Calorie Restriction Intervention Induces Enterotype-associated BMI Loss in Nonobese Individuals
Phase: NA
Conditions: Healthy Volunteer
Sponsor: BGI-Shenzhen

 
Clinical Study Protocol 
Calorie Restriction Intervention Induces Enterotype-associated  
BMI Loss in Nonobese Individuals 
 
 
 
Principal Investigator  
 
Kaiye Cai, Master 
 
 
 
 
 
BGI-Shenzhen 
 
 
 
 
 
Shenzhen, Guangdong, China 
Protocol identification number 
BGI-20170204 (dated 2017-3-20) 
Author  
 
 
 
Hua Zou, Bachelor 
 
 
 
 
 
BGI Education center 
 
 
 
 
 
University of Chinese Academy of Sciences 
 
 
 
 
 
Shenzhen, Guangdong, China 
Version 
 
 
 
0.4 
 
 
Confidentiality Statement 
 
This document contains confidential information that must not be disclosed to anyone other 
than the Investigator Team, and members of the Research Ethics Committee and 
Regulatory Authorities unless authorized to do so. 
 
 
 

 
Table of Contents 
Table of Contents .............................................................................................................. 2 
1.1 
Protocol title, number, date .................................................................................. 3 
1.2 
Name and address of investigator ....................................................................... 3 
1.3 
Investigator(s) signature page .............................................................................. 3 
1.4 
Protocol synopsis ................................................................................................... 4 
2.1       Background ........................................................................................................... 5 
2.2       Description of population to be studied, enrollment targets ............................. 5 
2.3 
Study duration ....................................................................................................... 6 
3.1 
Description of objectives and purpose ................................................................ 7 
4.1       Primary and secondary outcomes ....................................................................... 8 
5.1       Detailed study procedures .................................................................................. 10 
5.1.1     Screening information.................................................................................... 10 
5.1.2     Baseline period for recruiting participates. ................................................. 10 
5.1.3     Intervention period for Calorie restriction dietary intervention ............... 11 
6.1      Statistical methods to be employed .................................................................... 12 
 
 
 
 
 
 

1.1 Protocol title, number, date 
Title: Calorie restriction dietary intervention 
Protocol number: 
BGI-20170204 
Version: 
 
0.4 
Date:  
 
20 March 2017 
 
1.2 Name and address of investigator 
Kaiye Cai 
BGI-Shenzhen, Shenzhen, Guangdong, China 
 
1.3 Investigator(s) signature page 
The Principal Investigator is responsible for ensuring that the study is adhere to BGI-
Shenzhen all regulations and guidelines during and after study completion. 
 
I have read and understood the protocol specified above and agree on its content. I agree 
to conduct this study according to this protocol and guidelines in BGI-Shenzhen, the 
Declaration of BGI-Shenzhen, and the pertinent individual country laws/regulations and to 
comply with its obligations, subject to ethical and safety considerations. I shall not disclose 
the information contained in this protocol or any results obtained from this study without 
authorization. 
 
 
 
 
 
 
 
Kaiye Cai 
 
 
 
 
 
 
Principal Investigator    
Signature 
 
Site name or ID number  
 
Date 
 

1.4 Protocol synopsis 
Protocol Number and Title: BGI-20170204 Calorie Restriction Dietary Intervention 
Meals: The Normal Calorie diet at baseline and Calorie Restriction diet during Calorie 
Restriction period. 
Phase or Type of study: Interventional 
Study Objectives: The purpose of this intervention study is to evaluate the effect of calorie 
restriction on BMI loss, amino acid, and gut microbiota in healthy volunteers of two 
different enterotypes and provide useful insights for potential application of gut microbiome 
stratification in personalized nutrition intervention. 
 
Study design: The study included a one-week run-in period (baseline) and a three-week CR 
dietary intervention period. 
 
Sample Size: 41 individuals (24 females and 17 males aged 30 ± 6 years old) completed the 
whole intervention. 
 
Eligibility Criteria: 
 
Recruited volunteers should meet all the following criteria: 
1) BMI less than 28 kg/m2 and more than 18 kg/m2; 
2) Without antibiotics in the recent 2 months;  
3) Without prebiotic or probiotic supplements in the recent 2 months;  
4) Without hypertension, diabetes mellitus, gastrointestinal disease and other severe 
auto-immune diseases; 
5) Regular eating and lifestyle patterns;  
6) No international travel in the recent 3 months;  
7) Without prebiotic or probiotic supplements in the recent 2 months. 
 

Volunteers meeting any of the following criteria will not be eligible to participate in this 
study: 
1) With antibiotics in the recent 2 months; 
2) Failure to comply with the experimental requirement; 
 
 
2.1 Background 
Calorie restriction (CR), which has the potential effect on weight loss and blood amino 
acids, has been demonstrated to associate with gut microbiota in human, especially in obese 
individuals [1, 2]. Observational studies with long-term CR found that CR resulted in 
weight loss and decreasing chronic disease risk factors in nonobese persons [3-6]. A 4-
week CR intervention improved gut barrier integrity, reduced systemic inflammation on 
gut microbial diversity and BMI loss in obese women, suggesting a potential association 
among gut microbiota, CR and BMI [7]. Enterotype, a concept for stratifying individuals 
based on the gut microbiota, was first described in 2011 and was closely linked to long-
term dietary patterns [8, 9]. Plenty of studies have reported that individuals have shown 
microbial-dependent (enterotypes, Bacteroides to Prevotella ratio) metabolic responses to 
the same intervention, including changes of BMI and glycemic indices [9-14]. As for the 
association between CR and blood amino acids, Biolo et.al reported changes in blood 
amino acids of 9 healthy nonobese men after a 2-week CR diet [15]. Despite the plenty of 
studies on CR as we mentioned above, to our knowledge, there are no studies for 
simultaneously evaluating impacts of CR on BMI, gut microbiota and blood amino acids 
in nonobese individuals in a single study. 
2.2 Description of population to be studied, enrollment targets 
Volunteer-wanted posters were propagated at the China National Gene Bank in Shenzhen 
from March to April 2017. A non-obese healthy volunteer was considered if his/her BMI 
less than 28 kg/m2 [16]. 50 individuals met all the criteria and were recruited in this study, 

and 41 individuals (24 females and 17 males aged 30 ± 6 years old) completed the whole 
intervention (Table 1). In addition, recruited volunteers should meet all the following 
criteria: 1) without antibiotics in the recent 2 months; 2) without prebiotic or probiotic 
supplements in the recent 2 months; 3) without hypertension, diabetes mellitus, 
gastrointestinal disease and other severe auto-immune diseases; 4) regular eating and 
lifestyle patterns; 5) no international travel in the recent 3 month; 6) without prebiotic or 
probiotic supplements in the recent 2 months. The study was approved by the institutional 
review board on bioethics and biosafety of BGI-Shenzhen, Shenzhen (NO. BGI-IRB 
17020). All participates were fully informed of the design and purpose of this intervention 
study and signed a written informed consent letter.  
Table 1. Cohort Description 
 
Cohort 
  
(Mean ± SD) 
Number of subjects 
41 
Sex (female/male) 
24/17 
Age  
30 ± 6 
BMI (kg/m2) 
23.72 ± 2.81 
Weight(kg) 
64.84± 11.57 
 
 
 
 
2.3 Study duration 
During the first week (run-in period), all healthy volunteers consumed their usual diet and 
were encouraged to avoid yogurt, high-fat foods and alcohol. The CR diet was comprised 
of ~50% calories of a normal-calorie diet (female, 1000kcal/day; male, 1200kcal/day). It 
was designed with carbohydrate, fat and protein as approximately 55%, 30% and 15% of 
the total energy intake respectively, according to the Dietary Guidelines for Chinese 

Residents (2016) and nutritionally balanced [17] and a recent large nutritional study in 
China [18]. Common foods in low-calorie diets such as rice, vegetables, eggs, pork and 
beef were prepared in this study center to control experimental variables introduced by 
different foods and calorie estimation errors. Traditional Chinese cooking style - boiled, 
stir-fried and stewed, were applied for foods. For each meal, digital scales were used to 
measure the nutritional and caloric values of different foods and total meals for male and 
female respectively. BMI, blood and fecal samples of each volunteer were collected at this 
study center at baseline and after the 3-week CR intervention (Figure 1). BMI, blood and 
fecal samples of each volunteer were collected at this study center at baseline and after the 
3-week CR intervention.  
 
 
Figure 1. Overview of the experimental design. Illustration of experimental design, 
including a 1-week run-in period (baseline) and a 3-week calorie restriction (CR) dietary 
intervention trial with 50% energy deficit diet (male, ~1200Kcal/day; female, 
~1000Kcal/day). BMI, fasting blood samples and fecal samples of 41 enrolled healthy 
subjects were collected before and after the CR intervention to assess its effects on BMI, 
blood amino acids and gut microbiome in two enterotype groups.  
3.1 Description of objectives and purpose 
The purpose of this intervention study is to evaluate the effect of calorie restriction on BMI 

loss, amino acid, and gut microbiota in healthy volunteers of two different enterotypes and 
provide useful insights for potential application of gut microbiome stratification in 
personalized nutrition intervention. 
 
 
 
4.1 Primary and secondary outcomes 
The primary outcomes for this study are: 
 Change in BMI of all volunteers (n=41) following calorie restriction dietary 
intervention 
 Different changes in BMI between enterotypes Bacteroides (n=28) and enterotypes 
Prevotella (n=13) following calorie restriction dietary intervention 
BMI was measured via the clinic scale. To avoid intra-individual variations, BMI was 
collected multiple times of each volunteer during the last week of the CR intervention, and 
the averaged BMI value was used as his/her after-intervention BMI. 
 
 
The secondary outcomes for this study are: 
 Change in the concentration of 31 blood amino acids of 36 volunteers following 
calorie restriction dietary intervention 
 Different in the concentration of 31 blood amino acids between enterotypes 
Bacteroides (n=25) and enterotypes Prevotella (n=11) following calorie restriction 
dietary intervention 
 Change in the gut microbiota of all the volunteers (n=41) following calorie 
restriction dietary intervention 
 Different in the gut microbiota of between enterotypes Bacteroides (n=28) and 
enterotypes Prevotella (n=13) following calorie restriction dietary intervention 
The concentrations of 31 amino acids and derivatives in the fasting serum samples were 
measured by LC-MS/MS. Fasting blood samples were collected before and after the 

intervention for amino acid analysis. These blood samples were then centrifuged, and 
serum samples were collected and stored at -80 ℃. The concentrations of 31 amino acids 
and derivatives in the serum samples were then measured via ultra-high pressure liquid 
chromatography (UHPLC) coupled to an AB Sciex Qtrap 5500 mass spectrometry (AB 
Sciex, US) as described previously [19]. 
 
The gut microbial composition was determined using shotgun metagenomics sequencing 
of fecal DNA. Fecal samples were self-collected and then transferred to the laboratory on 
dry ice and kept frozen at -80°C before and after the CR intervention. Fecal DNA was 
extracted following a manual protocol as described previously [20]. The DNA 
concentration was estimated by Qubit (Invitrogen). Library construction and shotgun 
metagenomic sequencing were performed on qualified DNA samples based on the 
BGISEQ-500 protocol in the single-end 100bp mode [21]. Raw reads of BGISEQ-500 with 
SE100 mode were trimmed by an overall accuracy (OA) control strategy to control quality 
[21]. After trimming, averagely, 98.15% of the raw reads remained as high-quality reads. 
By using SOAP2.22 software, the high-quality reads were aligned to hg19 to remove reads 
from host DNA (identity ≥ 0.9). The retained clean reads were aligned to the integrated 
non-redundant gene catalog (IGC) using SOAP2.22 [22] and the average mapping rate and 
unique mapping rate were 80.18% and 65.76% respectively (identity ≥ 0.95). The relative 
abundance profiles of genes, genera, species and Kyoto Encyclopedia of Genes and 
Genomes orthologous groups (KEGG, KOs) of each sample were calculated by summing 
the relative abundances of their assigned IGC genes [22].  
 
 
 
 
 
 
 
 

5.1 Detailed study procedures 
5.1.1 Screening information 
 
Screening information 
Step Descriptor 
Details 
Appendix 
1 
Statement of Informed Consent 
Each participate must read, understand 
and sign the Statement of Informed 
Consent before enrolled in the study. The 
Principal Investigator or designer 
conducting the informed consent 
discussion must sign the consent form. 
Note: the participate must be provided 
with a signed copy of this document. 
  
2 
Demographics 
Age, sex, height, weight, body mass index 
  
3 
Healthy history 
Record the participates' dietary and 
medical and international travel history 
  
4 
Eligibility at screening 
All responses to Inclusion Criteria 
questions must be answered "yes", and all 
responses to Exclusion Criteria questions 
must be answered "no" for the participate 
to be considered eligible  
  
 
 
 
5.1.2 Baseline period for recruiting participates. 
 
Baseline information 
Step 
Descriptor 
Details 
Appendix 
1 
Baseline clue 
Participate will complete the baseline 
clue measures regarding their own 
contact dietary habits. 
exclude 9 
participates  
2 
BMI clue 
Record participates' BMI. 
  
3 
Measuring BMI 
BMI was measured via the clinic scale. 
  
4 
Collecting Fasting  
blood samples 
Fasting blood samples were collected at 
the end of baseline for extracting amino 
acids  
5 participates 
without 
 fasting blood 

5 
Collecting   
fecal samples 
Fecal samples were self-collected and 
then transferred to the laboratory on dry 
ice and kept frozen at -80°C at baseline. 
  
6 
Eligibility after  
Baseline Examination 
All responses to Inclusion Criteria 
questions must be answered "yes", and 
all responses to Exclusion Criteria 
questions must be answered "no" for the 
participate to be considered eligible.  
  
 
 
 
 
 
 
5.1.3 Intervention period for Calorie restriction dietary intervention 
 
Intervention information 
Step 
Descriptor 
Details 
Appendix 
1 
Prepare low-calorie meals 
Common foods in low-calorie diets such 
as rice, vegetables, eggs, pork and beef 
were prepared in this study center to 
control experimental variables 
introduced by different foods and calorie 
estimation errors. Traditional Chinese 
cooking style - boiled, stir-fried and 
stewed, were applied for foods. 
 
2 
Measuring BMI 
BMI was measured via the clinic scale. 
  
3 
Collecting Fasting  
blood samples 
Fasting blood samples were collected 
for extracting amino acids after the 
intervention 
5 participates 
without 
 fasting blood 
4 
Collecting   
fecal samples 
Fecal samples were self-collected and 
then transferred to the laboratory on dry 
ice and kept frozen at -80°C after the 
intervention 
  
5 
Eligibility after  
Baseline Examination 
All responses to Inclusion Criteria 
questions must be answered "yes", and 
all responses to Exclusion Criteria 
questions must be answered "no" for the 
participate to be considered eligible.  
  
 

6.1 Statistical methods to be employed 
All outcomes will be presented using descriptive statistics: Wilcoxon rank-sum test is used 
to detect the significant differences on phenotypes, the concentrations of blood amino acids 
and the relative abundances of genera and species between enterotypes. Wilcoxon signed-
rank test is used to detect the significant differences on phenotypes, the concentrations of 
blood amino acids and the relative abundances of genera and species in paired samples 
before and after the intervention. Permutational multivariate analysis of variance 
(PERMANOVA) is used to detect the association between enterotypes and the overall 
blood amino acid profile at baseline by using PERMANOVA with 9,999 permutations on 
enterotypes. Principal coordinate analysis is used to visual overall gut microbial based on 
the relative abundance profiles between baseline and after the intervention. Principal 
component analysis is used to visual overall amino acid composition based on the blood 
amino acid profiles between baseline and after the intervention. To investigate whether we 
could predict BMI loss ratio using omics features, we will perform a Lasso (Least absolute 
shrinkage and selection operator) regression analysis between baseline relative abundances 
of gut common species and the concentrations of blood amino acids (independent 
variables), and BMI loss ratio (dependent variables). We first normalize values of both 
independent and dependent variables (R, scale function). We then use the R function 
cv.glmnet to choose the most appropriate value for λ in the Lasso model (R glmnet package, 
alpha =1, family=“gaussian”, nfolds=10, alpha=1, nlambda=100). Here, λ is the tuning 
parameter (λ > 0) which controls the strength of the shrinkage of the variables. We then 
apply the Lasso feature selection process by shrinking the Lasso regression coefficients of 
non-informative variables to zero and selecting the variables of non-zero coefficients. To 
reduce overfitting with a limited sample size (n=41), we apply leave-one-out cross 
validation (LOOCV) to estimate the prediction performance of BMI loss ratio using a 
generalized linear model (GLM) of the selected features (creatFolds function in R caret 
package and the glm function in R base package). Likewise, we also use baseline BMI 
values for LOOCV to estimate its prediction performance for CR-associated BMI loss ratio. 
Spearman’s rho values will be calculated between actual BMI loss ratios and the predicted 
values. P-value adjustment is applied for multiple hypothesis testing on the concentrations 

of blood amino acids, the relative abundances of gut microbial genera and species use 
Benjamini-Hochberg (BH) method. R 3.5.0 will be used for all statistical analysis. 
 
 
 
 
 
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