Chapter 4 INSIG2 Polymorphism and BMI in Indian Population

Transcription

1 Chapter 4 INSIG2 Polymorphism and BMI in Indian Population

2 4.1 INTRODUCTION Diseases like cardiovascular disorders (CVD) are emerging as major causes of death in India (Ghaffar A et. al., 2004). Various studies have shown that higher body mass index (BMI) or accelerated gain in BMI is associated with an increased risk for CVD (Must A et. al., 1992; Dietz WH et. al., 1998; Eriksson JG et. al., 2001; Gunnell DJ et. al., 1998;). BMI is used as a surrogate measurement for obesity and is calculated as weight/(height) 2 in kg/m 2. Individuals with a BMI > 25 kg/m 2 are classified as overweight and those with a BMI >30 kg/m 2 are considered obese. The incidence of obesity worldwide is on the rise and has reached epidemic proportions in many developed and developing countries. According to the World Health Organization, there are an estimated 1 billion adults who are overweight and 300 million of these are considered clinically obese. The criterion of being obese can vary in different ethnic groups. South Asians have a lower muscle mass and a higher percentage body fat. They are more centrally obese than the whites of comparable ages and BMIs (McKeigue PM et. al., 1991; Chowdhury B et. al., 1996; Banerji MA et. al., 1999; Chandalia M et. al., 1999; Yajnik CS, 2004; Yajnik CS et. al., 2004). In India, modified World Health Organization guidelines are used to define obesity. According to these guidelines, individuals with BMI > 23kg/m 2 are classified as overweight while those with BMI > 25 kg/m 2 are defined as obese (Sachdev HS et. al., 2005). Obese individuals are at an increased risk of suffering from Type 2 Diabetes mellitus, CVD, metabolic syndrome, hypertension, stroke resulting in premature mortality (Vanhala M et. al., 1998). The current trend of decreased physical activity and increased caloric intake acting on a person s genetic makeup is mainly attributable to the rise in obesity. The common forms of obesity have a strong genetic component. Based on family studies and twin studies it has been estimated that the heritability of obesity ranges from 30% to 70% (Allison DB et. al., 1996). Several studies have shown associations between BMI or obesity and common genetic variants (Perusse L et. al., 2003; Duarte SF et. al., 2006). However, none of these have been consistently replicated. There are many interesting 80

3 candidate genes in the list including leptin (Duarte SF et. al., 2006), melanocortin 4 receptor (MC4R) (Perusse L et. al., 2003). The leptin and melanocortin pathways are assumed to play important roles in rare obesity cases and not the common forms of obesity. Linkage studies have also been carried out to identify regions linked to obesity albeit with limited success as the results vary greatly (Deng HW et. al., 2002). Recently, Herbert et. al. used a dense whole-genome scan of DNA samples from families enrolled in the Framingham Heart Study and identified a common genetic variant (rs ) about 10 kb upstream of the transcription start site of Insulin induced gene 2 (INSIG2) (Herbert A et. al., 2006). They reported the association of the CC genotype with obesity in three different family-based samples and three studies of unrelated individuals. Furthermore, a meta analysis of case-control samples showed that the CC genotype was significantly associated with obesity under recessive model. The protein product of INSIG2 inhibits the synthesis of fatty acid and cholesterol and thus in individuals with altered INSIG2 activity it can be perceived to result in obesity due to elevated triglyceride levels with subsequent storage in adipose tissues (Yabe D et. al., 2002). Interestingly, INSIG2 region has been identified as a factor in obesity by linkage studies both in mice and humans (Deng HW et. al., 2002; Cheverud JM et. al., 2004). In our AIIMS study, we have found that CAD patients had a significantly higher BMI than controls after adjusting for other confounding factors like diet, age, sex, homocysteine levels, vitamin B12 levels, hypertension, diabetes mellitus etc. Increased BMI increases the mortality in adult and elderly patients with CAD (Stevens J et. al., 1998; Rea TD et. al., 2001). In this study, we thus attempted to find out if the genetic variant rs upstream of INSIG2 gene is associated with BMI, as reported by Herbert et. al. in a cohort of 610 unrelated CAD patients and controls from our AIIMS study individuals. Since India has a large social, cultural, linguistic and biological diversity, we also screened more than 1500 individuals from various parts of the country and checked for the association of this variant with BMI. 81

4 4.2 MATERIALS AND METHODS Individuals were recruited as a part of the Indian Genome Variation Consortium (IGVC) project from various populations based on certain criteria that include linguistic lineages, ethnicity, geographical location etc. Keeping in view the linguistic and geographical diversity in India, we genotyped 1577 individuals from 37 sub-populations belonging to three linguistic lineages (Indo European, Dravidian and Tibeto-Burman) from different geographical locations of the country. Blood samples were also collected from a cohort consisting of 255 angiographically proven CAD patients and 355 controls recruited at a tertiary care centre, New Delhi, India. Individuals in this cohort consisted mainly of Indo-European linguistic lineage. The ethics committee approved this study. Written consent was obtained from all the participants. A detailed questionnaire was filled for each individual that included information about subject s height, weight measurements, etc. Blood samples were collected from volunteers into tubes containing anticoagulant and plasma was separated from the blood samples within an hour of collection. DNA was isolated from the blood samples after removal of the plasma using the modified salting out method as described earlier (Miller SA et. al., 1988). Oligonucleotides for PCR and genotyping were designed using the primer select module of the software DNA star (Table 4.1). Table 4.1: List of oligonucleotides used for PCR amplifications and genotyping. S.No. Primer Name Seuqence (5 to 3 ) Product length 1 INSIG2-For CTCCTACCTCCCTCCAATACC C INSIG2-Rev TCCCCGCCTCGTTTTCTAAG 3 INSIG2-SS CAGACCTAAAGGACCAC Polymerase chain reaction (PCR) conditions were optimized by varying MgCl2 concentrations and annealing temperature. PCR was carried 82

5 out using GeneAmp PCR system 9700 (Applied Biosystems, Foster City, USA), in a total volume of 10µl with 1.5mM MgCl2, 0.1mM of each dntp (Amersham Biosciences, New Jersey, USA), 20ng of genomic DNA, 0.1 picomol/µl of each forward and reverse primer, 0.03U/µl of Taq DNA polymerase (Bangalore Genie, India) and the buffer recommended by the supplier. SNaPshot reactions were set using ~50-75ng of the purified amplicon, 2 pmol of the genotyping primer, 0.5µl of the SNaPshot ready reaction mix (ABI, Foster City, CA) and milliq water to make the final volume up to 5µl. An aliquot (1 µl) of this product was analyzed by electrophoresis in a DNA analyzer (ABI Prism 3700). The software GeneScan analysis (ABI, Foster City, CA) was used to analyze the results. Genotypes were checked for the conformance of Hardy-Weinberg equilibrium using De-finetti program ( ). Genotypic associations were performed under the assumption of different models. Statistical analyses were performed using Simple Interactive Statistical Analysis (SISA) ( and Statistical Package for Social Sciences (SPSS) Windows, version 10. P-value <0.05 was interpreted as statistically significant. 83

6 4.3 RESULTS To study the relevance of the variant rs with respect to obesity in Indian population, we genotyped individuals from two studies- 1) AIIMS study and 2) IGVC study. General characteristics of the individuals recruited in the study are given in table 4.2. Genotypic and allelic distribution for the polymorphism rs in these cohorts is shown in table 4.3. Table 4.2: General/clinical characteristics of the individuals recruited for the study. Characteristics AIIMS Cohort IGV Cohort Age (Years)* 50 (45-58) 36 (27-48) Male (Number/%) 522 (86%) 900 (58%) BMI (Kg/m 2 )* 24.8 ( ) 22.0 ( ) Obese (Number/%) 301 (49%) 397 (26%) * Values shown are median (Interquartile Range). Table 4.3: Genotype distribution and allele frequencies of INSIG2 gene polymorphism in the two cohorts studied. Name of Cohort No of Genotypes Allele Frequency GG (%) GC (%) CC (%) G C AIIMS 313 (51.3) 253 (41.5) 44 (7.2) IGVC 774 (49.1) 659 (41.8) 144 (9.1) The minor allele frequency was found to be 0.30 and 0.28 in the two cohorts which was comparable to that reported by Herbert et. al. (0.37) (Herbert A et. al., 2006). The genotypic distribution in samples categorized as obese (BMI > 25 kg/m 2 ) and non-obese (BMI < 25 kg/m 2 ) is shown in table 4.4. The frequency of CC genotype was significantly higher in non-obese individuals as compared to the obese individuals under a recessive model in our AIIMS study (p=0.04) (Table 4.4). This trend of higher frequency of CC genotypes in non-obese individuals was also observed in the IGVC although the difference in the distribution was not statistically significant in this case (Table 4.4). These observations are in contrast to the earlier report where Herbert et. al. showed that the CC genotype was significantly more frequent in obese individuals. 84

7 Table 4.4: Genotypic variations at rs polymorphism between obese (BMI > 25 kg/m 2 ) and non-obese (BMI < 25 kg/m 2 ) under dominant and recessive models. Study Population Model Genotype Number (% Obese) Number (% Non-obese) Chi-square (p value) AIIMS Cohort Dominant GG 155 (52.2) 158 (50.5) 0.2 (0.67) CG+CC 142 (47.8) 155 (49.5) Recessive GG+CG 282 (95.0) 284 (90.8) 4.1 (0.04) CC 15 (5.05) 29 (9.2) IGVC Cohort Dominant GG 188 (49.5) 586 (49.0) 0.03 (0.9) CG+CC 192 (50.5) 611 (51.0) Recessive GG+CG 351 (92.4) 1082 (90.4) 1.4 (0.2) CC 29 (7.6) 115 (9.6) In general, Indian population to a large extent can be sub-structured on the basis of their ethnic origin as well as linguistic lineages. Linguistically, Indian population can be classified into four major families: Indo-European, Dravidian, Tibeto-Burman and Austro-Asiatic. When the samples of our IGVC were further classified on the basis of the linguistic lineage similar observations were obtained in Indo-European and Dravidian samples (Table 4.5). Table 4.5: Genotypic variations at rs polymorphism between obese (BMI > 25 kg/m2) and non-obese (BMI < 25 kg/m2) in three linguistic groups from IGVC cohort. Study Population Model Genotype Number (% Obese) Number (% Non-obese) Chi-squre (p value) Indo-European Dominant GG 130 (44.7) 449 (48.9) 1.6 (0.21) CG+CC 161 (51.5) 469 (50.3) Recessive GG+CG 271 (93.2) 829 (90.3) 2.3 (0.13) CC 20 (6.8) 89 (9.7) Dravidian Dominant GG 46 (67.7) 95 (50.3) 6.2 (0.01) CG+CC 22 (32.3) 94 (49.7) Recessive GG+CG 62 (91.2) 167 (88.4) 0.4 (0.52) CC 6 (8.8) 22 (11.6) Tibeto-Burman Dominant GG 10 (52.6) 41 (47.1) 0.2 (0.66) CG+CC 9 (47.4) 46 (52.9) Recessive GG+CG 16 (84.2) 83 (95.4) 2.5 (0.11) CC 3 (15.8) 4 (4.6) 85

8 The percentage of CC genotype was found to be 6.8 and 9.7 in Indo- European samples for obese and non-obese categories respectively. However, the difference was not statistically significant. Similarly, Tibeto-Burman population also did not show any association. However, in Dravidian population, the percentage of CC genotype was 8.8 and 11.6 in obese and nonobese categories respectively (Table 4.5). The difference in distribution of genotype with respect to obesity was found to be statistically significant under the assumption of recessive model. We also checked the status of BMI in our IGVC cohort as a function of age, sex and genotype. Under the assumption of a recessive model, BMI did not vary significantly in individuals with CC genotype as compared to those with GG or CG genotype regardless of age (Figure 4.1). Data for individuals below the age of 20 and over the age of 70 were excluded as there were very few samples with CC genotype in this age group. Even when the samples were segregated on the basis of sex, we didn t find significant difference in BMI as a function of age (Table 4.6). Figure 4.1: BMI as a function of age and genotype. Mean BMI comparison between individuals with CC genotype (homozygous variant) and CG+GG genotypes (homozygous wild type and heterozygous genotypes respectively). 86

9 Table 4.6: Effect of age, sex and genotype on BMI. Age Group (Years) Total Genotype Total Individuals Males Females GG+CG CC Total Genotype GG+CG CC Total Genotype GG+CG CC Upt (3.1) 20.9 (3.1) 20.1 (3.0) 20.9 (2.8) 20.9 (2.8) 20.8 (2.9) 20.6 (3.7) 20.8 (3.7) 17.7 (2.6) (10.5) 21.7 (3.3) 21.1 (3.4) 21.8 (3.2) 21.9 (3.2) 21.7 (2.7) 21.2 (3.6) 21.4 (3.5) 20.1 (4.2) (4.4) 22.7 (4.3) 22.4 (4.7) 22.8 (4.2) 22.8 (4.3) 22.6 (4.2) 22.6 (4.5) 22.7 (4.4) 22.3 (5.o) (4.8) 23.7 (4.6) 24.3 (6.3) 23.5 (4.8) 23.4 (4.6) 24.2 (6.8) 24.2 (4.8) 24.1 (4.7) 24.4 (5.7) (5.1) 23.7 (5.2) 21.8 (3.5) 23.2 (5.4) 23.4 (5.4) 19.5 (1.8) 23.9 (4.8) 24.0 (4.9) 23.1 (3.6) (4.8) 22.4 (4.9) 21.7 (4.2) 22.4 (4.6) 22.5 (4.7) 23.3 (6.3) 22.1 (5.2) 22.2 (5.3) 25.o* Values shown are mean BMI (Standard Deviation). *Only one individual was observed in this category. 87

10 4.4 DISCUSSION Obesity is emerging as an important health problem and acts as a strong risk factor for various diseases like CAD, type 2 diabetes, hypertension, stroke etc. Although several genetic variants have been associated with obesity none of these has been consistently replicated. In this regard, the polymorphism rs in the upstream region of INSIG2 gene has raised great hopes as it has been shown to be associated with obesity in four out of five different populations studied by Herbert et. al. (Herbert A et. al., 2006). However, we failed to find any significant association of this polymorphism with BMI in Indian population. The fact that polymorphism reported to be associated with obesity does not show any association in Indian population is not entirely surprising as several instances of differences between Indian and world populations are being increasingly revealed in complex disorders (Kumar J et. al., 2005; Kukreti R et. al., 2005; Sharma S et. al., 2005; Verma R et. al., 2005). Various other studies done in different cohorts from many countries have also failed to show the association in few of these cohorts (Smith et. al., 2007; Lyon HN et. al., 2008). Lyon et. al. tested the effect of INSIG2 polymorphism on about individuals from eight different populations using family based, population based and case-control study designs. Out of eight cohorts studied, they were able to find out the association in five cohorts but failed to observe any association in another three cohorts. Smith et. al. also failed to detect any association of the polymorphism in Caucasian, Afro-Caribbean and Indian individuals (Smith et. al., 2007). Differences in the genetic and environmental modifiers among populations might be a possibility for observing differences in associations. Since this SNP is present in the intergenic region, it might be possible that it is in linkage disequilibrium with some other SNP that is playing a role in obesity. Dense SNP map around this SNP needs to be scanned to resolve this issue. Further, more population needs to be screened to find out the exact position of the SNP in relation to obesity. Additional data regarding confounding factors for obesity also needs to be considered in the further studies to esimeate the exact role of this SNP. 88

11 In conclusion we have shown that the newly discovered association of the variant upstream of INSIG 2 gene (rs ) is not relevant in Indian population with respect to BMI. Further studies on the determinants of BMI are required to obtain a clear picture of high risk population for both extremes of BMI. 89