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Background: Differences in gut microbiota composition have been associated with obesity and metabolic alterations in children. The aim of this study was to analyze the abundance of the main bacterial families of the gut among children according to their body composition and metabolic markers. Methods: A cross-sectional study was conducted with 93 school-aged children (8.4 ± 1.6 years old). Anthropometric and body composition variables were measured and a blood sample was collected to determine glucose, insulin, lipid profile, C-reactive protein, leptin, and cytokines [interleukin 6, interleukin 10 (IL-10), tumor necrosis factor α (TNFα)]. DNA was extracted from stool samples and the abundance of bacterial families (Bacteroidaceae-Porphyromonadaceae-Prevotellaceae, Lactobacillaceae, Enterococcaceae, and Lachnospiraceae-Ruminococcaceae) was determined by qPCR assays. Results: Children with obesity and high waist/height ratio had lower Bacteroidaceae-Porphyromonadaceae-Prevotellaceae and higher abundance of Lactobacillaceae when compared with normal-weight children. TNFα was negatively associated and IL-10 was positively associated with Bacteroidaceae-Porphyromonadaceae-Prevotellaceae. Triglycerides showed a positive relationship with Lachnospiraceae-Ruminococcaceae whereas high-density lipoprotein-cholesterol was negatively associated with Lactobacillaceae. Conclusion: In rural Mexican school-aged children, a low abundance of Bacteroidaceae-Porphyromonadaceae-Prevotellaceae and a high abundance of Lactobacillaceae are associated with obesity and metabolic disturbances.
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ORIGINAL ARTICLE
Gut Bacterial Families Are Associated
with Body Composition and Metabolic Risk
Markers in School-Aged Children in Rural Mexico
Tania Aguilar, MNH,
1
Gerardo M. Nava, PhD,
2
Andrea M. Olvera-Ramı
´rez, PhD,
3
Dolores Ronquillo, MNH,
1
Mariela Camacho, MNH,
1
Gerardo A. Zavala, PhD,
4
Marı
´a C. Caaman˜o, PhD,
1
Karina Acevedo-Whitehouse, PhD,
5
Jorge L. Rosado, PhD,
1,6
and Olga P. Garcı
´a, PhD
1
Abstract
Background: Differences in gut microbiota composition have been associated with obesity and metabolic alterations in children.
The aim of this study was to analyze the abundance of the main bacterial families of the gut among children according to their body
composition and metabolic markers.
Methods: A cross-sectional study was conducted with 93 school-aged children (8.4 1.6 years old). Anthropometric and body
composition variables were measured and a blood sample was collected to determine glucose, insulin, lipid profile, C-reactive
protein, leptin, and cytokines [interleukin 6, interleukin 10 (IL-10), tumor necrosis factor a(TNFa)]. DNA was extracted from stool
samples and the abundance of bacterial families (BacteroidaceaePorphyromonadaceaePrevotellaceae, Lactobacillaceae,
Enterococcaceae,andLachnospiraceaeRuminococcaceae) was determined by qPCR assays.
Results: Children with obesity and high waist/height ratio had lower BacteroidaceaePorphyromonadaceaePrevotellaceae and
higher abundance of Lactobacillaceae when compared with normal-weight children. TNFawas negatively associated and IL-10 was
positively associated with BacteroidaceaePorphyromonadaceaePrevotellaceae. Triglycerides showed a positive relationship with
LachnospiraceaeRuminococcaceae whereas high-density lipoprotein-cholesterol was negatively associated with Lactobacillaceae.
Conclusion: In rural Mexican school-aged children, a low abundance of BacteroidaceaePorphyromonadaceaePrevotellaceae
and a high abundance of Lactobacillaceae are associated with obesity and metabolic disturbances.
Keywords: bacterial families; children; metabolic markers; microbiota; obesity
Introduction
Mexico has one of the highest rates of childhood
obesity. According to the National Health and
Nutrition Survey, there is a combined prevalence
of overweight and obesity of 33.2% among school-aged
children.
1
Obesity is a multifactorial disease and many
studies have observed that gut microbiota can be a crucial
factor in the development of obesity.
2–4
Over the past 10 years, studies have focused on identi-
fying the microbiota at different taxonomic levels, char-
acterizing the species or communities that could be playing
a role in the onset of obesity. A higher abundance of Fir-
micutes and a lower abundance of Bacteroidetes have been
associated with obesity in adults, adolescents, and chil-
dren.
5–7
The Firmicutes/Bacteroidetes ratio has been used
in a variety of studies as a novel biomarker of metabolic
alterations; however, results are not consistent.
8–14
The analysis at family level has increased in the last 5
years.
2,15–17
For instance in Mexican pediatric population
obese children have shown a lower abundance of Chris-
tensenellaceae,
2
Lactobacillaceae,
15
Bacteroidaceae,
16
and
1
Departamento de Investigacio
´n en Nutricio
´n Humana, Facultad de Ciencias Naturales,
2
Departamento de Ciencias de los Alimentos, Facultad de
Quı
´mica,
3
Cuerpo Acade
´mico Salud Animal y Microbiologı
´a Ambiental, Facultad de Ciencias Naturales,
5
Unidad de Microbiologı
´aBa
´sica y Aplicada,
Facultad de Ciencias Naturales, Universidad Auto
´noma de Quere
´taro, Quere
´taro, Me
´xico.
4
Faculty of Earth and Life Sciences, VU Amsterdam University, Amsterdam, The Netherlands.
6
CINDETEC, A.C., Quere
´taro, Me
´xico.
CHILDHOOD OBESITY
Month 2020 jVolume X, Number X
ªMary Ann Liebert, Inc.
DOI: 10.1089/chi.2019.0312
1
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higher abundance of Peptoestreptococcaceae,Coriobac-
teriaceae,
15
Prevotellaceae,
16
and Lachnospiraceae.
17
The sequencing of the 16s rRNA gene obtained from
fecal samples has revealed that there are at least seven
bacterial families that are representative of the human gut.
Within the Bacteroidetes phylum, there is a higher
abundance of Bacteroidaceae, Prevotellaceae,andPor-
phyromonadaceae, and among the Firmicutes phylum,
Lachnospiraceae, Ruminococcaceae, Lactobacillaceae,
and Enterococcaceae are the most predominant commu-
nities.
18,19
Up to 95% of all bacteria found in the human
gut can be classified within these seven families.
20
Based on these observations, the purpose of this study
was to analyze the abundance of the main bacterial families
in the gut of school-aged children according to their body
composition and metabolic markers using qPCR validated
primers as a cost-effective and sensitive method to analyze
differences in the microbiota structure.
Methods
Subjects and Experimental Design
A cross-sectional study was conducted with 93 school-
aged children (8.4 1.6 years old), 50 girls and 43 boys,
recruited from two rural communities (Santa Marı´a Be-
gon
˜a and Santa Cruz) of Quere´taro State, Me´xico. The
study was conducted according to the guidelines of the
Declaration of Helsinki (2013)
21
and all procedures in-
volving human patients, were approved by the Bioethics
Committee of the School of Natural Sciences at the Uni-
versidad Auto´noma de Quere´taro (UAQ) (Protocol
# 7335). Written and verbal informed consent was obtained
from parents and caretakers.
Children that had received any antibiotic treatment, mi-
cronutrient supplementation, prebiotic or probiotic supple-
mentation in the last 4 months, or who had any physical or
mental disability, were excluded from the study. Children’s
parents or caretakers were asked to attend their local health
clinic to provide their medical history and complete a so-
cioeconomic status questionnaire.
22,23
Participants received
a labeled sterile polypropylene flask with written instruc-
tions on how to collect the stool sample. The next morning,
children were transported from their local communities to
the Nutrition Clinic at UAQ for anthropometrical and body
composition measurements, and for the collection of stool
samples.
Anthropometry and Body Composition
Weight and height were measured in duplicate and in
nonconsecutively occasions by trained personnel follow-
ing the World Health Organization (WHO) procedures.
24
Weight was determined using a calibrated digital scale
(SECA Mod. 813, Hamburg, Germany) and height was
measured with a portable stadiometer (SECA Mod 206,
Hamburg, Germany). Nutritional status was calculated
based on the WHO criteria of BMI-for-age for children
5–19 years of age. Underweight was defined as two z-scores
below the WHO reference median, overweight as one
standard deviation, and obesity as two standard deviations
above the reference median of the BMI-for-age z-score.
25
Whole-body composition was measured by a certified
technician to determine abdominal fat mass, abdominal fat
percent, body fat mass, and body fat percent, using dual-
energy X-ray absorptiometry (DEXA) (Hologic Mod Ex-
plorer, Bedford, MA). Abdominal fat mass and abdominal
fat percent were estimated following a previously de-
scribed procedure; briefly the region of interest was de-
fined as quadrilateral boxes extending 10 cm above the
iliac crest and laterally to the edges of the abdominal soft
tissue, and all trunk tissue within this standardized region
was analyzed to determine fat mass and fat percent.
26
Excess body fat for girls was considered above 30%, and
for boys above 25%.
27,28
Blood Samples
A fasting blood sample was collected from each individual
on their first visit to the community health clinic. Children
had to fast for at least 12 hours before the blood sample was
collected early in the morning.
29
All analysis were performed
at the Human Nutrition Laboratory at UAQ.
High-density lipoprotein cholesterol (HDL) and low-
density lipoprotein cholesterol (LDL) were assessed by
spectrophotometry (Genesys 20; Thermo Scientific, MA)
using commercial kits (#HDLL0230 Cholesterol HDL;
Elitech, Se´es, France; #MX41023 Cholesterol LDL, Spin-
react, Sant Esteve de Bas, Spain). Triglycerides (TG) and
total cholesterol were determined in plasma with commer-
cial kits (#CHSL0250 Cholesterol, Elitech; #TGML0250
Triglycerides, Elitech) using a clinical chemical analyzer
(Bayer RA-50, Bayer Diagnostics, Dublin, Ireland). High-
value thresholds were, for total cholesterol 200 mg/dL;
for LDL cholesterol 130 mg/dL; and for TG, 100 mg/
dL for children <9yearsofageand130 mg/dL for
children >10 years of age. HDL concentrations were
considered low with values <40 mg/dL.
30
Fasting glucose was assessed by a colorimetric-
enzymatic method, using a commercial kit (#GHSL0250
Glucose; Elitech) and a clinical chemical analyzer (Bayer
RA-50, Bayer Diagnostics, Leverkusen, Germany). In-
sulin concentration in serum, was determined by a
commercial ELISA kit (#EZHI Millipore, MA) using a
microplate photometer (Multiskan Ascent; Thermo
Electron Corporation, Waltham, MA). Insulin resis-
tance was determined using the Homeostatic Model
Assessment (HOMA) with the following formula:
HOMA =(insulin ·glucose)/22.5.
31
Children with fast-
ing glucose 100 mg/dL was considered prediabetic,
indicating a high risk for diabetes.
32
Insulin resistance
was defined with a HOMA value >3.16.
31
Interleukin 6 (IL-6), interleukin 10 (IL-10), and tumor
necrosis factor a(TNFa) were quantified using commercial
kits (#EZHIL6, #EZHIL10, #EZHTNFA Millipore, MA),
C-reactive protein (hsCRP) was quantified in serum using a
2 AGUILAR ET AL.
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commercial high-sensitivity ELISA Kit (#BQ130C Bio-
quant, CA) and leptin was measured using a commercial
ELISA Kit (#EZHL). All ELISAs were read in a Multiskan
Ascent microplate photometer (Thermo Electron Corpora-
tion). Low-grade systemic inflammation was considered
when hsCRP >3mg/L.
33
Stool Samples
Parents or caretakers were asked to collect a stool
sample from their child in a sterile flask (provided) and to
store the flask in their fridge at 4C. Samples were col-
lected by the staff the next morning and once in the lab-
oratory, stool samples were kept at -20C until further
analysis.
Before DNA extraction, samples were washed with
500 lL of ethanol (#E7023 Sigma-Aldrich, MO), to elim-
inate fat from stool. DNA was extracted from 2.5 grams of
washed stool using a commercial extraction kit (#12855-50
PoweLyzer Power Soil DNA Isolation Kit, MoBio, CA)
following the manufacturer’s protocol. DNA quantity and
quality was verified using NanoDrop and electrophoresed
on a 1% agarose gel stained with Sybr Safe (#S33102
Thermo Fisher Scientific) to confirm DNA integrity.
The bacterial family abundance was determined by
quantitative PCR (qPCR) using a CFX96 Real-Time PCR
System (Bio-Rad, Hercules, CA) and SYBR Green PCR
technology (#RR420Q Clontech Laboratories, CA). Total
bacterial abundance was estimated using primers designed to
amplify a 359 bp fragment of the rpoB gene. This gene was
used because it does not present heterogeneity between
copies and provides a more reliable data about the amount of
bacteria in a given sample.
34
Each 15 lL qPCR mixture
consisted of 7.5 lL of 2X SYBR Green Master Mix, 2.25 lL
of BSA (100 lg/mL) (#B900S New England Biolabs, United
Kingdom), 0.6 lL of each primer (10 lM), rpoB1698f (50-
AACATCGGTTTGATCAAC-30) and rpoB2041r (50-CGTT
GCATGTTGGTACCCAT-30),
34
3lL of extracted genomic
DNA (4 ng/lL), and 1.05 lL PCR-grade water. PCR was
performed by initial denaturation at 94C(3minutes),35
cycles of denaturation at 94C (45 seconds), primer annealing
at 55C (30 seconds), and extension at 72C (30 seconds),
followed by one cycle of 72C(5minutes).
Density(ngofDNApergramoffeces)ofselected
bacterial families was determined by qPCR assays us-
ing previously validated bacterial group-specific 16S
rRNA gene primers. BacteroidaceaePorphyromonadaceae
Prevotellaceae (forward =50-GGTGTCGGCTTAAGTGCC
AT-30and reverse =50-CGGA(C/T)GTAAGGGCCGTGC-
30), LachnospiraceaeRuminococcaceae (forward =50-CGG
TACCTGACTAAGAAGC-30and reverse =AGTTT(C/T)A
TTCTTGCGAACG), Enterococcaceae (forward =50-CCC
TTATTGTTAGTTGCCATCATT-30and reverse =50-ACT
CGTTGTACTTCCCATTGT-30),
35
and Lactobacillaceae
(forward =50-AGCAGTAGGGAATCTTCCA-30and re-
verse =50- CACCGCTACACATGGAG-30).
36
Primer targets were checked using the Probe Match tool
from the RDP database. For all primer sets, an annealing
temperature of 55C was used, determined empirically by
a temperature gradient PCR. Each 15 lL qPCR mixture
consisted of 7.5 lL of 2X SYBR Green Master Mix, 1.5 lL
of BSA (100 lg/mL), 0.75 lL of each primer (10 lM), 3 lL
of extracted genomic DNA (4 ng/lL), and 1.5 lL PCR-
grade water. The cycling protocol for the four primer sets
was as follows: initial denaturation at 94C (3 minutes),
35 cycles of 94C (45 seconds), 55C (30 seconds), 72C
(30seconds),followedbyonecycleof72C (5 minutes).
Denaturation curves were determined from 60Cto95C
for all products for quality assurance.
For each qPCR assay, a calibration curve was deter-
mined using a stool pool from five random participants,
concentrations of DNA used in the standard curves ranged
from 10.9 to 0.003 ng/lL. Standard curves were amplified
at the same time as stool samples. All qPCR amplifications
were performed in triplicate. For each family, signals were
normalized using the amount of DNA determined divided
by the amount of DNA from rpoB gene. The abundance of
bacterial families was reported as the natural logarithm (ln)
of ng of DNA per gram of feces.
20
Statistical Analyses
Descriptive analyses were performed for all variables.
The distribution of dependent variables was explored to
confirm their normality with the Kolmogorov–Smirnov
test. Data that did not have a normal distribution was
transformed using the natural logarithm (TNFaand leptin).
General characteristics of children were examined and
compared among BMI categories with analysis of variance
(ANOVA). Between-group comparisons were evaluated
with the Tukey post hoc test.
Linear regression models were also used to examine the
association between the abundance of four bacterial
groups, as independent variables, with the anthro-
pometrical and biochemical variables as dependent vari-
ables. We checked the models for evidence of non-
normally distributed residuals, heterogeneity of variance,
and disproportionate influence of outliers. An ANOVA
was performed to examine whether bacterial abundance
varied between groups formed by altered anthro-
pometrical, body composition, and metabolic markers.
Variables that did not have cutoff points to determine
altered measures (abdominal fat %, cytokines and leptin)
were divided according to the median. Biochemical vari-
ables were adjusted by BMI for age Z score as a con-
founding factor and all statistical analyses were performed
with SPSS 18.0 (SPSS, Chicago) and p-values below 0.05
were considered significant.
Results
Anthropometric and Biochemical
Characteristics of the Children
Of the children included in the study, 53% were girls
and 46% boys. The prevalence of overweight and
CHILDHOOD OBESITY Month 2020 3
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obesity, determined by BMI for age Z score, was 23%
and 17%, respectively. Children with a waist to height
ratio 0.5 represented 38% of the population, and a total
of 59% of the children had excess body fat, according to
the total body fat mass percentage determined by DEXA.
Almost a third (30%) had high TG, 27% had low HDL,
and 21% had insulin resistance. Additionally, 21% had
low-grade systemic inflammation measured by hsCRP.
The main characteristics of the studied children are
shown in Table 1.
Table 1. Main Characteristics of the Studied Children According to BMI for Age (n=93)
Overall Normal weight Overweight Obesity
pn Mean SD nMean SD nMean SD nMean SD
Girls 50 27 15 8
Age (years) 93 8.4 1.6 56 8.2 1.6 21 8.8 1.9 16 8.5 1.6 0.36
Anthropometric
Weight (kg) 93 29.1 8.9 56 24.4
a
4.8 21 34.5
b
8.7 16 38.5
b
9.3 <0.001
Weight-for-age (z-zcore) 90 0.06 1.0 55 -0.6
a
0.7 21 0.9
b
0.6 14 1.6
b
0.4 <0.001
Height (cm) 93 127.4 10.2 56 125.1
a
9.1 21 131
b
11.3 16 130.5
b
11 0.032
Height-for-age (z-score) 90 -0.3 0.8 55 -0.6
a
0.7 21 -0.1
b
0.9 14 0.2
b
1 0.002
BMI-for-age (z-Score) 93 0.5 1.3 56 -0.4
a
0.7 21 1.5
b
0.3 16 2.3
c
0.3 <0.001
Waist circumference (cm) 93 62 9.8 56 55.9
a
4.8 21 68.9
b
8.1 16 74.3
c
7<0.001
Waist to height index 92 0.5 0.1 56 0.4
a
0 21 0.5
b
0 15 0.6
c
0<0.001
Abdominal fat (%) 92 26.5 8.8 56 21
a
5 21 32.8
b
6 15 38.3
c
5.2 <0.001
Total body fat (%) 92 29.9 7.2 56 25.7
a
4.8 21 34.8
b
4.8 15 39
c
3.7 <0.001
Biochemical
Total cholesterol (mg/dL) 93 150.6 26.3 56 150.5 25.3 21 149.2 27 16 152.8 30.2 0.91
TG (mg/dl) 93 95.2 45.2 56 76.5
a
29.8 21 106.2
b
35.1 16 146.3
c
57.9 <0.001
HDL (mg/dL) 93 47.5 10.3 56 50.6
a
9.6 21 45.6 b 10.4 16 39.3
b
7.7 <0.001
LDL (mg/dl) 93 82.1 18.2 56 81.2 16 21 82.2 20.5 16 85.2 22.6 0.74
Glucose (mg/dL) 93 81 7.7 56 79.9 8.6 21 82 6.3 16 83.5 5.2 0.19
Insulin (uU/mL) 87 12.7 5.1 51 10.8
a
3.1 20 15.9
b
6.7 16 14.5
b
5.7 <0.001
HOMA index 87 2.6 1.2 51 2.1
a
0.7 20 3.3
b
1.6 16 3
b
1.3 <0.001
Leptin (ng/mL)
d
88 4.9 5.6 52 1.8
a
1.4 21 8.3
b
7.2 15 10.7
c
5.2 <0.001
hsCRP (mg/L) 91 1.5 2.1 54 1
a
1.5 21 1.7
a
3.1 16 3.1
b
1.3 0.001
TNFa(pg/mL)
d
88 4.9 3.6 52 3.3
a
1.9 21 6 b 4.7 15 8.9
c
2.3 <0.001
Interleukin 6 (pg/mL) 87 3.9 5.1 51 2
a
0.9 21 3.9
a
7.8 15 10.3
b
4.1 <0.001
Interleukin 10 (pg/mL) 88 4.6 3.9 52 4.6 4.1 21 4.9 3.9 15 4 3.4 0.81
Microbiota
BPP (ln ng DNA per gram of feces) 93 5.6 2.0 56 6.3
a
1.9 21 6.1
a
1.8 16 4.1
b
1.9 <0.001
ECC (ln ng DNA per gram of feces) 93 10.6 3.4 56 11 3 21 10 4.2 16 9.9 3.6 0.36
LAC (ln ng DNA per gram of feces) 93 7.5 3.3 56 6.8
a
3.5 21 7.8
a,b
2.3 16 9.1
b
3.2 0.037
LRC (ln ng DNA per gram of feces) 93 9.7 2.2 56 9.9 2.4 21 9.5 2.1 16 9.4 1.7 0.65
a,b,c
Different letters within the same row are significantly different p<0.05 in post hoc comparison (Tukey test).
d
Variables were retransformed after using the natural logarithm.
BPP, BacteroidaceaePorphyromonadaceaePrevotellaceae; ECC, Enterococcaceae; HDL, high-density lipoprotein; HOMA, Homeostatic Model
Assessment; hsCRP, high-sensitive C reactive protein; LAC, Lactobacillaceae; LDL, low-density lipoprotein; ln, Natural logarithm;
LRC, LachnospiraceaeRuminococcaceae; SD, standard deviation; TG, triglycerides; TNFa, tumor necrosis factor a.
4 AGUILAR ET AL.
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Association between Bacterial Family
Abundance and Body Composition
The families BacteroidaceaePorphyromonadaceae
Prevotellaceae were negatively related BMI for age Z
score, waist circumference, waist to height index, ab-
dominal fat, abdominal fat percent, total body fat, and
total body fat percent. In contrast, Lactobacillaceae
showed a positive relationship BMI for age Z score, waist
circumference, waist to height index, abdominal fat, ab-
dominal fat percent, total body fat, and total body fat per-
cent (Table 2). Waist circumference presented the highest
adjusted R
2
; 16% of its variation was explained by a posi-
tive association with Lactobacillaceae and a negative
association with BacteroidaceaePorphyromonadaceae
Prevotellaceae.
Association between Bacterial Family
Abundance and Metabolic Risk Factors
The families BacteroidaceaePorphyromonadaceae
Prevotellaceae were negatively associated with levels of
TNFa(p=0.034) and positively associated with levels of
IL 10 ( p=0.043). Lactobacillaceae was negatively asso-
ciated with HDL levels ( p=0.012), and Lachnospiraceae
Ruminococcaceae families related positively with TG
Table 2. Linear Regression Models Between Anthropometric and Biochemical Variables
in Association with the Abundance of the Main Bacterial Families (n=93)
Bacteroidaceae
Porphyromonadaceae
Prevotellaceae Enterococcaceae Lactobacillaceae
Lachnospiraceae
Ruminococcaceae
(ln ng DNA
per gram of feces)
(ln ng DNA
per gram of feces)
(ln ng DNA
per gram of feces)
(ln ng DNA
per gram of feces)
B 95% CI B 95% CI B 95% CI B 95% CI
Anthropometric
Z score BMI for age -0.19 -0.32, -0.05
a
-0.04 -0.11, 0.04 0.09 0.01, 0.16
a
0.01 -0.11, 0.13
Waist Circumference (cm) -1.46 -2.46, -0.46
a
-0.46 -1.02, 0.10 0.75 0.19, 1.31
a
-0.1 -1.01, 0.80
Waist to height index -0.01 -0.02, 0.00
a
0.00 0.00, 0.00 0.01 0.00, 0.01
a
0.00 -0.01, 0.01
Abdominal fat (gram) -274.01 -514.67, -33.36
a
-87.32 -224.5, 49.87 210.88 75.21, 346.55
a
32.93 -184.66, 250.52
Abdominal fat (%) -0.98 -1.94, -0.02
a
-0.12 -0.67, 0.42 0.64 0.10, 1.18
a
0.42 -0.44, 1.29
Total body fat (gram) -546.19 -1021.37, -71.01
a
-151.52 -422.39, 119.35 402.59 134.71, 670.47
a
80.54 -349.1, 510.18
Total body fat (%) -0.76 -1.53, 0.02
a
-0.01 -0.45, 0.43 0.52 0.08, 0.96
a
0.39 -0.31, 1.1
Biochemical
Total cholesterol (mg/dL)
b
0.46 -2.59, 3.51 1.22 -0.43, 2.87 -1.43 -3.13, 0.27 0.19 -2.45, 2.84
TG (mg/dL)
b
-2.99 -7.16, 1.18 0.95 -1.31, 3.20 1.77 -0.55, 4.09 4.02 0.40, 7.63
a
HDL (mg/dL)
b
-0.17 -1.21, 0.87 0.01 -0.55, 0.57 -0.75 -1.32, -0.17
a
-0.61 -1.51, 0.29
LDL (mg/dL)
b
1.46 -0.65, 3.56 0.36 -0.78, 1.50 -0.69 -1.86, 0.48 0.77 -1.05, 2.60
Glucose (mg/dL)
b
-0.14 -1.03, 0.74 -0.05 -0.53, 0.43 0.07 -0.42, 0.57 -0.79 -1.56, -0.02
Insulin (uU/mL)
b
-0.23 -0.8, 0.34 -0.25 -0.55, 0.04 0.07 -0.24, 0.39 -0.12 -0.60, 0.36
HOMA index
b
-0.07 -0.19, 0.06 -0.06 -0.13, 0.01 0.01 -0.06, 0.09 -0.05 -0.16, 0.06
Leptin (ng/mL)
b,c
-0.02 -0.07, 0.04 0.00 -0.03, 0.03 0.01 -0.02, 0.04 0.01 -0.04, 0.06
hsCRP (mg/L)
b
0.13 -0.11, 0.37 0.00 -0.13, 0.12 0.03 -0.10, 0.17 0.02 -0.19, 0.22
TNF-a(pg/mL)
b,c
-0.06 -0.12, 0.00
a
0.00 -0.03, 0.03 -0.01 -0.04, 0.02 0.04 0.00, 0.09
Interleukin 6 (pg/mL)
b
-0.24 -0.81, 0.34 0.01 -0.29, 0.32 0.01 -0.3, 0.32 0.19 -0.28, 0.66
Interleukin 10 (pg/mL)
b
0.5 0.02, 0.98
a
0.06 -0.19, 0.31 -0.13 -0.39, 0.13 -0.04 -0.44, 0.36
Values are unstandardized coefficients B (95% CI).
a
Statistically associated p<0.05.
b
Model adjusted by BMI for age.
c
Variables were retransformed after using the natural logarithm (ln).
CHILDHOOD OBESITY Month 2020 5
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levels ( p=0.03) (Table 2). The rest of the biochemical
markers were not associated with any bacterial family.
Differences in Bacterial Family Abundance According
to Body Composition and Metabolic Risk Factors
The ANOVA test showed significant mean differences
among normal weight, overweight, and obesity accord-
ing to BMI for age Z score. Obese children had lower
abundance of BacteroidaceaePorphyromonadaceae
Prevotellaceae when compared with normal and over-
weight children. In addition, obese children showed
higher abundance of Lactobacillaceae when compared
with normal-weight subjects (Fig. 1A). Children with
high waist to height ratio (<0.5) had significantly lower
abundance of BacteroidaceaePorphyromonadaceae
Prevotellaceae and higher abundance of Lactobacilla-
ceae (Fig. 1B).
Children with abdominal fat percent above median
(>24%) showed higher abundance of Lactobacillaceae
(Fig. 1C). Regarding metabolic risk markers, children with
IL-10 levels above the median (>3.45 pg/mL) had higher
abundance of BacteroidaceaePorphyromonadaceae
Prevotellaceae (Fig. 1D). Bacterial abundance among all
other anthropometric and biochemical variable groups did
not show statistical significance (data not shown).
Discussion
The present study showed obesity, inflammation,
and lipids were associated with a lower abundance of
BacteroidaceaePorphyromonadaceaePrevotellaceae and
higher abundance of Lactobacillaceae when compared with
normal-weight children. Also, TG showed a positive rela-
tionship with LachnospiraceaeRuminococcaceae.
The lower abundance of BacteroidaceaePorphyro-
monadaceaePrevotellaceae found in children with obe-
sity and excess body fat, is similar to other cross-sectional
studies in children. For instance, a recent study found
that obese children had lower BacteroidesPrevotella
Prohyromonas spp. abundance when compared with
normal-weight children.
5
Similarly, a decrease in Bacter-
oidaceae was found in children with obesity according to
the BMI z-score.
3
In Mexican children, a study found that
Bacteroides plebeius had a higher abundance in normal-
Figure 1. Comparison of mean (ANOVA) between anthropometrical and biochemical variables and their association with the main
bacterial families studied. (A) Comparison between normal weight, overweight, and obesity according to BMI for age. (B) Comparison
between low waist to height ratio and high waist to height ratio. (C) Comparison between abdominal fat percent below and above the
median. (D) Comparison between IL-10 levels below median and above median. IL-10 was adjusted by Z score BMI for age. *p<0.05.
ANOVA, analysis of variance; BPP, BacteroidaceaePorphyromonadaceaeProvotellaceae; ECC, Enterococcaceae; LAC, Lactobacillaceae;
LRC, LachnospiraceaeRuminococcaceae.
6 AGUILAR ET AL.
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weight children when compared with children with obesity.
2
In obesity, a decrease in the abundance of Bacteroidaceae
PorphyromonadaceaePrevotellaceae seems to be associ-
ated with increased intestinal permeability and, hence, with
an increase in the absorption of lipopolysaccharide (LPS).
37–39
Obesity is associated with chronic subclinical inflam-
mation.
40
It has been reported that LPS derived from
intestinal Gram-negative bacteria is involved in the
production of inflammatory cytokines through activation
of Toll-like receptor, thus contributing to the inflamma-
tory state observed in obesity and insulin resistance.
41
Our findings suggest that an increased abundance of
BacteroidaceaePorphyromonadaceaePrevotellaceae
associates with lower TNF-alevels, which is consistent
with the findings of d’Hennezel.
42
Families such as
BacteroidaceaePorphyromonadaceaePrevotellaceae pres-
ent an underacylated lipid A form that produces a LPS iso-
form that inhibits Toll-like receptor 4 (TLR4) signaling,
reducing the production of TNF-a. These could be respon-
sible for the immunoinhibitory and immunosilent properties
of these bacterial families that contribute to immune
tolerance.
43
Additionally, we found that IL-10, an anti-inflammatory
cytokine, showed a positive relationship with the abundance
of BacteroidaceaePorphyromonadaceaePrevotellaceae.
Thus, the association of a higher abundance of Bacteroidaceae
PorphyromonadaceaePrevotellaceae with lower levels of
TNF-aand higher levels of IL-10 may ameliorate the per-
meability of the gut and improve the inflammatory profile
through the inhibition of TLR4 signaling. Nevertheless,
studies performed in Mexican children had revealed that
there is a higher abundance of the genus Prevotella in
obese children.
16,17
The lower abundance of BacteroidaceaePorphyro-
monadaceaePrevotellaceae in our obese children popu-
lation could be due to an interaction between these
families. Future analysis should consider the analysis of
these three families separately.
A direct relationship between the abundance of Lacto-
bacillaceae was observed in children with visceral obesity,
whereas an inverse association was observed between this
family and HDL levels. A study that compared obese,
normal weight, and anorectic patients, showed that the
obese group had a microbiota enriched in Lactobacillus.
12
Similarly, a high abundance of Lactobacillus species were
found in obese children in another study.
5
In Mexican
children, a study found that Lactobacillus were more
abundant in obese children.
15
A meta-analysis showed that some Lactobacillus species
such as L. acidophilus,L. ingluviei, and L. fermentum are
associated with weight gain, whereas L. gasseri and L.
plantarum are associated with weight loss.
44
These dif-
ferences lie in the genes that codify for different enzymes
among species. Lactobacillus that are linked to weight
gain, lack enzymes involved in fructose metabolism and
harbor enzymes involved in lipid metabolism. In contrast,
weight loss-associated species have enzymes implicated in
fructose, mannose, starch, and sucrose metabolism.
45
Ad-
ditionally, a meta-analysis comprising the effect of Lac-
tobacillus on lipid profile in both children and adults have
shown mixed results.
46,47
For instance, the supplementa-
tion of L. acidophilus showed a significant increase of
HDL cholesterol levels, probably as a consequence of the
inhibition of synthesis of fatty acids in the liver by the
short-chain fatty acids (SCFA) produced by this bacteria.
48
Altogether, the empirical evidence available indicates
that not all members of the Lactobacillaceae family are
beneficial to human health. In our study, a broad spectrum
of the Lactobacillaceae family was assessed, showing that
an increase in this family is associated with obesity and
metabolic disturbances. More studies are needed to define
which particular Lactobacillaceae members contribute to
the development of obesity and metabolic disturbances.
Finally, LachnospiraceaeRuminococcaceae associated
with higher levels of TG. Similar results were observed in
healthy adults where Coprocccus comes (Lachnospir-
aceae) correlated with TG levels.
49
Our results are similar
to a study made in Mexican children that showed that TG
levels are increased in overweight and obese populations.
These children also had altered propionic and butyric
concentrations in feces, and a higher abundance of Lach-
nospiraceae. The hypothesis is that an increase in SCFA
absorption may be responsible for the higher levels of TG
observed in these populations.
17
Our data show that the composition of fecal microbiota
at the family level could be used as a cost-effective method
to identify differences among anthropometrical and met-
abolic markers at the clinical field. Also, we consider that
the analysis at family level gives more specific information
about the changes in microbiota than when analyzing at the
phylum level. For instance, we found that not all members
of the Firmicutes remain high in obesity.
Our results suggest that Lactobacillaceae, a represen-
tative transient family of the human gut, seems to influence
the overall abundance of Firmicutes observed in some
studies.
3
However, these results should be interpreted with
caution. Even though we found association with bacteria at
the family level, groups as Lactobacillaceae should be
analyzed at a deeper level and functional analysis must
be considered to establish a metabolic profile. Also, we
must consider in future studies the quantification of
BacteroidaceaePorphyromonadaceaePrevotellaceae
independently, to assure a more detailed analysis of the
community, and the addition of some other bacterial families,
such as Christensenellaceae and Bifidobacteriaceae,could
give us broader information about the association of the mi-
crobiota with nutritional and metabolic variables.
The strengths and limitations of our study need to be
addressed. Due to the cross-sectional design of this study,
causality could not be established. For the microbiota
analysis, only one fecal sample was collected per indi-
vidual, which limits the accuracy of the family profiles.
The lack of 16S rRNA sequencing limits the results to the
targeted families. However, we could observe differences
CHILDHOOD OBESITY Month 2020 7
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in family abundances and were able to detect significant
associations between these abundances and clinical and
biochemical variables. To our knowledge, this is one of
few studies that assesses anthropometric and metabolic
markers and associates them with the microbiota at the
family level using validated qPCR primers for the seven
representative families of the human gut.
In conclusion, the abundance of Bacteroidaceae
PorphyromonadaceaePrevotellaceae and Lactobacilla-
ceae associates with obesity, HDL-cholesterol, TNFa,
and IL-10 in school-aged children. The analysis of mi-
crobiota at the family level can be used to determine their
association with obesity and metabolic risk markers.
Funding Information
This study was partially funded by Universidad Auto´n-
oma de Quere´taro (FOFI #FNN-2014-02) and by Consejo
Nacional de Ciencia y Tecnologı´a (CONACYT), which
provided the PhD grant (#332055).
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Olga P. Garcı
´a, PhD
Departamento de Investigacio
´n en Nutricio
´n Humana
Facultad de Ciencias Naturales
Universidad Auto
´noma de Quere
´taro
Avenida de las Ciencias S/N, Juriquilla
Quere
´taro CP 76230
Me
´xico
E-mail: olga.garcia@uaq.mx
CHILDHOOD OBESITY Month 2020 9
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Background: Childhood obesity is a serious public health problem in Mexico. Adult gut microbiota composition has been linked to obesity, but few studies have addressed the role of gut microbiota in childhood obesity. Objectives: The aim of this study is to compare gut microbiota composition in obese and normal-weight children and to associate gut microbiota profiles with amino acid serum levels and obesity-related metabolic traits. Methods: Microbial taxa relative abundance was determined by 16S rRNA sequencing in 67 normal-weight and 71 obese children aged 6-12 years. Serum amino acid levels were measured by mass spectrometry. Associations between microbiota composition, metabolic parameters and amino acid serum levels were tested. Results: No significant differences in phyla abundances or Firmicutes/Bacteroidetes ratios were observed between normal-weight and obese children. However, Bacteroides eggerthii abundance was significantly higher in obese children and correlated positively with body fat percentage and negatively with insoluble fibre intake. Additionally, Bacteroides plebeius and unclassified Christensenellaceae abundances were significantly higher in normal-weight children. Abundance of both these species correlated negatively with phenylalanine serum levels, a metabolite also found to be associated with obesity in Mexican children. Conclusions: The study identified bacterial species associated with obesity, metabolic complications and amino acid serum levels in Mexican children.
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An altered gut microbiota has been linked to obesity in adulty, though little is known about childhood obesity. The aim of this study was to characterize the composition of the gut microbiota in obese (n=42) and normal-weight (n=36) children aged 6 to 16. Using 16S rRNA gene-targeted sequencing, we evaluated taxa with differential abundance according to age- and sex-normalized body mass index (BMI z-score). Obesity was associated with an altered gut microbiota characterized by elevated levels of Firmicutes and depleted levels of Bacteroidetes. Correlation network analysis revealed that the gut microbiota of obese children also had increased correlation density and clustering of operational taxonomic units (OTUs). Members of the Bacteroidetes were generally better predictors of BMI z-score and obesity than Firmicutes, which was likely due to discordant responses of Firmicutes OTUs. In accordance with these observations, the main metabolites produced by gut bacteria, short chain fatty acids (SCFAs), were higher in obese children, suggesting elevated substrate utilization. Multiple taxa were correlated with SCFA levels, reinforcing the tight link between the microbiota, SCFAs, and obesity. Our results suggest that gut microbiota dysbiosis and elevated fermentation activity may be involved in the etiology of childhood obesity. This article is protected by copyright. All rights reserved.
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According to the hygiene hypothesis, the increasing incidence of autoimmune diseases in western countries may be explained by changes in early microbial exposure, leading to altered immune maturation. We followed gut microbiome development from birth until age three in 222 infants in Northern Europe, where early-onset autoimmune diseases are common in Finland and Estonia but are less prevalent in Russia. We found that Bacteroides species are lowly abundant in Russians but dominate in Finnish and Estonian infants. Therefore, their lipopolysaccharide (LPS) exposures arose primarily from Bacteroides rather than from Escherichia coli, which is a potent innate immune activator. We show that Bacteroides LPS is structurally distinct from E. coli LPS and inhibits innate immune signaling and endotoxin tolerance; furthermore, unlike LPS from E. coli, B. dorei LPS does not decrease incidence of autoimmune diabetes in non-obese diabetic mice. Early colonization by immunologically silencing microbiota may thus preclude aspects of immune education. Bacteroides species in the microbiota of children from countries with high susceptibility to autoimmunity produce a type of lipopolysaccharide (LPS) with immunoinhibitory properties. These properties may preclude early immune education and contribute to the development of type 1 diabetes.