Association of the UCP polymorphisms with susceptibility to obesity: case–control study and...

Association of the UCP polymorphisms with susceptibilityto obesity: case–control study and meta-analysis

Letıcia de Almeida Brondani • Bianca Marmontel de Souza •

Taıs Silveira Assmann • Ana Paula Boucas • Andrea Carla Bauer •

Luıs Henrique Canani • Daisy Crispim

Received: 9 December 2013 / Accepted: 5 April 2014

� Springer Science+Business Media Dordrecht 2014

Abstract This paper describes a case–control study and a

meta-analysis performed to evaluate if the following

polymorphisms are associated with presence of obesity:

-3826A/G (UCP1); -866G/A, Ala55Val and Ins/Del

(UCP2) and -55C/T (UCP3). The case–control study

enrolled 282 obese and 483 non-obese patients with type 2

diabetes. A literature search was made to identify all

studies that evaluated associations between UCP1–3

polymorphisms and obesity. In the case–control study the

distributions of the UCP variants did not differ between

obese and non-obese groups (P [ 0.05). Forty-seven

studies were eligible for the meta-analysis and the results

showed that the UCP2 -866G/A and UCP3 -55C/T

polymorphisms were associated with protection to obesity

in Europeans (OR = 0.89, 95 % CI 0.82–0.97 and

OR = 0.88, 95 % CI 0.80–0.97, respectively). The UCP2

Ala55 val polymorphism was associated with obesity in

Asians (OR = 1.61, 95 % CI 1.13–2.30). The UCP2 Ins/

Del polymorphism was associated with obesity mainly in

Europeans (OR = 1.19, 95 % CI 1.00–1.42). There was no

significant association of the UCP1 -3826A/G polymor-

phism with obesity. In our case–control study we were not

able to demonstrate any association between UCP poly-

morphisms and obesity in T2DM patients; however, in the

meta-analysis we detected a significant association of

UCP2 -866G/A, Ins/Del, Ala55Val and UCP3 -55C/T

polymorphisms with obesity.

Keywords Uncoupling proteins � Genetic

polymorphisms � Obesity � Meta-analysis

Introduction

Obesity and type 2 diabetes mellitus (T2DM) are common

and multifactorial conditions for which susceptibility is

determined by the combined actions of genetic and envi-

ronmental factors [1]. Prevalence of obesity and T2DM is

increasing worldwide at a disturbing rate, and both con-

ditions are associated with increased morbidity and mor-

tality rates [2, 3]. The remarkable increase in the

prevalence of these conditions during the past decades is

probably due to modifications in diet and physical activity

[4]. However, it is believed that these environmental

changes would only lead to obesity and/or T2DM under a

permissible genetic background [1]. Therefore, huge efforts

have been made to identify genes associated with these

disorders, and several studies have been focused on genes

encoding proteins related to energy expenditure, such as

uncoupling proteins (UCPs) [5, 6].

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11033-014-3371-7) contains supplementarymaterial, which is available to authorized users.

L. de Almeida Brondani � B. M. de Souza �T. S. Assmann � A. P. Boucas � A. C. Bauer �L. H. Canani � D. Crispim

Endocrinology Division, Hospital de Clınicas de Porto Alegre,

Porto Alegre, RS, Brazil

L. de Almeida Brondani � B. M. de Souza �T. S. Assmann � A. P. Boucas � L. H. Canani � D. Crispim

Postgraduate Program in Medical Sciences, Endocrinology,

Universidade Federal do Rio Grande do Sul., Porto Alegre, RS,

Brazil

D. Crispim (&)

Rua Ramiro Barcelos 2350, Predio 12, 4� Andar. CEP,

Porto Alegre, RS 90035-003, Brazil

e-mail: [email protected]

123

Mol Biol Rep

DOI 10.1007/s11033-014-3371-7

http://dx.doi.org/10.1007/s11033-014-3371-7

Uncoupling proteins 1, 2 and 3 are members of an

anion-carrier protein family located in the inner mito-

chondrial membrane [7]. These proteins have structural

similarities, but show different tissue expression in mam-

mals [7]. The original uncoupling protein, UCP1, is mostly

expressed in brown adipose tissue [8, 9]. Recently, it was

demonstrated that under some pathological conditions,

such as hyperglycemia, UCP1 is also expressed in white

adipose tissue, skeletal muscle, retina, and pancreatic islets

[8, 10]. Uncoupling protein 2 (UCP2) is broadly expressed

in several tissues while uncoupling protein 3 (UCP3) is

mainly expressed in the skeletal muscle [9].

During the last few years, numerous studies have

reported that UCPs reduce metabolic efficiency by

uncoupling substrate oxidation in mitochondria from ATP

synthesis by mitochondrial respiratory chain. This is

accomplished by promoting net translocation of protons

from the intermembrane space, across the inner mito-

chondrial membrane, to the mitochondrial matrix, thus

dissipating the potential energy available for ATP synthe-

sis, and therefore, decreasing ATP production [5, 8]. This

uncoupling effect subsequently leads to homologue- and

tissue-specific functions, such as thermogenesis and energy

expenditure (UCP1), regulation of free-fatty acids (FFAs)

metabolism (UCP2 and UCP3), decrease in reactive oxy-

gen species (ROS) production by mitochondria (UCP1-3)

and regulation of insulin secretion by pancreatic beta-cells

(UCP2) [5, 7], all associated with obesity and/or T2DM

pathogenesis.

For that reason, the relationship between UCP loci and

susceptibility to T2DM and obesity has been evaluated in a

number of genetic studies and special attention has been

given to the -3826A/G (rs1800592) polymorphism of the

UCP1 gene, the -866G/A (rs659366), Ala55Val (C/T;

rs660339) and Ins/Del polymorphisms of the UCP2 gene,

and the -55C/T (rs1800849) polymorphism of the UCP3

gene [5, 6, 11]. Two recent meta-analyses confirmed the

association between UCP2 Ala55Val and UCP3 -55C/T

polymorphisms and increased susceptibility for T2DM in

subjects of Asian descent [12, 13]. On the other hand,

results from studies that analyzed associations between

UCP1-3 polymorphisms and obesity are not consistent.

While some of them have reported associations between

one or more of these variants and obesity, others were

incapable to find any association between these polymor-

phisms and obesity [5, 11].

Thus, as part of the incessant attempt to examine the

hypothesis that UCP1-3 polymorphisms are associated

with obesity, we carried out a case–control study of white

subjects with T2DM followed by a meta-analysis of the

literature on the subject.

Subjects and methods

Case–control study

Case–control samples

The study population included 765 unrelated T2DM

patients belonging to a multicenter study that began

recruiting diabetic patients in Southern Brazil in 2002. That

project was designed to study risk factors for T2DM and its

chronic complications. It included four centers in teaching

hospitals located in the Brazilian state of Rio Grande do

Sul: the Grupo Hospitalar Conceicao, the Hospital Sao

Vicente de Paula, the Hospital Universitario de Rio Grande

and the Hospital de Clınicas de Porto Alegre. A complete

description of that project can be found elsewhere [14].

T2DM was diagnosed according to the American Diabetes

Association criteria [15]. The sample presented here was

already described in a previous study from our group,

which showed that any of the five analyzed UCP poly-

morphisms were associated with T2DM; although, some of

them were associated with risk for this disease in a meta-

analysis including several studies from different ethnicities

[13]. Thus, in the present case–control study, we aimed to

evaluate if these UCP polymorphism could be associated

with obesity in our T2DM sample.

All subjects reported European ancestry (mainly Portu-

guese, Spanish, Italian and German descent). Ethnicity was

determined by self-report. A standard questionnaire was used

to collect data about age, age at T2DM diagnosis and drug

treatment. All patients underwent physical evaluations and

laboratory tests. They were weighed wearing light clothing

and barefoot and have had their height measured. Body mass

index (BMI) was calculated as weight (kg)/height (m2).

The obese group (cases, n = 282) was defined by

BMI C 30 kg/m2, and the non-obese group (controls,

n = 483) was defined by BMI B 25 kg/m2. The charac-

teristics of the obese T2DM patients included in this study

were as follows: mean age ± SD was 57.3 ± 10.0 years

and mean BMI was 34.4 ± 4.3 kg/m2. Males comprised

37.4 % of this sample, and 78.7 % of all patients had

arterial hypertension. The characteristics of non-obese

T2DM patients were as follows: mean age was

59.2 ± 10.7 years and mean BMI was 25.8 ± 2.8 kg/m2.

Males comprised 53.1 % of this sample, and 64.2 % of all

patients had arterial hypertension.

The data obtained from this study did not influence

patients’ diagnosis or treatment. The study protocol was

approved by Ethic Committee in Research from Hospital

de Clınicas de Porto Alegre and all subjects gave informed

consent in writing.

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Genotyping

DNA was extracted from peripheral blood leukocytes using

a standardized salting-out procedure. The -866G/A

(rs659366) polymorphism in the promoter region of the

UCP2 gene was genotyped by digesting polymerase chain

reaction (PCR) products with the restriction enzyme MluI

(Invitrogen Life Technologies, Inc., San Diego, CA, USA)

as already described [16]. Digestion fragments were

resolved on 2 % agarose gels containing GelRedTM

Nucleic Acid Gel Stain (Biotium Inc., CA, USA) and

visualized under ultraviolet light. A DNA sample with a

known genotype was used as a positive control to confirm

the fullness of PCR product digestion. The 45 bp Ins/Del

polymorphism in the 30UTR region of exon 8 of the UCP2

gene was genotyped by PCR using primers already

described in the literature [17]. The primers amplified

products of 457 bp (insertion allele) or 412 bp (deletion

allele), which were then resolved on 2 % agarose gels

stained with GelRedTM Nucleic Acid Gel Stain and visu-

alized under ultraviolet light [18]. Genotypes of the

-866G/A and Ins/Del variants were verified using the

ImageMaster System VDS (GE HealthCare, London, UK).

The Ala55Val (C/T) variant (rs660339) in exon 4 of the

UCP2 gene, the -3826A/G (rs1800592) variant in the

promoter region of the UCP1 gene, and the -55C/T

(rs1800849) variant in the promoter region of the UCP3

were genotyped using primers and probes contained in the

409 Human Custom TaqMan Genotyping Assay (Assays-

By-Design Service; Life Technologies, Foster City, CA;

USA). Reactions were performed in a 96-well plate, in a

5 ll reaction volume using 2 ng of total DNA, TaqMan

Genotyping Master Mix 19 (Life Technologies), and

Custom TaqMan Genotyping Assay 1X specific for each

variant (Life Technologies). Plates were positioned in a

real-time PCR thermal cycler (7500 Fast Real Time PCR

System; Life Technologies) and heated for 10 min at

95 �C, followed by 50 cycles of 95 �C for 15 s and 63 �C

for 1 min.

Genotyping success rates were better than 95 % for all

analyzed polymorphisms and the calculated error rate for

PCR duplicates was fewer than 3 %.

Statistical analyses for the case–control study

Allele distributions were calculated by gene counting and

departures from the Hardy–Weinberg equilibrium (HWE)

were verified using v2 tests. Allele and genotype distribu-

tions were compared between groups using the v2 test.

Logistic regression analyses were done to evaluate inde-

pendent associations between the UCP polymorphisms and

obesity, adjusting for age and gender of the T2DM patients.

Statistical analyses were performed using SPSS version

18.0 (SPSS, Chicago, IL, USA). Results for which the P

value was under 0.05 were considered statistically

significant.

Meta-analysis

Search strategy and eligibility criteria

This study was designed and reported in agreement with

accepted guidelines for execution of systematic reviews

and meta-analyses [19, 20]. Both Embase and PubMed

repositories were searched systemically to identify all

available genetic studies of associations between obesity

and UCPs polymorphisms (UCP1 -3826A/G, UCP2

-866G/A, UCP2 Ala55Val, UCP2 Ins/Del and UCP3

-55C/T). The following medical subject headings (MeSH)

terms were searched: (‘‘Obesity’’ OR ‘‘Body mass index’’)

AND (‘‘mitochondrial uncoupling protein’’ OR

‘‘SLC25A27 protein, human’’ OR ‘‘mitochondrial uncou-

pling protein 20’ OR ‘‘mitochondrial uncoupling protein

30’) AND (‘‘mutation’’ OR ‘‘frameshift mutation’’ OR

‘‘germ-line mutation’’ OR ‘‘INDEL mutation’’ OR

‘‘mutation, missense’’ OR ‘‘point mutation’’ OR ‘‘codon,

nonsense’’ OR ‘‘sequence deletion’’ OR ‘‘polymorphism,

genetic’’ OR ‘‘polymorphism, single nucleotide’’ OR

‘‘polymorphism, restriction fragment length’’). The search

was restricted to human studies and English or Spanish

language papers and was finished on July 06, 2013. All of

the papers identified were also explored manually to

identify other relevant citation.

Two researchers (T.S.A and A.P.B.) separately reviewed

titles and abstracts of all selected papers with the purpose

of evaluate if the articles were eligible for inclusion in the

present meta-analysis. Divergences were solved by dis-

cussion between them and when required a third reviewer

(D.C.) was consulted. When the abstracts did not supply

sufficient information to fulfill the inclusion and exclusion

criteria, the full text of the article was retrieved for review.

We included observational studies that compared one or

more of the UCP polymorphisms between a known number

of obese and non-obese subjects. Articles were excluded

from the meta-analysis if the genotype frequencies in

control subjects deviated from those predicted by the

HWE, if they did not have enough data to estimate an OR

with 95 % CI, or if they did not used validated genotyping

techniques. If results were duplicated and had been pub-

lished more than once, the most complete article was

selected.

Data extraction and quality control assessment

Data were independently extracted by two researchers

(L.A.B. and B.M.S.) using a standardized abstraction form,

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and agreement was sought in all extracted items. When

agreement could not be attained, divergences in data

extraction were solved by a third researcher (D.C.) after

looking at the original publication. The data extracted from

each individual article was as follows: name of first author,

publication year, number of subjects in case and control

samples, gender, age, BMI, ethnicity, genotype and allele

distributions in case and control samples and OR (95 %

CI).

Two researchers (L.A.B. and B.M.S.) separately evalu-

ated the quality of all eligible studies using the Newcastle–

Ottawa Scale (NOS) for assessing quality of case–control

studies in meta-analysis [21]. The NOS score comprises

eight items divided into three dimensions, including

selection, comparability, and exposure. For each item, a

sequence of response options is supplied. A star scoring

system is used to permit a semi-quantitative evaluation of

article quality, such that the highest quality studies are

given a maximum of one star for each item, excluding the

comparability item, for which two stars can be assigned. As

a result, the total NOS score can vary between 0 and 9

stars.

Statistical analysis for meta-analysis

Genotype distributions in the control group were tested if

they deviate from those predicted by the HWE using v2

tests. Variant-disease associations were calculated using

OR (95 % CI) estimation based on allele contrast, additive,

recessive, dominant and co-dominant inheritance models

[22]. Heterogeneity was evaluated using a v2-based

Cochran’s Q statistic and inconsistency was calculated

using the I2 metric. Heterogeneity was considered signifi-

cant at P \ 0.10 for the Q statistic and I2 [ 50 % for the I2

metric. When a significant heterogeneity was observed, the

DerSimonian and Laird random effect model (REM) was

used to estimate OR (95 % CI) for each individual study

and for the pooled effect; when heterogeneity was not

observed, the fixed effect model (FEM) was used for these

calculations [23, 24].

Meta-regression and sensitivity analyses were per-

formed to recognize important studies with a considerable

impact on inter-study heterogeneity. The variables included

in meta-regression analyses were gender, age, ethnicity,

and BMI. Sensitivity analyses were done following strati-

fication of the studies by ethnicity, given that the UCP

polymorphisms might show variable frequencies across

ethnic groups.

Risk of publication bias was evaluated using funnel plot

graphics, analyzed both visually and using the Begg and

Egger statistic [25]. A significant publication bias was

considered when P \ 0.10. The Trim and Fills method was

used for adjusting for publication bias [26]. This method

assesses whether the publication bias is present and esti-

mates the effect when the biases are removed. All statis-

tical analyses were performed using Stata 11.0 software

(StataCorp, College Station, TX, USA).

Results

Case–control study in a T2DM population

Table 1 shows the genotype and allele distributions of the

UCP1 -3826A/G, UCP2 -866G/A, UCP2 Ala55Val,

UCP2 Ins/Del and UCP3 -55C/T variants in T2DM

patients broken down by the presence of obesity. The

genotype distributions of all polymorphisms were in

accordance with those predicted by the HWE in non-obese

patients (P [ 0.05) and were similar between obese and

non-obese samples (Table 1). This data did not change

following adjustment for age and gender (Table 1).

Moreover, the allele distributions of these variants did not

differ significantly between obese and non-obese T2DM

patients (Table 1). It is noteworthy that the frequencies of

these UCP polymorphisms also did not differ when

assuming different inheritance models (P [ 0.05).

Meta-analysis

Literature search and characteristics of eligible studies

Figure 1 is a flow diagram illustrating the strategy used

to identify and select articles for inclusion in this sys-

tematic review and meta-analysis. Three hundred-forty

three possible relevant articles were recovered by

searching Embase and PubMed repositories, and 256 of

them were excluded during the review of titles and

abstracts. Eighty-seven articles therefore appeared to be

eligible at this point and had their texts fully analyzed.

Nevertheless, following careful analyses of the texts,

other 40 articles were excluded because of missing data,

ineligible study design or since them genotyped other

UCP variants.

Thus, 48 articles satisfied the eligibility criteria and were

included in our meta-analyses: 47 that had been identified

during database searches [27–73] plus the case–control

study that we described above. Eleven of these articles

investigated the UCP1 -3826A/G polymorphism (2,488

cases/3,120 controls), 21 investigated the UCP2 -866G/A

polymorphism (6,852 cases/11,432 controls), 11 evaluated

the UCP2 Ala55Val polymorphism (1,792 cases/2,717

controls), 16 investigated the UCP2 Ins/Del polymorphism

(5,412 cases/5,057 controls), and 15 evaluated the UCP3

-55C/T polymorphism (4,578 cases/5,431 controls). Sup-

plementary Table 1 shows the genotype and allele

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distributions and OR (95 % CI) for the UCP polymor-

phisms in case and control groups from the different

studies analyzed.

Supplementary Table 2 lists the quality of each analyzed

article, evaluated using the NOS score. As already com-

mented, the highest quality articles were given 9 stars.

Most articles were classified as presenting good quality.

None of the articles were awarded with \6 stars, and

71.4 % of them received 8–9 stars. However, we did not

assess the quality of five studies because they were col-

lected from Qian et al. [74] and Liu et al. [75] meta-ana-

lysis and, therefore, we did not have access to the original

publications.

Quantitative synthesis

Table 2 depicts the pooled results for associations of the

UCP1-3 polymorphisms with obesity. Variant-obesity

associations were evaluated for allele contrast, additive,

recessive, dominant and co-dominant models of inheri-

tance. Figures 2 and 3 show pooled ORs for the associa-

tions between obesity and the UCP2 -866G/A, Ala55Val

and Ins/Del variants in European and Asian populations,

respectively, both assuming an allele contrast model. Fig-

ures 4 and 5 show the pooled OR for the associations

between UCP1 -3826A/G and UCP3 -55C/T polymor-

phisms and obesity in European and Asian populations,

respectively, also under an allele contrast model.

After stratification by ethnicity, it was not possible to

perform meta-analysis for the UCP1 -3826A/G poly-

morphism in Asians since there was only one study for this

polymorphism [53]. Despite of this, we included the indi-

vidual OR obtained from this study in the forest plot,

showing that this study did not find any significant asso-

ciation with obesity in Asians (Fig. 5). Our data was not

able to show any association between obesity and the

Table 1 Genotype and allele

distributions of UCP

polymorphisms in obese and

non-obese patients with type 2

diabetes

Data are presented as number of

carriers (%) or proportion of

sample. The control group was

composed by non-obese T2DM

patients and the case group was

composed by obese T2DM

patientsa P values were computed

using v2 tests to compare case

and control groupsb P values were computed

using logistic regression

analysis and are adjusted for age

and gender

UCP polymorphisms Obese subjects Non-obese subjects Unadjusted Pa Adjusted OR, 95 % CI/Pb

UCP1 -3826A/G n = 267 n = 454

A/A 132 (49.4) 212 (46.7) 0.529 1

A/G 103 (38.6) 194 (42.7) 0.847 (0.611–1.176)/0.321

G/G 32 (12.0) 48 (10.6) 1.178 (0.710–1.955)/0.526

A 0.687 0.681 0.839

G 0.313 0.319

UCP2 -866G/A n = 267 n = 456

G/G 95 (35.6) 159 (34.9) 0.906 1

G/A 128 (47.9) 216 (47.4) 1.010 (0.718–1.419)/0.956

A/A 44 (16.5) 81 (17.7) 0.949 (0.602–1.495)/0.822

G 0.595 0.586 0.751

A 0.405 0.414

UCP2 Ala55Val n = 269 n = 459

Ala/Ala 92 (34.2) 154 (33.6) 0.899 1

Ala/Val 128 (47.6) 215 (46.8) 1.006 (0.713–1.418)/0.973

Val/Val 49 (18.2) 90 (19.6) 0.941 (0.606–1.462)/0.787

Ala 0.580 0.570 0.745

Val 0.420 0.430

UCP2 Ins/Del n = 266 n = 458

Del/Del 141 (53.0) 215 (46.9) 0.277 1

Del/Ins 99 (37.0) 189 (41.3) 1.304 (0.773–2.200)/0.320

Ins/Ins 26 (10.0) 54 (11.8) 1.051 (0.615–1.797)/0.856

Del 0.716 0.676 0.121

Ins 0.284 0.324

UCP3 -55C/T n = 282 n = 483

C/C 192 (68.1) 328 (67.9) 0.992 1

C/T 80 (28.4) 137 (28.4) 0.965 (0.687–1.354)/0.835

T/T 10 (3.5) 18 (3.7) 0.962 (0.429–2.156)/0.925

C 0.823 0.821 0.985

T 0.177 0.179

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UCP1 -3826A/G polymorphism, independently of the

inheritance model assumed (Table 2).

The A allele of the UCP2 -866G/A polymorphism was

significantly associated with protection for obesity, but

only in Europeans assuming allele contrast (FEM OR 0.91,

95 % CI 0.85–0.97) or dominant (FEM OR 0.89, 95 % CI

0.82–0.97) inheritance models (Table 2). The UCP2 55 val

allele was associated with risk for obesity under allele

contrast (REM OR 1.18, 95 % CI 1.01–1.36) or recessive

(FEM OR 1.25, 95 % CI 1.04–1.51) inheritance models.

However, after stratification for ethnicity these associations

were maintained only in Asians (allele contrast: REM OR

1.30, 95 % CI 1.00–1.69; recessive: FEM OR 1.61, 95 %

CI 1.13–2.30).

In the overall population, the Ins allele of the UCP2 Ins/

Del polymorphism was associated with risk for obesity

under an allele contrast model (REM OR 1.12, 95 % CI

1.00–1.25). This allele was also associated with risk for

obesity in Europeans under a recessive model (FEM OR

1.19, 95 % CI 1.00–1.42).

The UCP3 -55C/T polymorphism was associated with

protection for obesity under a co-dominant inheritance

model (FEM OR 0.91, 95 % CI 0.83–0.99). This associa-

tion was confirmed in Europeans (FEM OR 0.88, 95 % CI

0.80–0.97) but not in Asians (FEM OR 1.12, 95 % CI

0.85–1.49).

To further investigate the significant heterogeneity

between studies presented in some analyses (I2 [ 50 %;

Table 2), the gender, age and BMI were included as

covariates in univariate and multivariate meta-regression

analyses performed for the five UCP polymorphisms under

allele contrast, additive, recessive, dominant and co-dom-

inant models of inheritance. However, none of these

covariates could individually or in combination explain the

heterogeneity observed (data not shown).

Sensitivity analyses were performed aiming to evaluate the

effect of each individual article on the meta-analysis data

acquired for the different inheritance models. This was carried

out by repeating the meta-analyses excluding a different study

at a time. These analyses demonstrated that only one study

[60] explained the heterogeneity identified in the meta-anal-

yses of the UCP2 Ala55Val variant (dominant and co-domi-

nant models) in the overall population. Moreover, another

study [34] explained the heterogeneity in the meta-analyses of

the UCP2 Ins/Del polymorphism (allele contrast and additive)

in the overall population. However, after exclusion of these

studies from the respective meta-analysis, the pooled OR

remained not significant.

Fig. 1 Flowchart illustrating

the search strategy used to

identify association studies of

UCP1-3 polymorphisms and

obesity for the meta-analysis

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Table 2 Pooled measures for associations between the UCP1 -3826A/G, UCP2 -866G/A, UCP2 Ala55Val, UCP2 Ins/Del and UCP3 -55C/T

polymorphisms and susceptibility to obesity

Inheritance model n studies n cases n controls I2(%) Pooled OR (95 % CI)

UCP1 -3826 A/G

Allele contrast overall 11 2,488 3,120 0.0 1.04 (0.95–1.13)

European 10 2,352 3,026 0.0 1.04 (0.95–1.13)

Additive overall 10 1,779 2,483 0.0 1.06 (0.83–1.36)

European 9 1,643 2,389 0.0 1.10 (0.85–1.41)

Recessive overall 10 1,779 2,483 0.0 1.06 (0.85–1.31)

European 9 1,643 2,389 0.0 1.09 (0.87–1.36)

Dominant overall 10 1,779 2,483 0.0 1.00 (0.88–1.14)

European 9 1,643 2,389 0.0 0.99 (0.87–1.13)

Co-dominant overall 10 1,779 2,483 0.0 0.98 (0.86–1.12)

European 9 1,643 2,389 0.0 0.96 (0.84–1.10)

UCP2 -866 G/A


Asian 10 1,981 4,109 58.7 1.12 (0.96–1.30)

European 11 6,121 7,323 17.1 0.91 (0.85–0.97)


Asian 10 1,981 4,109 56.5 1.20 (0.87–1.66)

European 10 5,412 6,686 20.5 0.88 (0.76–1.03)


Asian 10 1,981 4,109 45.8 1.16 (0.91–1.49)

European 10 5,412 6,686 42.8 0.99 (0.83–1.17)


Asian 10 1,981 4,109 52.4 1.14 (0.92–1.40)

European 10 5,412 6,686 22.9 0.89 (0.82–0.97)


Asian 10 1,981 4,109 98.1 1.39 (0.56–3.49)

European 10 5,412 6,686 35.3 0.90 (0.81–1.01)

UCP2 I/D


European 13 4,659 4,018 51.7 1.08 (0.98–1.21)

Asian 2 716 1,010 91.0 1.60 (0.70–3.67)


European 11 3,466 3,268 54.4 1.14 (0.85–1.52)

Asian 2 716 1,010 84.9 2.69 (0.51–14.17)


European 11 3,466 3,268 40.3 1.19 (1.00–1.42)

Asian 2 716 1,010 83.1 2.50 (0.53–11.71)


European 11 3,466 3,268 70.4 1.03 (0.85–1.24)

Asian 2 716 1,010 83.4 1.46 (0.73–2.90)


European 11 3,466 3,268 0.0 0.94 (0.85–1.04)

Asian 2 716 1,010 0.0 1.06 (0.84–1.33)

UCP2 Ala55 val


Asian 6 952 1,491 72.8 1.30 (1.00–1.69)

European 4 722 785 0.0 1.03 (0.90–1.18)


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Significant publication biases were detected in meta-

analyses of the UCP1 -3826A/G polymorphism (dominant

and co-dominant models). Nevertheless, after trim and fill

analyses the pooled OR did not change significantly; con-

sequently, the adjusted effect was essentially similar to the

original effect. This indicates that the number of missing

studies needed to reverse the bias is smaller than the

number of missing studies needed to nullify the effect. No

significant publication bias was detected in any of the other

meta-analyses performed (Fig. 6), which indicates that our

results are trustworthy.

Discussion

Uncoupling protein 1, UCP2 and UCP3 are candidate

genes for obesity and consequently T2DM because they

decrease mitochondrial membrane potential and mediate

proton leak [5, 8, 9]. Thus, polymorphisms decreasing the

activity or expression of these UCPs might decrease

energy expenditure by increasing coupling of oxidative

phosphorylation, therefore influencing susceptibility for

obesity and obesity-related disorders. This explains why

possible associations of the UCP1 -3826A/G, UCP2

-866G/A, UCP2 Ala55Val, UCP2 Ins/Del and UCP3

-55C/T polymorphisms with obesity have been widely

investigated in different populations; nevertheless, the

results for the association with obesity are still incon-

clusive (reviewed in [5, 11, 28, 74] ). Thus, aiming to

achieve a more definitive conclusion regarding the asso-

ciations of UCP polymorphisms with obesity, we carried

out a case–control study of Brazilian Caucasian subjects

with T2DM and meta-analyses of genetic association

studies on the subject.

Table 2 continued

Inheritance model n studies n cases n controls I2(%) Pooled OR (95 % CI)

Asian 5 476 957 55.9 1.53 (0.89–2.60)

European 3 852 1,085 0.0 1.04 (0.79–1.37)


Asian 6 980 1,191 18.2 1.61 (1.13–2.30)

European 3 694 1,085 0.0 1.06 (0.85–1.33)


Asian 6 908 1,071 66.2 1.19 (0.89–1.42)

European 3 694 1,085 0.0 1.01 (0.81–1.25)


Asian 6 908 1,071 61.8 0.93 (0.66–1.32)

European 3 694 1,085 0.0 0.96 (0.80–1.17)

UCP3 -55C/T


Asian 3 542 513 58.1 1.19 (0.78–1.80)

European 12 4,036 4,566 70.9 0.98 (0.85–1.14)


Asian 2 505 483 39.3 0.89 (0.50–1.58)

European 11 3,356 4,562 0.0 1.13 (0.92–1.40)


Asian 3 606 664 57.1 0.91 (0.43–1.93)

European 11 3,292 4,411 0.0 1.19 (0.97–1.47)


Asian 3 542 513 25.3 1.11 (0.85–1.46)

European 11 3,292 4,411 7.9 0.92 (0.84–1.01)


Asian 3 542 513 0.0 1.12 (0.85–1.49)

European 11 3,291 4,411 0.0 0.88 (0.80–0.97)

Where significant heterogeneity was detected (I2 [ 50 %), the DerSimonian and Laird random effect model (REM) was used to calculate OR

(95 % CI) for each individual study and for the pooled effect; where heterogeneity was not significant, the fixed effect model (FEM) was used for

this calculation. Stratification analysis was only performed for Europeans in UCP1 -3826A/G polymorphism, since only one study of Asian

ethnicity was identified for the UCP1 -3826A/G

Mol Biol Rep

123

Fig. 2 Forest plots showing

individual and pooled ORs

(95 % CI) for the association

between the UCP2 -866G/A,

Ala55Val and Ins/Del

polymorphisms and obesity in

European populations under an

allele contrast inheritance

model. The areas of the squares

reflect the weight of each

individual study and the

diamonds illustrate the random-

effects summary ORs (95 % CI)




between the UCP2 -866G/A,

Ala55Val and Ins/Del


Asian populations under an







Mol Biol Rep

123

Our case–control study indicated that genotype and allele

distributions of UCP1 -3826A/G, UCP2 -866G/A, UCP2

Ala55Val, UCP2 Ins/Del and UCP3 -55C/T polymorphisms

did not differ significantly between obese and non-obese

T2DM patients, suggesting that these variants are not impor-

tant risk factors for obesity in T2DM subjects. Certain factors

could have interfered with the results of our case–control

study. First, both obese and non-obese groups were T2DM

patients, so we cannot extrapolate our findings to healthy

subjects without diabetes from the same population. Second,

because only white subjects were included in the study, we can

not exclude the possibility of stratification bias. Therefore, our

case–control data should be read with prudence because we

did not evaluate the ancestral genetic background of our

samples, which would be the best method to exclude stratifi-

cation bias due to ethnic admixture. Third, we can not fully

rule out the occurrence of type II error when investigating

associations between the five UCP variants and obesity due to

the lack of enough statistical power. Despite these limitations,

we believe that is unlikely that these variants might play an

important role in susceptibility for obesity in white T2DM

patients from our population since the frequencies of these

polymorphisms are quite similar between obese and non-

obese T2DM patients.




between the UCP1 -3826A/G

and UCP3 -55C/T


European populations under an










between the UCP1 -3826A/G

and UCP3 -55C/T


Asian populations under an







Mol Biol Rep

123

Meta-analysis has been regarded as a powerful method

for pooling the data from different studies because it could

overcome the problem of small sample sizes as well as

insufficient statistical power of genetic association studies

for common diseases [20]. Therefore, to better investigate

the associations of the UCP1 -3826A/G, UCP2 -866G/A,

Ala55Val and Ins/Del and UCP3 -55C/T polymorphisms

with susceptibility for obesity, we also performed meta-

analyses of 47 published studies from different populations

plus the results from the present case–control study. Meta-

analysis results indicated that the UCP1 -3826A/G poly-

morphism is not associated with obesity neither in Asians

nor in Europeans. In Europeans, UCP2 -866A allele and

UCP3 -55C/T genotype were associated with protection

for obesity. On the other hand, the UCP2 55Val allele was

associated with risk for obesity in Asians, while the UCP2

Ins allele was marginally associated with risk for obesity.

The associations of the UCP2 -866G/A and UCP3 -55C/

T polymorphisms with protection against obesity in Euro-

peans and the association of the UCP2 Ala55Val with

obesity risk in Asians could be explained partially by dif-

ferences in lifestyle and body weight distributions between

Asian and Caucasian populations as well as by differences

in the genotype distributions of the investigated variants

across ethnicities. It is suggested that the effects of genetic

variants on obesity might be changed by nutritional char-

acteristic of the population [76]. Thus, it is possible that

variable diet patterns between European and Asian

populations might influence the effect of UCP polymor-

phisms on obesity.

Computational analyzes demonstrated that the UCP2

-866G/A polymorphism is involved in putative binding

sites for specific transcription factors, such as PAX6

(paired box gene 6) and HIF-1a (hypoxia-inducible factor-

1a) [33] and, thus, can be associated with an important

functional effect. Accordingly, the -866A allele has been

shown to increase UCP2 activity in transfected INS-1E

cells derived from rat beta cells [77]. Results in human

tissues are more conflicting, reporting either increased or

decreased UCP2 mRNA contents associated with the

-866A allele (reviewed in [6] ). In differentiated adipo-

cytes, the A allele has a 22 % more effective transcrip-

tional activity [33]; therefore, the present association of the

UCP2 -866A allele with protection for obesity in Euro-

peans appears to be biologically plausible since an

increased UCP2 gene expression in adipocytes would be

associated with increased energy expenditure.

The UCP2 Ala55Val variant leads to a conservative

amino acid change at position 55 of exon 4 and, until the

present date, there had been no proof that this polymor-

phism causes a functional change in the respective protein

[6, 11]. Therefore, it might be possible that this variant is

not a true disease-causing polymorphism, but could only be

in linkage disequilibrium with a functional polymorphism.

Considering that the Ala55Val polymorphism is in strong

linkage disequilibrium with the UCP2 -866G/A

Fig. 6 Funnel plot for contrast allele model for UCP polymorphisms

Mol Biol Rep

123

polymorphism (|D0| = 0.991) [18], which has a known

effect on UCP2 expression in a number of tissues [6, 11],

one could suggest that the -866G/A polymorphism should

be the candidate for the functional variant in the UCP2

gene. Nevertheless, our meta-analysis data showed a dif-

ferential association of the -866G/A and Ala55Val poly-

morphisms with obesity according to ethnicity: the -866A

allele was associated with protection against obesity in

Europeans whereas the 55Val allele was associated with

risk to obesity in Asians. This suggests that, at least in

Asians, the -866G/A polymorphism does not seem to be

the functional polymorphism explaining the association

between the Ala55Val polymorphism and obesity. Some

other unknown UCP2 functional variant in linkage dis-

equilibrium with the Ala55Val polymorphism might be

responsible for the association observed by us.

The biological significance of the UCP2 Ins/Del poly-

morphism is not well known. It is located in the 3’UTR

region of the gene, only 158 pb from the transcription stop

codon, and it could be functional due to a possible

involvement in mRNA processing or in the stability of the

transcript [73]. A lower stability of the transcript could lead

to a lower rate of UCP2 protein translation. Hypothetically,

any reduction in UCP2 protein could reduce the body

ability to remove excess calories through thermogenesis

and, consequently, predispose to obesity. In agreement

with this hypothesis, Ins allele carriers have been reported

to have higher BMI in different populations (reviewed in

[11] ). These data are in agreement with the association

between UCP2 Ins allele and risk for obesity reported by

us.

It is feasible that the UCP3 -55C/T polymorphism is

functional because it is located at 6 bp from the promoter

TATA box and 4 bp downstream of a putative PPAR

responsive region and, thus, could change the PPAR

responsiveness of the UCP3 gene [78, 79]. Hence, UCP3

gene could be one of the PPAR-c targets involved in the

regulation of lipid metabolism and sensitivity to insulin

[80]. In Pima Indians, subjects carrying the -55T allele

showed increased UCP3 mRNA expression in skeletal

muscle when compared with subjects carrying the C/C

genotype [55]. Moreover, decreased UCP3 expression has

been associated with increased BMI in Pima Indians [81].

In the present study, we showed that the heterozygous

genotype of the UCP3 -55C/T polymorphism was asso-

ciated with protection against obesity (co-dominant

model), which is an intriguing result, and needs to be

confirmed in additional studies.

It is worth noting that three previous meta-analyses

investigated one or more of the UCP polymorphisms

included in our meta-analysis regarding their associations

with obesity. Qian et al. [74] included in the meta-analysis

the UCP2 -866G/A (12 studies), UCP2 Ala55Val (9

studies) and UCP3 -55C/T (8 studies) polymorphisms;

however, the number of studies for each polymorphism

was smaller in their meta-analysis than ours (21 for the

-866G/A, 10 for the Ala55Val and 15 for the -55C/T in

our study). They did not analyze the UCP1 -3826A/G and

UCP2 Ins/Del polymorphisms. They showed that the

UCP2 -866G/A polymorphism was associated with

obesity in Europeans, which is in agreement with our data.

However, they were not able to find any association of the

UCP2 Ala55Val and UCP3 -55C/T polymorphisms with

obesity, possible due to a smaller sample size analyzed by

them. Other two meta-analyses [28, 75] analyzed only the

association between the UCP2 -866G/A polymorphism

and obesity. They also analyzed a smaller number of

studies than ours; nevertheless, in the same way as in the

present study, they showed an association between the

-866A allele and protection against obesity in Europeans.

The results of the present meta-analysis should be

interpreted within the context of a few limitations. First,

meta-analysis is susceptible to publication bias, and even

though we tried to trace unpublished studies, we can not

rule out the possibility that small negative studies were

overlooked. Second, because of the difficulty in retrieving

the full texts of studies published in different languages, we

only analyzed those articles wrote in English or Spanish.

Third, inter-studies heterogeneity is common in meta-

analysis for genetic association studies [82], and this can be

a significant problem when interpreting their results. Our

meta-analysis showed significant inter-study heterogeneity

for most of the UCP polymorphisms analyzed. To evaluate

this problem more detailed, meta-regression analyses were

done and demonstrated that age, ethnicity and gender did

not explain the inter-study heterogeneity. The heterogene-

ity found might be due to variations in sample selection,

genotyping techniques or gene-environment interactions

and, without more information on the metabolic and clin-

ical features of the articles analyzed, we can not rule out

the possibility that this heterogeneity might reduce our

power to detect true associations. ‘‘Leave one out’’ sensi-

tivity analyses showed that after excluding Wang et al. [60]

from the UCP2 Ala55Val analysis and Evans et al. [34]

from the UCP2 Ins/Del analysis, the heterogeneity for

these analyses decreased significantly. However, despite

the exclusion of these studies, the pooled OR in these meta-

analyses did not significantly change. Fourth, we also can

not exclude the occurrence of type II error when investi-

gating the associations of the UCP polymorphisms and

obesity after stratification by ethnicity. For the total sam-

ple, we had 80 % power (a = 0.05) to detect even modest

ORs (1.10–1.20) for almost all analyzed polymorphisms

under the allele contrast model, which indicates that our

data are reliable. Nevertheless, after stratification by eth-

nicity, we had 80 % power to detect modest OR in

Mol Biol Rep

123

Europeans (1.12–1.25) but not in Asians, except for the

UCP2 -866G/A polymorphism.

In conclusion, our results indicate that the UCP1

-3826A/G polymorphism is not an important risk factor

for obesity. However, our results suggest that the UCP2

-866G/A and UCP3 -55C/T are associated with protec-

tion against obesity in Europeans while the UCP2 Ala55-

Val and UCP2 Ins/Del polymorphisms are associated with

susceptibility to obesity in Asians and Europeans, respec-

tively. Since small sample sizes were obtained for some of

the analyses performed in Asians, further additional studies

with larger samples are necessary to elucidate the effects

possibly played by UCP polymorphisms in the pathogen-

esis of obesity in this ethnicity.

Acknowledgments The authors thank Dra. Caroline Kaercher

Kramer for her support with statistical analyses. This study was

partially supported by grants from the Fundacao de Amparo a Pes-

quisa do Estado do Rio Grande do Sul (FAPERGS), the Conselho

Nacional de Desenvolvimento Cientıfico e Tecnologico (CNPq) and

the Fundo de Incentivo a Pesquisa e Eventos (FIPE) at the Hospital de

Clınicas de Porto Alegre. The funders had no role in study design,

data collection and analysis, decision to publish or preparation of the

manuscript.

Conflict of interest There are no conflicts of interest.

References

1. Vimaleswaran KS, Loos RJ (2010) Progress in the genetics of

common obesity and type 2 diabetes. Expert Rev Mol Med 12:e7

2. Flegal KM, Graubard BI, Williamson DF, Gail MH (2005)

Excess deaths associated with underweight, overweight, and

obesity. JAMA 293(15):1861–1867

3. Narayan KM, Boyle JP, Thompson TJ, Sorensen SW, Williamson

DF (2003) Lifetime risk for diabetes mellitus in the United States.

JAMA 290(14):1884–1890

4. Hill JO, Wyatt HR, Reed GW, Peters JC (2003) Obesity and the

environment: where do we go from here? Science 299(5608):

853–855

5. Brondani LA, Assmann TS, Duarte GC, Gross JL, Canani LH,

Crispim D (2012) The role of the uncoupling protein 1 (UCP1) on

the development of obesity and type 2 diabetes mellitus. Arq Bras

Endocrinol Metabol 56(4):215–225

6. Souza BM, Assmann TS, Kliemann LM, Gross JL, Canani LH,

Crispim D (2011) The role of uncoupling protein 2 (UCP2) on the

development of type 2 diabetes mellitus and its chronic compli-

cations. Arq Bras Endocrinol Metabol 55(4):239–248

7. Fisler JS, Warden CH (2006) Uncoupling proteins, dietary fat and

the metabolic syndrome. Nutr Metab (Lond) 3:38

8. Azzu V, Brand MD (2010) The on-off switches of the mitochondrial

uncoupling proteins. Trends Biochem Sci 35(5):298–307

9. Ricquier D (2005) Respiration uncoupling and metabolism in the

control of energy expenditure. Proc Nutr Soc 64(1):47–52

10. Brondani LA, de Souza BM, Duarte GC, Kliemann LM, Esteves

JF, Marcon AS, Gross JL, Canani LH, Crispim D (2012) The

UCP1 -3826A/G polymorphism is associated with diabetic ret-

inopathy and increased UCP1 and MnSOD2 gene expression in

human retina. Invest Ophthalmol Vis Sci 53(12):7449–7457

11. Jia JJ, Zhang X, Ge CR, Jois M (2009) The polymorphisms of

UCP2 and UCP3 genes associated with fat metabolism, obesity

and diabetes. Obes Rev 10(5):519–526

12. Xu K, Zhang M, Cui D, Fu Y, Qian L, Gu R, Wang M, Shen C,

Yu R, Yang T (2011) UCP2 -866G/A and Ala55Val, and UCP3

-55C/T polymorphisms in association with type 2 diabetes sus-

ceptibility: a meta-analysis study. Diabetologia 54(9):2315–2324

13. de Souza BM, Brondani LA, Boucas AP, Sortica DA, Kramer

CK, Canani LH, Leitao CB, Crispim D (2013) Associations

between UCP1 -3826A/G, UCP2 -866G/A, Ala55Val and Ins/

Del, and UCP3 -55C/T polymorphisms and susceptibility to type

2 diabetes mellitus: case-control study and meta-analysis. PLoS

ONE 8(1):e54259

14. Canani LH, Capp C, Ng DP, Choo SG, Maia AL, Nabinger GB,

Santos K, Crispim D, Roisemberg I, Krolewski AS et al (2005)

The fatty acid-binding protein-2 A54T polymorphism is associ-

ated with renal disease in patients with type 2 diabetes. Diabetes

54(11):3326–3330

15. American Diabetes Association (2013) Diagnosis and classifica-

tion of diabetes mellitus. Diabetes Care 36(Suppl 1):S67–S74

16. Sasahara M, Nishi M, Kawashima H, Ueda K, Sakagashira S,

Furuta H, Matsumoto E, Hanabusa T, Sasaki H, Nanjo K (2004)

Uncoupling protein 2 promoter polymorphism -866G/A affects

its expression in beta-cells and modulates clinical profiles of

Japanese type 2 diabetic patients. Diabetes 53(2):482–485

17. Walder K, Norman RA, Hanson RL, Schrauwen P, Neverova M,

Jenkinson CP, Easlick J, Warden CH, Pecqueur C, Raimbault S

et al (1998) Association between uncoupling protein polymor-

phisms (UCP2-UCP3) and energy metabolism/obesity in Pima

indians. Hum Mol Genet 7(9):1431–1435

18. Crispim D, Fagundes N, dos Santos K, Rheinheimer J, Boucas A,

de Souza B, Macedo G, Leiria L, Gross J, Canani L (2010)

Polymorphisms of the UCP2 gene are associated with prolifera-

tive diabetic retinopathy in patients with diabetes mellitus. Clin

Endocrinol (Oxf) 72(5):612–619

19. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P (2009) Pre-

ferred reporting items for systematic reviews and meta-analyses: the

PRISMA statement. Ann Intern Med 151(4):264–269 W264

20. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD,

Rennie D, Moher D, Becker BJ, Sipe TA, Thacker SB (2000)

Meta-analysis of observational studies in epidemiology: a pro-

posal for reporting. Meta-analysis of observational studies in

epidemiology (MOOSE) group. JAMA 283(15):2008–2012

21. Stang A (2010) Critical evaluation of the Newcastle–Ottawa scale

for the assessment of the quality of nonrandomized studies in

meta-analyses. Eur J Epidemiol 25(9):603–605

22. Minelli C, Thompson JR, Abrams KR, Thakkinstian A, Attia J

(2005) The choice of a genetic model in the meta-analysis of

molecular association studies. Int J Epidemiol 34(6):1319–1328

23. Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a

meta-analysis. Stat Med 21(11):1539–1558

24. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Mea-

suring inconsistency in meta-analyses. BMJ 327(7414):557–560

25. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in

meta-analysis detected by a simple, graphical test. BMJ

315(7109):629–634

26. Duval S (2000) Tweedie R (2000) Trim and fill: a simple funnel-

plot-based method of testing and adjusting for publication bias in

meta-analysis. Biometrics 56(2):455–463

27. Alonso A, Martı A, Corbalan MS, Martınez-Gonzalez MA, Forga

L, Martınez JA (2005) Association of UCP3 gene -55C [ T

polymorphism and obesity in a Spanish population. Ann Nutr

Metab 49(3):183–188

28. Andersen G, Dalgaard LT, Justesen JM, Anthonsen S, Nielsen T,

Thørner LW, Witte D, Jørgensen T, Clausen JO, Lauritzen T et al

(2013) The frequent UCP2 -866G [ A polymorphism protects

Mol Biol Rep

123

against insulin resistance and is associated with obesity: a study

of obesity and related metabolic traits among 17 636 Danes. Int J

Obes (Lond) 37(2):175–181

29. Berentzen T, Dalgaard LT, Petersen L, Pedersen O, Sørensen TI

(2005) Interactions between physical activity and variants of the

genes encoding uncoupling proteins -2 and -3 in relation to body

weight changes during a 10-y follow-up. Int J Obes (Lond)

29(1):93–99

30. Dalgaard LT, Andersen G, Larsen LH, Sørensen TI, Andersen T,

Drivsholm T, Borch-Johnsen K, Fleckner J, Hansen T, Din N et al

(2003) Mutational analysis of the UCP2 core promoter and

relationships of variants with obesity. Obes Res

11(11):1420–1427

31. Dalgaard LT, Sørensen TI, Andersen T, Hansen T, Pedersen O

(1999) An untranslated insertion variant in the uncoupling protein

2 gene is not related to body mass index and changes in body

weight during a 26-year follow-up in Danish Caucasian men.

Diabetologia 42(12):1413–1416

32. Dalgaard LT, Sørensen TI, Drivsholm T, Borch-Johnsen K,

Andersen T, Hansen T, Pedersen O (2001) A prevalent poly-

morphism in the promoter of the UCP3 gene and its relationship

to body mass index and long term body weight change in the

Danish population. J Clin Endocrinol Metab 86(3):1398–1402

33. Esterbauer H, Schneitler C, Oberkofler H, Ebenbichler C, Paul-

weber B, Sandhofer F, Ladurner G, Hell E, Strosberg AD, Patsch

JR et al (2001) A common polymorphism in the promoter of

UCP2 is associated with decreased risk of obesity in middle-aged

humans. Nat Genet 28(2):178–183

34. Evans D, Minouchehr S, Hagemann G, Mann WA, Wendt D,

Wolf A, Beisiegel U (2000) Frequency of and interaction

between polymorphisms in the beta3-adrenergic receptor and in

uncoupling proteins 1 and 2 and obesity in Germans. Int J Obes

Relat Metab Disord 24(10):1239–1245

35. Forga L, Corbalan M, Marti A, Fuentes C, Martınez-Gonzalez

MA, Martınez A (2003) Influence of the polymorphism 03826 A

– [ G in the UCP1 gene on the components of metabolic syn-

drome. An Sist Sanit Navar 26(2):231–236

36. Gagnon J, Lago F, Chagnon YC, Perusse L, Naslund I, Lissner L,

Sjostrom L, Bouchard C (1998) DNA polymorphism in the

uncoupling protein 1 (UCP1) gene has no effect on obesity

related phenotypes in the Swedish Obese Subjects cohorts. Int J

Obes Relat Metab Disord 22(6):500–505

37. Heidari J, Akrami SM, Heshmat R, Amiri P, Fakhrzadeh H, Pa-

jouhi M (2010) Association study of the -866G/A UCP2 gene

promoter polymorphism with type 2 diabetes and obesity in a

Tehran population: a case control study. Arch Iran Med

13(5):384–390

38. Herrmann SM, Wang JG, Staessen JA, Kertmen E, Schmidt-

Petersen K, Zidek W, Paul M, Brand E (2003) Uncoupling pro-

tein 1 and 3 polymorphisms are associated with waist-to-hip ratio.

J Mol Med 81(5):327–332

39. Kosuge K, Soma M, Nakayama T, Aoi N, Sato M, Haketa A,

Uwabo J, Izumi Y, Matsumoto K (2008) Human uncoupling

protein 2 and 3 genes are associated with obesity in Japanese.

Endocrine 34(1–3):87–95

40. Kring SI, Larsen LH, Holst C, Toubro S, Hansen T, Astrup A,

Pedersen O, Sørensen TI (2008) Genotype-phenotype associa-

tions in obesity dependent on definition of the obesity phenotype.

Obes Facts 1(3):138–145

41. Labruna G, Pasanisi F, Nardelli C, Tarantino G, Vitale DF,

Bracale R, Finelli C, Genua MP, Contaldo F, Sacchetti L (2009)

UCP1 -3826 AG ? GG genotypes, adiponectin, and leptin/

adiponectin ratio in severe obesity. J Endocrinol Invest

32(6):525–529

42. Liu X, Zhang B, Shen Y, Li J, Zhao N, Ma L, Du Q, Liu L, Zhao

J (2012) Wang X (2012) A 45-bp insertion/deletion

polymorphism in uncoupling protein 2 is not associated with

obesity in a Chinese population. Biochem Genet

50(9–10):784–796

43. Maestrini S, Podesta F, Di Blasio AM, Savia G, Brunani A,

Tagliaferri A, Mencarelli M, Chiodini I, Liuzzi A (2003) Lack of

association between UCP2 gene polymorphisms and obesity

phenotype in Italian Caucasians. J Endocrinol Invest

26(10):985–990

44. Mancini FP, Sabatino L, Colantuoni V, Pasanisi F, Finelli C,

Contaldo F, Masulli M, Riccardi G, Vaccaro O (2003) Variants of

uncoupling protein-2 gene and obesity: interaction with peroxi-

some proliferator-activated receptorgamma2. Clin Endocrinol

(Oxf) 59(6):817–822

45. Meirhaeghe A, Amouyel P, Helbecque N, Cottel D, Otabe S,

Froguel P, Vasseur F (2000) An uncoupling protein 3 gene

polymorphism associated with a lower risk of developing Type II

diabetes and with atherogenic lipid profile in a French cohort.

Diabetologia 43(11):1424–1428

46. Mottagui-Tabar S, Hoffstedt J, Brookes AJ, Jiao H, Arner P,

Dahlman I (2008) Association of ADRB1 and UCP3 gene

polymorphisms with insulin sensitivity but not obesity. Horm Res

69(1):31–36

47. Nieters A, Becker N, Linseisen J (2002) Polymorphisms in can-

didate obesity genes and their interaction with dietary intake of n-

6 polyunsaturated fatty acids affect obesity risk in a sub-sample

of the EPIC-Heidelberg cohort. Eur J Nutr 41(5):210–221

48. Ochoa MC, Santos JL, Azcona C, Moreno-Aliaga MJ, Martınez-

Gonzalez MA, Martınez JA, Marti A, Members G (2007) Asso-

ciation between obesity and insulin resistance with UCP2-UCP3

gene variants in Spanish children and adolescents. Mol Genet

Metab 92(4):351–358

49. Oktavianthi S, Trimarsanto H, Febinia CA, Suastika K, Saraswati

MR, Dwipayana P, Arindrarto W, Sudoyo H, Malik SG (2012)

Uncoupling protein 2 gene polymorphisms are associated with

obesity. Cardiovasc Diabetol 11:41

50. Otabe S, Clement K, Dina C, Pelloux V, Guy-Grand B, Froguel

P, Vasseur F (2000) A genetic variation in the 5’ flanking region

of the UCP3 gene is associated with body mass index in humans

in interaction with physical activity. Diabetologia 43(2):245–249

51. Papazoglou D, Papathanasiou P, Papanas N, Papatheodorou K,

Chatziangeli E, Nikitidis I, Kotsiou S, Maltezos E (2012)

Uncoupling protein-2 45-base pair insertion/deletion polymor-

phism: is there an association with severe obesity and weight loss

in morbidly obese subjects? Metab Syndr Relat Disord

10(4):307–311

52. Pinelli M, Giacchetti M, Acquaviva F, Cocozza S, Donnarumma

G, Lapice E, Riccardi G, Romano G, Vaccaro O, Monticelli A

(2006) Beta2-adrenergic receptor and UCP3 variants modulate

the relationship between age and type 2 diabetes mellitus. BMC

Med Genet 7:85

53. Proenza AM, Poissonnet CM, Ozata M, Ozen S, Guran S, Palou

A, Strosberg AD (2000) Association of sets of alleles of genes

encoding beta3-adrenoreceptor, uncoupling protein 1 and lipo-

protein lipase with increased risk of metabolic complications in

obesity. Int J Obes Relat Metab Disord 24(1):93–100

54. Ramis JM, Gonzalez-Sanchez JL, Proenza AM, Martınez-Larrad

MT, Fernandez-Perez C, Palou A, Serrano-Rıos M (2004) The

Arg64 allele of the beta 3-adrenoceptor gene but not the -3826G

allele of the uncoupling protein 1 gene is associated with

increased leptin levels in the Spanish population. Metabolism

53(11):1411–1416

55. Schrauwen P, Xia J, Walder K, Snitker S, Ravussin E (1999) A

novel polymorphism in the proximal UCP3 promoter region:

effect on skeletal muscle UCP3 mRNA expression and obesity in

male non-diabetic Pima Indians. Int J Obes Relat Metab Disord

23(12):1242–1245

Mol Biol Rep

123

56. Schauble N, Geller F, Siegfried W, Goldschmidt H, Remschmidt

H, Hinney A, Hebebrand J (2003) No evidence for involvement

of the promoter polymorphism -866 G/A of the UCP2 gene in

childhood-onset obesity in humans. Exp Clin Endocrinol Diabe-

tes 111(2):73–76

57. Srivastava N, Prakash J, Lakhan R, Agarwal CG, Pant DC, Mittal

B (2010) A common polymorphism in the promoter of UCP2 is

associated with obesity and hyperinsulenemia in northern Indi-

ans. Mol Cell Biochem 337(1–2):293–298

58. Urhammer SA, Dalgaard LT, Sørensen TI, Møller AM, Andersen

T, Tybjaerg-Hansen A, Hansen T, Clausen JO, Vestergaard H,

Pedersen O (1997) Mutational analysis of the coding region of the

uncoupling protein 2 gene in obese NIDDM patients: impact of a

common amino acid polymorphism on juvenile and maturity

onset forms of obesity and insulin resistance. Diabetologia

40(10):1227–1230

59. Valve R, Heikkinen S, Rissanen A, Laakso M, Uusitupa M

(1998) Synergistic effect of polymorphisms in uncoupling protein

1 and beta3-adrenergic receptor genes on basal metabolic rate in

obese Finns. Diabetologia 41(3):357–361

60. Wang TN, Huang MC, Lin HL, Hsiang CH, Ko AM, Chang WT,

Ko YC (2007) UCP2 A55 V variant is associated with obesity

and related phenotypes in an aboriginal community in Taiwan. Int

J Obes (Lond) 31(11):1746–1752

61. Xu R (2005) Association between uncoupling protein 2 gene

polymorphisms and obesity in Japanese. Thesis, WanFang

Database

62. Shen X (2007) Relationship of uncoupling protein 2 gene -866

G/A polymorphism with metabolic syndrome in Chinese Han.

Thesis, WanFang Database

63. Otabe S, Clement K, Rich N, Warden C, Pecqueur C, Neverova

M, Raimbault S, Guy-Grand B, Basdevant A, Ricquier D et al

(1998) Mutation screening of the human UCP 2 gene in normo-

glycemic and NIDDM morbidly obese patients: lack of associa-

tion between new UCP 2 polymorphisms and obesity in French

Caucasians. Diabetes 47(5):840–842

64. Jiffri EH (2012) Association of the UCP2 45 bp insertion/dele-

tion polymorphism with diabetes type 2 and obesity in Saudi

population. Egypt J Med Hum Genet 13:257–262

65. Xiu L, Weng J, Sui Y, Wang J, Yan J (2004) Common variants in

beta 3-adrenergic receptor and uncoupling protein-2 genes are

associated with type 2 diabetes and obesity. Zhonghua Yi Xue Za

Zhi 84:375–379

66. Yang M, Jia W, Fang Q, Tang J, Bao Y (2004) An association of

resting energy expenditure, total and regional body fat with the

polymorphism of UCP2 gene. Shanghai Med J 27:237–240

67. Zhou H, Guo D, Li Y, Guo M, Tian Y, Zhang L (2011) The

association of uncoupling protein 2 -866 G/A (UCP2 -866 G/A)

polymorphism with obesity. Mol Cardiol China 11:158–163

68. Csernus K, Pauler G, Erhardt E, Lanyi E (2013) Molnar D (2013)

Uncoupling protein-2 gene polymorphisms are associated with

obesity in Hungarian children. Acta Paediatr 102(5):e200–e204

69. Gioli-Pereira L, Santos PC, Sugaya LS, Ferreira NE, Krieger JE,

Pereira AC, Hueb WA (2013) Association between UCP2 A55 V

polymorphism and risk of cardiovascular events in patients with

multi-vessel coronary arterial disease. BMC Med Genet 14:40

70. Oguzkan-Balci S, Col-Araz N, Nacak M, Araz M, Sabanci H,

Balat A, Pehlivan S (2013) Mitochondrial uncoupling protein 2

(UCP2) gene polymorphisms are associated with childhood

obesity and related metabolic disorders. J Pediatr Endocrinol

Metab 26(3–4):277–283

71. Kubota T, Mori H, Tamori Y, Okazawa H, Fukuda T, Miki M, Ito

C, Fleury C, Bouillaud F, Kasuga M (1998) Molecular screening

of uncoupling protein 2 gene in patients with noninsulin-depen-

dent diabetes mellitus or obesity. J Clin Endocrinol Metab

83(8):2800–2804

72. Gu G (2005) Relantionships between the functional polymor-

phism in the promoter of uncoupling protein 2 gene and obesity

and type 2 diabetes. Thesis, (wwwresearch-degreethesis)

73. Marti A, Corbalan MS, Forga LL, Martinez-Gonzalez MA,

Martinez JA (2004) Higher obesity risk associated with the exon-

8 insertion of the UCP2 gene in a Spanish case-control study.

Nutrition 20(6):498–501

74. Qian L, Xu K, Xu X, Gu R, Liu X, Shan S, Yang T (2013) UCP2

-866G/A, Ala55Val and UCP3 -55C/T polymorphisms in

association with obesity susceptibility—a meta-analysis study.

PLoS ONE 8(4):e58939

75. Liu L, Zhao X, Kang S, Zhang D (2013) An association between

-866G/A polymorphism in the promoter of UCP2 and obesity: a

meta-analysis. Gene 514(1):41–47

76. Luan JA, Wong MY, Day NE, Wareham NJ (2001) Sample size

determination for studies of gene-environment interaction. Int J

Epidemiol 30(5):1035–1040

77. Cassell PG, Neverova M, Janmohamed S, Uwakwe N, Qureshi A,

McCarthy MI, Saker PJ, Albon L, Kopelman P, Noonan K et al

(1999) An uncoupling protein 2 gene variant is associated with a

raised body mass index but not Type II diabetes. Diabetologia

42(6):688–692

78. Solanes G, Pedraza N, Iglesias R, Giralt M, Villarroya F (2000)

The human uncoupling protein-3 gene promoter requires MyoD

and is induced by retinoic acid in muscle cells. FASEB J

14(14):2141–2143

79. Acın A, Rodriguez M, Rique H, Canet E, Boutin JA, Galizzi JP

(1999) Cloning and characterization of the 5’ flanking region of

the human uncoupling protein 3 (UCP3) gene. Biochem Biophys

Res Commun 258(2):278–283

80. Liu Y-J, Liu P-Y, Long J, Lu Y, Elze L, Recker RR, Deng H-W

(2005) Linkage and association analyses of the UCP3 gene with

obesity phenotypes in Caucasian families. Physiol Genomics

22(2):197–203

81. Schrauwen P, Xia J, Bogardus C, Pratley RE, Ravussin E (1999)

Skeletal muscle uncoupling protein 3 expression is a determinant

of energy expenditure in Pima Indians. Diabetes 48(1):146–149

82. Munafo MR, Flint J (2004) Meta-analysis of genetic association

studies. Trends Genet 20(9):439–444

Mol Biol Rep

123

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Association of the UCP polymorphisms with susceptibility to obesity: case–control study and...

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