Subjects and Phenotypic Characterization
The characteristics of study population, the inclusion and exclusion criteria of SAPPHIRe study were detailed in several previous publications [9, 10]. Notably, diabetic subjects diagnosed based on the WHO criteria were excluded [27]. This study incorporated both the concordant (both hypertension) and the discordant sib-pairs (one hypertension, the other hypotension) in design. A total of 2525 subjects of Japanese or Chinese descendents were recruited from 6 centers at San Francisco, Hawaii and Taiwan. In this report, 1793 subjects including parents and sibs from 601 families were genotyped. Written informed consent was obtained form all participants.
The study was approved by the ethics board of each participating institute.
Characterization of the subjects by fasting plasma glucose, insulin, triglyceride, total cholesterol, lipoprotein profiles, OGTT and anthropometric measurements, including height, weight, waist and hip circumferences were previously described in details previously [9, 10]. A 75-g oral glucose tolerance test (OGTT) was performed and plasma glucose and insulin levels at 1- hour and 2 hour2-hour post-glucose load wereas measured [9, 10]. The fasting plasma sample was obtained right before the OGTT was conducted.
Extraction of genomic DNA and genotyping
Total genomic DNA was purified from peripheral blood leukocytes using DNA extraction kit of Puregene (Minneapolis, MN, USA), following the manufacturer’s protocol. The primers used for PCR amplification for the exon 2 of human adiponectin gene were: 5’-TAG AAG TAG ACT CTG CTG AGA TG-3’ and 5’-CTC CCT GTG TCT AGG CCT TAG–3’. The PCR reactions were performed in a total volume of 15 l containing 20 ng of genomic DNA with an initial denaturation at 94oC for 5 min, followed by denaturing at 94oC for 30 sec, annealing at 68 to 60oC for 1 min with the annealing temperature stepping down 2oC for every 5 cycles, and polymerization at 72oC for 40 sec; then by a final extension at 72oC for 10 min. Four
l of the amplified DNA was digested with BspHI enzyme (New England BioLabs, Inc., Beverly, MA, USA), and then electrophoresed on a 2% agarose gel. The
Statistical Analyses
Association analysis for adiponectin genotypes- All variables were in S.I. units except that in HOMA-IR. The data were given in means and S.E. In order to compare each outcome variable between the G variant-carrying individuals (including the genotypes T/G and G/G) and their sibs with the genotype T/T, only the siblings with discordant genotypes in T94G polymorphism of the adiponectin gene were included in the analysis. Paired analysis was used to test whether there was difference in variables of interest between the sibs discordant for genotypes after adjustment for age, gender, BMI, ethnicity and area of enrollment BMI by employing analysis of covariance with these variables as covariates. The residuals were then used to test the null hypothesis that there is no difference in the adjusted variables between genotype-discordant sibs among all families (Table 2). The analyses were performed using the SAS version 8.0 PROC GLM. We also performed analysis of covariance using a mixed model to assess whether the adiponectin genotypes affect the outcome variables controlling for covariates age, gender, BMI, ethnicity and area of enrollment BMI as fixed effects, and controlling for clustering among families as a random effect (Ttable 23). The analyses were performed using the SAS 8.0 PROC MIXED. All statistical tests were two-tailed. The p-values less than 0.05 were considered statistically significant. These statistical analyses were previously described [9, 10].
Analyses on adiponectin gene and PPAR2 gene interactions- In order to assess the interactions between the adiponectin and PPAR2 genotypes, we performed analysis of covariance using a mixed model to evaluate the contribution of the main effects of adiponectin T49G, PPAR2 Pro12Ala respectively, and their interactions for each variable of interest in the same model controlling for covariates age, gender, BMI ethnicity and BMI area of enrollment as fixed effects and entering family as a random effect (Ttable 34). The analyses were performed using the SAS 8.0 PROC MIXED.
The differences of several variables between the subjects with different adiponectin genotypes within fixed PPAR2 genotypes after adjusting for age, gender, ethnicity, BMI, area of enrollment and family effects were compared (Ttable 45, Ffigure 1). The values/bars reported are the least square means for each subgroup. The p values were obtained using the SAS PROC MIXED.
RESULTS
The genotype and allele frequencies of adiponectin T94G polymorphism in Japanese and Chinese subjects were comparable (Table 1A). The genotype and allele frequencies in the hypertensive probands were similar and did not deviate from Hardy-Weinberg equilibrium (Table 1B). The genotype and allele frequencies, and the basic characteristics of these subjects categorized by the PPAR2 genotypes were reported previously [9]. Using a sibling-based comparison for phenotypic variables, we found that the subjects with the T/T genotype (n=282) had significantly higher mean plasma insulin level at 1 hour (Ins60) during oral glucose tolerance tests (OGTT) than that of the sibs with the discordant genotypes (T/G and G/G, n=312) after adjustment for age, gender, BMI, ethnicity and the area of enrollment BMI ((562±27 vs. 483±18 pmol/L, p=Table 20.0067). The differences in three other variables including in the mean area under curve of plasma insulin (AUCi) in OGTT, total cholesterol and LDL cholesterol wasere of borderline statistical significance (Table 2828±37 vs. 743±27 pmol.h/L, p=0.0578). The differences between dis-cocordant adiponectin genotypes in the other variables did not reach statistical significance (data not shown).
To confirm the above findings, we also used a mixed model to correct for familial effects. This method enabled us to include more subjects for analyses. The basic characteristics of the subjects included in this analysis were shown in Table 1(C).
After adjusting for age, gender, BMI, ethnicity and area of enrollment BMI, only the means in Ins60 and AUCi in OGTT were significantly different between subjects with the T/T genotype and those otherwise (Table 32). Post-glucose load hyperinsulinemia among subjects with the T/T genotypes in order to normalize plasma glucose levels shown in these two analyses suggests that they may be more insulin resistant. We also performed the analyses by categorize the subjects into three adiponectin genotypes (n=844 of T/T, n=701 of T/G and n=168 of G/G). The results were quite similar. Only the mean Ins60 (525±18 vs. 455±20 vs. 461±35 pmol/L, p=0.0035) and AUCi (785±26 vs. 702±28 vs. 719±50 pmol.h/L, p=0.0204) were significantly different among the three genotypes. The differences were primarily between the T/T and the other two genotypes, suggesting a dominant effect of the G allele in association with insulin sensitivity. Thus this allowed us to pool G/T and G/G genotypes together in all the analyses. No significant difference among the three genotypes was observed in the other variables (data not shown).
Next we investigated the effects of genetic interactions between the adiponectin T94G and the PPARγ2 Pro12Ala polymorphisms. Previously, we found in the same study population that the PPARγ2 Ala12 variant was significantly associated with better glucose tolerance at 0 (Glu0) and 1 hour (Glu60) in OGTT and with lower
insulin resistant index by HOMA (HOMAIR) [9]. Using two-way ANOVA analysis, we found that there were significant interactions between the adiponectin and PPAR2 genotypes in insulin levels at 0 (Ins0) and 2-hour (Ins120) in OGTT and HOMAIR, with adjustment for age, gender, BMI, ethnicity and area of enrollment BMI, whereas their interactions in Glu0 and AUCi were lessof borderline significantnce (tTable 43).
Interestingly, the main effects of adiponectin genotypes were all significant on every parameter (Ins0, Ins60, Ins120 and AUCi) of insulin in OGTT and HOMAIR. The main effect of the adiponectin genotypes on Glu0 was of borderline significance (Ttable 3). In contrast, the main effects of PPAR2 genotypes were only significant on Glu0 Glu60 and area under curve of glucose (AUCg) in OGTT (Ttable 43).
To more clearly illustrate the effects of interactions, these subjects were grouped by their genotypes. It was apparent that the subjects carrying both the adiponectin G and PPAR2 Ala12 alleles were more insulin sensitive. They tended to have lower fasting plasma glucose and insulin, plasma insulin levels in OGTT, and HOMAIR
(Table 54, Figure 1). Subjects with the adiponectin G allele even when coupled with PPAR2 Pro/Pro still had lower insulin levels at 1-hour and AUCi in OGTT than subjects with the T/T genotype (Table 5 4 and Figure 1A).
DISCUSSION
In this report, we demonstrated that the adiponectin T94G genotypes indeed influenced wereas associated with the variation in insulin sensitivity, indicated indirectly by the serum insulin levels and insulin resistance index by HOMA (HOMAIR), in a cohort of Chinese and Japanese hypertension families recruited in the SAPPHIRe study. The subjects with the T/T genotype lower post-glucose load insulin levels but similar glucose levels than those with the other genotypes, indicating insulin resistance associated with T/T genotypes. It should be noted that diabetic subjects were excluded in the recruitment of SAPPHIRe study [10]. Compared with four recent population-based genetic association studies [15-18], the advantage of the current study is that this is a family-based association study utilizing siblings as controls. Because they have relatively higher sharing in both genetic and environmental backgrounds than the population controls, the observed phenotypic differences between them are more likely the results of genetic differences in interest [11].
There has been a great interest in tackling the genetic make-up of human complex traits or diseases, such as diabetes, obesity and hypertension. Although a huge amount of genetic data of such has accumulated in the past, still not very many have addressed the issue of genetic interactions [4, 5]. To our knowledge, our study is one of the earliest to investigate the genetic interactions in connection with insulin resistance. Significant interactions between the genotypes of adiponectin and PPAR2 in relation to fasting insulin and post-glucose load insulin level at 2-h and HOMAIR
were noted, providing a piece of genetic evidence in humans that these two genes may participate either in the same or in two interdependent pathways regulating related to the biological processes of insulin sensitivity. Consistent with this, previous studies in cell cultures, in animals and in humans have shown that adiponectin expression either at mRNA or at protein levels could be up-regulated by PPARγ2 activation [21-25].
This study also demonstrated that a genetic association study is capable of detecting genetic epistasis.
The molecular mechanisms behind the genetic interactions between adiponectin and PPAR2 genotypes are unclear at present, but would be fascinating to elucidate.
The T94G polymorphism of adiponectin is a silent mutation for Gly15 (GGT to GGG).
We speculated that it might be in linkage dis-equilibrium with the other genetic alterations, probably a regulatory mutation. On the other hand, the PPAR2 Ala12 variant was previously shown to have reduced transactivating ability [8]. Although only a half-site of PPAR response element (PPRE) was recognized in the proximal promoter of adiponectin by sequence analysis, synthetic PPAR2 agonists were
demonstrated to enhance transcription of luciferase driven by the proximal 2 kb of adiponectin promoter [22]. How PPAR2 variants may activate adiponectin gene in different promoter contexts is an important question to address in the future.
In conclusion, we found that the T94G polymorphism of the adiponectin gene was associated with insulin sensitivity, represented by serum insulin levels and HOMAIR, in the subjects from a large hypertensive family cohort. More importantly, the effect of adiponectin was modified by the PPAR2 Pro12Ala genotypes, indicating a genetic interaction in modulatingthe association with insulin sensitivity. These observations suggest that both the adiponectin and PPAR2 genes and the interactions between them may play a role in the etiopathogenesis of insulin resistance.
Acknowledgments. The authors would like to thank Ms. Chia-Ling Chao and Kuan-Ching Lee for their technical assistance. We thank patients and their families for participating in this study. We also thank Stephen Mockrin and Susan Old of the National Heart, Lung and Blood Institute, and the other members of the SAPPHIRe project for their help. This study was supported by grants (NSC -85-2331-B002-350Y, NSC -86-2314-B002-345Y, and NSC -87-2312-B002-021Y, and NSC 91-3112-B-002-019) from the National Science Council, a grant (BS-090-pp-01) from National Health Research Institutes, and a grant from the Department of Education (89-B-FA01-1-4), Taiwan, R.O.C. and; a grant (UO1 HL54527-0151) from the National Heart Lung and Blood Institute (USA). We also apologize to the authors whose work on PPAR2 could not be cited here because of the limitations on the number of references.
References.
4. Jansen H, Waterworth DM, Nicaud V, Ehnholm C, Talmud PJ (2001) Interaction of the common apolipoprotein C-III (APOC3 -482C > T) and hepatic lipase (LIPC -514C > T) promoter variants affects glucose tolerance in young adults. European Atherosclerosis Research Study II (EARS-II). Ann Hum Genet 65:237-243
5. Hsueh WC, Cole SA, Shuldiner AR et al. (2001) Interactions between variants in the beta3-adrenergic receptor and peroxisome proliferator-activated receptor-gamma2 genes and obesity. Diabetes Care 24:672-677
6. Altshuler D, Hirschhorn JN, Klannemark M et al. (2000) The common PPARgamma Pro12Ala polymorphism is associated with decreased risk of type 2 diabetes. Nat Genet 26:76-80
7. Beamer BA, Yen CJ, Andersen RE et al. (1998) Association of the Pro12Ala variant in the peroxisome proliferator-activated receptor-gamma2 gene with obesity in two Caucasian populations. Diabetes 47:1806-1808
8. Deeb SS, Fajas L, Nemoto M et al. (1998) Pro12Ala substitution in PPARgamma2 associated with decreased receptor activity, lower body mass index and improved insulin sensitivity. Nat Genet 20:284-287
9. Chuang LM, Hsiung CA, Chen YD et al. (2001) Sibling-based association study of the PPARgamma2 Pro12Ala polymorphism and metabolic variables in Chinese and Japanese hypertension families: a SAPPHIRe study. Stanford Asian-Pacific Program in Hypertension and Insulin Resistance. J Mol Med 79:656-664
10. Ranade K, Wu KD, Risch N et al. (2001) Genetic variation in aldosterone synthase predicts plasma glucose levels. Proc Natl Acad Sci USA 98:13219-13224
11. Stumvoll M, Haring H (2002) The peroxisome proliferator-activated receptor-gamma2 Pro12Ala polymorphism. Diabetes 51:2341-2347
12. Fruebis J, Tsao TS, Javorschi S et al. (2001) Proteolytic cleavage product of 30-kDa adipocyte complement-related protein increases fatty acid oxidation in muscle and causes weight loss in mice. Proc Natl Acad Sci USA 98:2005-2010
13. Berg AH, Combs TP, Du X, Brownlee M, Scherer PE (2001) The adipocyte-secreted protein Acrp30 enhances hepatic insulin action. Nat Med 7:947-953
14. Yamauchi T, Kamon J, Waki H et al. (2001) The fat-derived hormone adiponectin reverses insulin resistance associated with both lipoatrophy and obesity. Nat Med 7:941-946
15. Stumvoll M, Tschritter O, Fritsche A et al. (2002) Association of the T-G polymorphism in adiponectin (exon 2) with obesity and insulin sensitivity:
interaction with family history of type 2 diabetes. Diabetes 51:37-41
16. Hara K, Boutin P, Mori Y et al. (2002) Genetic variation in the gene encoding adiponectin is associated with an increased risk of type 2 diabetes in the Japanese population. Diabetes 51:536-540 haplotypes in the both proximal promoter and exon 3 of the APM1 gene modulate adipocyte-secreted adiponectin hormone levels and contribute to the genetic risk for type 2 diabetes in French Caucasians. Hum Mol Genet 11:2607-2614
20. Maeda N, Shimomura I, Kishida K et al. (2002) Diet-induced insulin resistance in mice lacking adiponectin/ACRP30. Nat Med 8:731-737
21. Kubota N, Terauchi Y, Yamauchi T, et al. (2002) Disruption of Adiponectin Causes Insulin Resistance and Neointimal Formation. J Biol Chem 277:25863-25866
22. Yang WS, Jeng CY, Wu DJ et al. (2002) Synthetic peroxisome proliferator–activated receptor-agonist, rosiglitazone, increases plasma levels of adiponectin in type 2 diabetic patients. Diabetes Care 25:376-380
23. Maeda N, Takahashi M, Funahashi T et al. (2001) PPARgamma ligands increase expression and plasma concentrations of adiponectin, an adipose-derived protein.
Diabetes 50:2094-2099
24. Hirose H, Kawai T, Yamamoto Y, et al. (2002) Effects of pioglitazone on metabolic parameters, body fat distribution, and serum adiponectin levels in Japanese male patients with type 2 diabetes. Metabolism 51:314-317
25. Combs TP, Wagner JA, Berger J, et al. (2002) Induction of adipocyte
complement-related protein of 30 kilodaltons by PPARgamma agonists: a potential mechanism of insulin sensitization. Endocrinology 143:998-1007
26. Yu JG, Javorschi S, Hevener AL et al. (2002) The effect of thiazolidinediones on plasma adiponectin levels in normal, obese, and type 2 diabetic subjects. Diabetes 51:2968-2974
27. World Health Organization (1985) Diabetes Mellitus: Report of a WHO Study Group. Geneva, World Health Org., (Tech. Rep. Ser., no. 727)
Legends to the figures:
Figure 1 A. Comparisons of AUCi between the subjects with different combinations of the adiponectin and PPAR2 genotypes, the bars showed meansS.E., * p=0.0357,
** p=0.0118. B. Comparisons of AUCg between the subjects with different combinations of the adiponectin and PPAR2 genotypes, the bars showed meansS.E.
Comparisons of HOMAIR between the subjects with different combinations of the adiponectin and PPAR2 genotypes, the bars showed meansS.E.M., *** p=0.0192.
C. Comparisons of HOMAIR between the subjects with different combinations of the adiponectin and PPAR2 genotypes, the bars showed meansS.E., ***p=0.0201.
Table 1. The proportions (%) and the numbers of subjects with specific adiponectin T94G genotypes and alleles among Chinese or Japanese subjects (A) andor hypertensive probands (B). Characteristics of the subjects discordant for the adiponectin genotypes. Data are compared in subjects with the T/T vs. those with the T/G or G/G (C).
Gender (male/female) 372/472 389/480 NS
Ethnicity (Chinese/Japanese) 619/225 631/238 NS Area
(Taiwan/San Francisco/Hawaii) 513/96/235 509/92/268 NS
Age (years) 50.780.38 50.880.38 NS
BMI* (kg/m2) 25.650.16 25.960.16 NS
Waist (cm) 85.710.47 86.640.47 NS
Waist/hip ratio 0.880.00 0.890.00 NS
*Abbreviation: BMI, body mass index
** NS for p >0.1
Table 2. Pair comparisons of metabolic variables between siblings discordant for the adiponectin genotypes after adjustment for age, gender, ethnicity and BMI
Variables1 T/T
(N=282)
T/G or G/G (N=312)
MeanS.D. MeanS.D. P=3
Glu0 (mmol/L) 5.17±0.89 5.22±1.11 NS
Glu60 (mmol/L) 9.72±2.44 9.72±2.67 NS
Glu120 (mmol/L) 7.83±2.44 7.83±2.72 NS
AUCg 16.15±3.59 16.19±3.98 NS
Ins0 (pmol/L) 52±37 56±40 NS
Ins60 (pmol/L) 562±453 483±318 0.0064
Ins120 (pmol/L) 491±470 461±408 NS
AUCi (pmol.h/L) 828±623 743±482 0.0565
HOMAIR2 1.73±1.42 1.89±1.92 NS
1Abbreviations: Glu, plasma glucose at the specific time point in OGTT; Ins, plasma insulin at the specific tome point in OGTT; AUCg, area under curve of plasma glucose in OGTT, AUCi, area under curve of plasma insulin in OGTT. 2 HOMA:
homeostasis model assessment calculated by [Insulin/22.5e-In(Glucose)
]
3 P-values greater than or equal to 0.1 are marked NS (not significant), P-values less than 0.05 are in bold.
Table 32. Comparisons of metabolic variables between subjects with discordant adiponectin genotypes after adjustment for age, gender, BMI, ethnicity and area of enrollment BMI, using a mixed model corrected for family effects.
MeanS.E. MeanS.E. P=
Glu0 5.39±0.05 5.33±0.05 NS
Glu60 9.67±0.13 9.50±0.13 NS
Glu120 7.89±0.13 7.78±0.13 NS
AUCg 16.30±0.20 16.06±0.19 NS
Ins0 54±1 53±1 NS
Ins60 529±17 496±17 0.0008
Ins120 476±18 448±18 NS
AUCi 790±24 711±24 0.0058
HOMAIR 1.85±0.06 1.85±0.06 NS
Variables*
T/T (N=844)
T/G or G/G (N=869)
MeanS.E. MeanS.E. P=***
Glu0 96.325.35±0.900.05 95.545.31±0.890.05 NS Glu60 171.319.52±2.440.14 168.369.35±2.440.14 NS Glu120 140.667.81±2.480.14 138.817.71±2.490.14 NS AUCg 289.9316.11±3.760.21 285.515.86±3.760.21 NS
Ins0 7.654.5±0.211.5 7.4453.4±0.211.5 NS
Ins60 73.17525.0±2.5718.4 63.54455.9±2.5718.4 0.0008 Ins120 66.25475.3±2.7920.0 62.31447.1±2.820.0 NS AUCi 109.36784.7±3.6626.3 98.28705.2±3.6526.3 0.0056
HOMAIR** 1.84±0.06 1.84±0.06 NS
1*Abbreviations: Glu, plasma glucose at the specific time point in OGTT; Ins, plasma insulin at the specific tome point in OGTT; AUCg, area under curve of plasma glucose in OGTT, AUCi, area under curve of plasma insulin in OGTT.
2** HOMA: homeostasis model assessment calculated by [Insulin/22.5e-In(Glucose)
] 3*** P-values greater than or equal to 0.1 are marked NS (not significant), P-values less than 0.05 are in bold.* All abbreviations, units, and P-values are as in Table 2.
Table 43.Table 3. Significance levels of the adiponectin or PPAR2 genotypes on the main effects and their interactions after adjustment for age, gender, ethnicity, BMI, and area of enrollment using a mixed model corrected for family effects.
Variables*
*All abbreviations, units, and P-values are as in Table 2.
** F(d.f.s): F-value (degrees of freedom for the numerator and denominator of the relevant F statistics) Table 3. Significance levels of the adiponectin or PPAR2 genotypes on the main effects and their interactions after adjustment for age, gender, BMI, ethnicity and area of enrollment using a mixed model corrected for family effects.
Variables*
Adiponectin T94G Main effects
PPAR2 Pro12Ala Main effects
Interactions
P=** P= P=
Glu0 0.0671† 0.5491 0.0859†
Glu60 0.4951 0.0167 0.9271
Glu120 0.5459 0.1167 0.7640
AUCg 0.4063 0.0210 0.7751
Ins0 0.0301 0.5243 0.0285
Ins60 0.0034 0.4468 0.1795
Ins120 0.0189 0.4083 0.0435
AUCi 0.0027 0.3937 0.0691†
HOMAIR 0.0408 0.4255 0.0154
* All abbreviations, units, and P-values are as in Table 2.
** P-values greater than or equal to 0.1 are marked NS (not significant), P-values less than 0.05 are in bold.
Table 4. Comparisons between the subjects with different combinations of adiponectin and PPAR2 genotypes. The p values in the 1st column from the right indicate the comparisons between the different adiponectin genotypes within a group with the same PPAR2 genotypes. The p-values at the bottom indicate the comparisons between the different PPAR2 genotypes within a group with the same adiponectin genotypes. All the abbreviations, units and p-values used are as in Table 2.
Variables* PPAR2 genotypes Adiponectin genotypes P=**
T/T G/-
752.9 55.3 adiponectin or PPAR2 genotypes on the main effects and their interactions after
adjustment for age, gender, ethnicity, and BMI using a mixed model corrected
** P-values greater than or equal to 0.1 are marked NS (not significant), P-values less than 0.05 are in bold.* All abbreviations, units, and P-values are as in Table 2.