5±92.5**

0.037

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assessment-insulin resistance, HOMA-IR Paired T test within group

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delta 1month baseline between dietary counseling group and sleeve gastrectomy group

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delta 3month baseline between dietary counseling group and sleeve gastrectomy group

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*P<0.05; **P<0.01; ***P<0.001

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Legend to Figures: Figure 1. The individual change of diversity in the dietary counseling (DC, Figure 1A & 1B) and sleeve gastrectomy (SG, Figure 1C & 1D) group after intervention (n=10 in each group;

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subject A~J: DC group; subject K~T: SG group). Each of the lines represents each subject in

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either the DC or SG group. This shows the diversity of gut flora quantified in two ways.

Figure 2. The Venn diagram compares and contrasts the number of species that changed after three months of treatment between and within SG and DC groups (all p < 0.05). Each of the

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the diagram.

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Figure 3. The distribution of significantly gut microbiota change after three-months

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intervention within DC, SG group and between SG and DC group. This shows the direction change among these four groups (a total of 55 species among group 1~4 were identified to be significantly different (all p < 0.05); After further adjusted for weight change in the treatment period (three months), only four species (Peptoniphilus lacrimalis, Selenomonas_4 sp.,

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Prevotella_2 sp., and Pseudobutyrivibrio sp.) were found to have significantly changed (all p

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Figure 1.

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Figure 2.

Atopobium sp.

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Group1

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Holdemanella sp.

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Group2

Bifidobacterium adolescentis Ruminococcace NK4A214 group sp. Eubacterium dolichum Group3 30

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Actinomyces sp. Bacteroidales S24 7 group sp. Bacteroidales S24 7 group sp.

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Dorea formicigenerans ATCC 27755 Flavonifractor sp.

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Anaerotruncus sp. Ruminiclostridium 9 sp. Cronobacter sakazakii

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Fretibacterium sp.

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Group4

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Scardovia sp.

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Bifidobacterium catenulatum DSM 16992=JCM

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1194 =LMG 11043 Rothia sp. Rothia sp.

Adlercreutzia equolifaciens Eggerthella lenta

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Group 4 continued Slackia sp. Porphyromonadaceae sp.

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Prevotella2 sp. Prevotellaceae NK3B31 group sp.

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Staphylococcus_aureus_subsp. Peptoniphilus_lacrimalis_315_B Eubacterium brachy ATCC 33089

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Family XIII AD3011 group sp.

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Pseudobutyrivibrio sp.

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Tyzzerella 4 sp.

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Shuttleworthia satelles DSM 14600

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Lachnospiraceae NK4A136 group sp. Peptostreptococcus_sp. Human gut metagenome sp. Catenibacterium sp.

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Solobacterium moorei Clostridium ramosum Dialister sp.

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Dialister sp. Selenomonas 3 sp.

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Selenomonas 4 sp. Leptotrichia sp. Lautropia sp.

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Haemophilus sp.

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Figure 3.

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