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eISSN: 2473-0815

Endocrinology & Metabolism International Journal

Research Article Volume 5 Issue 3

Are we Predisposed to Type 2 Diabetes Risk: a Case-Control Study from Urban Population in Western India

Jayesh J Sheth,1 Sunil Trivedi,1 Ankna Shah,1 Nutan Nabar,1 Navneet Shah,3 Premal Thakor,4 Rama Vaidya,2 Frenny Sheth1

1Department of Biochemistry and Molecular Genetics FRIGE, India
2Endocrine and Metabolic Disorders, Kasturba Health Society, Medical Research Centre, India
3Department of Diabetes and Endocrinology, Sterling Hospital, India
4Gujarat Diabetic Association, India

Correspondence: Jayesh J Sheth, Department of Biochemistry and Molecular Genetics, FRIGE, s Institute of Human Genetics, FRIGE House, Jodhpur Gam Road, Satellite, Ahmedabad 380015, India, Tel +91 79-26921414

Received: July 12, 2017 | Published: August 11, 2017

citation: Sheth J, Trivedi S, Shah A, Nabar N, Shah N et al. (2017) Are we Predisposed to Type 2 Diabetes Risk: a Case-Control Study from Urban Population in Western India. Endocrinol Metab Int J 5(3): 00122. DOI: 10.15406/emij.2017.05.00122

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Abstract

Background: Type 2 diabetes mellitus (T2DM) is driven by genetic and environmental factors. Sequence variants of candidate genes are known to predispose Indians to T2DM. ‘Asian Indian Phenotype’ is a cluster of abnormality described earlier; links possible genetic proneness of Indians. PPARγ2 gene SNP rs1801282, a missense variant is associated with improved insulin sensitivity, reduced T2DM risk and adipogenesis. This polymorphism does not confer this benefit to Indian Asians as seen in Caucasians. ADRβ3 gene SNP rs4994 produce variant receptor protein with altered affinity; is associated to development of dyslipidemia, obesity, insulin resistance (IR) and early T2DM onset. Combined influence of both these alleles on hemoglobin glycation, insulin resistance and dyslipidemia is not reported which is addressed in this case-control study from urban western Indian population.     

Methods: T2DM subjects (n=506) and non-diabetic controls (n=544) largely from urban Hindu community were enrolled through consecutive sampling and studied for fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), fasting insulin (FI), HOMA-IR, total cholesterol (TC), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), Low-density lipoprotein cholesterol (LDL-C) and non-HDL-C through biochemical methods. SNPs were studied with restriction fragment length polymorphism (RFLP). Statistical analysis performed by Statav12 and IBM-SPSS included a variety of tests. 

Results: T2DM subjects had higher FPG, HbA1c, HOMA-IR, TG and lower FI, HDL-C, LDL-C as compared to non-diabetic healthy individuals, while no differences in TC and non-HDL-C were seen in subgroups. The frequencies of reference and variant alleles of rs1801282 of PPARγ2and rs4994 of ADRβ3 individually were similar in T2DM and controls.PPARγ2 ref allele bearing T2DM subjects had a higher HbA1c and HOMA-IR in dyslipidemic as well as non-dyslipidemic than PPARγ2variant allele bearing counterpart. On allelic combination, presence of PPARγ2ref+ADRβ3va was associated with high HbA1c and dyslipidemia than PPARγ2va+ADRβ3ref allelic type. Taken together, reference allele of PPARγ2plus variant ADRβ3allelic type is likely to predispose to higher HbA1c, HOMA-IR and dyslipidemia.

Conclusion: Coexistence of reference allele of PPARγ2 gene and variant allele of ADRβ3 gene, dyslipidemia and obesity in an individual is likely to confer increased insulin resistance thereby increasing susceptibility to the development of T2DM.

Keywords: Type 2 diabetes mellitus; HbA1c; Dyslipidemia; PPARγ2; ADRβ3

Abbreviations

T2DM: Type 2 Diabetes Mellitus; PPARγ2: Peroxisome Proliferator-Activated Receptor g2; ADRβ3: Beta-3-Adrenergic Receptor; IR: Insulin Resistance; FPG: Fasting Plasma Glucose; HbA1c: Glycosylated Hemoglobin; FI: Fasting Insulin; HOMA-IR: Homeostatic Model Assessment Insulin Resistance; TC: Total Cholesterol; TG: Triglyceride; HDL-C: High-Density Lipoprotein Cholesterol; LDL-C: Low-Density Lipoprotein Cholesterol; RFLP: Restriction Fragment Length Polymorphism; WHR: Waist to Hip Ratio; SNP: Single Nucleotide Polymorphism

Introduction

Type 2 diabetes mellitus (T2DM; OMIM 125853) is a complex metabolic disease triggered by the influence of numerous genetic and environmental factors [1,2]. Several studies have addressed the role of sequence variants of specific candidate genes [3,4]. Radha & Mohan [5,6]emphasized on a higher genetic predisposition of Indians to T2DM. Indians are reported to have a higher plasma insulin (compared to matched Europeans) [7], a higher WHR (waist to hip ratio) for any given BMI (compared to other ethnic groups) [8] and a greater insulin resistance (IR) [compared to other ethnic groups] [9-11]. Studies on migrant Indians have also shown a higher prevalence of T2DM (compared to indigenous population) [12-14]. Collectively, these studies point towards possibility of genetic proneness (genetic predisposition) of Indians to T2DM. In addition, fat tends to accumulate more in the abdominal region in Indians and therefore may be more prone to develop insulin resistance [10,15]. This cluster of metabolic abnormality is collectively referred to as ‘Asian Indian Phenotype’ [16].

PPARγ2 , a candidate gene is predominantly expressed in adipose tissue and plays a vital role in insulin action and glucose homeostasis [17,19]. Moreover, it also act as a ‘fatty acid sensor’ with affinity towards free fatty acids [20,21]. SNP rs1801282 in the PPARγ2 gene is a missense variant associated with improved insulin sensitivity, decreased risk of T2DM and adipogenesis; thus suggesting its role in glucose deregulation, dyslipidemia and obesity [17,18,20]. Furthermore, a recent meta-analysis of 41 studies showed that the presence of this variant allele was associated with T2DM (Meta-analysis; OR= 0.79, 95% CI= 0.66-0.95, I2= 69%) implicating its role in disease risk [22]. In addition, the Ala allele of this polymorphism was associated with a decreased risk of type 2 diabetes [23]. Moreover, Radha et al concluded that despite the frequency of the Ala allele at the PPARg-Pro12Ala locus was the same in individuals of South Asian descent, as in Caucasians, this polymorphism does not appear to improve insulin sensitivity or decrease risk for type 2 diabetes in South Asians (Asian Indians), as it does in Caucasians [5].

Likewise, ADRβ3 is primarily expressed in adipose tissue and is implicated in lipolysis regulation [24]. SNP rs4994 of this gene is a missense variant that produces a protein (receptor) with altered affinity to norepinephrine.24Additionally, this variant has been shown to have an altered interaction with G-protein coupled receptors in adipocytes that may promote decrease in lipolysis resulting into development of dyslipidemia, obesity, insulin resistance (IR) and an earlier onset of T2D [24,25]. This may likely to be due to its influence on the glucose and lipid metabolism. However, inconsistent results on the association between the above variant alleles and T2DM risk may likely be due to the studies being carried out in individuals of different ethnicity [24,26]. The ADRB3 rs4994 polymorphism however, is reported to be associated with T2DM from Mexico city [27]. Moreover, Several past studies have connected this non-synonymous rs4994 polymorphism to T2DM, obesity, insulin resistance and hypertension [28-31].

Despite the availability of the association results with aforementioned variants for several ethnicity [18,19,25,26] no study has been reported the association between these variant alleles and T2DM in the western Indian population. Interestingly, interactions of alleles of PPARγ2 (Pro12Ala) and ADRβ3 (Trp64Arg) have been found to be related to obesity risk in Spanish children and adolescents as well as Mexican-American adults [32,33].

The current study takes into account the synergism of polymorphisms of (Pro12Ala in PPARγ2 and Trp64Arg in ADRβ3) and aim to catch their influence on genetic proneness in a case-control study in largely Hindu urban Western Indian population by juxtaposing them to hemoglobin glycation, insulin resistance and dyslipidemia.

Materials and methods

Study population

A total of 1050 subjects were recruited for the study with 506 T2DM cases and 544 non-diabetic healthy controls from western India. They were enrolled after informed consent by consecutive sampling technique during April, 2012 to December, 2014 from the out-patient departments of several diabetes clinics and through weekly camps at two institutes (FRIGE, Ahmedabad and KHS’s Medical Research Centre, Mumbai).The study were approved by the institutional Ethics committees of the institutions. The study participants were largely from Hindu community. The inclusion criteria for T2DM subjects were (a) age (≥25 years); (b) duration of diabetes (≥6 months from the date of diagnosis) and; (c) fasting plasma glucose levels (FPG ≥ 126.0mg/dl). Non-diabetic healthy controls were recruited with an inclusion criterion of (a) age (≥25 years); (b) HbA1c level ≤ 6.5% and; (c) fasting plasma glucose levels (FPG ≤ 110.0mg/dl). Subjects with Type 1 Diabetes, lactating and pregnant mothers, and those with concomitant chronic illness were excluded from the study.

Epidemiological questionnaire and sample collection

Each participant completed a standardized baseline questionnaire in person at each institute. The questionnaire included medical history, family history and demographic data. Blood samples were collected in fluoride, serum and EDTA vaccutainers in the morning (0800–1100 hrs) after 12 hours fasting for biochemical analysis [fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), fasting insulin (FI), insulin resistance by HOMA-IR and lipid profile] and SNP genotyping. Serum samples were separated within 30-45 minutes of collection and stored at -20°C till further analysis.

Biochemical investigations

FPG and lipid profile including total cholesterol (TC), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) were measured by colorimetric method with respective calibrator and biological standards by commercially available kits using an auto-analyzer BTS 330 (Biosystem, Spain)]. HbA1c was determined using affinity assay [NycoCard reader-II (Axis-Shield, Norway)] from whole blood. Serum FI levels were measured by Immuno Radiometric Assay [using commercial kit (Immunotech, France) with γ-counter system PC-RIA MAS (Stratec Biometrical System AG, Germany)]. Insulin resistance was calculated by the Homeostasis Model of Assessment-Insulin Resistance Index (HOMA-IR) [34]. Low-density lipoprotein cholesterol (LDL-C) and non-HDL-C were calculated by standard formula [LDL = TC - HDL-C - (TG/5) and non-HDL-C = TC - HDL-C]. The intra-assay coefficients of variation for lipid parameters were TC= 1.39%, TG= 0.50% and HDL-C= 1.67% whereas inter-assay coefficients of variations for aforementioned lipid parameters were 2.65%, 1.65% and 4.69% respectively.

SNP genotyping

Genomic DNA was extracted from whole blood by salting out method [35]. SNP rs1801282 and rs4994 were genotyped using restriction fragment length polymorphism (RFLP) PCR. The detailed PCR conditions are mentioned in supplementary Table 1. The amplified and digested PCR products were separated on 3% agarose gel and visualized under UV light based E-Gel imager (Life technologies, USA).

 

T2DM (N=506)

Non-Diabetic Healthy Controls (N=544)

Anthropometric Data (Mean±SD)

Age (Years)

56.88±10.44*

48.82±12.91

BMI (Kg/m2)

27.28±5.14*

26.01±4.68

WHR

0.93±0.11*

0.91±0.07

Biochemical Parameters (Mean±SD)

FPG (mg/dl)

139.62±48.37*

88.89±12.68

HbA1c (%)

8.14±1.77*

5.70±0.52

FI (µIU/ml)

10.77±6.52$

11.89±5.40

HOMA-IR

3.74±3.33*

2.67±1.54

TC (mg/dl)

176.92±51.42

180.98±46.49

TG (mg/dl)

141.28±73.55*

112.79±53.57

HDL-C (mg/dl)

53.86±16.66@

56.40±17.19

LDL-C (mg/dl)

94.32±49.28@

101.43±45.20

Non-HDL-C (mg/dl)

126.48±52.34

124.59±47.23

Table 1: Anthropometric and biochemical characteristics of study subjects.

@ p <0.02; * p <0.001; $ p <0.002.

SNP ID

Primers
(Forward & Reverse)

PCR
Conditions

No. of PCR
Cycles

Restriction
Enzyme
used for
RFLP

Digested
Product
Allele

Product Size

Temp. (°C)

Time (sec)

Method

 

rs-1801282

5’-ACTCTGGGAGATTCTCCTATTGGC-3’

5’-CTGGAAGACAAACTACAAGAG-3’

Denaturation

94

20

28 Cycles

HaeIII

Wild Type
(Pro12Pro)

132 + 23bp

Annealing

56

20

Heterozygous
Variant
(Pro12Ala)

155 + 132+ 23bp

Extension

72

20

Homozygous
Variant
(Ala12Ala)

155bp

rs-4994

5’-CGCCCAATACCGCCAACAC-3’
  5’-CCACCAGGAGTCCCATCACC-3’

Denaturation

94

30

32 Cycles

MspI

Wild
type
(Trp64Trp)

152 + 58bp

Annealing

69

30

Heterozygous
Variant
(Trp64Arg)

82 +70+ 58 +152bp

Extension

72

30

Homozygous
Variant
(Arg64Arg)

82+70+ 58bp

Supplementary Table 1

Statistical analysis

The sample size (current study) yielded a margin error of 5% and confidence limit of 95% with an on-line calculator [36]. HbA1c values were mentioned as percentage of total hemoglobin and all other biochemical parameters were expressed as mg/dl. Student’s t-test was conducted to compare the distribution of continuous variables between cases and controls. Correlation between the SNP, HbA1c and HOMA-IR was carried out using least squares linear regression. Hardy-Weinberg equilibrium (HWE) test for SNPs was performed in controls. Comparison of allele frequencies of SNPs between populations was carried out using Fisher’s exact test. Binomial logistic regression was performed to yield odds ratios (ORs). ROC analysis to establish the cut-offs for HOMA-IR was performed in the study population. The significance threshold for two-sided type 1 error (P-value) in all tests was set at 0.05. False positive error/Type I error rate was 0.05. All statistical analysis was carried out in Stata v12 [for Macintosh OS (Stata Corp., College Station, USA)] and IBM-SPSS v19.0.

The cut-offs used for all the parameters for regression analysis were as follow: HbA1c >7% in T2DM subjects and ≤5.7% in non-diabetic control subjects [37]. HOMA-IR of ≤3.95 were considered as normal (as determined by ROC curve analysis). The cut-offs for lipids in accordance with NCEP guidelines [38] were: Cholesterol (220 mg/dl), triglyceride (150 mg/dl), HDL-C (40 mg/dl), LDL-C (130 mg/dl), non-HDL-C (160 mg/dl). One or more abnormal serum lipid biomarker concentration was regarded as dyslipidemia [38].

Results

Assessment of baseline anthropometric and biochemical characteristics

There were 50.95% males [N=535 (N=273 T2DM, N=262 non diabetic healthycontrols)] and 49.05% females [N=515 (N=233 T2DM, N=282 non diabetic healthy controls)] from amongst a total of 1050 study subjects. Base line anthropometric and biochemical characteristics are summarized in Table 1. The mean duration of diabetes amongst the T2DM subjects was 8.62 years (range: 6 months to 40 years) and had significantly higher FPG, HbA1c, HOMA-IR, TG and significantly lower FI, HDL-C, LDL-C as compared to controls (non-diabetic healthy individuals), while no differences were seen in TC and non-HDL-C amongst the two subgroups. T2DM subjects had also a higher BMI (Body Mass Index) and WHR (Waist to hip ratio) than controls.

Allelic polymorphism rs1801282 of PPARγ2 gene

The overall frequency of C allele (Pro, Reference allele) of rs1801282 of PPARγ2gene in the study cohort was 0.90 (81.41%, N=852) while frequency of G allele (Ala, Variant allele) was 0.10 (18.86%, N=198; Figure 1). There was a uniform distribution of both alleles in the study subjects by Hardy-Weinberg equilibrium (p=1.0, df=1). The Pro allele had a frequency of 0.91 (83.40%, N=422) in T2DM and 0.89 (79.04%, N=430) in controls while the Ala allele had a frequency of 0.09 (16.60%, N=84) in T2DM and 0.11 (20.96%, N=114) in controls respectively. Considering the lower frequency of Ala12Ala allele in T2DM (0.01%, N=5) and in control subjects (0.01%, N=7) respectively, subjects with Pro12Ala (heterozygous) and Ala12Ala (homozygous) were grouped together for further analysis. The frequencies of Pro (reference) and Ala (variant) allele were not different in the current study similar to Finnish, Spanish, North- and South- Indian subset populations; while in other populations, the frequencies were significantly different (Figure 1).

Figure 1: Comparison of allelic frequencies of rs1801282 of PPARγ2gene and rs4994 of ADRβ3gene.

Allelic polymorphism rs4994 of ADRβ3 gene

The overall frequency of A (Trp, Reference allele) allele rs4994 of ADRβ3 gene in the study population was 0.91 (82.86%, N=870) while the frequency of G (Arg, Variant allele) allele was 0.09 (17.14%, N=180; Figure 2). Both alleles showed a uniform distribution in the study subjects by Hardy–Weinberg equilibrium (p=0.740, df=1). The Trp allele showed a frequency of 0.89 in T2DM (81.03%, N=410) and 0.92 in control subjects (84.56%, N=460) while Arg allele showed a frequency of 0.11 in T2DM (16.79%, N=85) and 0.08 in control subjects (14.89%, N=81). Considering the low frequency of Arg64Arg allele in T2DM (0.02%, N=11) and in control subjects (0.01%, N=3) respectively, those with Trp64Arg (heterozygous) and Arg 64 Arg (homozygous) were grouped together for further analysis. The reported frequencies of Trp (reference) and Arg (variant) allele were no different in the present study similar to Han Chinese (Beijing), Chinese in Denver (USA), Gujarati Indian (Houston), Japanese (Tokyo), Mexican (LA, USA),Masai in Kenya, Spanish and significantly different in other population subsets (Figure 1).  

Figure 2: Comparison of allelic combinations of rs1801282 of PPARγ2gene and rs4994 of ADRβ3 gene with HbA1c and HOMA-IR.

Association of allelic polymorphism of rs1801282 and rs4994 with T2DM risk

rs1801282and rs4994 were processed through logistic regression to identify their association with increased or decreased T2DM risk. However, we could not find any significant association of T2DM with variant allele of PPARγ2 gene (OR= 0.85, 95% CI: 0.63-1.14, p=0.27), but the presence of the variant allele of ADRβ3 gene was significantly associated with an increased risk of T2DM (OR= 1.48, 95% CI: 1.10-2.00, p=0.01).

Association of allelic polymorphism of rs1801282 and rs4994 to biochemical parameters

HbA1c and HOMA-IR were similar in diabetics bearing reference allele [(PPARγ2; Pro/Pro) or (ADRβ3; Trp/Trp)] or variant allele [(PPARγ2 gene; Pro/Ala) or (ADRβ3 gene; Trp/Arg)]. Control subjects with PPARγ2 reference allele had significantly higher (p=0.04) HbA1c (though in the normal range) as compared to PPARγ2 variant allele announcing a possibility of its diabetogenic role. In contrast, control subjects bearing ADRβ3 reference allele had significantly lower HbA1c [though in the normal range (p=0.05)] than controls harboring variant ADRβ3 allele suggesting a likelihood of its protective role (Table 2).  

 

PPARG2

 

T2DM

Non-Diabetic Healthy Controls

 

Reference Allele Pro/Pro (N=422)

Variant Allele Pro/Ala (N=84)

Reference Allele Pro/Pro (N=430)

Variant Allele Pro/Ala (N=114)

HbA1c (%)@

8.16±1.82

8.01±1.47

5.72±0.52*

5.62±0.06*

HOMA-IR@

3.76±2.45

3.66±2.67

2.68±1.63

2.62±1.13

 

ADRB3

 

Reference Allele Trp/Trp (N=410)

Variant Allele Trp/Arg (N=96)

Reference Allele Trp/Trp (N=460)

Variant Allele Trp/Arg (N=84)

HbA1c (%)@

8.4±1.80

8.15±1.60

5.68±0.52**

5.80±0.52**

HOMA-IR@

3.68±2.53

4.00±2.64

2.68±1.60

2.63±1.16

Table 2: Influence of allelic polymorphism on HbA1c and Insulin resistance.

@ Values expressed as Mean±SD
* p=0.04 ; ** p=0.05

When presence or absence of dyslipidemia was added to both diabetics and non-diabetic controls, dyslipidemic diabetic individuals with reference PPARγ2 allele had significantly higher HbA1c (p=0.048; Linear regression: r2=0.102, p<0.0010) and HOMA-IR (p=0.001) than variant bearing dyslipidemic diabetic individuals (Table 3). Amongst the non-dyslipidemic diabetics also, a similar observation was evident [higher HbA1c (p=0.001) and HOMA-IR (p=0.043) in PPARγ2 reference allele bearing individuals than variant PPARγ2 bearing individuals, (Table 3)]. Collectively these results point towards a diabetogenic role of PPARγ2reference allele than the variant allele amongst diabetic subjects. Control subjects on the other hand, did not show such difference in HbA1c or HOMA-IR.

 

 

T2DM

Non-Diabetic Healthy Controls

 

 

Dyslipidemia

 

 

PPARG2

 

 

Reference Allele
Pro/Pro (N=225)

Variant Allele
Pro/Ala (N=41)

Reference Allele Pro/Pro (N=205)

Variant Allele Pro/Ala (N=57)

HbA1c (%)

Mean ± SD

8.27±1.63*

7.74±1.24*

5.77±0.50

5.65±0.51

R2

0.102£

0.053

0.011

0.111

HOMA-IR

Mean ± SD

4.06±1.49**

3.24±1.32**

2.77±1.84

2.71±1.01

R2

0.004

0.006

0.072

0.172@

 

 

ADRB3

 

 

Reference Allele
Trp/Trp (N=209)

Variant Allele
Trp/Arg (N=57)

Reference Allele
Trp/Trp (N=220)

Variant Allele
Trp/Arg (N=240)

HbA1c (%)

Mean ± SD

8.28±1.72

8.33±1.76

5.75±0.51

5.88±0.54

R2

0.098

0.019

0.087**

0.085

HOMA-IR

Mean ± SD

3.84±1.99$

4.61±1.02$

2.75±1.78

2.80±1.21

R2

0.057Ω

0.031

0.013

0.059

 

 

No Dyslipidemia

 

 

PPARG2

 

 

Reference Allele
Pro/Pro (N=197)

Variant Allele Pro/Ala (N=43)

Reference Allele Pro/Pro (N=225)

Variant allele Pro/Ala (N=57)

HbA1c (%)

Mean ± SD

8.17±1.68**

7.26±1.63**

5.68±0.53

5.58±0.54

R2

0.044

0.161

0.095£

0.024

HOMA-IR

Mean ± SD

3.64±1.74#

3.06±1.49#

2.61±1.41

2.53±1.24

R2

0.011

0.285W

0.012

0.113

 

 

ADRB3

 

 

Reference Allele
Trp/Trp N=201)

Variant Allele
Trp/Arg (N=39)

Reference Allele
Trp/Trp (N=240)

Variant Allele
Trp/Arg (N=42)

HbA1c (%)

Mean ± SD

7.76±1.28

8.01±1.87

5.62±0.52

5.71±0.47

R2

0.018

0.392Δ

0.066

0.212

HOMA-IR

Mean ± SD

3.09±2.22

3.54±1.99

2.45±1.05

2.61±1.42

R2

0.072δ

0.232

0.026

0.054

Table 3: Influence of allelic polymorphism and lipid status on hemoglobin glycation
and insulin resistance.

Linear Regression (LR) mentioned as R2
P values: *0.048; ** 0.001; $0.005; # 0.043; £ 0.000; @ 0.040; Ω 0.019; Δ 0.002; 0.003; δ 0.012

ADRβ3 variant allele bearing dyslipidemic diabetic individuals exhibited a higher HOMA-IR (p=0.005; Linear regression: r2=0.057, p=0.019) than the reference ADRβ3 allele bearing dyslipidemic diabetics suggesting a possible connection of variant allele to dyslipidemia and diabetogenicity; while such a difference was not seen in non-dyslipidemic diabetics. Control subjects did not show such difference (Table 3).

Assessment of rs1801282 and rs4994 allelic combinations with biochemical characteristics

rs1801282 of PPARγ2 gene and rs4994 of ADRβ3 gene allelic combinations yielded four allelic types [Allelic type-1: PPARγ2ref+ADRβ3ref (Pro/Pro+Trp/Trp); Allelic type-2: PPARγ2ref+ADRβ3va (Pro/Pro+Trp/Arg); Allelic type-3: PPARγ2va+ADRβ3ref (Pro/Ala+Trp/Trp); Allelic type-4: PPARγ2va+ADRβ3va (Pro/Ala+Trp/Arg)]. For assessment of interactions, we dichotomized biochemical parameters viz. HbA1c, HOMA-IR into normal/high and lipid status into dyslipidemia/non-dyslipidemia [HbA1c≥7.0% in T2DM and HbA1c≥5.7% in controls was regarded as high in accordance with IDF recommendations].22The cut-off of HOMA-IR (3.95) was determined on ROC analysis [specificity: 87.8% (95% CI: 84.7-90.4%); sensitivity: 32.45% (95% CI: 28.3-36.8); +LR: 2.66 (95% CI: 2.1-3.4); -LR: 0.77 (95% CI: 0.7-0.8); +PV: 77.7%; -PV 58.9%; Supplementary Figure 1].

Supplementary Figure 1

We compared the Mean±SD of HbA1c and HOMA-IR with aforementioned four allelic combinations. A higher HbA1c and HOMA-IR was seen in allelic combination-2 as compared to combination-3 (HbA1c: T2DM p=0.050, Controls p=0.001) and (HOMA-IR: T2DM p=0.021, Controls p=0.001); Figure 2] suggesting a likelihood of association of PPARγ2ref and ADRβ3va alleles with poor glycation and insulin resistance in our study population.

When presence or absence of dyslipidemia was added to these allelic combinations, allelic type-2 bearing T2DM individuals but not control individuals had higher HbA1c (p=0.042) and HOMA-IR (p=0.002) than allelic type-3 (Figure 3A & B). Similarly, non-dyslipidemic individuals with allelic type-2 also demonstrated higher HbA1c (T2DM p=0.033; Controls p=0.001) and HOMA-IR (T2DM p=0.010; Controls p=0.001) than individuals (both diabetics and controls) with allelic type-3 again suggesting a likelihood of association of reference allele of PPARγ2 gene and variant allele of ADRβ3 gene to poor glycation and insulin resistance.

Figure 3: Comparison of allelic combinations of rs1801282 of PPARγ2gene and rs4994 of ADRβ3gene with HbA1c and HOMA-IR in dyslipidemic and non-dyslipidemic subjects.

We further elaborated our results with binary logistic regression model [where all these bifurcated (low/high; dyslipidemic/non-dyslipidemic) biochemical parameters were juxtaposed and compared to all six possible combinations of allelic types (Table 4)]. Again, presence of high HbA1c [p=0.034; LR (Likelihood Ratio): 4.559, p=0.033] and dyslipidemia [p=0.012; LR: 6.372, p=0.012] were significantly connected to allelic type-2. On the other hand, higher HOMA-IR was seen in all allelic combinations with type-4 [Higher Exp(B) between

  1. allelic type-1 vs allelic type-4 (LR: 3.870, p=0.049),
  2. allelic type-2 vs allelic type-4 (LR: 3.675, p=0.055), and
  3. Allelic type-3 vs allelic tye-4 (LR: 5.808, p=0.016); Table 4] possibly suggesting a connection of ADRβ3 variant allele to higher HOMA-IR.

 

 

 

Logistic Regression

 

Exp(B)

Allelic type-1
vs
Allelic type-2

HbA1c (%)

Normal

1.362

High

1.296

HOMA-IR

Normal

1.152

High

0.977

Lipid Status

No Dyslipidemia

1.145

Dyslipidemia

1.379

Allelic type-1
vs
Allelic type-3

HbA1c (%)

Normal

0.785

High

0.909

HOMA-IR

Normal

0.888

High

0.736

Lipid Status

No Dyslipidemia

0.897

Dyslipidemia

0.772

Allelic type-1
vs
Allelic type-4

HbA1c (%)

Normal

1.029

High

1.170

HOMA-IR

Normal

1.084

High

857.944

Lipid Status

No Dyslipidemia

1.168

Dyslipidemia

1.111

Allelic type-2
vs
Allelic type-3

HbA1c (%)

Normal

0.814

High

0.452*

HOMA-IR

Normal

0.685

High

0.554

Lipid Status

No Dyslipidemia

0.838

Dyslipidemia

0.449**

Allelic type-2
vs
Allelic type-4

HbA1c (%)

Normal

0.943

High

1.255

HOMA-IR

Normal

1.103

High

25420.268

Lipid Status

No Dyslipidemia

1.289

Dyslipidemia

1.017

Allelic type-3
vs
Allelic type-4

HbA1c (%)

Normal

1.967

High

1.936

HOMA-IR

Normal

1.614

High

1166721302.765

Lipid Status

No Dyslipidemia

1.983

Dyslipidemia

2.688

Table 4: Interactions of allelic combinations with glycated hemoglobin, HOMA-IR and lipid status.

* p=0.034; ** p=0.012;
Allelic type-1: PPARG2ref +ADRB3ref (Pro/Pro + Trp/Trp);
Allelic type-2: PPARG2ref +ADRB3va (Pro/Pro + Trp/Arg);
Allelic type-3: PPARG2va+ADRB3ref (Pro/Ala + Trp/Trp);
Allelic type-4: PPARG2va+ADRB3va (Pro/Ala + Trp/Arg)

Discussion

T2DM is the most common complex metabolic disorder influenced by genetic, environmental and lifestyle factors. Although the causes of T2DM cannot be pinpointed, many studies have shown a strong genetic link [20,39] to interactions of several candidate genes as an underlying etiology [25,26]. rs1801282 of nuclear receptor PPARγ2 stimulates transcription of several genes implicated in adipogenesis, lipid metabolism and insulin signaling and might possibly affect insulin resistance. Alanine (G allele, variant allele) is shown to favor α-helix formation of PPARγ2 gene product and activate amino terminal resulting into activation of ligand-independent transcription. Conversely, Proline (C allele, reference allele) modulates α-helix formation in a manner that predisposes to insulin resistance [20,21,23]. Thus, the variant allele is likely to have a protective role in insulin resistance, pathophysiology of T2DM and dyslipidemia. A study reported the relation of Ala allele of PPARγ2 gene to lower the T2DM risk in Caucasians, and in over weight subjects it was found to be associated with lower insulin with increased insulin sensitivity [22]. Conversely, the Pro allele of PPARγ2 gene is likely to increase insulin resistance and subsequent dyslipidemia in a meta-analysis [40]. The authors reported a frequency of Ala (variant allele) in the range of 8.0% to 51% in Caucasians (a subset of entire population). HAPMAP reported frequency of Ala (Variant) allele is in the range of 0 to 10%. We observed a frequency of Ala (variant) allele similar to GIH (Gujarati Indians in Houston, Texas, 9.09%) population of HAPMAP [41]. We observed that 79.80% (N=158/198) individuals bearing variant allele of PPARγ2 had HOMA-IR below ROC cut-off supporting the previous reports of its likely association to insulin sensitivity. Conversely, high HOMA-IR (> ROC cut-off) was observed in 188/852 (22.06%) individuals bearing reference allele favoring its association to insulin resistance irrespective of the diabetic status. Two studies reported an association of Ala allele to reduced binding affinity to promoter elements and reduced ability to transactivate response elements of the responsive genes and improved insulin sensitivity [23] and reduced risk of insulin resistance with reduced TG levels in non-diabetics [21]. Though in the current study, we did not observe differences in incidence of dyslipidemia amongst diabetics and controls bearing Ala allele of PPARγ2 gene. Mohan et al5 also concluded that despite the frequency of the Ala allele at the PPARg-Pro12Ala locus being the same in individuals of South Asian descent, as in Caucasians, this particular polymorphism does not appear to improve insulin sensitivity or decrease risk for type 2 diabetes in South Asians (Asian Indians), as it does in Caucasians.

Nonetheless, all dyslipidemic individuals of our study with Pro allele of PPARγ2 gene had higher HbA1c and HOMA-IR than those with Ala allele (of PPARγ2gene) again favouring a likely association of this variant to predisposition for poor glycation and insulin resistance. A meta-analysis exhibited Ala allele of PPARγ2 gene, to confer a risk reduction of T2DM by 21% [17]. Thus, there has been a consensus that PPARγ2 variant allele confers ‘protection’ against T2DM and its likely manifestations in the form of reduced insulin resistance, reduced BMI and decreased lipid parameters. Conversely, presence of reference allele of PPARγ2 gene has an opposite effect. Similarly, a study performed on South Asians (a subset of entire population) concluded that variant allele of PPARγ2gene does not offer any protective role [26].

ADRβ3 gene variant (rs4994) product, largely found in visceral adipocytes regulate lipolysis and mediate catecholamine induced cAMP activation by G-proteins [24]. The exact mechanism of ADRβ3 gene in modulation of insulin secretion is not known, but glucose stimulated insulin secretion from pancreatic β cells is known to be potentiated by epinephrine. Lower levels of cAMP with variant allele of ADRβ3 gene in pancreatic β cells may reduce insulin release [24]. Polymorphism of aforesaid gene (A allele, Trp64Arg) is shown to be associated to abdominal obesity and insulin-resistance [24,42,43]. Visceral obesity is also known to be associated with reduced FFA uptake and impaired insulin action that result into insulin resistance in skeletal muscle due to higher free fatty acids (FFA) or triglycerides [42,43]. Present study demonstrate significant association of variant allele rs4994 of ADRβ3 gene with an increased risk of T2DM, most likely to be due to higher HOMA-IR in dyslipidemic T2DM patients. The variant allele of ADRβ3 gene is also reported to be linked to insulin resistance in obese Italian males [44] and metabolic syndrome (obesity and low HDL-C) in Kyrgyz people [45]. HAPMAP reported frequencies of reference- (A allele) and variant (G allele) alleles of rs4994 of ADRβ3 gene ranges from 80-96% and 3.9-19.1% respectively. We found a relatively higher frequency of variant allele (17.14%) similar to HAPMAP population of CHB (H) [Han Chinese in Beijing, China; 12.40%], CHD (D) [Chinese in Metropolitan Denver, Colorado; 14.67%], GIH (G) [Gujarati Indians in Houston, Texas; 13.36%], JPT (J) [Japanese in Tokyo, Japan; 19.19%], MEX (M) [Mexican ancestry in Los Angeles, California; 12.04%] and MKK (K) [Masai in Kinyawa, Kenya; 13.14%] [46-48]. This allele is reported to be connected to insulin resistance in a meta-analysis of 31 studies [44].

While juxtaposing aforementioned alleles to link IR, T2DM and dyslipidemia, we observed a significantly higher HbA1c, increased IR and dyslipidemia in individuals with allelic type-2 compared to allelic type-3.  This point towards a possibility that co-existence of reference allele of PPARγ2 gene and variant allele of ADRβ3gene in an individual may predispose to develop biochemical deregulation connected to T2DM, poor hemoglobin glycation, dyslipidemia and subsequent metabolic syndrome. Likewise, co-existence of reference allele of ADRβ3gene and variant allele of PPARγ2 gene is likely to be protective against T2DM. A similar finding has been reported by Moon et al. in Korean population [49].

The present study has also shown the likelihood of high HOMA-IR when allelic type-4 was present compared to type-1 to type 3 [as shown by high Exp(B) on logistic regression]. A similar report by Ochoa et al. also demonstrated higher BMI, serum leptin and insulin with co-existence of variants of PPARγ2 gene and ADRβ3gene [32].

Coexistence of alleles in population is a natural phenomenon. Such allelic combinations along with life style predisposition may trigger epigenomic changes by increased DNA methylation of the genes and hold a clue to design tailored diagnostic, therapeutic and preventive efforts to control or subvert the disease. The functionality of the resultant proteins and their impact on metabolic pathways is greatly modified by epigenetic studies and protein-protein interaction. The allelic interactions reported here need further insight into epigenetics of these allelic combinations in pathophysiology of T2DM [32,33,50].

Conclusion

The present study demonstrate that coexistence of reference allele of PPARγ2 gene and variant allele of ADRβ3 gene together with dyslipidemia in subjects with T2DM is likely to confer poor hemoglobin glycation and  increased insulin resistance; thus demonstrating a likelihood of T2DM predisposition in western Indian population.

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