# Characterization of a Novel Polymorphism in PPARG Regulatory Region Associated with Type 2 Diabetes and Diabetic Retinopathy in Italy

Authors: Valerio Costa; Amelia Casamassimi; Katherine Esposito; Angela Villani; Mariaelena Capone; Rosa Iannella; Bruno Schisano; Miryam Ciotola; Carmen Di Palo; Feliciantonia Capone Corrado; Franco Santangelo; Dario Giugliano; Alfredo Ciccodicola
Journal: Journal of Biomedicine and Biotechnology
 (2009)

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## Abstract

Peroxisome proliferator-activated receptor gamma polymorphisms have been widely associated with type 2 diabetes, although their role in the pathogenesis of vascular complications is not yet demonstrated. In this study, a cohort of 211 type 2 diabetes, 205 obese, and 254 control individuals was genotyped for Pro12Ala, C1431T, C-2821T polymorphisms, and for a newly identified polymorphism (A-2819G). The above-mentioned polymorphisms were analyzed by gene-specific PCR and direct sequencing of all samples. A significant difference was found for -2819G frequency when patients with type 2 diabetes—particularly diabetic women with the proliferative retinopathy—were compared with healthy control individuals. In conclusion, we identified a novel polymorphism, A-2819G, inPPARG gene, and we found it to be associated with type 2 diabetes and proliferative retinopathy in diabetic females. In the analyzed population, this variant represents a genetic risk factor for developing the diabetic retinopathy, whereas Pro12Ala and C1431T do not.


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## Body

## 1. Introduction

Peroxisome proliferator-activated receptor gamma
(PPARG) gene is a member of nuclear hormone receptor superfamily,
and it regulates the
transcription of several genes involved in glucose metabolism, adipocyte differentiation, lipid oxidation, angiogenesis, and inflammation [1].
PPARγ displays different protein isoforms generated by different promoters and
alternative splicing [2, 3]. The functional role of PPARγ is well documented, and its alterations have
been widely associated with metabolic diseases, such as type 2 diabetes and obesity [4, 5].Indeed, the most analyzedPPARG polymorphism, Pro12Ala in the exon B [6],
exerts an unclear effect on receptor
structure, and it seems to reduce the receptor binding affinity with the
responsive elements, also inducing a decrease in transcriptional activity both
with and without PPARγ
agonists [7, 8]. The common Pro12Ala polymorphism has been associated with increased risk
of developing type 2 diabetes, even though a recent hypothesis suggests Pro12Ala
could be involved in the aetiology of metabolic disorders not by itself, but
because of linkage disequilibrium with other single nucleotide polymorphisms
(SNPs) in the PPARG promoter [9, 10].Moreover,PPARG gene
polymorphisms have also been associated with micro- and macrovascular
complications of diabetes, although the few available studies do not
unambiguously confirm the role of PPARG in the pathogenesis of these complications, especially diabetic retinopathy [11–13].
Indeed, recent studies have demonstrated that PPARγ ligands could both inhibit intraocular
angiogenesis and upregulate VEGF gene expression in several cell types [14].To date, most of the mutation analyses focused on the coding region of
thePPARG gene [9], and only few studies have analyzed SNPs in the
regulatory region of PPARG possibly
associated with metabolic disorder susceptibility [15, 16]. A novel functional variant in
the PPARγ2 promoter, C-2821T, that associates with predictors of type 2 diabetes and
obesity, was recently reported in Pima Indians population [16].In attempt to analyze this variant in Italian individuals affected by
type 2 diabetes, we have performed an SNP
analysis on 211 unrelated type 2 diabetic patients from Campania County,
Southern Italy. Subsequently, we have extended
this analysis to a cohort of 205
unrelated obese patients and 254 control individuals.Furthermore, we have genotyped the
above-mentioned cohort (diabetic, obese, and control individuals), for two extensively studied SNPs in high linkage disequilibrium, Pro12Ala and C1431T.


## 2. Materials and Methods

### 2.1. Patients and Clinical Characteristics

Genomic DNA was extracted from blood samples by
standard procedures [17]. SNP analysis was performed on genomic DNA from 211 unrelated Italian
type 2 diabetic patients (102 of which suffered from proliferative
retinopathy), 205 unrelated obese patients, and 254 control individuals. All the subjects were recruited from the Department
of Geriatrics and Metabolic Diseases, Second University of Naples, Italy, and from the Department of Neuroscience, “Federico
II” University of Naples, Italy. All procedures were approved by the Ethics Board of the participating institutes and adhered to the tenets of the Declaration of Helsinki. All samples were acquired after informed consent was obtained from the patients.The diabetes and obese status were determined
according to the criteria of World Health Organization [18]. Patients with type 2 diabetes were eligible for the study if they had background or proliferative diabetic retinopathy, similar age (±1 year) and similar duration of diabetes (±1 year). No other inclusion criteria had to be met. The clinical characteristics (age, sex, duration of T2DM, 2h-plasma glucose, HbA1c and BMI) of the 102 diabetic patients with proliferative retinopathy were not different from those of the 109 diabetic patients with background retinopathy (see Table 1). Retinopathy condition was evaluated by ocular fundus photography.Table 1
Clinical characteristics of type 2 diabetic patients.
CharacteristicProliferativeBackgroundP valueDR groupDR groupn102109Age (years)63  ±  1062  ±  90.44Male (%)45 (44,1)54 (49,5)0.51Female (%)57 (55,9)55 (50,5)0.72Duration of
T2DM11,5  ±  2,912,0  ±  2.50.182h-plasma
glucose (mg/dl)230  ±  90172  ±  600.001HbA1c8,9  ±  1, 07,9  ±  0,90.001BMI (Kg/m2)31,2  ±  4,430,4  ±  4,50.19


### 2.2. SNP Analysis and Genotyping

Pro12Ala SNP analysis was performed with specific
primers pairs (Pro12AlaF 5′-TTT TAA CGG ATT GAT CTT TTG C-3′ and Pro12AlaR
5′-ACC CTT ACA TAA ATG CCC CC-3′) amplifying a fragment of 214 bp. C1431T SNP
in the exon 6 of PPARG gene was
analyzed by PCR amplification of a 358 bp fragment using specific primers pairs
(C1431TF 5′-CTG AAC CCC CTG TTG TGT TT-3′ and C1431TR 5′-GGT GTC AGA TTT TCC
CTC AGA-3′). The screening of C-2821T and A-2819G SNPs was carried out on a 453 bp genomic region of PPARγ2-specific promoter, PCR amplified with specific
oligonucleotides (C-2821TF 5′-TAC ACC AAT GGG TTG TCA-3′; C-2821TR 5′-GTA GCC
AAA GAC AGG TTC TG-3′) by Muller et al. (2003) [16].PCR reactions set-up with AmpliTaq Gold (Applied Biosystems, Calif, USA)  were
performed using an ABI 9700 automatic thermal cycler under the following
conditions: 95°C for 10 minutes; then 35 cycles of 95°C for 1 minute, at
annealing temperature (Ta) for 1 minute, of 72°C for 2
minutes; and a final step of 72°C for 10 minutes. PCR products were analyzed
by denaturing high performance liquid chromatography (DHPLC), and those with a DHPLC
pattern different from controls were sequenced using the ABI Prism Big Dye
Terminator Cycle Sequencing V2.0 (Applied Biosystems).


### 2.3. Statistical Analysis

Statistical analysis was performed using standard
chi-square, Fisher, and
student’st-tests. For continuous variables, the general estimating equation procedure was used to
adjust for covariates. The association of polymorphisms with type 2 diabetes, obesity, and retinopathy was
analyzed by exact chi-square test, comparing SNPs frequencies.Allele and genotype frequencies (Pearsonχ2 statistics), O.R., and P values, as
well as dominant and recessive genetic models, were analyzed using FINETTI
(http://ihg2.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl). Hardy-Weinberg
equilibrium of tested groups and Armitage’s trend test (ATT) were also
calculated using FINETTI. ATT takes into account genotypes rather than alleles,
avoiding a possible bias due to doubling of sample size [19]. It assumes
additive (or codominant) disease model where all disease allele are independent
and have the same contribution to the disease risk.Continuous clinical data, such as duration of type 2
diabetes (years), HbA1c and BMI (body mass index-Kg/m2), different
between background and proliferative retinopathy populations, were compared by
unpaired student’s t-test (two-tailed test); a resulting P value less than 0.05 (C.I. 95%) was
considered significant.


## 2.1. Patients and Clinical Characteristics

Genomic DNA was extracted from blood samples by
standard procedures [17]. SNP analysis was performed on genomic DNA from 211 unrelated Italian
type 2 diabetic patients (102 of which suffered from proliferative
retinopathy), 205 unrelated obese patients, and 254 control individuals. All the subjects were recruited from the Department
of Geriatrics and Metabolic Diseases, Second University of Naples, Italy, and from the Department of Neuroscience, “Federico
II” University of Naples, Italy. All procedures were approved by the Ethics Board of the participating institutes and adhered to the tenets of the Declaration of Helsinki. All samples were acquired after informed consent was obtained from the patients.The diabetes and obese status were determined
according to the criteria of World Health Organization [18]. Patients with type 2 diabetes were eligible for the study if they had background or proliferative diabetic retinopathy, similar age (±1 year) and similar duration of diabetes (±1 year). No other inclusion criteria had to be met. The clinical characteristics (age, sex, duration of T2DM, 2h-plasma glucose, HbA1c and BMI) of the 102 diabetic patients with proliferative retinopathy were not different from those of the 109 diabetic patients with background retinopathy (see Table 1). Retinopathy condition was evaluated by ocular fundus photography.Table 1
Clinical characteristics of type 2 diabetic patients.
CharacteristicProliferativeBackgroundP valueDR groupDR groupn102109Age (years)63  ±  1062  ±  90.44Male (%)45 (44,1)54 (49,5)0.51Female (%)57 (55,9)55 (50,5)0.72Duration of
T2DM11,5  ±  2,912,0  ±  2.50.182h-plasma
glucose (mg/dl)230  ±  90172  ±  600.001HbA1c8,9  ±  1, 07,9  ±  0,90.001BMI (Kg/m2)31,2  ±  4,430,4  ±  4,50.19


## 2.2. SNP Analysis and Genotyping

Pro12Ala SNP analysis was performed with specific
primers pairs (Pro12AlaF 5′-TTT TAA CGG ATT GAT CTT TTG C-3′ and Pro12AlaR
5′-ACC CTT ACA TAA ATG CCC CC-3′) amplifying a fragment of 214 bp. C1431T SNP
in the exon 6 of PPARG gene was
analyzed by PCR amplification of a 358 bp fragment using specific primers pairs
(C1431TF 5′-CTG AAC CCC CTG TTG TGT TT-3′ and C1431TR 5′-GGT GTC AGA TTT TCC
CTC AGA-3′). The screening of C-2821T and A-2819G SNPs was carried out on a 453 bp genomic region of PPARγ2-specific promoter, PCR amplified with specific
oligonucleotides (C-2821TF 5′-TAC ACC AAT GGG TTG TCA-3′; C-2821TR 5′-GTA GCC
AAA GAC AGG TTC TG-3′) by Muller et al. (2003) [16].PCR reactions set-up with AmpliTaq Gold (Applied Biosystems, Calif, USA)  were
performed using an ABI 9700 automatic thermal cycler under the following
conditions: 95°C for 10 minutes; then 35 cycles of 95°C for 1 minute, at
annealing temperature (Ta) for 1 minute, of 72°C for 2
minutes; and a final step of 72°C for 10 minutes. PCR products were analyzed
by denaturing high performance liquid chromatography (DHPLC), and those with a DHPLC
pattern different from controls were sequenced using the ABI Prism Big Dye
Terminator Cycle Sequencing V2.0 (Applied Biosystems).


## 2.3. Statistical Analysis

Statistical analysis was performed using standard
chi-square, Fisher, and
student’st-tests. For continuous variables, the general estimating equation procedure was used to
adjust for covariates. The association of polymorphisms with type 2 diabetes, obesity, and retinopathy was
analyzed by exact chi-square test, comparing SNPs frequencies.Allele and genotype frequencies (Pearsonχ2 statistics), O.R., and P values, as
well as dominant and recessive genetic models, were analyzed using FINETTI
(http://ihg2.helmholtz-muenchen.de/cgi-bin/hw/hwa1.pl). Hardy-Weinberg
equilibrium of tested groups and Armitage’s trend test (ATT) were also
calculated using FINETTI. ATT takes into account genotypes rather than alleles,
avoiding a possible bias due to doubling of sample size [19]. It assumes
additive (or codominant) disease model where all disease allele are independent
and have the same contribution to the disease risk.Continuous clinical data, such as duration of type 2
diabetes (years), HbA1c and BMI (body mass index-Kg/m2), different
between background and proliferative retinopathy populations, were compared by
unpaired student’s t-test (two-tailed test); a resulting P value less than 0.05 (C.I. 95%) was
considered significant.


## 3. Results

To investigate whether SNPs in thePPARG gene could be a risk factor for type 2 diabetes and
obesity, an extensive polymorphism analysis in a population of 670 individuals
from Campania County, in Southern Italy, was performed. The diabetes and obese status were determined
according to the criteria of World Health Organization.We genotyped three SNPs, Pro12Ala, C1431T—in the
coding region ofPPARG gene—and C-2821T in the regulatory region [16], in 211 individuals with type 2 diabetes, 205 suffering from obesity, and 254 control
individuals. Our analysis revealed that
T-2821 allele was not found in 670 individuals, and consequently, C-2821 allele
frequency was 100%.Allelic frequencies of Pro12Ala and C1431T variants (listed
in Table2) are in agreement to the data previously reported by Doney et al., 2004 [20], although 1431T
allele was shown to be less frequent in obese than in control individuals in
the analyzed population.Table 2
Genotypes and allele frequencies in diabetic, obese, and control groups (statistical significant values are in bold).
(a)Control groupDiabetic groupObese groupn254211205Pro12AlaPro/Pro222 (87,5%)179 (84,6%)176 (86%)Pro/Ala32 (12,5%)32 (15,4%)28 (13,6%)Ala/Ala0 (0%)0 (0%)1 (0,4%)C1431TC/C199 (78,3%)171 (81%)176 (85,9%)C/T44 (17,4%)38 (18%)27 (13,2%)T/T11 (4,3%)2 (1%)2 (0,9%)A-2819GA/A208 (81,7%)141 (66,7%)165 (80,6%)A/G39 (15,4%)64 (30,6%)36 (17,6%)G/G7 (2,9%)6 (2,7%)4 (1,8%)(b)SNPRisk alleleFrequency of risk alleleRisk allele odds ratio (95% C.I.)P value; statistical power (%)Pro12AlaAlaControl groupDiabetic groupObese groupControl/DiabeticControl/ObeseControl/DiabeticControl/Obese0.060.080.081.2 (0.73–2.03)1.1 (0.70–1.96)0.4;
23%0.5;
12.2%C1431TT0.130.100.070.74
(0.49–1.1)1.83
(1.17–2.86)0.1;
17.6%0.008;68.3%A-2819GG0.100.180.102.25
(1.46–3.44)1.03
(0.67–1.57)1.9×10−4;98%0.9;
9%Moreover, during the SNP analysis of the C-2821T SNP, we
have characterized a novel SNP, A-2819G, located two base pair downstream,
included within the same putative E2 box [16]. This novel
SNP was genotyped on the entire cohort of 670 individuals using the same
standard procedures. Genotypes and allelic
frequencies of A-2819G are reported in Table 2.The -2819G minor allele  frequency, observed in 211
individuals with type 2 diabetes, was
about 2-fold than the frequency of the risk allele in the control and obese
groups (see Table2). In logistic
regression analyses, aimed to test the hypothesis of A-2819G as risk factor for
type 2 diabetes under the assumption of a codominant, recessive and dominant
model, the dominant model best fitted the data (P=1.9×10−4, Table 3). According to this model, -2819G allele carriers (AG
and GG genotype) showed an O.R.  =  2.24 (95% C.I. 1.46–3.44) of
developing type 2 diabetes compared to the noncarriers (Table 3). The A-2819G
allele is in Hardy-Weinberg disequilibrium (DHW) in the control group (P=.01
see Table  3).Table 3
Hardy-Weinberg equilibrium and association tests for A-2819G SNP.
Hardy-Weinberg equilibriumTests for association (C.I.: 95% confidence  interval)ControlsDiabetic groupRisk alleleArmitage’s trend testn=254n=211[AA versus GG][AA versus AG+GG]Common odd ratioO.R.  =  2.42O.R.  =  2.24O.R.  =  1.60C.I.=[0.41–3.84]C.I.=[1.46–3.44]P=.01P=.8P=.6P=1.9×10−4P=.0013The newly identified risk allele -2819G was not found
to be associated with obesity (Table2) and it
was estimated to be not in linkage disequilibrium with Pro12Ala and C1431T SNPs. The possible association of
A-2819G SNP with some specific clinical features of both type 2 diabetes and
obesity was also considered. Specifically, BMI in obese individuals, blood
glucose, and HA1c levels and the presence of proliferative retinopathy in type
2 diabetic patients were evaluated. The analysis revealed that -2819G frequency
in diabetic females with proliferative retinopathy was 2-fold increased with
respect to background retinopathy female (P=.02; see Table 4 and Figure
1).Table 4
Genotypes and -2819G
frequency in diabetic women with and without proliferative retinopathy (statistical significant
values are in bold).
(a)Diabetic female with background retinopathyDiabetic female with proliferative retinopathyn5557A-2819GA/A43 (78%)34 (60%)A/G11 (22%)21 (36.7%)G/G0 (0%)2 (3.3%)(b)SNPRisk alleleFrequency of risk alleleRisk allele odds ratio (95% C.I.)P value; statistical power (%)A-2819GGDiabetic female with background retinopathyDiabetic female with proliferative retinopathyBackground/proliferativeBackground/proliferative0.110.222.30 (1.09–4.83)0.02;66.3%Figure 1
Comparison of A-2819G frequencies in diabetic women with background or
proliferative retinopathy and control women.In logistic regression analyses, the dominant model
best fitted the data (P=.03; see Table 5). According
to this model, type 2 diabetes female with -2819G allele (AG and GG genotype)
showed an O.R.  =  2.42 (95%  C.I.=1.057–5.56) of developing proliferative retinopathy compared to the noncarrier diabetic female  (Table 5). The A-2819G allele is in Hardy-Weinberg equilibrium in both background
and proliferative groups (Table 5).
Moreover, diabetic females with proliferative retinopathy had more than 3-fold
risk compared to control females (data not shown).Table 5
Hardy-Weinberg equilibrium and association tests for A-2819G and
proliferative retinopathy.
Hardy-Weinberg equilibriumTests for association (C.I.: 95% confidence interval)BackgroundProliferativeRisk alleleArmitage’s trend testn=55n=57[AA versus GG][AA versus AG+GG]Common odd ratioO.R.  =  6.30O.R.  =  2.42O.R.  =  3.57C.I.=[0.29–135.68]C.I.=[1.057–5.56]P=.37P=.57P=.12P=.03P=.02In conclusion, our results show that a novel nucleotide variant A-2819G, in the
regulatory region ofPPARG, is
associated with type 2 diabetes and with proliferative retinopathy in females
in an Italian cohort.


## 4. Discussion

Previous studies have shown that nucleotide
variations, both point mutations and SNPs, in thePPARG coding region, are associated with an increased
susceptibility of developing type 2 diabetes and obesity [9].
Particularly, the well-known Pro12Ala substitution has been widely associated
with both type 2 diabetes and obesity, or predictors of these diseases, in
different populations [10, 12]. The Pro12Ala polymorphism has been recently predicted
to confer protection, through unknown mechanisms, from type 2 diabetes and nephropathy [12, 15, 21, 22].Conversely, it has also been shown that Pro12Ala does
not associate with diabetic retinopathy [11, 13]. The Pro12Ala SNP has been
described in high linkage disequilibrium with C1431T polymorphism, located in exon 6 of PPARG gene. Several studies have shown T minor allele to be
associated, in obese individuals, with low plasmatic leptin levels [14]. The
same variation has been associated with high BMI values in obese female from
Finland in coassociation with Ala12 allele [23].In the present study, we demonstrated that Pro12Ala
and C1431T variants are not associated with type 2 diabetes, obesity, BMI
variation, and also microangiopatic complication of diabetes, in a cohort of
670 individuals from Campania County, in the Southern Italy. We also found that
-2821T allele, described in the Pima
Indians population from Arizona, was not present in the analyzed population.In contrast, a novel nucleotide variation, A-2819G,
two base pair downstream the C-2821T SNP described by Muller et al. [16], was
identified. We demonstrated that the A-2819G nucleotide variant is associated
with type 2 diabetes, but not with obesity or BMI variations (Tables 2 and 3). The A-2819G allele is in DHW in the
control group. DHW may be due to several factors such as genotyping errors,
population stratification, selection, inbreeding, the presence of a causative
allele or simply by the chance. We can therefore exclude with high confidence
that DHW in the control group could be due to genotyping errors. For A-2819G
SNP, as well as for all the examined SNPs, all the subjects were analyzed by DHPLC
and then sequenced. We cannot exclude that DHW is due to other confounding
factors; however, at least for the autosome genes, the presence of DHW in
controls is compatible with the segregation of a causative variant according to
a dominant model [24].The association tests for the newly identified A-2819G
SNP, revealed statistical significant association with a common and detrimental
microvascular complication of type 2 diabetes, namely, the diabetic
proliferative retinopathy, in the diabetic females of the examined population
(O.R.  =  2.43; C.I.1.09–4.83; P=0.02). This
microvascular complication represents a leading cause of blindness,
characterized by an increased vascular permeability, haemostatic abnormalities,
increased tissue ischemia, and also neoangiogenesis.The newly identified A-2819G SNP and the C-2821T [16] are both located in a binding
site for a transcriptional repressor δEF1. The polymorphisms described by Muller et al., 2003 [16],
leading to higher levels of PPARG transcription, has been associated with an
increased risk of development of type 2 diabetes and obesity in Pima Indians [16]. This finding is supported by the notion that
supraphysiological activation of PPARγ by thiazolidinedione (TZD) agonists increases triglyceride (TG) content of white adipose
tissue, thereby decreasing TG levels in the liver and muscles, resulting in
increased insulin sensitivity and type 2 diabetes [25].In this study, we demonstrated that a novel
polymorphism, in the regulatory region of PPARG, associates to proliferative retinopathy in diabetic females, suggesting
a possible involvement of the hormonal female component, such as the estrogen
receptor signaling that interferes with the physiological PPARγ intracellular pathway [26]. We can reject sex differences due to genotype bias, since males and
females were analyzed together, in blind fashion, and the gender was assigned
after the analysis.We postulate that PPARγ may be involved in the development and
progression of diabetic retinopathy via a plethora of mechanisms, although its
pathogenic effect on the development of the microvascular complications in type
2 diabetic patients is not yet known.Indeed, Muller et al. (2003) concluded that -2821T
allele, via its effect on PPARγ expression, may exert functional consequences on
different PPARγ2-activated
pathways and that its association with the well-known Pro12Ala may contribute
to PPARγ2-related phenotypes [16].PPARγ is a multivalent receptor able to bind different
ligands. Full and partial TZD agonists can induce distinct receptor
combinations, leading to selective gene expression. Ligand-receptor complexes,
assuming different three-dimensional conformations, show unique and
differential interactions with cofactors and other transcription factors [27].Particularly, PPARγ2 is highly
and heterogeneously expressed in the human eye, where its ligand-dependent activation evokes potent and robust
inhibition of corneal angiogenesis [27]. Moreover, altered lipid oxidation in the retina supporting cells
(retinal pigment epithelium, RPE) and in the eye vessels, and an increased
glucose uptake from RPE cells, has pathological effects on eye vessels [28, 29]. However, it was already shown that lipid peroxidation is
influenced by C1431T polymorphism, also involved in the susceptibility to
premature myocardial infarction [30].We hypothesize that the newly identified A-2819G
nucleotide variation—possibly altering
PPARγ2 transcript abundance—may affect the
complex and highly regulated gene expression network in the eye, conferring in
turn the susceptibility to diabetic retinopathy.Further studies enrolling a greater number of
subjects, both patients and controls, would be useful to better elucidate the
involvement of this novel SNP in the susceptibility of developing diabetic
retinopathy. However, these findings strengthen the
hypothesis that PPARγ could be a novel pharmacological
target of angiostatic agents in the treatment of diabetic retinopathy.



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