Hindawi
PPAR Research
Volume 2021, Article ID 8880042, 9 pages
https://doi.org/10.1155/2021/8880042
Research Article
PPARα Gene Is Involved in Body Composition Variation in
Response to an Aerobic Training Program in Overweight/Obese
Glêbia A. Cardoso ,1,2 Mateus D. Ribeiro ,1,2 Bruno R. V. Sousa ,1,3
Yohanna de Oliveira ,3 Klécia F. Sena ,1,2 Joane R. E. Batista ,1
Antônio E. M. Almeida ,4 João M. Filho ,4 Raquel S. B. Silva ,1,2 Darlene C. Persuhn ,3
and Alexandre S. Silva 1,2
1
Laboratory of Applied Studies in Physical Training to Performance and Health-LETFADS, Department of Physical Education,
Federal University of Paraíba, João Pessoa, CEP: 58059-900 Paraíba, Brazil
2
Associate Graduate Program in Physical Education-UPE/UFPB, Department of Physical Education, Federal University of Paraíba,
João Pessoa, CEP: 58059-900 Paraíba, Brazil
3
Graduate Program in Nutrition Sciences, Department of Nutrition, Federal University of Paraíba-PPGCN/UFPB, João Pessoa,
CEP: 58051-900 Paraíba, Brazil
4
Lauro Wanderley University Hospital-HULW-Federal University of Paraíba-UFPB, João Pessoa, CEP: 58059-900 Paraíba, Brazil
Correspondence should be addressed to Alexandre S. Silva; alexandresergiosilva@yahoo.com.br
Received 28 August 2020; Revised 25 June 2021; Accepted 27 July 2021; Published 10 August 2021
Academic Editor: Pascal Froment
Copyright © 2021 Glêbia A. Cardoso et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is
properly cited.
The objective of this study was to investigate the relationship of the polymorphism in Intron 7 G/C (rs 4253778) of the peroxisome
proliferator-activated receptor alpha (PPARα) gene with the magnitude of changes in the body composition of an overweight and
obese population that underwent an aerobic training program. Fifty-eight previously inactive men and women, body mass index
(BMI) 31:5 ± 2:8 kg/m2 , 46.5% (n = 27) genotyped as CC genotype and 53.5% (n = 31) as CA+AA, underwent a 12-week aerobic
training (walking/running). Aerobic capacity (ergospirometry), body composition (DXA), and nutritional assessment were made
before and 48 h after the experimental protocol. Two-way ANOVA, chi-square test, and logistic regression were used (p < 0:05).
Twenty-seven volunteers (46.5%) were identified as CC genotype and 31 (53.5%) as CA+AA genotype. Time-group interaction
showed that there was no difference in these between two allele groups. However, differences in distribution of respondents or
nonresponders according to allele A were identified for fat mass (p ≤ 0:003), percentage fat mass (p ≤ 0:002), the waist (p ≤ 0:009
), abdomen (p ≤ 0:000), and hip (p ≤ 0:001), this difference being independent for the fat mass. Meanwhile, sex, age, and
nutritional management have also been found to be influential factors. It is concluded that the PPARα gene is involved in
varying body composition in response to an aerobic training program.
1. Introduction
Although obesity is considered a top public contemporary
health concern pointing to an increase in its prevalence, as
seen from 1999 to 2000 through 2017–2018, US obesity
prevalence increased from 30.5% to 42.4% [1], and weight
loss is still a big challenge, which can be noticed by the dis-
creet magnitude of weight loss seen in publications on this
subject. Systematic reviews and meta-analysis show clinically
discrete weight reduction ranging from 1.4 to 2.5 kg of the
fat mass in a training program lasting 6 to 12 months [2,
3], in each of the original studies. However, there is an individual variation considerably above these reported averages,
as results between -9.5 to +2.6 kg [4], -10.2 to +1.7 kg [5],
2
-11.0 to +4.0 kg fat mass (FM) [6], and -4.4 to +4.9 kg FM
[7]. Although this variability is contested by the absence of
an adequate comparator sample [8], in a recent published
clinical trial [9], while the variation in the experimental
group was from -6 to +2.7 kg; in the control group, it was
much smaller (-2.4 to +2.5 kg).
Adherence to the training program and nutritional
behavior can be considered as intervening factors in individual variability in response to training [10], but these factors
have not been statistically analyzed in the data of these studies. Genetic factors may contribute, at least in part, to explain
individual differences in the obesogenic process [11, 12].
Among the genes that have been shown to influence body
composition, the peroxisome proliferator-activated receptor
(PPAR), composed of three subtypes: PPARα involved in
glucose metabolism [12], PPARγ related to lipid metabolism
[13], and PPARσ more linked to energy balance [14]. In a
multicenter trial conducted with 3.234 participants, a variant
in PPARγ was positively associated with body mass index
(BMI) and visceral adiposity [15]. The same was also demonstrated for the Pro12Ala variant of PPARγ [16]. Besides that,
in a genomic association study, which included 307 individuals in the Chinese population, a variant (Thr394Thr) of
the PPARα gene was associated with central obesity, and
about 43.7% of AG genotyped patients were diabetic [17].
However, none of these studies involved physical training
and the slimming process.
In a previous study [18], we found that PPARγ2 did not
influence exercise-induced weight loss, despite previous data
indicating that this gene is involved in adipocyte regulation,
growth, and differentiation [19]. On the other hand, the
receptor activated by peroxisome-α proliferators (PPARα)
is most expressed in tissues such as liver, heart, skeletal muscle, intestinal mucosa, and brown adipose tissue [20]. In
addition, PPARα is the most involved of the PPAR family
in fatty acid metabolism, and its activation lowers lipid levels
[21]. However, also for this gene, its influence on exerciseinduced weight loss is not known. Considering the involvement of PPARα in lipid metabolism, we can raise the hypothesis that this gene may specifically be involved in the lipolytic
process and, consequently, in weight loss induced by a physical training program. This would be particularly relevant
from the point of view of using physical training as a tool
to reduce obesity levels.
To elucidate this genetic involvement in exerciseinduced weight loss, the objective of this study was to investigate the relationship of the polymorphism in Intron 7 G/C
(rs 4253778) of the PPARα gene with the magnitude of
changes in the body composition of an overweight and obese
population that underwent an aerobic training program
aimed at reducing body weight and relating sex, age, and
nutritional factors (consumption of proteins, fibers, carbohydrates, and fat in the sixth week of intervention) as possible confounding variables.
2. Materials and Methods
This was a clinical trial with 58 adult men and women
(33:1 ± 7:6 years) who underwent a 12-week aerobic physical
PPAR Research
training program. The participants were categorized according to the presence of the A allele, so that the statistical analysis was performed with two trained and genotyped groups
(CC, n = 27, and CA+AA, n = 31). To be eligible, participants
had to be adults (ages 20 to 45 years), previously classified as
insufficiently active (<150 minutes/week of moderate to
severe physical activity) as determined by the International
Physical Activity Questionnaire [22]; have a BMI of between
25 and 39.9 kg/m2 for at least six months; have not changed
more than 2 kg in the last three months; do not smoke or
consume alcohol (more than two doses/day); do not use
medicine, supplements, or thermogenic substances which
alter the metabolism; and do not have any diseases (diabetes,
coronary artery disease, or hormonal diseases); for women,
they should not be menopausal or present symptoms related
to the climacteric period. Those who missed two consecutive
weeks or 25% of the physical training program or who began
dietary intervention, physical exercise, or medication during
the program period, as well as those who were injured, were
excluded from the study.
The flowchart in Figure 1 shows the trajectory of recruiting participants from the 630 interested parties who
responded to the invitation to participate in the study carried
out via social networks until the final samples, considering
the study’s eligibility and exclusion criteria.
The experimental protocol was approved by the Human
Research Ethics Committee of the Health Science Center
(CCS) of the Federal University of Paraíba (UFPB), Brazil,
under protocol number 1.981.304, and was registered at ClinicalTrials.gov (registration number: NCT03568773). All volunteers who agreed to participate in the study provided
written consent after being clarified about procedures and
potential risks.
2.1. Study Design. Participants were involved in a 12-week
consecutive physical training program. Nutritional assessment, ergospirometry and dual-energy X-ray absorptiometry
(DXA) were made before the program started, in the 6th
week and at the end of the program. Oral mucosa collection
was performed for subsequent genotyping (Figure 2).
2.2. DNA Extraction and Genotyping. Genomic DNA
extracted from the oral epithelial cells were obtained with a
3% sucrose wash of the participants in the experimental group.
Extracted DNA was washed with 70% alcohol and resuspended in 40 μL TE buffer (pH 8.0) (25). Polymorphism in
Intron 7 G/C (rs 4253778) of the PPARα gene was determined
by restriction fragment length polymerase-polymorphism
chain reaction (PCR-RFLP), followed by digestion through
the restriction enzyme TaqI, generating the following
fragments: 266 bp (CC); 266, 216, and 50 bp (CA); and 216
and 50 bp (AA). PCR primers were as follows: the sequence
of forward is ACAATCACTCCTTAAATATGGTGG (24
bases); reverse is AAGTAGGGACAGACAGGACCAGTA
(24 bases). Cycling conditions were an initial denaturation of
95°C for 2 min, 30 cycles of denaturation 95°C/30 sec, annealing at 56°C/30 sec and extension at 72°C/30 sec, followed by a
final extension at 72° C for 5 min.
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Transparent reporting of trials
CONSORT 2010 Flow Diagram
Assessed for eligibility (n = 630)
Enrollment
Excluded (n = 427)
Not meeting inclusion criteria (n = 405)
Declined to participate (n = 15)
Other reasons (n = 7)
Aerobic exercise training
Eligible for intervention (n =134)
Made the initial evaluations (n = 92)
Did not receive intervention (did not perform all the exams,
started another training protocol, performed exams but did not
attend at the beginning of the training) (n = 42)
Initiated the intervention (n = 92)
Discontinued intervention (gave up the intervention, lost more
than 25% or two consecutive weeks, hurt) (n = 34)
Analyzed (n = 58)
Homozygous - CC (n = 27)
Homozygous – CA + Recessive homozygote -AA (n = 31)
Figure 1: CONSORT flow diagram.
Exercise protocol
Adaptation
0
E
x
a
m
s
1
2
1
2
3
4
5
6
7
8
9 10 11 12
E
x
a
m
s
Nutritional assessment
genotyping
Nutritional assessment ergospirometry dual-energy
X-ray absorptiometry
Figure 2: Study design.
E
x
a
m
s
2.3. Nutritional Control. Volunteers underwent a nutritional
assessment in order to monitor nutrition through the 24 h
recall following a protocol suggested by the Dietary Recommendation Intake [23] behavior throughout the study; it
was recommended to maintain the usual dietary patterns.
Nutritional assessments were carried out in triplicate, two
referring to days of the week and one referring to the weekend. For calculations, AVANUTRI software was used, version 4.0 (Avanutri & Nutrição Computer Services, Três
Rios, RJ, Brazil). This assessment was repeated in the sixth
and final week.
2.4. Body Composition. The volunteer was asked to lie on
his back for a full body scanner, using dual-energy X-ray
absorptiometry equipment—DXA equipment (LUNAR
ADVANCE DF+13.4038 Radiation (GE Lunar Corporation,
USA)); the guidelines and procedures of calibration provided
by the exams were performed considering the threecompartment model, and its components were divided into
the lean tissue, adipose tissue, and bone tissue.
4
Additionally, circumference measurements were made
with a flexible and inextensible measuring tape, with a precision of 1.0 mm (Sanny, São Paulo, Brazil), with the volunteers in an orthostatic position, with the abdomen relaxed,
the arms parallel to the body and the feet together, with
the tape not compressing the skin and supported parallel
to the floor. Waist circumference was measured around the
abdomen, taking as reference the average distance between
the last floating rib and the iliac crest, abdomen (area with
greater abdomen perimeter), and hips (greater gluteal prominence). These measurements took place at the preexperiment moment and 48 hours after intervention. The women
were instructed to be in the postmenstrual period before
the evaluation.
2.5. Aerobic Capacity and Anaerobic Threshold. The ergospirometry test was performed following the individualized
ramp protocol [24] with incremental loads at every 3 minutes
(Centurion-200 Micromed, Brazil) for determining the
maximum VO2. The test was performed by a qualified cardiologist. Cardiac monitoring was performed through continuous electrocardiographic tracing (ErgoPC Elite, Micromed,
Brazil), always through 13 leads. Blood pressure measurement was performed with a properly calibrated mercury
column sphygmomanometer. Exhaled gas was measured
using a Metalyzer 3B-Cortex (Leipzig, Germany), which
was measured with each breath, associated with the ErgoPC
Elite (Micromed, Brazil), and VO2 peak was considered as
the maximum consumption reached in the last seconds of
the exercise. Anaerobic limit (L1) was determined by the
agreement of two methods: the V-slope and the ventilatory
equivalent. Finally, the respiratory compensation point was
determined from the moment of sustained drop in the final
expiratory pressure of CO2 (PEF CO2) and elevation of the
expiratory pressure of O2 (PEF O2). For the interruption of
the test, we followed the protocol of [25].
2.6. Exercise Protocol. The exercise protocol is shown in
Table 1. After two weeks of adaptation with the modality,
participants completed 12 consecutive weeks of aerobic
training consisting of walking or running. Training prescription was based on the aerobic (L1) and anaerobic (L2) thresholds of each volunteer, according to the results obtained in
the ergospirometry test. Initially, the volunteers performed
two weeks of adaptation with two sessions/week of 20 to
40 min and intensity below L1 on the treadmill. Then, the
training protocol started, from the first to the fourth week;
the volunteers performed three sessions/week of 40 to 60
minutes, with intensity at L1. At the fifth week, the frequency
and duration of training remained and the intensity was
increased to between L1 and 1/2 L2. From the sixth to the
8th week, the training frequency was increased to five sessions/week, with three sessions being held in the laboratory,
supervised by the researchers and the application, and two
volunteers chose the practice location and did it only with
the use of the application for smartphone, to ensure that
the training does not leave the zone. From the ninth to the
twelfth week, the training frequency and duration remained
and the intensity was increased to 1/2 L2 to L2.
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Table 1: Exercise protocol.
Week
Sessions/week
Time (min)
Intensity
2
1
2–3
4
5
6–8
9–12
2
3
3
3
3
5
5
20–40
40
50
60
60
60
60
<L1
L1
L1
L1
L1–1/2 L2
L1–1/2 L2
1/2 L2–L2
<L1: below anaerobic threshold; L1: anaerobic threshold; L1–1/2 L2: between
anaerobic threshold and half the respiratory compensation point; 1/2 L2–L2:
half of respiratory compensation point and respiratory compensation point.
The training took place in an open environment during
the months of March and April 2018, during which time
the city’s climate is stable (temperature between 24°C and
30°C throughout the year), with little precipitation (rain).
At the time of the sessions (between 6 am and 8 am), the temperature was between 26°C and 28°C with relative humidity
of around 80%. These climatic conditions facilitated adherence to the training program and the stress induced by the
sessions. All sessions were supervised by an exercise physiologist, and the heart rate was continuously monitored with
heart rate monitors (Polar®, model FT1 (Polar Electro Oy,
Kempele, Finland)). In addition, each volunteer used a
smartphone app to assess distance and training intensity
(Endomondo Sports Tracker, version 17.5.1).
2.7. Statistical Analysis. Data are presented as mean and standard deviation or the absolute values as variables. Data were
tested for normal distribution and homogeneity of variance
using Kolmogorov-Smirnov and Levene tests prior to the statistical analyses. Two-way ANOVA for repeated measures
(considering the time × allele interaction) to compare differences in body composition variables and nutritional data
between the absence (CC) and presence (CA+AA) of allele
A. additionally, a chi-square test was used to check possible
differences in the distribution of subjects in responders (that
reduced the body composition parameters evaluated) and
nonresponders (no variation or increase in the selected variables) in the function of the genotype (CC vs. CA+AA).
Finally, a binary logistic regression was performed to verify
the influence of possible confounding variables on weight
loss responses (sex, age, and nutritional factors during the
intervention). Data were analyzed using the statistical package SPSS Statistics (v.20, IBM SPSS, Chicago, IL, USA), and
the level of significance was set at p ≤ 0:05.
3. Results
The characteristics of the volunteers in this study are shown
in Table 2. From 58 volunteers who completed the training
protocol, 70.7% were women (n = 41) and 46.5% (n = 27)
were identified as the CC genotype and 53.5% (n = 31) as
the CA+AA genotype. The CC group was composed of 10
(37%) subjects overweight and 10 and 17 (63%) obese, and
the CA+AA group was composed of 9 (29%) subjects
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Table 2: Baseline characteristics of participants according to the 7
G/C (rs 4253778) polymorphism of the PPARα gene.
Variables
N (%)
CC
Alleles
CA+AA
p value
Age (years)
27 (46.5%)
31:6 ± 7:4
31 (53.5%)
34:4 ± 7:7
0.14
PA (min/week)
74:2 ± 27:2
71:1 ± 31:3
0.54
0.69
VO2max (mL/kg min)
28:5 ± 5:7
29:1 ± 6:3
Weight (kg)
84:6 ± 11:0
85:3 ± 11:7
0.78
BMI (kg/m2)
31:3 ± 2:6
31:7 ± 3:1
0.67
LM (kg)
45:0 ± 8:6
45:4 ± 11:4
0.88
FM (kg)
36:2 ± 6:1
37:2 ± 6:1
0.52
% FM
43:7 ± 5:8
44:5 ± 7:6
0.69
WC (cm)
92:5 ± 8:1
94:0 ± 7:8
0.51
Abdomen (cm)
104:3 ± 7:4
106:1 ± 7:5
0.34
HC (cm)
110:9 ± 5:5
110:6 ± 6:6
0.85
Nutrition
Energy (kcal)
1988:8 ± 542:6 1766:1 ± 561:1
0.13
Carbohydrate (g)
262:3 ± 77:0
235:4 ± 80:7
0.20
Lipids (g)
65:5 ± 20:5
55:3 ± 18:4
0.05∗
Protein (g)
86:1 ± 38:2
79:6 ± 35:8
0.49
Fibers (g)
14:9 ± 6:2
13:8 ± 6:7
0.53
Data are means ± SD. N: number of participants; PA: physical activity; BMI:
body mass index; LM: lean mass; FM: fat mass; %FM: fat mass percentage;
WC: waist circumference; HC: hip circumference. ∗ Between group-group
differences (one-way ANOVA) (p ≤ 0:05).
overweight and 22 (71%) obese. When the Hardy-Weinberg
Balance was calculated considering p ≤ 0:05, we observed
that the study sample is consistent with the expected distribution (C = 70% and A = 30%) according to the allele frequency of the European Centre d’Etude du Polymorphisme
Humain (45%) reported by the International HapMap Project (http://www.hapmap). When comparing the genotypes,
they had similar characteristics in terms of age, level of physical activity compatible with the insufficiently active classification [21], and aerobic capacity between regular and weak
according to Brazilian Society of Cardiology [26, 27]. When
comparing body composition data, differences were also
not found. In addition, the eating pattern was similar.
Time-group interaction analysis is shown in Table 3. Both
groups showed significant reductions in the variable’s fat mass,
percentage fat mass, and circumferences (waist, abdomen,
and hip). However, time-group interaction showed that there
was no difference in these reductions between two allele
groups. For weight and BMI, we observed that only the group
with allele A showed an intragroup reduction, but this difference was not maintained in the time-group interaction. As
for the lean mass, there was an increase only in the
intragroup for CC but also without differences in the timegroup interaction. Aerobic capacity showed a significant
increase after the intervention, with this improvement occurring in both genotypic groups evaluated, and without differences between the groups.
Figure 3 shows that there were responders and nonresponders for body composition variables. 38 volunteers
(62%) reduced body weight, while 22 volunteers (38%) did
not reduce or increase weight. The biggest weight reduction
was 6.2 kg, and the biggest increase was 4.7 kg. For percentage
fat mass, the biggest increase was 1.4% and the biggest reduction was 5.8%; meanwhile, fat mass had its biggest increase of
2.7 kg and biggest reduction of 5.9 kg. It is worth noting that a
variation related to lean mass can also be observed, with volunteers gaining up to 3.6 kg and others reducing up to 3.1 kg.
Although differences in the time × group interaction
were not found, when the volunteers were categorized as
responders and nonresponders, important differences in
responses to training were observed. Among the CC volunteers, it can be seen that 66.7% were respondents for fat mass,
while among the CA+AA volunteers, about 90.3% were
respondents. Differences can also be noted for the percentage
of fat (p ≤ 0:002) and circumferences [waist (p ≤ 0:009),
abdomen (p ≤ 0:000), and hip (p ≤ 0:001)] (Table 4).
A multiple binary logistic regression to verify sex, age,
and nutritional factors (consumption of proteins, fibers, carbohydrates, and fat in the sixth week of intervention) as possible confounding variables was noted; the highest frequency
of distribution of the AA allele among subjects who were
responsive to reduction of fat mass was independent of these
confounding variables, while these variables were shown to
influence the percentage of fat mass and circumferences
(waist, abdomen, and hip), indicating that genetic influence
is not independent (Table 5).
4. Discussion
This study showed that the polymorphism in the PPARα
gene does not influence the magnitude of weight loss induced
by an aerobic training program; however, it showed that the
weight loss responsiveness to the training program was
genetic-dependent. This dependence was not only genetic
because sex, age, and nutritional intake were also influential,
but the ability to respond to fat mass was independent of the
confounding factors that were considered.
Variability in magnitude of individual responses seen in
previous studies was also found in the present study. Meanwhile, some people reduced between 0.07 and 5.99 kg of the
fat mass and others increased this measure from 0.03 to
2.70 kg; in previous studies, the results between -9.5 to
+2.6 kg [4], -10.2 to +1.7 kg [5], -11.0 to +4.0 kg FM [6],
and -4.4 to +4.9 kg FM [7] were observed.
Although the magnitude of weight loss in response to the
physical training program has been well demonstrated
through the mean, responsiveness to programs is relevant,
since it is notorious to identify people who are not able to lose
weight after physical training. While our data pointed to an
important prevalence of 38% of people who were unresponsive to weight loss, this data has not been presented in previous studies (results presented only as mean and inferential
statistics) [8].
Although genetic factors can be easily suggested as possible causes of the variability in slimming responses and
responsiveness to training programs, this possibility has so
6
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Table 3: Comparison between effect of training on aerobic capacity and body composition of people with allele C versus A.
Variables
CC (n = 27)
After
Baseline
-1
∗
CA+AA (n = 31)
After
Baseline
▲
∗
▲
VO2max (mL kg )
28:5 ± 5:7
33:2 ± 6:9
4:7 ± 3:9
29:1 ± 6:3
36:5 ± 10:9
7:3 ± 7:1
Weight (kg)
84:6 ± 11:0
84:0 ± 12:0
−0:6 ± 2:9
85:3 ± 11:7
84:1 ± 11:2∗
−1:2 ± 2:3
BMI (kg/m2)
31:3 ± 2:6
31:1 ± 2:8
−0:2 ± 1:3
31:7 ± 3:1
31:2 ± 3:2∗
−0:5 ± 0:9
LM (kg)
45:0 ± 8:6
∗
45:7 ± 8:6
+0:7 ± 1:3
45:4 ± 11:4
45:8 ± 11:1
+0:4 ± 1:6
FM (kg)
36:2 ± 6:1
35:1 ± 7:1∗
−1:1 ± 2:3
37:2 ± 6:1
35:7 ± 6:7∗
−1:5 ± 1:7
%FM (%)
43:7 ± 5:8
42:6 ± 5:8∗
−1:1 ± 1:5
44:5 ± 7:6
43:2 ± 8:2∗
−1:3 ± 1:5
WC (cm)
92:5 ± 8:1
91:0 ± 8:4∗
−1:5 ± 3:5
94:0 ± 7:8
91:7 ± 7:9∗
−2:3 ± 2:1
Abdomen (cm)
104:3 ± 7:4
HC (cm)
110:9 ± 5:5
101:5 ± 8:6
∗
108:8 ± 5:6
∗
−2:8 ± 3:6
−2:8 ± 2:5
106:1 ± 7:5
110:6 ± 6:6
101:9 ± 7:7
∗
−4:2 ± 3:4
107:8 ± 6:9
∗
−2:8 ± 2:5
7
6
5
4
3
2
1
0
–1
–2
–3
–4
–5
–6
–7
Δ lean mass (kg)
Δ weight (kg)
Data are means ± SD. BMI: body mass index; LM: lean mass; FM: fat mass; %FM: fat mass percentage; WC: waist circumference; HC: hip circumference. ∗
Intragroup differences vs. baseline (pairwise Student’s t-test) (p < 0:05). Differences in time × group interaction were not found (two-way ANOVA) (p ≤ 0:05).
7
6
5
4
3
2
1
0
–1
–2
–3
–4
–5
–6
–7
Volunteers
Volunteers
CC
CA+AA
CC
CA+AA
7
6
5
4
3
2
1
0
–1
–2
–3
–4
–5
–6
–7
(b)
Δ fat mass (%)
Δ fa tmass (kg)
(a)
7
6
5
4
3
2
1
0
–1
–2
–3
–4
–5
–6
–7
Volunteers
CC
CA+AA
Volunteers
CC
CA+AA
(c)
(d)
Figure 3: Interindividual difference in the variation of the participants’ body composition after intervention. Data are (a) weight, (b) lean
mass, (c) fat mass, and (d) fat mass percentage.
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Table 4: Distribution test between the polymorphism in Intron 7 G/C (rs 4253778) of the PPARα gene and responders and nonresponders to
weight loss training program.
Variables
CC n (%)
PPARα
CA+AA n (%)
△weight
Responders
Nonresponders
16 (59.3)
11 (40.7)
20 (64.5)
11 (35.5)
△BMI
Responders
Nonresponders
16 (59.3)
11 (40.7)
19 (61.3)
12 (38.7)
△LM
Responders
Nonresponders
18 (66.7)
9 (58.1)
18 (33.3)
13 (41.9)
△FM
Responders
Nonresponders
18 (66.7)
9 (33.3)
28 (90.3)
3 (9.7)
△ %FM
Responders
Nonresponders
20 (74.1)
7 (25.9)
25 (80.6)
6 (19.4)
△WC
Responders
Nonresponders
18 (66.7)
9 (33.3)
25 (80.6)
6 (19.4)
△abdomen
Responders
Nonresponders
22 (81.5)
5 (18.5)
26 (83.9)
5 (16.1)
△HC
Responders
Nonresponders
20 (74.1)
7 (25.9)
27 (87.1)
4 (12.9)
McNemar test
RR (95% IC)
0.150
0.80 (0.27–2.31)
0.200
0.92 (0.32–2.63)
0.122
1.44 (0.49–4.21)
0.003
0.21 (0.05–0.90)
0.002
0.68 (0.19–2.36)
0.009
0.48 (0.14–1.59)
0.000
0.84 (0.21–3.30)
0.001
0.42 (0.10–1.64)
Data are frequency of responders (who obtained some weight loss) and nonresponders (who did not lose weight or increased any variable related to weight loss).
N: number of participants; △: variation between pre- and postintervention; BMI: body index mass; FM: fat mass; %FM: fat mass percentage; WC: waist
circumference; HC: hip circumference. Chi-square test (McNemar test).
Table 5: Logistic regression model verifying the influence of sex,
age, and nutritional factors between Intron 7 G/C polymorphism
(rs 4253778) and body composition.
β
p
RR (95% IC)
FM
−3:226 ± 1:150
0.005
0.04 (0.004–0.378)
%FM
−0:788 ± 0:796
0.322
0.455 (0.095–2.165)
WC
−950 ± 0:719
0.187
0.387 (0.95–1.584)
Abdomen
−0:161 ± 0:824
0.845
0.851 (0.169–4.280)
HC
−1:289 ± 0:896
0.150
0.276 (0.048–1.595)
Dependent variable. △: variation between pre- and postintervention; FM: fat
mass; %FM: fat mass percentage; WC: waist circumference; HC: hip
circumference. Independent variables: G/C polymorphism, sex, age, and
nutritional factors (ingestion carbohydrates, fat, proteins, and fibers in the
sixth week of intervention). For each body composition variable, a separate
model was made.
far been scarcely investigated. After mapping the human
genome, broad association studies (GWAS) have managed
to identify a variety of genes involved in the etiology of obesity, among them MC4R, FTO, and PPAR family genes [28].
However, at least as far as we know, few studies have verified
the genetic influence on weight loss, specifically exerciseinduced weight loss.
Our laboratory has been trying to identify causal factors
for this variability and prevalence of responsiveness. In a previous study, we tested PPARγ and found no influence of this
gene on the responses obtained after a continuous aerobic
training program [18]. However, PPAR is a family with
PPARα, PPARγ, and PPARσ, so that, following this previous
study, we decided to check the PPARα gene, since this gene is
the most involved of the PPAR family in the fatty acid metabolism, and its activation lowers lipid levels [21]. In fact, this is
the first time that it has been demonstrated that some gene
influences the occurrence of weight loss induced by a training
program, where it was demonstrated that carriers of the allele
A have a higher frequency of people who have shown themselves to be responsive. We found evident associations that
the genotype influences the frequency of distribution between
responders and nonresponders for an aerobic training program in the variables FM, %FM, CC, abdomen, and HC.
Statistical procedures showed this difference in response
from the A allele only for the frequency of responsiveness
8
(Table 4), but not in the magnitude of weight loss (Table 3).
However, it was noted that despite the lack of difference in
the time-group interaction, allele A patients had a greater
descriptive response to all weight loss-associated variables.
Therefore, we do not rule out that the presence of the PPARα
allele A also influences magnitude. We therefore suggest
studies with a larger sample size to better test this possibility.
Although our data shows that there is a genetic participation in this process, this participation does not seem to work
alone, since most of the associations found (%FM, WC,
abdomen, and HC) were not maintained, due to confounding
factors such as sex, age, and nutritional factors (consumption
of proteins, fibers, carbohydrates, and fat in the sixth week of
intervention). In fact, the literature presents some factors that
influence the responses found by physical exercise, such as
genetic, physiological, environmental, and ethnic factors, in
addition to age, training history, level of physical activity,
and social formation [29]. Therefore, the data from the present study, while demonstrating consistently the influence of
this polymorphism in the responsiveness to weight loss, also
corroborate the fact that environmental influence has a
determining role in this process [30].
It is known that the activation of PPARα positively regulates the expressions of several enzymes involved in mitochondrial β-oxidation and peroxisome and in microsomal
ω-oxidation, as well as in the transcriptional regulation of
genes necessary to maintain redox balance during fatty oxidative acid catabolism [31]. In addition, PPARα is largely
related to molecular actions in lipid metabolism and inflammation and is involved not only in glucose and lipid metabolism but also in inflammation modulation pathways [32].
With regard to physical exercise, the regulation of the expression of this gene can increase the oxidative capacity of skeletal muscle in relation to endurance training [33]; thus, this
gene is often found in athletes of this modality [34].
Considering that exercise-induced weight loss and its
magnitude are already well defined in several previous studies, our data add the information that responsiveness to the
training program is something that must be considered and
that genetic and environmental factors determine differences
in individual responses, even in the face of a sample made
homogeneous before the inclusion criteria. Our data provide
ways for health professionals to advance in their work, starting from the classic intervention and evaluating the results
for a second moment of the intervention, where causal factors or the absence of weight loss can be identified and remedied, when possible, in order to restore the individual weight
loss capacity, pointing out that managing nutritional intake
and genetic characteristics is an initial path for this second
intervention.
5. Conclusion
This study showed that people with CC allele in the PPARα
gene have less responsiveness to weight loss induced by a
physical training program, while people with CA and AA
alleles have 26% higher weight loss. However, our data
showed that this dependence was not only genetic because
sex, age, and nutritional intake were also influential.
PPAR Research
Data Availability
The data of this study are available from the corresponding
author upon reasonable request.
Additional Points
Highlights. (i) The polymorphism in the PPARα gene influences the weight loss induced by twelve weeks of physical
training with aerobic exercises. (ii) Responsiveness to the
training program is something that must be considered,
and genetic and environmental factors determine differences
in individual responses.
Ethical Approval
The studies involving human participants were reviewed and
approved by the Human Research Ethics Committee of the
Health Sciences Center (CCS) of the Federal University of
Paraíba (UFPB), Brazil, under protocol number 1.981.304.
Consent
The patients/participants provided their written informed
consent to participate in this study.
Conflicts of Interest
The authors declare that they have no competing interests.
Authors’ Contributions
GC and AS conceived the idea for the manuscript, agreed on
the content, contributed to the writing and editing the manuscript, and approved the final draft of the manuscript. DP,
MR, BS, YO, KF, JB, AA, JM-F, and RS conceived the editing
of the manuscript and approved the final draft of the
manuscript.
Acknowledgments
We thank the Coordenação de Aperfeiçoamento de Pessoal
de Nível Superior (CAPES) and the Conselho Nacional de
Desenvolvimento Científico e Tecnológico (CNPq) for their
financial support and finally the Federal University of Paraiba for the logistical support.
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