Skip to main content
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2009 Sep 28.
Published in final edited form as: Obesity (Silver Spring). 2008 Dec 18;17(3):510–517. doi: 10.1038/oby.2008.583

The monounsaturated fatty acid intake modulates the effect of ADIPOQ polymorphisms on obesity

Daruneewan Warodomwichit *,, Jian Shen *, Donna K Arnett , Michael Y Tsai §, Edmond K Kabagambe , James M Peacock , James E Hixson **, Robert J Straka ††, Michael Province ‡‡, Ping An §§, Chao-Qiang Lai *, Laurence D Parnell *, Ingrid Borecki ‡‡, Jose M Ordovas *
PMCID: PMC2753535  NIHMSID: NIHMS105003  PMID: 19238139

Abstract

Objective

To explore whether dietary fat modulates the association of genetic variations at ADIPOQ locus with obesity traits in White US subjects.

Methods and Procedures

we genotyped two promoter single nucleotide polymorphisms (SNPs) at adiponectin gene, -11391G>A and -11377C>G, in 1083 participants (515 men and 568 women) in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study. Dietary intake, anthropometric and biochemical variables and serum adiponectin were assessed.

Results

Serum adiponectin was higher for carriers of the -11391A allele (P=0.001) but lower for the -11377G allele carriers (P=0.017). The significant association with obesity traits was found in the -11391G>A SNP (but not the -11377C>G); the -11391A allele carriers had significantly lower weight (P=0.029), BMI (P=0.019), waist (P=0.003) and hip circumferences (P=0.004) compared to the GG homozygotes. Furthermore, the associations of the -11391G>A with BMI and obesity risk were further modified by monounsaturated fatty acids (MUFA) intake (P-interaction=0.021 and 0.034 for BMI and obesity risk respectively). In subjects with MUFA intake ≥13% of energy intake (median level), the -11391A carriers had lower BMI (27.1 kg/m2 for GA+AA versus 29.1 kg/m2 for GG, P=0.002) and decreased obesity risk (OR for -11391A = 0.52, 95%CI; 0.28-0.96; P=0.031). No genotype-related differences for BMI or obesity for either allele of -11391A>G in subjects with MUFA intake <13%.

Discussion

Our findings support the functionality of both promoter SNPs and provide the association between -11391G>A SNPs and obesity-related traits and new insights into their modulations by MUFA intake.

Keywords: adiponectin, monounsaturated fatty acid, ADIPOQ single polynucleotide polymorphism, genetic susceptibility, nutrigenomics

Introduction

Adiponectin, the most abundant adipocyte-derived protein, exhibits antiatherosclerosis, antidiabetic as insulin sensitizer, and anti inflammatory properties (1, 2). Hypoadiponectinemia has been consistently observed in association with type 2 diabetes (T2D), coronary artery disease, and obesity (3-6). Weight reduction, either by the lifestyle modification or by bariatric surgery, is associated with increased in adiponectin concentrations as well as improved insulin sensitivity (7, 8). Adiponectin is encoded by the ADIPOQ gene located on chromosome 3q27 (9), where a susceptibility locus for T2D (10, 11) and the metabolic syndrome (12) has been mapped. Serum adiponectin concentrations are highly heritable (∼50%) and are linked to the ADIPOQ locus (13-17). Two promoter single nucleotide polymorphisms (SNPs) at the ADIPOQ locus including the -11391G>A and -11377C>G have been shown to alter the plasma adiponectin concentration and consequently affect the risk of T2D (16). In vitro studies further support the functionality of the -11391G>A SNP, demonstrating that the A allele significantly increases transcriptional activity and plasma adiponectin concentrations as compared with the G allele (18).

Four ADIPOQ SNPs including rs17300539 (-11391G>A) and rs266729 (-11377C>G) in the promoter region, rs2241766 (45T>G) in exon2 and rs1501299 (276G>T) in intron 2 have been extensively studied with regard to the association with insulin resistance and obesity traits. However, results from previous reports are inconsistent. For instance, the ADIPOQ +45G allele was associated with a higher risk of obesity and insulin resistance in a German population (19) but protective among Taiwanese (20, 21). Lack of consistency has also been observed for the ADIPOQ 276G>T polymorphism in which the increased risk of obesity and insulin resistance was associated with the 276T allele among Italians (22) but with 276G allele among Greek women (23). The observed inconsistent associations could be due to a number of factors, such as different ethnic origin, sample size of the study, genetic heterogeneity, but they could be also due to environmental exposures with diet being the major factor. Therefore, our aim was to examine the potential modification of dietary factors on the associations between functional SNPs at the ADIPOQ locus and insulin resistance and body mass index (BMI) in a well-characterized US-White population.

Materials and Methods

Subjects and study design

The study subjects consisted of 515 men and 568 women, aged 17-92 years, with predominantly Caucasian origin, who participated in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study and who had complete and consistent dietary record as the previous report (24). The detailed design and methodology of the GOLDN study has been described in previous publications (24, 25). The protocol was approved by the Institutional Review Boards at the University of Alabama, the University of Minnesota, the University of Utah, and Tufts University. Written informed consent was obtained from all participants.

Dietary and lifestyle assessment

The habitual dietary intake was estimated using the Dietary history questionnaire (DHQ), a cognitively-based food frequency questionnaire, developed by the National Cancer Institute (available online at http://riskfactor.cancer.gov/DHQ/). The ability to assess dietary intake has been validated, primarily in White US subjects and the average correlation coefficients between the DHQ and four 24-hours dietary recalls was 0.62 (26). Intake of total fat, saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA), including n-3 and n-6 PUFA, were presented as percentage of total daily energy intake. We grouped the dietary intake of α-linolenic acid, eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid as n-3 PUFA. Linoleic acid and arachidonic acid were combined as n-6 PUFA. Self reported use of hormone treatment was included. Physical activity was described as metabolic equivalent task (MET) hours. Smoking status was classified in 3 groups as non smoker, former smoker or current smoker. Alcohol consumption was expressed as numbers of serving of alcohol per week.

Anthropometric measurement

Anthropometric data including height, weight, waist and hip circumferences, and BMI were measured using a standard technique: height without shoes by a stadiometer, weight in light clothes by a beam balance, waist circumference over the unclothed abdomen at the umbilicus in the end of a normal expiration, and hip circumference at a maximal diameter by a non-stretchable standard tape. Body mass index (BMI) was calculated by dividing the weight in kilograms by height in meters squared. We defined obesity as the BMI ≥ 30 kg/m2 (27)

Biochemical analyses

Venous blood was obtained after an overnight fast and all plasma samples were analyzed together at the end of the study. Triglycerides were measured by glycerol blanked enzymatic method on the Roche COBAS FARA centrifugal analyzer (Roche Diagnostics). Total cholesterol and HDL cholesterol were measured by the Hitachi 911 Automatic Analyzer (Roche Diagnostics) using a cholesterol esterase, cholesterol oxidase reaction (Chol R1; Roche Diagnostics). A homogeneous assay for direct measuring LDL cholesterol (LDL Direct Liquid Select™ Cholesterol Reagent; Equal Diagnostics) on the Hitachi 911 was used. Glucose was determined by a hexokinase-mediate reaction on the Hitachi 911 (Roach Diagnostic). Insulin was measured by radioimmunoassay with commercial kit (Linco Research Inc, St. Charles, MO). Insulin resistance was assessed by HOMA-IR [HOMA-IR = fasting insulin (μIU/mL)*fasting glucose (mmol/L)/22.5](28). Serum adiponectin was measured by using a Quantikine Human Adiponectin Immunoassays (R&D Systems, Minneapolis, MN). The standard procedures of measurement of inflammatory markers; serum TNF-alpha, IL-6 and highly sensitive C-reactive protein (hsCRP) have been described previously (29).

DNA isolation and ADIPOQ genotyping

Genomic DNA was isolated from peripheral blood leukocytes using an AUTOPURE LS™ (Gentra Systems, Minneapolis, MN). Two promoter SNPs at ADIPOQ (rs17300539, -11391G>A; rs266729, -11377C>G) were genotyped by using a Taqman assay with allele-specific probes on the ABIPrism 7900HT Sequence Detection System (Applied Biosystems) (30) according to standardized laboratory protocols.

Statistical analysis

Statistical analyses were carried out using SAS for Windows version 9.0 (SAS Institute, Cary, NC). Continuous variables were tested for normal distribution and logarithmic transformations were applied to triglyceride, BMI, glucose, insulin, HOMA-R, adiponectin, TNF-alpha, IL-6 and hsCRP. Data for those variables are presented as back-transformed geometric means and 95% confidence intervals (CI). Pearson χ2 and Fisher’s exact tests were also used to test ADIPOQ genotypes for departure from Hardy-Weinberg equilibrium. We used the generalized estimating equation (GEE) linear and logistic regression with exchangeable correlation structure to adjust for the correlated observations due to familial relationship. For binary outcomes, the presented results (odds ratio and 95%CI) were the exponentiated beta coefficient and 95% CI, which were computed at natural log-scale. The analyses were performed for the whole sample and for men and women separately in order to verify the homogeneity of genetic effect among men and women. To determine the interaction between ADIPOQ SNPs and dietary fat intake, the median levels of different fat intake in this population were used as the cut-point to dichotomize the corresponding variables. The interactions between SNPs and dietary fat intake (as dichotomous) were tested in the multivariate interaction model. Covariates included age, gender, BMI, smoking (never, former and current smoker), alcohol consumption, physical activity expressed as metabolic equivalent task (MET)-hours based on self-reported types and durations of activities over 24 hours, diabetes status, hormone treatment, total energy intake, and percentage of carbohydrate and protein intake. The pairwise linkage disequilibrium (LD) between SNPs was estimated as correlation coefficient using the Helix Tree software package (Golden Helix). A two-tailed P value of <0.05 was considered as statistically significant.

Bioinformatics analysis

Analysis of the genomic DNA sequence segment centered on the promoter variation at ADIPOQ was conducted with MAPPER (31) to identify potential transcription factor binding sites.

Results

Characteristics of the study participants are shown in Table 1. Men had significantly higher (P <0.05) waist circumference, weight, fasting plasma glucose, HOMA-R, and triglycerides and lower HDL-c, hsCRP and adiponectin concentrations compared to women. The prevalence of obesity or diabetes was not difference in both groups but women had significantly more abdominal obesity according to the ATP III classification (44.1% and 57.5% in men and women respectively, P<0.001). The genotype distributions did not deviate from Hardy-Weinberg expectation (P>0.05) and the minor allele frequencies of the ADIPOQ -11391G>A and -11377C>G were 0.07 and 0.25, respectively. The pair-wise LD coefficient (R) between -11397G>A and -11377C>G was 0.16, indicating the haplotypic independence of both SNPs.

Table 1.

General characteristics of participants at the baseline according to gender

Men (n=515) Women (n=568)
Age, years 49.9 (0.8) 48.9 (0.7)
Weight, kg* 90.6 (0.9) 76.2 (0.9)
Height, m* 1.78 (0.004) 1.65 (0.004)
BMI, kg/m2 28.7 (0.3) 28.1 (0.3)
Waist circumference, cm* 101.0 (0.7) 92.9 (0.9)
Hip circumference, cm* 105.9 (0.5) 108.9 (0.7)
Total cholesterol, mg/dL 192.6 (2.2) 193.7 (2.2)
LDL-cholesterol, mg/dL* 124.7 (1.7) 120.9 (1.7)
HDL-cholesterol, mg/dL* 41.7 (0.5) 52.6 (0.7)
Triglycerides, mg/dL* 158.0 (7.9) 127.7 (4.2)
Fasting glucose, mg/dL* 105.7 (1.0) 98.1 (0.8)
Fasting insulin, 14.2 (0.4) 13.4 (0.4)
HOMA-IR* 3.8 (0.1) 3.4 (0.1)
Adiponectin, ng/mL* 6392 (187) 10199 (244)
IL-6, pg/mL 2.09 (0.19) 1.89 (0.07)
hsCRP, mg/dL* 0.19 (0.02) 0.30 (0.03)
TNF-alpha, pg/mL 3.7 (0.3) 3.2 (0.1)
Current smoker, n (%) 39 (7.6) 42 (7.4)
Current drinker, n (%) 256 (49.7) 291 (51.2)
Hormonal treatment, n (%)* 0 (0) 119 (21))
Obesity, n (%) 169 (32.8) 192 (33.8)
Diabetes, n (%) 50 (9.7) 56 (9.9)
ADIPOQ -11391G>A, n (%)
 GG 442 (86.8) 488 (86.5)
 GA 64 (12.6) 71 (12.6)
 AA 3 (0.6) 5 (0.9)
ADIPOQ -11377C>G, n (%)
 CC 285 (55.9) 319 (56.8)
 CG 191 (37.5) 208 (37.0)
 GG 34 (6.7) 35 (6.2)

Data are mean (SEM) or n (%),

*

Statistically significant difference between men and women after adjusted for sex and family relationship.

Genotyping study was successfully obtained in 509 men and 564 women for -11391G>A and 510 men and 562 women for -11377C>G.

Because of no significant interactions between genotype and gender for any of the main outcomes, we performed gender-adjusted analysis in all subjects combined. The promoter SNPs were significantly associated with serum adiponectin concentrations in a multvitiate adjusted model (adjusted for age, gender, family relationship, smoking, alcohol consumption, physical activity, hormone and diabetes status). Higher adiponectin levels were associated with the minor allele A of the -11391G>A SNP (P<0.001), whereas the minor allele G of the -11377C>G was associated with a lower adiponectin concentrations (P=0.027). Moreover, carriers of the -11391A allele had significantly lower weight (84.0±1.6 kg versus 86.8±1.3 kg, P=0.029), waist (97.3±1.3 cm versus 100.5±1.1 cm, P=0.002) and hip circumferences (107.0±1.0 cm versus 109.2±0.8 cm, P=0.004), and BMI (28.6±0.5 kg/m2 versus 29.6±0.4 kg/m2, P=0.019) compared with GG individuals. Consistent with these findings, we found lower insulin resistance determined by lower fasting insulin (P=0.013) and HOMA-IR (P=0.009) in carriers of the minor allele. However, the significant difference of insulin resistance parameters across genotypes disappeared after adjusting for BMI (Table 2). None of these significant associations were observed for the -11377G>C SNP. We did not find significant associations between the two promoter polymorphisms and inflammation biomarkers, with the exception of a borderline significant association between the minor -11377G allele and higher TNF-alpha levels (P=0.045) (Table 2).

Table 2.

Anthropometric and metabolic parameters according to ADIPOQ -11391G>A and -11377C>G genotype

-11391G>A P* value P** value -11377C>G P* value P** value
GG (n=930) GA+AA (n=143) CC (n=604) CG+GG (n=468)
Weight, kg 86.8 (1.3) 84.0 (1.6) 0.029 86.6 (1.4) 86.4 (1.4) 0.846
Height, m 1.71 (0.005) 1.71 (0.008) 0.812 1.71 (0.005) 1.71 (0.006) 0.782
BMI, kg/m2 29.6 (0.4) 28.6 (0.5) 0.019 29.5 (0.2) 29.4 (0.2) 0.823
Waist circumference, cm 100.5 (1.1) 97.3 (1.3) 0.002 0.197 100.3 (1.2) 100.2 (1.2) 0.909 0.887
Hip circumference, cm 109.2 (0.8) 107.0 (1.0) 0.004 0.438 108.8 (0.9) 109.2 (0.9) 0.615 0.192
Cholesterol, mg/dL 193.0 (2.8) 192.7 (4.1) 0.939 0.892 193.2 (3.0) 192.2 (3.0) 0.652 0.660
LDL-c, mg/dL 121.4 (2.2) 120.8 (3.4) 0.804 0.938 121.0 (2.4) 121.2 (2.4) 0.926 0.904
HDL-c, mg/dL 44.8 (0.7) 45.3 (1.2) 0.622 0.949 45.2 (0.8) 44.3 (0.8) 0.229 0.181
Triglycerides, mg/dL† 137.8 (126.6-150.0) 133.4 (118.7-150.0) 0.532 0.929 137.8 (100.2-150.7) 137.5 (125.1-151.0) 0.956 0.945
Glucose, mg/dL† 112.6 (109.8-115.5) 111.4 (108.1-114.7) 0.190 0.477 112.3 (109.5-115.3) 112.6 (109.6-115.8) 0.672 0.576
Insulin, μU/mL† 13.5 (12.6-14.3) 12.0 (11.0-13.2) 0.013 0.139 13.5 (12.6-14.4) 13.0 (12.1-14.0) 0.296 0.351
HOMA-R† 3.7 (3.5-4.0) 3.3 (3.0-3.6) 0.009 0.121 3.7 (3.5-4.0) 3.6 (3.3-3.9) 0.374 0.452
Adiponectin, ng/mL† 6403 (5944-6898) 7920 (7086-8853) <0.001 0.001 6828 (6307-7391) 6340 (5861-6858) 0.027 0.017
IL-6, pg/mL† 1.79 (1.65-1.93) 1.64 (1.46-1.83) 0.089 0.343 1.75 (1.60-1.91) 1.83 (1.67-1.99) 0.280 0.211
hsCRP, mg/dL† 0.15 (0.13-0.18) 0.14 (0.11-0.18) 0.518 0.693 0.15 (0.13-0.18) 0.15 (0.13-0.18) 0.838 0.662
TNF-alpha, pg/mL† 3.09 (2.93-3.26) 3.21 (2.99-3.45) 0.268 0.167 3.04 (2.88-3.20) 3.23 (3.03-3.44) 0.037 0.036

Values are mean (SEM) or geometric mean (95%CI),

*

P value after adjusted for age, gender, family relationship, smoking, alcohol consumption, physical activity, diabetes status, and hormone treatment.

**

Further adjusted for BMI

Next, we examined the interaction between ADIPOQ SNPs and dietary fat in determining adiposity and insulin resistance. We dichotomized the percentage of intakes for total fat (35%), SFA (12%), MUFA (13%), PUFA (7%), n-3 PUFA (0.7%) and n-6 PUFA (7%) according to their respective medians. There was significant interaction between the -11391G>A and MUFA intake in relation to BMI after multivariate adjustment for age, gender, family relationship, smoking, alcohol consumption, physical activity, diabetes status, daily energy intake, and percentage of protein and carbohydrate (P-interaction=0.021). At low MUFA intakes (≥13% of total energy intake), we did not find significant allele-related differences for BMI (P=0.902). On the other hand, at high MUFA intakes (>13% of total energy intake), GG homozygotes, had significantly higher BMI (+2 kg/m2, P=0.002) as compared with -11391A allele carriers (Table 3.). No gene-diet interaction on BMI was not observed for the ADIPOQ-11377C>G (P-interaction=0.277). Neither -11391G>A nor -11377C>G SNPs showed significant interactions with dietary fat intake in relation to waist circumference or insulin resistance parameters (data not presented).

Table 3.

Body mass index (kg/m2) stratified by ADIPOQ genotype and categories of energy from dietary fat.

-11391G>A P* value P** for interaction -11377C>G P* value P** for interaction
GG (n=930) GA+AA (n=143) CC (n=604) CG+GG (n=468)
Total fat, % 0.117 0.254
  < 35 28.5 (27.6-29.5) 28.5 (27.2-29.8) 0.878 28.4 (27.4-29.5) 28.7 (27.8-29.7) 0.579
  ≥ 35 29.0 (28.0-30.0) 27.5 (26.2-28.8) 0.019 29.0 (27.9-30.2) 28.6 (27.7-29.6) 0.346
SFA, % 0.990 0.818
  < 12 29.2 (28.3-30.2) 28.4 (27.2-29.6) 0.105 29.1 (28.0-30.1) 29.2 (28.2-30.2) 0.819
  ≥ 12 28.6 (27.6-29.7) 28.0 (26.5-29.7) 0.430 28.6 (27.5-29.8) 28.4 (27.3-29.6) 0.697
MUFA, % 0.021 0.277
  < 13 28.5 (27.6-29.5) 28.6 (27.2-30.0) 0.902 28.4 (27.3-29.5) 28.7 (27.7-29.8) 0.468
  ≥ 13 29.1 (28.1-30.1) 27.1 (26.0-28.2) 0.002 29.1 (28.0-30.2) 28.7 (27.7-29.7) 0.365
PUFA, % 0.695 0.762
  < 7 28.0 (27.0-29.0) 27.5 (26.0-29.0) 0.387 27.8 (26.7-29.0) 28.0 (26.9-29.1) 0.768
  ≥ 7 29.6 (28.6-30.6) 28.6 (27.3-30.0) 0.127 29.5 (28.4-30.6) 29.5 (28.4-30.5) 0.995
n-3 PUFA, % 0.154 0.364
  < 0.7 27.8 (26.9-28.8) 27.7 (26.3-29.2) 0.901 27.6 (26.6-28.7) 28.0 (27.0-29.0) 0.477
  ≥ 0.7 29.9 (28.9-31.0) 28.4 (27.1-29.8) 0.025 29.8 (28.7-30.9) 29.6 (28.6-30.7) 0.702
n-6 PUFA, % 0.838 0.087
  < 7 28.0 (27.0-29.0) 27.4 (25.9-29.0) 0.334 27.8 (26.7-29.0) 28.0 (26.9-29.1) 0.750
  ≥ 7 29.6 (28.6-30.6) 28.7 (27.4-30.1) 0.195 29.5 (28.4-30.6) 29.4 (28.4-30.5) 0.965
TFA, % 0.197 0.067
  <2 29.1 (28.2-30.1) 28.9 (27.5-30.3) 0.679 28.9 (27.9-29.9) 29.5 (28.5-30.5) 0.177
  ≥ 2 28.9 (27.8-29.9) 27.5 (26.1-28.9) 0.047 28.9 (27.7-30.1) 28.4 (27.4-29.5) 0.380

All dietary fat data were categorized by their median value of population

Data are adjusted geometric mean (95 %CI)

P for genotype (*) and interaction term (**) were obtained from the multivariate model after adjustment for age, gender, family relationship, smoking, alcohol consumption, physical activity, diabetes status, hormone treatment, total energy intake and percentage of carbohydrate and protein

Furthermore, we used logistic regression to test the effect of the ADIPOQ SNPs and dietary fat interaction on the risk of obesity. We found a significant interaction between the -11391G>A polymorphism and MUFA intake as categorical variable on the risk of obesity (P-interaction= 0.034) in a multivariate adjusted model (Table 4). At low MUFA intake (<13% of total energy), carriers of the -11391A allele had a non-significant higher risk of obesity (OR=1.30, 95%CI; 0.77-2.20; P=0.347). In contrast, when the MUFA intake was high (≥13% of total energy), carriers of the -11391A allele had lower risk of obesity (OR= 0.52, 95%CI; 0.28-0.96; P=0.031) compared with GG subjects. No statistically significant interaction on obesity risk was observed for -11377C>G polymorphism (P-interaction=0.553).

Table 4.

Odds Ratios (OR) and 95% confidence interval ((95% CI) for the obesity prevalence stratified by ADIPOQ -11391G>A and -11377C>G SNPs and the median value of MUFA intake

MUFA intake < 13% MUFA intake ≥ 13% P** for interaction
OR 95%CI P* OR 95%CI P*
-11391G>A
GG 1 1
GA+AA 1.30 (0.77-2.20) 0.347 0.52 (0.28-0.96) 0.031 0.034
-11377C>G
CC 1 1
CG+GG 0.87 (0.95-1.09) 0.527 0.70 (0.48-1.04) 0.083 0.553

P value for the genotype (*) and interaction (**) term obtained in the corresponding logistic regression model after adjusting for age, gender, family relationships, tobacco smoking, alcohol consumption, physical activity, diabetes status, hormone, daily energy intake, and percentage of carbohydrate and protein in the corresponding strata of MUFA intake.

The Computational analysis of the upstream region of the ADIPOQ gene with MAPPER, a tool designed to identify putative transcription factor binding sites, revealed potential AP4 and CP2 binding sites only for the ADIPOQ -11391A variant allele. No putative binding site for transcription factors at the ADIPOQ -11377C>G was detected.

Discussion

In this population of US Whites, we demonstrated a novel interaction between the ADIPOQ -11391G>A polymorphism and dietary MUFA intake affecting BMI and obesity risk. This gene-diet interaction was not observed in another promoter SNP (-11377C>G) of the same gene despite its association with plasma adiponectin concentrations. Our data are consistent with previous reports (16, 32-35) demonstrating that the association between both promoter SNPs and serum adiponectin. The minor allele A at the ADIPOQ -11391G>A SNP was associated with higher while the minor allele G at ADIPOQ -11377C>G SNP with lower plasma adiponectin concentrations. Despite these significant associations with plasma adiponectin concentrations, only the ADIPOQ -11391G>A SNP was significantly associated with a number of obesity-related traits including insulin resistance, BMI, weight and waist and hip circumferences. Previous studies on ADIPOQ polymorphisms have shown a wide range of inconsistent outcomes. Among Hispanics, the minor allele of -11391G>A was associated with significantly lower visceral fat, but no significant associations were observed for BMI, waist circumference or waist-to-hip ratio (WHR) (36). Another study in French Caucasians demonstrated that a GG haplotype, constructed from two promoter SNPs (-11391G>A and -11377C>G), was associated with low circulating adiponectin concentrations and with type 2 diabetes, but they did not find significant associations with fasting insulin or HOMA-R (16). Moreover, they also demonstrated that the promoter haplotype was associated with the risk of type 2 diabetes only in obese individuals (35). This finding supports our result that the association of the -11391G>A polymorphism with insulin resistance (assessed by fasting insulin and HOMA-R) is entirely accounted for by BMI, suggesting that the impact of ADIPOQ variations on glucose homeostasis may be dependent on body fatness and/or the presence of insulin resistance. Likewise, Buzzetti et al. failed to find an association with HOMA-R (32) in obese subjects, but statistically significant allele-related differences were observed with hyperinsulinemic-euglycemic clamp data. Only one study on Italian obese children found a significant association between the -11391A allele and higher serum adiponectin, lower fasting insulin and HOMA-R (33). The potential reasons for the lack of consistency among studies are multiple. First, inadequate sample sizes to determining the effect because of low allele frequencies, very different study designs, including subjects from different ethnic groups, ages and health conditions were used; secondly, there are major differences in the methods (clamp, fasting insulin, HOMA-R) used to assess insulin sensitivity/resistance; third, the environmental factors surrounding the participants varied across studies; and forth, there are major differences in the approaches used for statistical analyses.

Our computational analysis of the upstream region of the ADIPOQ gene with MAPPER (31), a tool designed to identify putative transcription factor binding sites, revealed potential AP4 and CP2 binding sites only for the ADIPOQ -11391A variant allele. No transcription binding site was predicted for the ADIPOQ -11377C>G. The association of the ADIPOQ -11391A with hyperadiponectinemia may be accounted for by increases in transcription activity (18). Several transcription factors regulate adiponectin gene expression, including peroxisome proliferation activated receptor gamma (PPAR-γ), liver receptor homolog-1 (37, 38), CCAAT/enhancer-binding protein (C/EBP), nuclear factor-Y (NF-Y) (39) and sterol-regulatory-element-binding protein-1c (SREBP-1c) (40). Park et al. suggested that C/EBP is critical for the regulation of adiponectin expression in response to nutrients and in the course of adipocyte differentiation (39). However, there is no experimental data showing a direct relationship between AP-4 or CP-2 and ADIPOQ promoter activity or serum adiponectin. Nevertheless, there is support for a relationship between C/EBP and AP-4 through the E-box, upstream regulatory factor of the C/EBP gene, that was required for full activity of the C/EBP promoter. Purification of the E-box binding protein during adipocyte differentiation resulted in the finding that USF-1 and USF-2 stimulates C/EBP expression, whereas AP-4 represses it (41). Biddinger et al. reported that the genetically insulin resistance mice with high fat feeding increased hepatic triglycerides and MUFAs through SREBP-1c and stearoyl-CoA desaturase 1 (SCD1) by increased in both SREBP-1c mRNA expression and nuclear protein, and SCD1 mRNA and activity(42). Moreover, fatty acids both MUFA and PUFA bind directly to PPAR-α and PPAR-γ and regulate gene expression (43, 44).

Consistent with the hypothesis that the genetic impact of this SNP on anthropometric and metabolic traits may be modulated by dietary factors, we found that in the highest 50th percentile, the carriers of the -11391A allele had lower BMI and decreased 48% risk of obesity compared with GG homozygotes. On the other hand, in the lowest 50th percentile of MUFA intake, -11391A allele was not associated with BMI or obesity risk. No interaction was observed for the ADIPOQ -11377C>G SNP, suggesting different mechanisms driving the activity of these two promoter sites. A report from the NUGENOB Study examined the ADIPOQ -11391G>A SNP and total fat intake and found no significant interactions modulating the development of obesity. However, the population examined consisted only of obese women and interaction with specific fatty acids was not reported (45). The previous findings in the Nurses’ Health Study (46) for the PPAR-γ Pro12Ala SNP and in the Framingham Heart Study (47) for the APOA5 -1131T>C SNP showing associations between higher MUFA intake and lower BMI only in those subjects carrying the minor alleles at the respective loci supported the concept of gene-diet interaction on the developing of complex traits in genetic susceptibility individuals.

The reported interaction needs to be interpreted with caution and be maintained within the context of the specific circumstances of the population examined. In the American diet, the main source of MUFA is not olive oil (48) as in Mediterranean countries. Moreover, it is important to note that the cross-sectional design of our study cannot provide evidence for the causality of this interaction and its mechanistic basis. Therefore, intervention studies are required to confirm whether the observed gene-MUFA interactions modulating obesity risk extend to other populations and dietary habits.

In conclusion, our findings support the current knowledge regarding significant associations between the promoter polymorphism at ADIPOQ (-11391G>A and -11377G>A) and serum adiponectin concentrations, however, only ADIPOQ -11391G>A influence the obesity traits. Moreover, we found a novel interaction between this SNP and dietary MUFA modulating BMI and obesity risk. This finding, if supported by other population, intervention and mechanistic studies could contribute to more personalized approaches to weight control and disease prevention.

Acknowledgements

This work was supported by National Heart, Lung, and Blood Institute Grant U 01 HL72524, Genetic and Environmental Determinants of Triglycerides Grant HL-54776, by contracts 53-K06-5-10 and 58-1950-9-001 from the US Department of Agriculture Research Service.

Footnotes

Disclosure information: All authors have nothing to declare

References

  • 1.Fasshauer M, Paschke R, Stumvoll M. Adiponectin, obesity, and cardiovascular disease. Biochimie Recent advances in lipid metabolism and related disorders. 2004;86:779–84. doi: 10.1016/j.biochi.2004.09.016. [DOI] [PubMed] [Google Scholar]
  • 2.Goldstein BJ, Scalia R. Adiponectin: A Novel Adipokine Linking Adipocytes and Vascular Function. J Clin Endocrinol Metab. 2004;89:2563–8. doi: 10.1210/jc.2004-0518. [DOI] [PubMed] [Google Scholar]
  • 3.Frystyk J, Berne C, Berglund L, Jensevik K, Flyvbjerg A, Zethelius B. Serum Adiponectin Is a Predictor of Coronary Heart Disease: A Population-Based 10-Year Follow-Up Study in Elderly Men. J Clin Endocrinol Metab. 2007;92:571–6. doi: 10.1210/jc.2006-1067. [DOI] [PubMed] [Google Scholar]
  • 4.Kanaya AM, Wassel Fyr C, Vittinghoff E, et al. Serum Adiponectin and Coronary Heart Disease Risk in Older Black and White Americans. J Clin Endocrinol Metab. 2006;91:5044–50. doi: 10.1210/jc.2006-0107. [DOI] [PubMed] [Google Scholar]
  • 5.Koenig W, Khuseyinova N, Baumert J, Meisinger C, Lowel H. Serum Concentrations of Adiponectin and Risk of Type 2 Diabetes Mellitus and Coronary Heart Disease in Apparently Healthy Middle-Aged Men: Results From the 18-Year Follow-Up of a Large Cohort From Southern Germany. Journal of the American College of Cardiology. 2006;48:1369–77. doi: 10.1016/j.jacc.2006.06.053. [DOI] [PubMed] [Google Scholar]
  • 6.Weyer C, Funahashi T, Tanaka S, et al. Hypoadiponectinemia in Obesity and Type 2 Diabetes: Close Association with Insulin Resistance and Hyperinsulinemia. J Clin Endocrinol Metab. 2001;86:1930–5. doi: 10.1210/jcem.86.5.7463. [DOI] [PubMed] [Google Scholar]
  • 7.Yang W-S, Lee W-J, Funahashi T, et al. Weight Reduction Increases Plasma Levels of an Adipose-Derived Anti-Inflammatory Protein, Adiponectin. J Clin Endocrinol Metab. 2001;86:3815–9. doi: 10.1210/jcem.86.8.7741. [DOI] [PubMed] [Google Scholar]
  • 8.Esposito K, Pontillo A, Di Palo C, et al. Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. Jama. 2003;289:1799–804. doi: 10.1001/jama.289.14.1799. [DOI] [PubMed] [Google Scholar]
  • 9.Maeda K, Okubo K, Shimomura I, Funahashi T, Matsuzawa Y, Matsubara K. cDNA cloning and expression of a novel adipose specific collagen-like factor, apM1 (AdiPose Most abundant Gene transcript 1) Biochem Biophys Res Commun. 1996;221:286–9. doi: 10.1006/bbrc.1996.0587. [DOI] [PubMed] [Google Scholar]
  • 10.Mori Y, Otabe S, Dina C, et al. 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;51:1247–55. doi: 10.2337/diabetes.51.4.1247. [DOI] [PubMed] [Google Scholar]
  • 11.Vionnet N, Hani EH, Dupont S, et al. Genomewide search for type 2 diabetes-susceptibility genes in French whites: evidence for a novel susceptibility locus for early-onset diabetes on chromosome 3q27-qter and independent replication of a type 2-diabetes locus on chromosome 1q21-q24. Am J Hum Genet. 2000;67:1470–80. doi: 10.1086/316887. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Kissebah AH, Sonnenberg GE, Myklebust J, et al. Quantitative trait loci on chromosomes 3 and 17 influence phenotypes of the metabolic syndrome. PNAS. 2000;97:14478–83. doi: 10.1073/pnas.97.26.14478. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Chuang L-M, Chiu Y-F, Sheu WH-H, et al. Biethnic Comparisons of Autosomal Genomic Scan for Loci Linked to Plasma Adiponectin in Populations of Chinese and Japanese Origin. J Clin Endocrinol Metab. 2004;89:5772–8. doi: 10.1210/jc.2004-0640. [DOI] [PubMed] [Google Scholar]
  • 14.Comuzzie AG, Funahashi T, Sonnenberg G, et al. The Genetic Basis of Plasma Variation in Adiponectin, a Global Endophenotype for Obesity and the Metabolic Syndrome. J Clin Endocrinol Metab. 2001;86:4321–5. doi: 10.1210/jcem.86.9.7878. [DOI] [PubMed] [Google Scholar]
  • 15.Lindsay RS, Funahashi T, Krakoff J, et al. Genome-Wide Linkage Analysis of Serum Adiponectin in the Pima Indian Population. Diabetes. 2003;52:2419–25. doi: 10.2337/diabetes.52.9.2419. [DOI] [PubMed] [Google Scholar]
  • 16.Vasseur F, Helbecque N, Dina C, et al. 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;11:2607–14. doi: 10.1093/hmg/11.21.2607. [DOI] [PubMed] [Google Scholar]
  • 17.Heid IM, Wagner SA, Gohlke H, et al. Genetic Architecture of the APM1 Gene and Its Influence on Adiponectin Plasma Levels and Parameters of the Metabolic Syndrome in 1,727 Healthy Caucasians. Diabetes. 2006;55:375–84. doi: 10.2337/diabetes.55.02.06.db05-0747. [DOI] [PubMed] [Google Scholar]
  • 18.Bouatia-Naji N, Meyre D, Lobbens S, et al. ACDC/Adiponectin Polymorphisms Are Associated With Severe Childhood and Adult Obesity. Diabetes. 2006;55:545–50. doi: 10.2337/diabetes.55.02.06.db05-0971. [DOI] [PubMed] [Google Scholar]
  • 19.Stumvoll M, Tschritter O, Fritsche A, et al. Association of the T-G Polymorphism in Adiponectin (Exon 2) With Obesity and Insulin Sensitivity: Interaction With Family History of Type 2 Diabetes. Diabetes. 2002;51:37–41. doi: 10.2337/diabetes.51.1.37. [DOI] [PubMed] [Google Scholar]
  • 20.Wei-Shiung Y, Pei-Ling T, Wei-Jei L, et al. Allele-specific differential expression of a common adiponectin gene polymorphism related to obesity. Journal of Molecular Medicine. 2003;V81:428–34. doi: 10.1007/s00109-002-0409-4. [DOI] [PubMed] [Google Scholar]
  • 21.Yang WS, Hsiung CA, Ho LT, et al. Genetic epistasis of adiponectin and PPAR?2 genotypes in modulation of insulin sensitivity: a family-based association study. Diabetologia. 2003;V46:977–83. doi: 10.1007/s00125-003-1136-2. [DOI] [PubMed] [Google Scholar]
  • 22.Filippi E, Sentinelli F, Trischitta V, et al. Association of the human adiponectin gene and insulin resistance. Eur J Hum Genet. 2004;12:199–205. doi: 10.1038/sj.ejhg.5201120. [DOI] [PubMed] [Google Scholar]
  • 23.Xita N, Georgiou I, Chatzikyriakidou A, et al. Effect of Adiponectin Gene Polymorphisms on Circulating Adiponectin and Insulin Resistance Indexes in Women with Polycystic Ovary Syndrome. Clin Chem. 2005;51:416–23. doi: 10.1373/clinchem.2004.043109. [DOI] [PubMed] [Google Scholar]
  • 24.Corella D, Arnett DK, Tsai MY, et al. The -256T>C Polymorphism in the Apolipoprotein A-II Gene Promoter Is Associated with Body Mass Index and Food Intake in the Genetics of Lipid Lowering Drugs and Diet Network Study. Clin Chem. 2007 doi: 10.1373/clinchem.2006.084863. clinchem.2006.084863. [DOI] [PubMed] [Google Scholar]
  • 25.Lai C-Q, Arnett DK, Corella D, et al. Fenofibrate Effect on Triglyceride and Postprandial Response of Apolipoprotein A5 Variants. The GOLDN Study. Arterioscler Thromb Vasc Biol. 2007 doi: 10.1161/ATVBAHA.107.140103. ATVBAHA.107.140103. [DOI] [PubMed] [Google Scholar]
  • 26.Millen AE, Midthune D, Thompson FE, Kipnis V, Subar AF. The National Cancer Institute Diet History Questionnaire: Validation of Pyramid Food Servings. Am. J. Epidemiol. 2006;163:279–88. doi: 10.1093/aje/kwj031. [DOI] [PubMed] [Google Scholar]
  • 27.National Institutes of Health Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report. Obes Res. 1998;6(Suppl 2):51S–209S. [PubMed] [Google Scholar]
  • 28.Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9. doi: 10.1007/BF00280883. [DOI] [PubMed] [Google Scholar]
  • 29.Tsai MY, Hanson NQ, Straka RJ, et al. Effect of influenza vaccine on markers of inflammation and lipid profile. Journal of Laboratory and Clinical Medicine. 2005;145:323–7. doi: 10.1016/j.lab.2005.03.009. [DOI] [PubMed] [Google Scholar]
  • 30.Livak KJ. Allelic discrimination using fluorogenic probes and the 5′ nuclease assay. Genet Anal. 1999;14:143–9. doi: 10.1016/s1050-3862(98)00019-9. [DOI] [PubMed] [Google Scholar]
  • 31.Marinescu V, Kohane I, Riva A. MAPPER: a search engine for the computational identification of putative transcription factor binding sites in multiple genomes. BMC Bioinformatics. 2005;6:79. doi: 10.1186/1471-2105-6-79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Buzzetti R, Petrone A, Zavarella S, et al. The glucose clamp reveals an association between adiponectin gene polymorphisms and insulin sensitivity in obese subjects. Int J Obes (Lond) 2006 doi: 10.1038/sj.ijo.0803419. [DOI] [PubMed] [Google Scholar]
  • 33.Petrone A, Zavarella S, Caiazzo A, et al. The promoter region of the adiponectin gene is a determinant in modulating insulin sensitivity in childhood obesity. Obesity (Silver Spring) 2006;14:1498–504. doi: 10.1038/oby.2006.172. [DOI] [PubMed] [Google Scholar]
  • 34.Schwarz PE, Towers GW, Fischer S, et al. Hypoadiponectinemia is associated with progression toward type 2 diabetes and genetic variation in the ADIPOQ gene promoter. Diabetes Care. 2006;29:1645–50. doi: 10.2337/dc05-2123. [DOI] [PubMed] [Google Scholar]
  • 35.Vasseur F, Helbecque N, Lobbens S, et al. 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;V48:892–9. doi: 10.1007/s00125-005-1729-z. [DOI] [PubMed] [Google Scholar]
  • 36.Beth SS, Stefanie W, Carl DL, et al. Genetic analysis of adiponectin and obesity in Hispanic families: the IRAS Family Study. Human Genetics. 2005;117:107–18. doi: 10.1007/s00439-005-1260-9. [DOI] [PubMed] [Google Scholar]
  • 37.Maeda N, Takahashi M, Funahashi T, et al. PPAR{gamma} Ligands Increase Expression and Plasma Concentrations of Adiponectin, an Adipose-Derived Protein. Diabetes. 2001;50:2094–9. doi: 10.2337/diabetes.50.9.2094. [DOI] [PubMed] [Google Scholar]
  • 38.Iwaki M, Matsuda M, Maeda N, et al. Induction of Adiponectin, a Fat-Derived Antidiabetic and Antiatherogenic Factor, by Nuclear Receptors. Diabetes. 2003;52:1655–63. doi: 10.2337/diabetes.52.7.1655. [DOI] [PubMed] [Google Scholar]
  • 39.Park S-k, Oh S-Y, Lee M-Y, Yoon S, Kim K-S, Kim J-w. CCAAT/Enhancer Binding Protein and Nuclear Factor-Y Regulate Adiponectin Gene Expression in Adipose Tissue. Diabetes. 2004;53:2757–66. doi: 10.2337/diabetes.53.11.2757. [DOI] [PubMed] [Google Scholar]
  • 40.Seo JB, Moon HM, Noh MJ, et al. Adipocyte Determination- and Differentiation-dependent Factor 1/Sterol Regulatory Element-binding Protein 1c Regulates Mouse Adiponectin Expression. J. Biol. Chem. 2004;279:22108–17. doi: 10.1074/jbc.M400238200. [DOI] [PubMed] [Google Scholar]
  • 41.Kim J-w, Monila H, Pandey A, Lane MD. Upstream stimulatory factors regulate the C/EBP[alpha] gene during differentiation of 3T3-L1 preadipocytes. Biochemical and Biophysical Research Communications. 2007;354:517–21. doi: 10.1016/j.bbrc.2007.01.008. [DOI] [PubMed] [Google Scholar]
  • 42.Biddinger SB, Almind K, Miyazaki M, Kokkotou E, Ntambi JM, Kahn CR. Effects of Diet and Genetic Background on Sterol Regulatory Element-Binding Protein-1c, Stearoyl-CoA Desaturase 1, and the Development of the Metabolic Syndrome. Diabetes. 2005;54:1314–23. doi: 10.2337/diabetes.54.5.1314. [DOI] [PubMed] [Google Scholar]
  • 43.Kliewer SA, Sundseth SS, Jones SA, et al. Fatty acids and eicosanoids regulate gene expression through direct interactions with peroxisome proliferator-activated receptors alpha and gamma. Proceedings of the National Academy of Sciences. 1997;94:4318–23. doi: 10.1073/pnas.94.9.4318. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Wolfrum C. Cytoplasmic fatty acid binding protein sensing fatty acids for peroxisome proliferator activated receptor activation. Cellular and Molecular Life Sciences (CMLS) doi: 10.1007/s00018-007-7279-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Jose LS, Philippe B, Camilla V, et al. Genotype-by-nutrient interactions assessed in European obese women. European Journal of Nutrition. 2006;45:454–62. doi: 10.1007/s00394-006-0619-6. [DOI] [PubMed] [Google Scholar]
  • 46.Memisoglu A, Hu FB, Hankinson SE, et al. Interaction between a peroxisome proliferator-activated receptor {gamma} gene polymorphism and dietary fat intake in relation to body mass. Hum. Mol. Genet. 2003;12:2923–9. doi: 10.1093/hmg/ddg318. [DOI] [PubMed] [Google Scholar]
  • 47.Corella D, Lai C-Q, Demissie S, et al. APOA5 gene variation modulates the effects of dietary fat intake on body mass index and obesity risk in the Framingham Heart Study. Journal of Molecular Medicine. 2007;85:119–28. doi: 10.1007/s00109-006-0147-0. [DOI] [PubMed] [Google Scholar]
  • 48.Ordovas JM, Corella D, Demissie S, et al. Dietary Fat Intake Determines the Effect of a Common Polymorphism in the Hepatic Lipase Gene Promoter on High-Density Lipoprotein Metabolism: Evidence of a Strong Dose Effect in This Gene-Nutrient Interaction in the Framingham Study. Circulation. 2002;106:2315–21. doi: 10.1161/01.cir.0000036597.52291.c9. [DOI] [PubMed] [Google Scholar]

RESOURCES