Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2013 Dec 1.
Published in final edited form as: Mol Genet Metab. 2012 Oct 13;107(4):721–728. doi: 10.1016/j.ymgme.2012.10.003

ASSOCIATION BETWEEN ADIPOQ SNPS WITH PLASMA ADIPONECTIN AND GLUCOSE HOMEOSTASIS AND ADIPOSITY PHENOTYPES IN THE IRAS FAMILY STUDY

S Sandy An 1,2,3, Anthony J G Hanley 7, Julie T Ziegler 5, W Mark Brown 5, Steven M Haffner 8, Jill M Norris 9, Jerome I Rotter 10, Xiuqing Guo 10, Y-D Ida Chen 10, Lynne E Wagenknecht 6, Carl D Langefeld 5, Donald W Bowden 1,2,3,4, Nicholette D Palmer 1,2,3
PMCID: PMC3504195  NIHMSID: NIHMS414848  PMID: 23102667

Abstract

Context

Adiponectin is an adipocytokine associated with a variety of metabolic traits. These associations in human studies, in conjunction with functional studies in model systems, have implicated adiponectin in multiple metabolic processes.

Objective

We hypothesize that genetic variants associated with plasma adiponectin would also be associated with glucose homeostasis and adiposity phenotypes.

Design and Setting

The Insulin Resistance Atherosclerosis Family Study was designed to identify the genetic and environmental basis of insulin resistance and adiposity in the Hispanic- (n=1,424) and African-American (n=604) population.

Main Outcome Measures

High quality metabolic phenotypes, e.g. insulin sensitivity (SI), acute insulin response (AIR), disposition index (DI), fasting glucose, body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and waist circumference, were explored.

Results

Based on association analysis of more than 40 genetic polymorphisms in the adiponectin gene (ADIPOQ), we found no consistent association of ADIPOQ variants with plasma adiponectin levels and adiposity phenotypes. However, there were two promoter variants, rs17300539 and rs822387, associated with plasma adiponectin levels (P=0.0079 and 0.021, respectively) in the Hispanic-American cohort that were also associated with SI (P=0.0067 and 0.013, respectively). In contrast, there was only a single promoter SNP, rs17300539, associated with plasma adiponectin levels (P=0.0018) and fasting glucose (P=0.042) in the African-American cohort. Strikingly, high impact coding variants did not show evidence of association.

Conclusions

The lack of consistent patterns of association between variants, adiponectin levels, glucose homeostasis, and adiposity phenotypes suggests a reassessment of the influence of adiponectin in these pathways.

Keywords: adiponectin, single nucleotide polymorphisms, glucose homeostasis, adiposity, African Americans, Hispanic Americans

1. Introduction

Adiponectin is an adipocytokine that has been implicated in glucose homeostasis and fatty acid oxidation [1, 2]. It is the most abundant adipocytokine found in plasma, accounting for 0.01% of plasma protein. Unlike other well-known adipocytokines such as leptin which increases in circulation with body mass, adiponectin levels are inversely correlated with body mass. Lower levels of adiponectin have been correlated with a variety of conditions including obesity [3, 4], type 2 diabetes (T2D) [2, 5], hypertension [6, 7], dyslipidemia [8], and metabolic syndrome [9]. In addition to these correlations, there have been multiple reports of a negative correlation between adiponectin levels and insulin resistance [10–12], a trait frequently observed in metabolic syndrome and T2D.

Plasma adiponectin levels have been genetically linked to many loci [9, 13–16] in various populations. However, chromosome 3q27 the location of the ADIPOQ gene encoding adiponectin remains the strongest signal identified from genome-wide association studies (GWAS) [16, 17]. In addition, this locus has been implicated in genetic studies of T2D [18–20] and metabolic syndrome [21], making ADIPOQ a candidate gene of interest in studies of insulin resistance, adiposity, and T2D.

The purpose of this study was to explore the relationship between adiponectin and glucose homeostasis and adiposity phenotypes. We sought to examine this relationship through association analysis of ADIPOQ single nucleotide polymorphisms (SNPs) with plasma adiponectin levels, glucose homeostasis, and adiposity measures such as insulin sensitivity, fasting glucose, and body mass index (BMI).

2. Materials and Methods

2.1 Insulin Resistance Atherosclerosis Family Study (IRASFS)

The study design, recruitment, and phenotyping for IRASFS have been described in detail [22]. Briefly, the IRASFS was designed to identify the genetic and environmental basis of insulin resistance and adiposity. Hispanic-American subjects included in this report were recruited from clinical centers in San Luis Valley, Colorado and San Antonio, Texas while African Americans were recruited from Los Angeles, California. The IRAS-FS protocol was approved by local institutional review committees and all participants gave informed consent. While a diagnosis of diabetes was not a requirement to participate, approximately 12% of the subjects had diabetes. Families were recruited to obtain an average of 12–13 members. The exam included a fasting blood draw and medical history interview. The clinical examination included an insulin-modified frequently sampled glucose tolerance test (FSIGT) using the reduced sampling protocol [23]. Measures of glucose homeostasis, fasting glucose, insulin sensitivity (SI), acute insulin response (AIR), and disposition index (DI), were computed using the results of the FSIGT combined with the MINMOD analysis program [24]. Height, weight, and waist circumference were measured and computed tomography was used to estimate visceral and subcutaneous fat (VAT and SAT, respectively). The characteristics of the IRASFS cohort are summarized in Table 1.

Table 1.

Summary statistics for Insulin Resistance Atherosclerosis Family Study participants.

Trait Class Trait Hispanic Americans African Americans
n* mean standard deviation n* mean standard deviation
Gender (n, female, %) 1183 691 58.4 566 337 59.5
Age (years) 1183 42.9 14.5 566 42.8 13.9
T2D (n, affected, percent total) 1180 146 12.4 566 64 11.3
Adiponectin (ug/mL) 1073 13.5 6.9 555 9.01 5.24
Glucose Homeostasis Insulin Sensitivity (SI; x10−5 min−1 [pmol/L] −1) 975 2.45 1.87 490 1.63 1.17
Acute Insulin Response (AIR; pmol/L) 975 765 657 489 1007 822
Fasting Glucose (mg/dL) 1032 93.5 9.5 501 94.6 9.7
Disposition Index (DI; x10−5 min−1) 975 1317 1233 489 1432 1276
Adiposity Body Mass Index (BMI; kg/m2) 1183 29 6.2 566 30.1 6.8
Visceral Adipose Tissue (VAT; cm2) 1132 115 61 520 93.3 58
Subcutaneous Adipose Tissue (SAT; cm2) 1132 340 155 520 358 191
Visceral Subcutaneous Ratio (VSR) 1091 0.38 0.21 502 0.3 0.18
Waist Circumference (cm) 1182 90.1 14.3 565 91.8 15.1
Open in a new tab
*

Sample sizes range from 964 to 1151 due to incomplete data for some measures. Specifically, the FSIGT was not performed in those with diabetes, reducing the sample size for SI and AIR.

2.2 Laboratory Methods

Biomarkers

Total plasma adiponectin levels were measured by radioimmunoassay (RIA; Linco Research, St. Charles, MO). This RIA uses a polyclonal anti-adiponectin antibody which recognizes trimers and higher multimers of adiponectin and includes recognition of the globular domain. In addition, a subset of 200 samples has been measured with a monoclonal antibody-based enzyme-linked immunosorbent assay (ELISA), with good correlation (r=0.88) with the RIA [25].

DNA Isolation

Genomic DNA was purified using PUREGENE DNA isolation kits (Gentra Inc., Minneapolis, MN, USA). Total genomic DNA was quantified using a fluorometric assay by Hoefer DyNA Quant 200 fluorometer (Hoefer Pharmacia Biotech Inc., San Francisco, CA, USA).

ADIPOQ SNP Genotyping

In addition to database searches, SNPs identified by direct sequencing of ADIPOQ were evaluated in this study. Sequencing and analysis have been described (An, submitted). Briefly, ADIPOQ sequencing targeted promoter (region as defined by Kita et al [26]) and exonic sequences of ADIPOQ in Hispanic and African Americans. In addition to novel variants identified from sequencing, common SNPs across the ADIPOQ gene and the surrounding +/−50kb region were selected using HapMap YRI and CEU populations (www.hapmap.org). Thirty-eight haplotype tagging SNPs were selected with an r2 threshold of 0.8 and MAF >5%. In addition, SNPs identified in prior GWAS studies [10, 27], functional studies [28], low frequency (LF) and rare variants identified from DNA sequencing described above, and studies in diverse populations as summarized in Waki et al [28] were also identified for genotyping, resulting in a total of 43 and 44 SNPs genotyped in the IRASFS Hispanic- and African-American samples, respectively. In addition, data from analysis of the previously described G45R mutation in Hispanic Americans, a high impact, low frequency coding variant [25] was included for completeness. SNP genotyping was performed on a Sequenom MassARRAY Genotyping System (Sequenom, San Diego, CA) using methods previously described [29]. The genotyping efficiency was >90% and 134 blind duplicate samples included to evaluate genotyping accuracy were 100% concordant.

2.3 Statistical Analysis

All variants were examined for Mendelian inconsistencies using PedCheck [30] in the IRASFS cohorts, resulting in <0.05% discrepancies which were converted to missing. In the Hispanic-American sample, there were five SNPs found to be monomorphic (T31I, C36S, G51G, R55C, and rs1865762) and thus excluded from further analysis. In the African-American cohort, there were four variants found to be monomorphic (C36S, G45R, G84R, and G90S) and thus excluded from further analysis. All SNPs were consistent with Hardy-Weinberg equilibrium. To best approximate the distributional assumptions of the tests (i.e., conditional normality given age, gender, BMI, recruitment center, and admixture and homogeneity of variance), each trait was examined for most appropriate transformations. Specifically, plasma adiponectin levels, BMI, and waist measures were natural log transformed, insulin sensitivity was natural log transformed after the addition of 1, acute insulin response and disposition index were sign square root, and visceral and subcutaneous adipose tissue were square root transformed. No transformation was required for fasting glucose.

Tests of association between the variants and quantitative traits were computed using a variance component model as implemented in Sequential Oligogenic Linkage Analysis Routines (SOLAR) [31]. For the tests of association, the inference was based on the additive genetic model adjusting for age, gender, BMI, recruitment center and admixture, except in the analysis with BMI. Admixture in the IRASFS African Americans was calculated with principle component analysis based on 36 ancestry informative markers (AIMs) [32]. In the IRASFS Hispanic Americans, admixture was estimated using a principal component analysis of 80 AIMs [33]. Multiple comparison correction was not performed owing to the a priori hypothesis of association between the variants examined and adiponectin levels and the primary hypothesis that ADIPOQ SNPs are associated with glucose homeostasis and adiposity as a consequence of these metabolic derangements.

3. Results

There were a total of 38 and 40 SNPs analyzed in the Hispanic- and African-American cohorts, respectively. In the Hispanic-American cohort, minor allele frequencies for the genotyped SNPs ranged from 0 to 49%. Six SNPs (excluding G45R) were nominally associated (P<0.05) with plasma adiponectin levels (Table 2A) with p-values ranging from 0.0072 to 0.025. In addition to the previously described G45R variant, only one of these SNPs, rs62625753 was a coding variant (G90S; located in exon 3). The remaining SNPs associated with adiponectin levels were found 5′ of the gene or in intron 1. The SNP with the strongest association with plasma adiponectin was rs822391, with a p-value of 0.0072. Individuals with zero (13.57 ± 7.84 μg/mL), one (13.49 ± 6.67 μg/mL), and two (15.59 ± 7.09 μg/mL) copies of the minor allele had modest differences in plasma adiponectin levels (An et al submitted). In the African-American cohort, minor allele frequencies for the genotyped SNPs ranged from 0 to 44% (Table 3A). There were seven SNPs associated with plasma adiponectin levels, with a coding variant, R55C, being the most strongly associated (p=0.00030). Individuals with this variant (1.20 ± 0.37 μg/mL) had significantly lower levels of plasma adiponectin levels compared to those without the variant (9.06 ± 5.01 μg/mL) (An et al submitted). The remaining associated variants were found 5′ of the gene, in intron 1, or 3′ of the gene.

Table 2A.

ADIPOQ SNP associations with glucose homeostasis phenotypes in Hispanic Americans.

SNP Position (hg18) Alleles (Major/Minor) MAF AA change Location Adiponectin SI AIR DI Fasting Glucose
beta P-value beta P-value beta P-value beta P-value beta P-value
rs7648121 68183280 C/T 0.060 5′ −1.04 0.25 0.073 0.082 −2.26 0.030 −1.18 0.43 −1.04 0.21
rs6810075 188031259 T/C 0.44 5′ 0.39 0.74 −0.0010 0.96 −0.60 0.25 −0.41 0.58 0.39 0.33
rs10937273 188032389 G/A 0.33 5′ −0.47 0.062 0.012 0.60 0.95 0.090 0.95 0.23 −0.47 0.28
rs1648707 188034405 A/C 0.42 5′ 0.21 0.66 −0.0014 0.95 −0.36 0.49 −0.22 0.76 0.21 0.61
rs864265 188036986 G/T 0.19 5′ 0.20 0.25 0.022 0.39 −0.58 0.38 −0.09 0.92 0.20 0.69
rs822387 188038731 T/C 0.060 5′ 0.36 0.021 −0.12 0.013 0.31 0.79 −1.37 0.41 0.36 0.68
rs16861194 188042119 A/G 0.14 5′ −0.041 0.67 −0.017 0.54 0.066 0.93 −1.01 0.31 −0.041 0.94
rs17300539 188042154 G/A 0.047 5′ 0.18 0.0079 −0.13 0.0067 1.43 0.23 −1.92 0.25 0.18 0.84
rs266729 188042168 C/G 0.28 5′ 0.42 0.72 0.0039 0.86 −0.45 0.43 0.14 0.86 0.42 0.33
rs710445 188044212 A/G 0.44 intron 1 0.24 0.67 0.0055 0.79 −0.57 0.27 −0.50 0.50 0.24 0.55
rs16861205 188044328 G/A 0.13 intron 1 −0.54 0.64 0.0013 0.96 0.39 0.60 −0.22 0.84 −0.54 0.36
rs16861209 188045808 C/A 0.064 intron 1 −0.0049 0.068 −0.14 0.0013 1.29 0.25 −2.34 0.14 −0.0049 1.00
rs822391 188046497 T/C 0.19 intron 1 0.23 0.0072 0.0044 0.86 −0.42 0.51 −0.60 0.50 0.23 0.63
rs822394 188049422 C/A 0.19 intron 1 0.33 0.013 0.0079 0.75 −0.53 0.40 −0.57 0.52 0.33 0.50
rs822396 188049571 A/G 0.20 intron 1 0.27 0.025 0.0064 0.79 −0.43 0.49 −0.46 0.60 0.27 0.59
rs12495941 188050874 G/T 0.32 intron 1 −1.01 0.090 0.016 0.47 0.49 0.38 1.08 0.18 −1.01 0.022
rs9877202 188052301 A/G 0.0066 intron 1 −0.19 0.81 0.15 0.37 3.24 0.44 10.23 0.091 −0.19 0.95
rs2036373 188052885 T/G 0.044 intron 1 −1.26 0.67 0.12 0.010 0.12 0.92 3.25 0.051 −1.26 0.18
rs17366568 188053147 G/A 0.066 intron 1 0.025 0.24 0.086 0.056 −1.97 0.078 0.34 0.83 0.025 0.98
rs2241766 188053586 T/G 0.18 G15G exon 2 −0.33 0.32 −0.0051 0.84 1.44 0.026 1.56 0.090 −0.33 0.52
G45R* 188053674 G/C 0.011 G45R exon 2 −1.17 5.0E-40 0.0049 0.96 −4.97 0.047 −3.75 0.29 3.69 0.058
rs2241767 188053890 A/G 0.18 intron 2 −0.40 0.30 −0.0051 0.84 1.58 0.016 1.66 0.072 −0.40 0.43
rs3774261 188054253 G/A 0.43 intron 2 −0.25 0.85 −0.0025 0.90 1.10 0.034 0.61 0.40 −0.25 0.53
rs3774262 188054508 G/A 0.18 intron 2 −0.38 0.48 −0.010 0.72 1.54 0.019 1.51 0.11 −0.38 0.46
rs62625753 188054720 G/A 0.0044 G90S exon 3 0.91 0.025 −0.21 0.21 1.62 0.70 −2.23 0.70 0.91 0.78
rs17366743 188054783 T/C 0.016 Y111H exon 3 3.67 0.77 −0.17 0.092 −0.83 0.74 −3.72 0.30 3.67 0.062
rs6444174 188055883 T/C 0.029 3′ UTR −0.80 0.97 0.11 0.061 −3.29 0.030 −1.47 0.50 −0.80 0.51
rs6773957 188056399 G/A 0.43 3′ UTR −0.25 0.90 −0.0024 0.91 1.11 0.032 0.67 0.36 −0.25 0.53
rs2082940 188056858 C/T 0.19 3′ UTR −0.33 0.35 −0.0030 0.91 1.27 0.050 1.34 0.14 −0.33 0.51
rs9842733 188058176 A/T 0.0044 3′ UTR 0.69 0.77 0.029 0.94 1.28 0.90 3.02 0.83 0.69 0.93
rs1403697 188059387 A/G 0.0045 3′ 0.69 0.77 0.029 0.94 1.28 0.90 3.02 0.83 0.69 0.93
rs7639352 188061168 C/T 0.24 3′ −0.081 0.44 −0.0060 0.80 0.30 0.62 −0.46 0.60 −0.081 0.87
rs7641507 188061267 C/T 0 3′ 0.41 0.53 0.058 0.81 −6.98 0.26 −1.74 0.84 0.41 0.93
rs6444175 188062438 G/A 0.25 3′ −0.017 0.54 −0.011 0.66 0.20 0.75 −0.74 0.39 −0.017 0.97
rs1403696 188062560 C/T 0.024 3′ −2.07 0.17 0.13 0.067 −3.66 0.037 −0.53 0.83 −2.07 0.14
rs13085499 188063534 G/A 0.49 3′ 0.39 0.78 −0.014 0.49 −0.24 0.64 −0.33 0.65 0.39 0.32
rs7628649 188068075 C/T 0.21 3′ −0.29 0.36 0.022 0.39 0.56 0.38 1.46 0.11 −0.29 0.57
rs17373414 188068221 C/T 0.051 3′ −1.66 0.052 −0.050 0.37 0.086 0.95 −1.58 0.42 −1.66 0.12
Open in a new tab

SNPs are ordered by position (hg18) and annotated with alleles (major/minor), minor allele frequency (MAF) determined in the largest set of unrelated individuals (n=229), amino acid (AA) change and location relative to the ADIPOQ gene. For each phenotype, adiponectin, insulin sensitivity (SI), acute insulin response (AIR), disposition index (DI), and fasting glucose, the effect size and direction (beta) and additive P-values are given.

*

G45R data was previously published by Bowden et al. in Hum Mol Genet. 2010 Oct 15;19(20):4112-20. Epub 2010 Aug 5.

Table 3A.

ADIPOQ SNP associations with glucose homeostasis phenotypes in African Americans.

SNP Position (hg18) Alleles (Major/Minor) MAF AA change Location Adiponectin SI AIR DI Fasting Glucose
beta P-value beta P-value beta P-value beta P-value beta P-value
rs7648121 68183280 C/T 0.061 5′ 0.15 0.0052 −0.072 0.12 0.66 0.66 −1.19 0.55 0.15 0.90
rs6810075 188031259 T/C 0.43 5′ 0.011 0.72 −0.016 0.54 −0.16 0.85 −0.82 0.46 −0.29 0.65
rs10937273 188032389 G/A 0.13 5′ 0.063 0.15 0.040 0.28 −0.27 0.82 0.66 0.68 1.26 0.15
rs1648707 188034405 A/C 0.39 5′ −0.072 0.017 −0.027 0.30 0.11 0.90 −1.05 0.35 −0.69 0.26
rs864265 188036986 G/T 0.065 5′ −0.0060 0.89 0.031 0.42 −1.41 0.26 −0.55 0.74 0.71 0.44
rs822387 188038731 T/C 0.33 5′ −0.042 0.23 −0.046 0.13 1.43 0.14 1.21 0.35 −0.54 0.46
rs16861194 188042119 A/G 0.32 5′ 0.010 0.79 −0.0067 0.83 0.36 0.71 −0.49 0.71 −1.11 0.13
rs17300539 188042154 G/A 0.028 5′ 0.32 0.0018 0.12 0.16 2.46 0.37 6.71 0.061 −4.12 0.042
rs266729 188042168 C/G 0.11 5′ −0.040 0.44 0.034 0.46 −1.69 0.25 −1.17 0.55 0.58 0.59
rs710445 188044212 A/G 0.44 intron 1 0.0054 0.86 0.014 0.61 0.19 0.83 −0.38 0.74 −1.11 0.085
rs16861205 188044328 G/A 0.26 intron 1 0.011 0.76 0.010 0.76 0.82 0.43 0.19 0.89 −0.70 0.35
rs16861209 188045808 C/A 0.14 intron 1 0.086 0.034 −0.013 0.71 0.31 0.78 0.21 0.89 −0.81 0.32
rs822391 188046497 T/C 0.031 intron 1 −0.031 0.67 −0.024 0.69 −1.56 0.44 −1.83 0.49 1.37 0.35
rs822394 188049422 C/A 0.029 intron 1 −0.010 0.88 0.017 0.76 1.50 0.41 3.59 0.13 −0.22 0.87
rs822396 188049571 A/G 0.19 intron 1 −0.071 0.060 −0.015 0.63 0.34 0.74 0.24 0.86 0.79 0.29
rs12495941 188050874 G/T 0.39 intron 1 −0.0091 0.77 −0.032 0.23 0.13 0.88 −0.63 0.58 −0.052 0.94
rs9877202 188052301 A/G 0.13 intron 1 −0.059 0.21 −0.053 0.20 2.71 0.042 1.43 0.41 0.54 0.58
rs2036373 188052885 T/G 0.083 intron 1 −0.0092 0.88 −0.025 0.62 −2.05 0.21 −1.41 0.52 1.75 0.15
rs17366568 188053147 G/A 0.037 intron 1 0.052 0.56 0.12 0.11 −0.33 0.89 1.20 0.71 −0.41 0.82
rs2241766 188053586 T/G 0.031 G15G exon 2 0.043 0.62 −0.0071 0.92 2.11 0.37 4.30 0.17 0.30 0.86
G38D 188053654 G/A 0.0185 G38D exon 2 −0.041 0.76 −0.19 0.075 −1.43 0.68 −4.94 0.27 4.05 0.11
R55C 188053704 C/T 0.010 R55C exon 2 −1.00 3.0E-04 −0.369 0.16 9.73 0.26 2.43 0.83 9.71 0.13
rs2241767 188053890 A/G 0.028 intron 2 0.087 0.31 −0.0018 0.98 2.08 0.37 4.62 0.13 0.34 0.84
rs3774261 188054253 G/A 0.40 intron 2 0.041 0.16 0.018 0.46 −0.61 0.44 0.018 0.99 1.05 0.076
rs3774262 188054508 G/A 0.028 intron 2 0.088 0.30 −0.0034 0.96 2.13 0.35 4.63 0.13 0.32 0.85
rs17366743 188054783 T/C 0.010 Y111H exon 3 −0.20 0.35 0.014 0.94 −11.12 0.061 −13.15 0.088 2.49 0.57
I164T 188054943 T/C 0 I164T exon 3 −0.0019 0.996 0.31 0.38 −3.66 0.74 7.17 0.61 −10.93 0.20
rs6444174 188055883 T/C 0.21 3′ UTR −0.056 0.16 0.0041 0.91 −0.15 0.89 −0.89 0.54 2.13 0.010
rs6773957 188056399 G/A 0.40 3′ UTR 0.041 0.16 0.018 0.46 −0.61 0.44 0.018 0.99 1.05 0.076
rs2082940 188056858 C/T 0.23 3′ UTR −0.047 0.20 0.0037 0.90 0.16 0.87 0.056 0.97 1.76 0.019
rs9842733 188058176 A/T 0.13 3′ UTR 0.015 0.78 0.019 0.68 1.45 0.33 1.02 0.60 1.52 0.17
rs1403697 188059387 A/G 0.18 3′ 0.0055 0.90 −0.0092 0.81 0.46 0.71 −0.20 0.90 1.85 0.042
rs7639352 188061168 C/T 0.33 3′ 0.072 0.026 0.0079 0.77 −0.48 0.57 0.083 0.94 −0.19 0.76
rs7641507 188061267 C/T 0.094 3′ −0.030 0.61 0.051 0.30 1.21 0.45 2.14 0.31 −1.33 0.25
rs1865762 188062094 A/G 0.010 3′ 0.19 0.17 0.074 0.56 −0.69 0.87 2.27 0.67 −1.57 0.61
rs6444175 188062438 G/A 0.33 3′ 0.076 0.024 0.013 0.64 −0.95 0.30 −0.013 0.99 −0.044 0.95
rs1403696 188062560 C/T 0.26 3′ −0.032 0.39 −0.010 0.76 0.058 0.95 −1.01 0.44 0.97 0.19
rs13085499 188063534 G/A 0.34 3′ −0.054 0.072 0.011 0.67 0.19 0.82 0.36 0.74 −0.69 0.25
rs7628649 188068075 C/T 0.33 3′ −0.023 0.48 −0.028 0.29 0.79 0.35 −0.030 0.98 0.54 0.40
rs17373414 188068221 C/T 0.020 3′ 0.096 0.47 0.10 0.36 3.12 0.39 7.05 0.13 −0.59 0.83
Open in a new tab

SNPs are ordered by position (hg18) and annotated with alleles (major/minor), minor allele frequency (MAF) as determined in the largest set of unrelated individuals (n=58), amino acid (AA) change and location relative to the ADIPOQ gene. For each phenotype, adiponectin, insulin sensitivity (SI), acute insulin response (AIR), disposition index (DI), fasting glucose, effect size and direction (beta) and additive P-values are given.

Since adiponectin has been associated in the literature with insulin sensitivity and other metabolic derangements, ADIPOQ SNPs were evaluated for association with glucose homeostasis (insulin sensitivity (SI), acute insulin response (AIR), disposition index (DI), and fasting glucose) and adiposity (body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and waist circumference) traits. When ADIPOQ variants were tested with these traits (Tables 2A, 2B, 3A and 3B) there was no consistent evidence of association between SNPs associated with plasma adiponectin levels and association with glucose homeostasis phenotypes in either cohort. Within the Hispanic-American sample, a single SNP, rs12495941 located in intron 1, was nominally associated with fasting glucose (P=0.022) and trending toward association with adiponectin (P=0.090). Four SNPs were associated with insulin sensitivity (P=0.013–0.0013) with two of the variants, rs822387 and rs17300539, associated with plasma adiponectin levels (P=0.021 and 0.0079, respectively). There were 10 SNPs associated with AIR, which is a measure of the first phase insulin response. However, only the ADIPOQ G45R variant was associated with AIR (P=0.047) and adiponectin levels (P=5.0E-40). There were no SNPs associated with disposition index, which is the product of SI and AIR. There were three variants associated with BMI (P=0.032–0.0071), none of which were associated with adiponectin levels and two variants associated with VAT, one of which, rs2036373, was also associated with BMI (P=0.032) and SI (P=0.010). Three variants were associated with SAT, but none were associated with adiponectin. One of these SNPs, rs12495941, was also associated with fasting glucose (P=0.022). Finally, there were five variants associated with waist circumference (P=0.042-9.5E-04). Two of the five variants were also associated with SAT (P<0.039) and one of the two was associated with fasting glucose (rs12495941, P=0.022). A 3′ variant (rs13085499) associated with waist circumference (P=0.0034) was also associated with VAT (P=0.035).

Table 2B.

ADIPOQ SNP associations with adiposity phenotypes in Hispanic Americans.

SNP Position (hg18) Alleles (Major/Minor) MAF AA change Location BMI VAT SAT WAIST
beta P-value beta P-value beta P-value beta P-value
rs7648121 68183280 C/T 0.060 5′ −0.0084 0.23 −0.29 0.073 −1.04 0.38 −0.0026 0.64
rs6810075 188031259 T/C 0.44 5′ 0.0019 0.29 0.079 0.32 0.39 0.52 0.0033 0.23
rs10937273 188032389 G/A 0.33 5′ −0.0018 0.30 −0.054 0.53 −0.47 0.021 −0.0037 0.20
rs1648707 188034405 A/C 0.42 5′ 0.0021 0.40 0.11 0.16 0.21 0.52 0.0045 0.093
rs864265 188036986 G/T 0.19 5′ 0.0011 0.98 −0.031 0.75 0.20 0.64 0.0011 0.76
rs822387 188038731 T/C 0.060 5′ −0.0081 0.22 −0.29 0.10 0.36 0.10 −0.010 0.12
rs16861194 188042119 A/G 0.14 5′ 0.0028 0.42 −0.023 0.84 −0.041 0.96 −0.0006 0.87
rs17300539 188042154 G/A 0.047 5′ −0.0062 0.26 −0.23 0.21 0.18 0.11 −0.0058 0.37
rs266729 188042168 C/G 0.28 5′ 0.00083 0.51 0.14 0.090 0.42 0.17 0.0060 0.042
rs710445 188044212 A/G 0.44 intron 1 0.0011 0.27 0.059 0.46 0.24 0.44 0.0044 0.11
rs16861205 188044328 G/A 0.13 intron 1 −0.0011 0.19 −0.12 0.30 −0.54 0.41 −0.0026 0.51
rs16861209 188045808 C/A 0.064 intron 1 −0.0076 0.55 0.0052 0.98 −0.0049 0.091 −0.0049 0.40
rs822391 188046497 T/C 0.19 intron 1 0.0028 0.77 −0.068 0.47 0.23 0.28 0.0051 0.13
rs822394 188049422 C/A 0.19 intron 1 0.0027 0.75 −0.055 0.57 0.33 0.30 0.0046 0.17
rs822396 188049571 A/G 0.20 intron 1 0.0031 0.95 −0.041 0.67 0.27 0.27 0.0057 0.085
rs12495941 188050874 G/T 0.32 intron 1 −0.00063 0.067 −0.10 0.25 −1.01 0.039 −0.0063 0.034
rs9877202 188052301 A/G 0.0066 intron 1 0.0029 0.72 −0.061 0.92 −0.19 0.68 0.010 0.62
rs2036373 188052885 T/G 0.044 intron 1 −0.0016 0.032 −0.42 0.022 −1.26 0.19 −0.015 0.023
rs17366568 188053147 G/A 0.066 intron 1 −0.0066 0.51 0.012 0.95 0.025 0.012 −0.020 9.5E-04
rs2241766 188053586 T/G 0.18 G15G exon 2 0.000036 0.17 −0.15 0.12 −0.33 0.41 −0.0021 0.55
G45R* 188053674 G/C 0.011 G45R exon 2 0.040 0.35 −0.22 0.57 −0.77 0.064 0 0.72
rs2241767 188053890 A/G 0.18 intron 2 −0.00086 0.24 −0.16 0.11 −0.40 0.58 −0.0025 0.48
rs3774261 188054253 G/A 0.43 intron 2 −0.0023 0.85 −0.13 0.11 −0.25 0.66 −0.0046 0.091
rs3774262 188054508 G/A 0.18 intron 2 −0.00079 0.26 −0.18 0.081 −0.38 0.39 −0.0032 0.36
rs62625753 188054720 G/A 0.0044 G90S exon 3 0.013 0.88 −0.10 0.86 0.91 0.15 −0.015 0.49
rs17366743 188054783 T/C 0.016 Y111H exon 3 0.0063 0.19 0.36 0.32 3.67 0.56 0.014 0.28
rs6444174 188055883 T/C 0.029 3′ UTR −0.00014 0.45 0.15 0.52 −0.80 0.84 −0.0063 0.44
rs6773957 188056399 G/A 0.43 3′ UTR −0.0020 0.96 −0.12 0.13 −0.25 0.61 −0.0045 0.094
rs2082940 188056858 C/T 0.19 3′ UTR −0.0010 0.23 −0.17 0.086 −0.33 0.74 −0.0038 0.27
rs9842733 188058176 A/T 0.0044 3′ UTR −0.074 0.0072 1.25 0.32 0.69 0.19 0.0091 0.79
rs1403697 188059387 A/G 0.0045 3′ −0.074 0.0071 1.25 0.32 0.69 0.19 0.0078 0.82
rs7639352 188061168 C/T 0.24 3′ −0.0015 0.32 −0.026 0.78 −0.081 0.34 −0.0034 0.29
rs7641507 188061267 C/T 0 3′ −0.029 0.15 −1.52 0.13 0.41 0.49 −0.0067 0.85
rs6444175 188062438 G/A 0.25 3′ −0.0018 0.30 −0.035 0.70 −0.017 0.44 −0.0029 0.37
rs1403696 188062560 C/T 0.024 3′ −0.0056 0.72 0.13 0.63 −2.07 0.49 −0.0049 0.60
rs13085499 188063534 G/A 0.49 3′ 0.0022 0.70 0.16 0.035 0.39 0.36 0.0079 0.0034
rs7628649 188068075 C/T 0.21 3′ −0.0015 0.13 −0.14 0.14 −0.29 0.81 −0.0058 0.085
rs17373414 188068221 C/T 0.051 3′ −0.0027 0.13 0.034 0.87 −1.66 0.12 −0.0046 0.53
Open in a new tab

SNPs are ordered by position (hg18) and annotated with alleles (major/minor), minor allele frequency (MAF) determined in the largest set of unrelated individuals (n=229), amino acid (AA) change and location relative to the ADIPOQ gene. For each phenotype, adiponectin, body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and waist circumference (WAIST), the number of observations (n), effect size and direction (beta) and additive P-values are given.

*

G45R data was previously published by Bowden et al. in Hum Mol Genet. 2010 Oct 15;19(20):4112-20. Epub 2010 Aug 5.

Table 3B.

ADIPOQ SNP associations with adiposity phenotypes in African Americans.

SNP Position (hg18) Alleles (Major/Minor) MAF AA change Location Adiponectin BMI VAT SAT WAIST
beta P-value beta P-value beta P-value beta P-value beta P-value
rs7648121 68183280 C/T 0.061 5′ 0.15 0.0052 0.0039 0.76 0.38 0.076 −0.17 0.53 0.0016 0.82
rs6810075 188031259 T/C 0.43 5′ 0.011 0.72 −0.00014 0.36 0.071 0.57 0.0071 0.96 0.0028 0.47
rs10937273 188032389 G/A 0.13 5′ 0.063 0.15 −0.0014 0.21 −0.086 0.63 −0.050 0.83 −0.0010 0.86
rs1648707 188034405 A/C 0.39 5′ −0.072 0.017 0.0022 0.45 0.094 0.44 0.032 0.84 0.00051 0.89
rs864265 188036986 G/T 0.065 5′ −0.0060 0.89 0.021 0.50 0.17 0.36 −0.27 0.21 −0.0032 0.57
rs822387 188038731 T/C 0.33 5′ −0.042 0.23 0.0029 0.70 0.17 0.22 0.16 0.36 0.0064 0.16
rs16861194 188042119 A/G 0.32 5′ 0.010 0.79 −0.00064 0.31 −0.076 0.58 −0.24 0.18 −0.0031 0.49
rs17300539 188042154 G/A 0.028 5′ 0.32 0.0018 0.0068 0.25 −0.23 0.58 −0.35 0.50 0.0025 0.85
rs266729 188042168 C/G 0.11 5′ −0.040 0.44 0.0023 0.95 −0.065 0.76 0.47 0.076 −0.0026 0.70
rs710445 188044212 A/G 0.44 intron 1 0.0054 0.86 −0.00039 0.24 −0.14 0.26 0.010 0.95 −0.0045 0.26
rs16861205 188044328 G/A 0.26 intron 1 0.011 0.76 −0.0017 0.47 −0.20 0.17 −0.25 0.18 −0.0022 0.64
rs16861209 188045808 C/A 0.14 intron 1 0.086 0.034 0.00048 0.97 −0.023 0.89 0.23 0.27 −0.00042 0.94
rs822391 188046497 T/C 0.031 intron 1 −0.031 0.67 −0.0060 0.91 0.077 0.79 0.27 0.46 0.0031 0.74
rs822394 188049422 C/A 0.029 intron 1 −0.010 0.88 −0.0053 0.87 −0.14 0.61 0.094 0.78 −0.0026 0.77
rs822396 188049571 A/G 0.19 intron 1 −0.071 0.060 −0.00076 0.99 0.15 0.29 −0.071 0.69 0.00092 0.84
rs12495941 188050874 G/T 0.39 intron 1 −0.0091 0.77 0.00033 0.15 0.092 0.47 0.0061 0.97 0.0044 0.27
rs9877202 188052301 A/G 0.13 intron 1 −0.059 0.21 −0.0013 0.95 0.35 0.061 0.14 0.56 0.0071 0.24
rs2036373 188052885 T/G 0.083 intron 1 −0.0092 0.88 −0.000074 0.98 0.27 0.25 0.061 0.83 −0.00051 0.95
rs17366568 188053147 G/A 0.037 intron 1 0.052 0.56 0.0087 0.17 −0.067 0.85 0.50 0.25 0.0033 0.76
rs2241766 188053586 T/G 0.031 G15G exon 2 0.043 0.62 0.00011 0.80 −0.26 0.45 −0.41 0.34 −0.0035 0.75
G38D 188053654 G/A 0.0185 G38D exon 2 −0.041 0.76 0.0034 0.96 0.40 0.46 −0.95 0.15 0.0021 0.90
R55C 188053704 C/T 0.010 R55C exon 2 −1.00 3.0E-04 0.015 0.62 0.33 0.74 0.83 0.50 0.057 0.081
rs2241767 188053890 A/G 0.028 intron 2 0.087 0.31 0.0019 0.90 −0.16 0.64 −0.45 0.28 0.00077 0.94
rs3774261 188054253 G/A 0.40 intron 2 0.041 0.16 0.00022 0.37 0.013 0.91 −0.26 0.067 −0.00033 0.93
rs3774262 188054508 G/A 0.028 intron 2 0.088 0.30 0.0021 0.87 −0.16 0.64 −0.45 0.28 0.00081 0.94
rs17366743 188054783 T/C 0.010 Y111H exon 3 −0.20 0.35 0.0012 0.66 −0.74 0.39 0.86 0.43 −0.051 0.070
I164T 188054943 T/C 0 I164T exon 3 −0.0019 0.996 0.0034 0.96 −2.24 0.18 −0.22 0.92 −0.054 0.31
rs6444174 188055883 T/C 0.21 3′ UTR −0.056 0.16 0.00060 0.33 −0.13 0.42 0.16 0.43 −0.0030 0.56
rs6773957 188056399 G/A 0.40 3′ UTR 0.041 0.16 0.00022 0.37 0.013 0.91 −0.26 0.067 −0.00033 0.93
rs2082940 188056858 C/T 0.23 3′ UTR −0.047 0.20 0.0017 0.52 −0.17 0.26 −0.031 0.86 −0.0032 0.48
rs9842733 188058176 A/T 0.13 3′ UTR 0.015 0.78 0.023 0.37 −0.20 0.38 0.24 0.39 0.0055 0.42
rs1403697 188059387 A/G 0.18 3′ 0.0055 0.90 0.0019 0.65 −0.058 0.75 0.13 0.57 0.0035 0.54
rs7639352 188061168 C/T 0.33 3′ 0.072 0.026 −0.00069 0.81 0.14 0.27 −0.21 0.18 −0.00031 0.94
rs7641507 188061267 C/T 0.094 3′ −0.030 0.61 0.0012 0.15 0.36 0.13 −0.32 0.30 −0.0073 0.32
rs1865762 188062094 A/G 0.010 3′ 0.19 0.17 −0.0049 0.093 −0.45 0.42 −1.03 0.14 −0.023 0.21
rs6444175 188062438 G/A 0.33 3′ 0.076 0.024 −0.0012 0.77 0.077 0.57 −0.30 0.071 −0.0021 0.62
rs1403696 188062560 C/T 0.26 3′ −0.032 0.39 0.0022 0.43 −0.097 0.52 0.074 0.69 0.0026 0.57
rs13085499 188063534 G/A 0.34 3′ −0.054 0.072 −0.0017 0.26 −0.073 0.54 0.090 0.54 −0.0021 0.57
rs7628649 188068075 C/T 0.33 3′ −0.023 0.48 0.0015 0.79 −0.071 0.58 0.0075 0.96 0.0025 0.53
rs17373414 188068221 C/T 0.020 3′ 0.096 0.47 0.0077 0.93 0.27 0.61 0.051 0.94 0.027 0.11
Open in a new tab

SNPs are ordered by position (hg18) and annotated with alleles (major/minor), minor allele frequency (MAF) as determined in the largest set of unrelated individuals (n=58), amino acid (AA) change and location relative to the ADIPOQ gene. For each phenotype, adiponectin, body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and waist circumference (WAIST), effect size and direction (beta) and additive P-values are given.

In the African-American cohort, results were broadly similar to those observed in the Hispanic-American sample. There were a total of seven SNPs associated with plasma adiponectin levels (Table 3A; P<0.034). One was the coding variant, R55C, which was also the most strongly associated variant (P=3.0E-04) within the 40 SNPs analyzed. There were no ADIPOQ SNPs associated with SI, DI, BMI, VAT, SAT, or waist circumference although four SNPs associated with fasting glucose (P<0.042). However, rs17300539 was the only SNP associated with fasting glucose (P=0.042) that was also associated with plasma adiponectin levels (P=0.0018). Only a single SNP located in intron 1 was associated with AIR, rs9877202 (P=0.042) and was not associated with plasma adiponectin levels (P=0.21).

4. Discussion

The purpose of this study was to evaluate whether ADIPOQ SNPs were associated with plasma adiponectin levels as well as glucose homeostasis and adiposity measures. There is an abundant literature investigating the association between adiponectin variants and glucose homeostasis and adiposity measures [10, 11, 20, 21, 34, 35]. Much of this literature has been inconclusive, but if adiponectin actively participates in metabolic disease, we would hypothesize that variants that alter circulating adiponectin levels should be associated with metabolic measures. Overall, our results were not consistent with this hypothesis: no consistent pattern of association was observed between SNPs, adiponectin levels, and metabolic traits. The few nominal associations, e.g. rs17300539 and rs822387, that followed from adiponectin levels into metabolic traits are less convincing when strict corrections for multiple comparisons (for Hispanic Americans, 40 SNPs; P≤0.0013 and for African Americans, 42 SNPs; P≤0.0012) were made.

Recently Hivert et al [10] observed association with two ADIPOQ promoter SNPs, rs17300539 and rs822387, with plasma adiponectin levels in the Framingham Offspring Study, a study which is comprised almost exclusively of individuals of European descent. We observed similar association with plasma adiponectin levels in the IRASFS Hispanic-American cohort with p-values of 0.0079 (rs17300539) and 0.021 (rs822387) for the two correlated SNPs (r2=0.69). In addition, in the IRASFS African-American cohort, rs17300539 was also associated with plasma adiponectin levels (P=0.0018), while rs822387 was not associated (P=0.23) potentially due to decreased correlation among the variants (r2=0.06). Interestingly, Hivert et al did not find association with glucose homeostasis and adiposity measures with either of these variants whereas in the IRASFS, there was association of rs17300539 and rs822387 with SI in the Hispanic- American cohort (P=0.0067 and 0.013, respectively) and association of rs17300539 with fasting glucose in the African-American cohort (P=0.042). The association of rs17300539 and rs822387 with SI did not result in dramatic differences in SI in individuals with zero (2.16 ± 1.86 × 10−5 min−1[pmol/L] −1; 2.14 ± 0.84 × 10−5 min−1[pmol/L] −1), one (2.08 ± 1.85 × 10−5 min−1[pmol/L] −1; 2.26 ± 2.11 × 10−5 min−1[pmol/L] −1), and two (1.94 × 10−5 min−1[pmol/L] −1; 1.94 × 10−5 min−1[pmol/L] −1) copies of the minor allele, respectively (Appendix Table 1). The association of rs17300539 with fasting glucose in the African Americans yielded similar modest differences in the genotypic means for 0 (94.77 ± 9.74 mg/dL) and 1 (91.91 ± 8.38 mg/dL) copy of the minor allele. We explored the rs17300539 SNP further. Although the SNP was associated with fasting glucose in the independent African-American and Hispanic-American cohorts, the beta coefficients were in the opposite direction and in a meta-analysis there was no evidence for association (P=0.51, data not shown). Hivert et al was able to find association between the coding variant rs17366743 and fasting glucose (P=0.0004) and diabetes risk (P=0.01) [10]. However, in the IRASFS Hispanic- and African-American cohorts, there was no evidence of association of rs17366743 with glucose homeostasis or adiposity measures. Dastani et al [13] recently reported evidence for nominal association (P=0.003–0.048) between T2D and a collection of adiponectin-associated variants across the genome in a large European sample, however, the promoter SNP, rs17300539, was not found to be associated with T2D, only associated with high molecular weight adiponectin in the Cardiovascular Health Study (P=3.0 × 10−16)[13].

In addition to common variants we evaluated multiple low frequency coding variants. Similar to the G45R mutation which we have previously described [25], the R55C coding variant in African Americans corresponds to very low adiponectin levels and no evidence of association with the glucose homeostasis and adiposity phenotypes studied here (Table 3A and 3B). The frequency of the R55C is low (1.0%) which limits power to assess association, but one would suspect that a mutation that reduces the amount of circulating adiponectin 80–85% would result in a difference in metabolic measures. It is noteworthy that in a cross-ethnic (Hispanic American and African American) analysis, cross-SNP (R55C and G45R) meta-analysis of these high impact variants, there was no evidence of association with any of the metabolic and adiposity traits evaluated here (data not shown).

While there are reports of association between SI and plasma adiponectin levels [36, 37], it is difficult to discern the exact relationship between the two due to a lack of consistent association between variants associated with plasma adiponectin levels and SI. Within the IRASFS, there were only two SNPs that were associated with both plasma adiponectin and SI in the Hispanic-American cohort, and none within the African-American cohort. There are also some mixed reports of association between fasting glucose and plasma adiponectin levels [4, 38]. These inconclusive findings across ancestries could be attributed to reduced power in the IRASFS African American cohort in comparison to the Hispanic American cohort as a result of sample size differences (Appendix Table 2) and compounded by LD differences between the two ethnicities. Therefore, it is not possible to draw firm conclusions based on these results, but we hypothesize that if adiponectin and fasting glucose are associated with one another, there may be factors other than ADIPOQ variants that drive the association.

As an adipocytokine, adiponectin has been reported to be associated with adiposity phenotypes [3, 36, 39]. In the IRASFS Hispanic-American cohort, there were several SNPs associated with adiposity phenotypes such as BMI, visceral and subcutaneous adipose tissue, and waist circumference. However, none of the variants were concomitantly associated with plasma adiponectin levels. This finding leads us to speculate that variants associated with plasma adiponectin levels are not similarly related to adipose tissue distribution or overall body mass, unlike many other reports. Interestingly, in the African-American cohort of the IRASFS, there were no ADIPOQ variants associated with adiposity phenotypes.

Within the IRASFS Hanley et al [40] previously reported strong correlations between plasma adiponectin levels and SI, fasting glucose, fasting insulin, BMI, waist circumference, and visceral adipose tissue. The current study failed to find consistent association between variants associated with plasma adiponectin and glucose homeostasis and adiposity phenotypes. One of the greatest strengths of the IRASFS is a unique study design with well phenotyped and comprehensively genotyped samples of African and Hispanic Americans. These results are limited in the number of individuals available within the sample, 1183 Hispanic and 566 African Americans. Due to the high quality phenotyping performed in the IRASFS, this also limits replication samples since this type of phenotyping is unavailable in most other cohorts. In spite of this, this study performed a comprehensive tagging of the ADIPOQ gene to assess the association of adiponectin variants with glucose homeostasis and adiposity phenotypes.

5. Conclusions

We evaluated approximately 40 ADIPOQ variants for association with plasma adiponectin levels, glucose homeostasis and adiposity phenotypes in a Hispanic- and African-American cohort. We were able to identify two promoter variants associated with plasma adiponectin and SI within the Hispanic-American cohort and one promoter variant associated with plasma adiponectin and fasting glucose within the African-American cohort. We did not find any consistent association of ADIPOQ variants with plasma adiponectin and other glucose homeostasis or adiposity phenotypes. These results question the role of adiponectin in glucose homeostasis and adiposity; whether it plays a causative role as previously reported or whether it is more a consequence of these metabolic derangements.

Supplementary Material

01

Highlights.

  • Focus on the Hispanic- and African-American population, an ethnic minority disproportionately affected by T2D and obesity and under-represented in genetic studies of common, complex disease.

  • This study utilizes high quality metabolic phenotypes, e.g. insulin sensitivity (SI), acute insulin response (AIR), disposition index (DI), fasting glucose, body mass index (BMI), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and waist circumference.

  • Evaluation of quantitative measures of glucose homeostasis, SI, AIR and DI, contributes to the understanding of the physiological architecture of T2D and obesity.

  • The lack of consistent patterns of association between variants, adiponectin levels, glucose homeostasis, and adiposity phenotypes suggests a reassessment of the influence of adiponectin in these pathways.

Acknowledgments

This research was supported in part by NIH grants HL060894, HL060931, HL060944, HL061019, HL061210, DK066358, DK085175 and DK91076.

Footnotes

The authors have nothing to disclose.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  • 1.Fruebis J, Tsao TS, Javorschi S, Ebbets-Reed D, Erickson MR, Yen FT, Bihain BE, Lodish HF. 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 U S A. 2001;98:2005–2010. doi: 10.1073/pnas.041591798. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Hotta K, Funahashi T, Arita Y, Takahashi M, Matsuda M, Okamoto Y, Iwahashi H, Kuriyama H, Ouchi N, Maeda K, Nishida M, Kihara S, Sakai N, Nakajima T, Hasegawa K, Muraguchi M, Ohmoto Y, Nakamura T, Yamashita S, Hanafusa T, Matsuzawa Y. Plasma concentrations of a novel, adipose-specific protein, adiponectin, in type 2 diabetic patients. Arterioscler Thromb Vasc Biol. 2000;20:1595–1599. doi: 10.1161/01.atv.20.6.1595. [DOI] [PubMed] [Google Scholar]
  • 3.Arita Y, Kihara S, Ouchi N, Takahashi M, Maeda K, Miyagawa J, Hotta K, Shimomura I, Nakamura T, Miyaoka K, Kuriyama H, Nishida M, Yamashita S, Okubo K, Matsubara K, Muraguchi M, Ohmoto Y, Funahashi T, Matsuzawa Y. Paradoxical decrease of an adipose-specific protein, adiponectin, in obesity. Biochem Biophys Res Commun. 1999;257:79–83. doi: 10.1006/bbrc.1999.0255. [DOI] [PubMed] [Google Scholar]
  • 4.Yamamoto Y, Hirose H, Saito I, Tomita M, Taniyama M, Matsubara K, Okazaki Y, Ishii T, Nishikai K, Saruta T. Correlation of the adipocyte-derived protein adiponectin with insulin resistance index and serum high-density lipoprotein-cholesterol, independent of body mass index, in the Japanese population. Clin Sci (Lond) 2002;103:137–142. doi: 10.1042/cs1030137. [DOI] [PubMed] [Google Scholar]
  • 5.Scherer PE, Williams S, Fogliano M, Baldini G, Lodish HF. A novel serum protein similar to C1q, produced exclusively in adipocytes. J Biol Chem. 1995;270:26746–26749. doi: 10.1074/jbc.270.45.26746. [DOI] [PubMed] [Google Scholar]
  • 6.Adamczak M, Wiecek A, Funahashi T, Chudek J, Kokot F, Matsuzawa Y. Decreased plasma adiponectin concentration in patients with essential hypertension. Am J Hypertens. 2003;16:72–75. doi: 10.1016/s0895-7061(02)03197-7. [DOI] [PubMed] [Google Scholar]
  • 7.Ohashi K, Kihara S, Ouchi N, Kumada M, Fujita K, Hiuge A, Hibuse T, Ryo M, Nishizawa H, Maeda N, Maeda K, Shibata R, Walsh K, Funahashi T, Shimomura I. Adiponectin replenishment ameliorates obesity-related hypertension. Hypertension. 2006;47:1108–1116. doi: 10.1161/01.HYP.0000222368.43759.a1. [DOI] [PubMed] [Google Scholar]
  • 8.Matsubara M, Maruoka S, Katayose S. Decreased plasma adiponectin concentrations in women with dyslipidemia. J Clin Endocrinol Metab. 2002;87:2764–2769. doi: 10.1210/jcem.87.6.8550. [DOI] [PubMed] [Google Scholar]
  • 9.Comuzzie AG, Funahashi T, Sonnenberg G, Martin LJ, Jacob HJ, Black AE, Maas D, Takahashi M, Kihara S, Tanaka S, Matsuzawa Y, Blangero J, Cohen D, Kissebah A. The genetic basis of plasma variation in adiponectin, a global endophenotype for obesity and the metabolic syndrome. J Clin Endocrinol Metab. 2001;86:4321–4325. doi: 10.1210/jcem.86.9.7878. [DOI] [PubMed] [Google Scholar]
  • 10.Hivert MF, Manning AK, McAteer JB, Florez JC, Dupuis J, Fox CS, O’Donnell CJ, Cupples LA, Meigs JB. Common variants in the adiponectin gene (ADIPOQ) associated with plasma adiponectin levels, type 2 diabetes, and diabetes-related quantitative traits: the Framingham. Offspring Study Diabetes. 2008;57:3353–3359. doi: 10.2337/db08-0700. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Vasseur F, Helbecque N, Lobbens S, Vasseur-Delannoy V, Dina C, Clement K, Boutin P, Kadowaki T, Scherer PE, Froguel P. Hypoadiponectinaemia and high risk of type 2 diabetes are associated with adiponectin-encoding (ACDC) gene promoter variants in morbid obesity: evidence for a role of ACDC in diabesity. Diabetologia. 2005;48:892–899. doi: 10.1007/s00125-005-1729-z. [DOI] [PubMed] [Google Scholar]
  • 12.Yang WS, Hsiung CA, Ho LT, Chen YT, He CT, Curb JD, Grove J, Quertermous T, Chen YD, Kuo SS, Chuang LM. Genetic epistasis of adiponectin and PPARgamma2 genotypes in modulation of insulin sensitivity: a family-based association study. Diabetologia. 2003;46:977–983. doi: 10.1007/s00125-003-1136-2. [DOI] [PubMed] [Google Scholar]
  • 13.Dastani Z, Hivert MF, Timpson N, Perry JR, Yuan X, Scott RA, Henneman P, Heid IM, Kizer JR, Lyytikainen LP, Fuchsberger C, Tanaka T, Morris AP, Small K, Isaacs A, Beekman M, Coassin S, Lohman K, Qi L, Kanoni S, Pankow JS, Uh HW, Wu Y, Bidulescu A, Rasmussen-Torvik LJ, Greenwood CM, Ladouceur M, Grimsby J, Manning AK, Liu CT, Kooner J, Mooser VE, Vollenweider P, Kapur KA, Chambers J, Wareham NJ, Langenberg C, Frants R, Willems-Vandijk K, Oostra BA, Willems SM, Lamina C, Winkler TW, Psaty BM, Tracy RP, Brody J, Chen I, Viikari J, Kahonen M, Pramstaller PP, Evans DM, St Pourcain B, Sattar N, Wood AR, Bandinelli S, Carlson OD, Egan JM, Bohringer S, van Heemst D, Kedenko L, Kristiansson K, Nuotio ML, Loo BM, Harris T, Garcia M, Kanaya A, Haun M, Klopp N, Wichmann HE, Deloukas P, Katsareli E, Couper DJ, Duncan BB, Kloppenburg M, Adair LS, Borja JB, Wilson JG, Musani S, Guo X, Johnson T, Semple R, Teslovich TM, Allison MA, Redline S, Buxbaum SG, Mohlke KL, Meulenbelt I, Ballantyne CM, Dedoussis GV, Hu FB, Liu Y, Paulweber B, Spector TD, Slagboom PE, Ferrucci L, Jula A, Perola M, Raitakari O, Florez JC, Salomaa V, Eriksson JG, Frayling TM, Hicks AA, Lehtimaki T, Smith GD, Siscovick DS, Kronenberg F, van Duijn C, Loos RJ, Waterworth DM, Meigs JB, Dupuis J, Richards JB. Novel Loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 2012;8:e1002607. doi: 10.1371/journal.pgen.1002607. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Guo X, Saad MF, Langefeld CD, Williams AH, Cui J, Taylor KD, Norris JM, Jinagouda S, Darwin CH, Mitchell BD, Bergman RN, Sutton B, Chen YD, Wagenknecht LE, Bowden DW, Rotter JI. Genome-wide linkage of plasma adiponectin reveals a major locus on chromosome 3q distinct from the adiponectin structural gene: the IRAS family study. Diabetes. 2006;55:1723–1730. doi: 10.2337/db05-0428. [DOI] [PubMed] [Google Scholar]
  • 15.Lindsay RS, Funahashi T, Krakoff J, Matsuzawa Y, Tanaka S, Kobes S, Bennett PH, Tataranni PA, Knowler WC, Hanson RL. Genome-wide linkage analysis of serum adiponectin in the Pima Indian population. Diabetes. 2003;52:2419–2425. doi: 10.2337/diabetes.52.9.2419. [DOI] [PubMed] [Google Scholar]
  • 16.Ling H, Waterworth DM, Stirnadel HA, Pollin TI, Barter PJ, Kesaniemi YA, Mahley RW, McPherson R, Waeber G, Bersot TP, Cohen JC, Grundy SM, Mooser VE, Mitchell BD. Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity (Silver Spring) 2009;17:737–744. doi: 10.1038/oby.2008.625. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Heid IM, Henneman P, Hicks A, Coassin S, Winkler T, Aulchenko YS, Fuchsberger C, Song K, Hivert MF, Waterworth DM, Timpson NJ, Richards JB, Perry JR, Tanaka T, Amin N, Kollerits B, Pichler I, Oostra BA, Thorand B, Frants RR, Illig T, Dupuis J, Glaser B, Spector T, Guralnik J, Egan JM, Florez JC, Evans DM, Soranzo N, Bandinelli S, Carlson OD, Frayling TM, Burling K, Smith GD, Mooser V, Ferrucci L, Meigs JB, Vollenweider P, Dijk KW, Pramstaller P, Kronenberg F, van Duijn CM. Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals. Atherosclerosis. 2010;208:412–420. doi: 10.1016/j.atherosclerosis.2009.11.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Bostrom MA, Freedman BI, Langefeld CD, Liu L, Hicks PJ, Bowden DW. Association of adiponectin gene polymorphisms with type 2 diabetes in an African American population enriched for nephropathy. Diabetes. 2009;58:499–504. doi: 10.2337/db08-0598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Mori Y, Otabe S, Dina C, Yasuda K, Populaire C, Lecoeur C, Vatin V, Durand E, Hara K, Okada T, Tobe K, Boutin P, Kadowaki T, Froguel P. Genome-wide search for type 2 diabetes in Japanese affected sib-pairs confirms susceptibility genes on 3q, 15q, and 20q and identifies two new candidate Loci on 7p and 11p. Diabetes. 2002;51:1247–1255. doi: 10.2337/diabetes.51.4.1247. [DOI] [PubMed] [Google Scholar]
  • 20.Vasseur F, Helbecque N, Dina C, Lobbens S, Delannoy V, Gaget S, Boutin P, Vaxillaire M, Lepretre F, Dupont S, Hara K, Clement K, Bihain B, Kadowaki T, Froguel P. Single-nucleotide polymorphism 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. 2002;11:2607–2614. doi: 10.1093/hmg/11.21.2607. [DOI] [PubMed] [Google Scholar]
  • 21.Kissebah AH, Sonnenberg GE, Myklebust J, Goldstein M, Broman K, James RG, Marks JA, Krakower GR, Jacob HJ, Weber J, Martin L, Blangero J, Comuzzie AG. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. Proc Natl Acad Sci U S A. 2000;97:14478–14483. doi: 10.1073/pnas.97.26.14478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Henkin L, Bergman RN, Bowden DW, Ellsworth DL, Haffner SM, Langefeld CD, Mitchell BD, Norris JM, Rewers M, Saad MF, Stamm E, Wagenknecht LE, Rich SS. Genetic epidemiology of insulin resistance and visceral adiposity. The IRAS Family Study design and methods. Ann Epidemiol. 2003;13:211–217. doi: 10.1016/s1047-2797(02)00412-x. [DOI] [PubMed] [Google Scholar]
  • 23.Steil GM, Volund A, Kahn SE, Bergman RN. Reduced sample number for calculation of insulin sensitivity and glucose effectiveness from the minimal model. Suitability for use in population studies. Diabetes. 1993;42:250–256. doi: 10.2337/diab.42.2.250. [DOI] [PubMed] [Google Scholar]
  • 24.Bergman RN, Finegood DT, Ader M. Assessment of insulin sensitivity in vivo. Endocr Rev. 1985;6:45–86. doi: 10.1210/edrv-6-1-45. [DOI] [PubMed] [Google Scholar]
  • 25.Bowden DW, An SS, Palmer ND, Brown WM, Norris JM, Haffner SM, Hawkins GA, Guo X, Rotter JI, Chen YD, Wagenknecht LE, Langefeld CD. Molecular basis of a linkage peak: exome sequencing and family-based analysis identify a rare genetic variant in the ADIPOQ gene in the IRAS Family Study. Hum Mol Genet. 2010;19:4112–4120. doi: 10.1093/hmg/ddq327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Kita A, Yamasaki H, Kuwahara H, Moriuchi A, Fukushima K, Kobayashi M, Fukushima T, Takahashi R, Abiru N, Uotani S, Kawasaki E, Eguchi K. Identification of the promoter region required for human adiponectin gene transcription: Association with CCAAT/enhancer binding protein-beta and tumor necrosis factor-alpha. Biochem Biophys Res Commun. 2005;331:484–490. doi: 10.1016/j.bbrc.2005.03.205. [DOI] [PubMed] [Google Scholar]
  • 27.Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, McCarroll SA, Papadakis K, Qi L, Randall JC, Roccasecca RM, Sanna S, Scheet P, Weedon MN, Wheeler E, Zhao JH, Jacobs LC, Prokopenko I, Soranzo N, Tanaka T, Timpson NJ, Almgren P, Bennett A, Bergman RN, Bingham SA, Bonnycastle LL, Brown M, Burtt NP, Chines P, Coin L, Collins FS, Connell JM, Cooper C, Smith GD, Dennison EM, Deodhar P, Elliott P, Erdos MR, Estrada K, Evans DM, Gianniny L, Gieger C, Gillson CJ, Guiducci C, Hackett R, Hadley D, Hall AS, Havulinna AS, Hebebrand J, Hofman A, Isomaa B, Jacobs KB, Johnson T, Jousilahti P, Jovanovic Z, Khaw KT, Kraft P, Kuokkanen M, Kuusisto J, Laitinen J, Lakatta EG, Luan J, Luben RN, Mangino M, McArdle WL, Meitinger T, Mulas A, Munroe PB, Narisu N, Ness AR, Northstone K, O’Rahilly S, Purmann C, Rees MG, Ridderstrale M, Ring SM, Rivadeneira F, Ruokonen A, Sandhu MS, Saramies J, Scott LJ, Scuteri A, Silander K, Sims MA, Song K, Stephens J, Stevens S, Stringham HM, Tung YC, Valle TT, Van Duijn CM, Vimaleswaran KS, Vollenweider P, Waeber G, Wallace C, Watanabe RM, Waterworth DM, Watkins N, Witteman JC, Zeggini E, Zhai G, Zillikens MC, Altshuler D, Caulfield MJ, Chanock SJ, Farooqi IS, Ferrucci L, Guralnik JM, Hattersley AT, Hu FB, Jarvelin MR, Laakso M, Mooser V, Ong KK, Ouwehand WH, Salomaa V, Samani NJ, Spector TD, Tuomi T, Tuomilehto J, Uda M, Uitterlinden AG, Wareham NJ, Deloukas P, Frayling TM, Groop LC, Hayes RB, Hunter DJ, Mohlke KL, Peltonen L, Schlessinger D, Strachan DP, Wichmann HE, McCarthy MI, Boehnke M, Barroso I, Abecasis GR, Hirschhorn JN. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41:25–34. doi: 10.1038/ng.287. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Waki H, Yamauchi T, Kamon J, Ito Y, Uchida S, Kita S, Hara K, Hada Y, Vasseur F, Froguel P, Kimura S, Nagai R, Kadowaki T. Impaired multimerization of human adiponectin mutants associated with diabetes. Molecular structure and multimer formation of adiponectin. J Biol Chem. 2003;278:40352–40363. doi: 10.1074/jbc.M300365200. [DOI] [PubMed] [Google Scholar]
  • 29.Beaulieu M. Application Note. 2003. Multiplexing the Homogeneous MassEXTEND Assay. [Google Scholar]
  • 30.O’Connell JR, Weeks DE. PedCheck: a program for identification of genotype incompatibilities in linkage analysis. Am J Hum Genet. 1998;63:259–266. doi: 10.1086/301904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998;62:1198–1211. doi: 10.1086/301844. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Palmer ND, Mychaleckyj JC, Langefeld CD, Ziegler JT, Williams AH, Bryer-Ash M, Bowden DW. Evaluation of DLG2 as a positional candidate for disposition index in African-Americans from the IRAS Family Study. Diabetes Res Clin Pract. 2010;87:69–76. doi: 10.1016/j.diabres.2009.10.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Palmer ND, Langefeld CD, Ziegler JT, Hsu F, Haffner SM, Fingerlin T, Norris JM, Chen YI, Rich SS, Haritunians T, Taylor KD, Bergman RN, Rotter JI, Bowden DW. Candidate loci for insulin sensitivity and disposition index from a genome-wide association analysis of Hispanic participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study. Diabetologia. 2010;53:281–289. doi: 10.1007/s00125-009-1586-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ukkola O, Santaniemi M, Rankinen T, Leon AS, Skinner JS, Wilmore JH, Rao DC, Bergman R, Kesaniemi YA, Bouchard C. Adiponectin polymorphisms, adiposity and insulin metabolism: HERITAGE family study and Oulu diabetic study. Ann Med. 2005;37:141–150. doi: 10.1080/07853890510007241. [DOI] [PubMed] [Google Scholar]
  • 35.Morandi A, Maffeis C, Lobbens S, Bouatia-Naji N, Heude B, Pinelli L, Meyre D, Froguel P. Early detrimental metabolic outcomes of rs17300539-A allele of ADIPOQ gene despite higher adiponectinemia. Obesity (Silver Spring) 2010;18:1469–1473. doi: 10.1038/oby.2009.403. [DOI] [PubMed] [Google Scholar]
  • 36.Yatagai T, Nagasaka S, Taniguchi A, Fukushima M, Nakamura T, Kuroe A, Nakai Y, Ishibashi S. Hypoadiponectinemia is associated with visceral fat accumulation and insulin resistance in Japanese men with type 2 diabetes mellitus. Metabolism. 2003;52:1274–1278. doi: 10.1016/s0026-0495(03)00195-1. [DOI] [PubMed] [Google Scholar]
  • 37.Yamamoto Y, Hirose H, Saito I, Nishikai K, Saruta T. Adiponectin, an adipocyte-derived protein, predicts future insulin resistance: two-year follow-up study in Japanese population. J Clin Endocrinol Metab. 2004;89:87–90. doi: 10.1210/jc.2003-031163. [DOI] [PubMed] [Google Scholar]
  • 38.Kim SY, Lee SJ, Park HK, Yun JE, Lee M, Sung J, Jee SH. Adiponectin is Associated with Impaired Fasting Glucose in the Non-Diabetic Population Epidemiol. Health. 2011;33:e2011007. doi: 10.4178/epih/e2011007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Ryo M, Nakamura T, Kihara S, Kumada M, Shibazaki S, Takahashi M, Nagai M, Matsuzawa Y, Funahashi T. Adiponectin as a biomarker of the metabolic syndrome. Circ J. 2004;68:975–981. doi: 10.1253/circj.68.975. [DOI] [PubMed] [Google Scholar]
  • 40.Hanley AJ, Bowden D, Wagenknecht LE, Balasubramanyam A, Langfeld C, Saad MF, Rotter JI, Guo X, Chen YD, Bryer-Ash M, Norris JM, Haffner SM. Associations of adiponectin with body fat distribution and insulin sensitivity in nondiabetic Hispanics and African-Americans. J Clin Endocrinol Metab. 2007;92:2665–2671. doi: 10.1210/jc.2006-2614. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

01
NIHMS414848-supplement-01.docx (23.9KB, docx)

RESOURCES