OBJECTIVE

To test the association of regional fat depots with circulating adiponectin and resistin concentrations and to assess the potential mediating effect of adipokines on associations between abdominal fat depots and cardiometabolic risk factors.

RESEARCH DESIGN AND METHODS

Participants from the Framingham Heart Study offspring cohort (n = 916, 55% women; mean age 59 years) free of cardiovascular disease underwent computed tomography measurement of visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), pericardial fat, and intrathoracic fat volumes and assays of circulating adiponectin and resistin.

RESULTS

VAT, SAT, pericardial fat, and intrathoracic fat were negatively correlated with adiponectin (r = −0.19 to −0.34, P < 0.001 [women]; r = −0.15 to −0.26, P < 0.01 [men] except SAT) and positively correlated with resistin (r = 0.16–0.21, P < 0.001 [women]; r = 0.11–0.14, P < 0.05 [men] except VAT). VAT increased the multivariable model R2 for adiponectin from 2–4% to 10–13% and for resistin from 3–4% to 3–6%. Adjustment for adipokines did not fully attenuate associations between VAT, SAT, and cardiometabolic risk factors.

CONCLUSIONS

Adiponectin and resistin are correlated with fat depots cross-sectionally, but none of the adipokines can serve as surrogates for the fat depots. Relations between VAT, SAT, and cardiometabolic risk factors were not fully explained by adiponectin or resistin concentrations.

The risk of cardiometabolic diseases increases with fat accumulation in ectopic depots (1,,,5). Visceral adipose tissue (VAT) is more associated with an increased risk of developing diabetes, hypertension, dyslipidemia, and metabolic syndrome compared with subcutaneous adipose tissue (SAT) (1,2). Pericardial fat and intrathoracic fat may also confer additional metabolic risk (4,5). The increased metabolic risk associated with ectopic fat depots may be due to their associations with adiponectin (6,7). However, prior studies (6,8) have been limited in size or age distribution and used only one or two computed tomography (CT) slices to measure abdominal fat area, which might lead to inaccuracy in measurement of actual regional fat deposits. In addition, it is not clear whether indexes of regional adiposity contribute to interindividual variation in systemic adipokine concentrations beyond anthropometric measures such as BMI and waist circumference (6,8). In this study, we investigated the cross-sectional associations of regional fat depots and circulating levels of adiponectin and resistin and assessed whether these adipokines mediate the association of abdominal fat depots with cardiometabolic risk factors.

Participants were selected from the Framingham Heart Study multidetector CT substudy of the community-based Framingham Heart Study offspring cohort; inclusion criteria have been described previously (1). Beginning in 1971, the offspring study enrolled the offspring and spouses of the offspring whose parents were in the original cohort of the Framingham Heart Study. A subset of the offspring cohort had measurements of adipokine concentrations at their seventh examination cycle between 1998 and 2001.

Of 1,418 participants in the offspring cohort of the multidetector CT study, 1,342 participants had interpretable CT measurements of visceral, subcutaneous, pericardial, and intrathoracic fat volume between 2002 and 2005. Plasma adiponectin and resistin concentrations were available in 1,060 of 1,342 participants. After excluding participants with clinically prevalent cardiovascular disease (CVD), prior coronary artery bypass graft, and an incomplete covariate profile, the final sample size was 916 (501 women and 415 men). The institutional review boards of Boston University Medical Center and Massachusetts General Hospital approved this study. Written informed consent was obtained from all participants.

Adipokine measurements

Circulating concentrations of adiponectin and resistin were measured by enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, MN) in plasma collected after a >8-h fast and frozen at −80°C. Intra-assay coefficients of variation were 5.8% for adiponectin and 9.0% for resistin. Single, random plasma adiponectin and resistin concentrations have been shown to be stable over a 1-year period (9).

Volumetric adipose tissue imaging

An 8-slice multidetector CT (LightSpeed Ultra; General Electric, Milwaukee, WI) was used to image the abdomen and thorax. While lying in a supine position, participants had an average of 25 contiguous 5-mm-thick cross-sectional images (120 kVp; 40 mA; gantry rotation time, 500 ms; table feed, 3:1) of the abdomen and 48 contiguous 2.5-mm-thick cross-sectional images of the heart (120 kVp; 400 mA; temporal resolution, 330 ms). The abdominal images spanned 125 mm above the level of S1. The specific protocols used to obtain images of the abdomen and thorax have been described elsewhere (5,10).

Abdominal and thoracic adipose tissue measurements

Visceral, subcutaneous, pericardial, and thoracic fat volumes were measured (Aquarius 3D Workstation; TeraRecon, San Mateo, CA) using an image display window width of −195 to −45 Hounsfield units (HU) and window center of −120 HU. The readers manually traced the abdominal muscular wall separating the visceral and subcutaneous depots and outlined the pericardium to distinguish pericardial from thoracic fat. Pericardial fat volume included all adipose tissue visualized within the pericardial sac. Total thoracic fat volume consisted of adipose tissue from the right pulmonary artery to the diaphragm and from the chest wall to the descending aorta, including fat within the pericardial sac. Interreader reproducibility was excellent for visceral, subcutaneous, pericardial, and thoracic fat volume measurements (1,10). Intrathoracic fat was derived after subtracting pericardial fat from total thoracic fat to identify two mutually exclusive fat compartments.

Risk factor and covariate assessment

Cardiometabolic risk factors and other covariates were assessed at the contemporaneous examination. BMI was measured as weight (in kilograms) divided by the square of height (in meters). Waist circumference was measured at the level of the umbilicus. Tobacco and alcohol use were self-reported through physician-administered questions. Participants were classified as current smokers if they smoked on average at least one cigarette per day in the previous year. Women who drank ≥7 alcoholic drinks per week and men who consumed ≥14 alcoholic drinks per week were considered alcohol users. The physical activity index was based on the average number of hours per day spent sleeping and doing sedentary, slight, moderate, and heavy activity. Women were considered postmenopausal if they had not had any menstrual periods for at least 1 year.

Hypertension was diagnosed as a systolic blood pressure ≥140 mmHg, a diastolic blood pressure ≥90 mmHg, or treatment with antihypertensive agents. Fasting morning plasma samples were collected to measure fasting plasma glucose, total cholesterol, HDL cholesterol, and triglycerides. Diabetes was defined as a fasting plasma glucose ≥126 mg/dl (≥7 mmol/l) or treatment with insulin or a hypoglycemic medication. Metabolic syndrome was defined based on modified Adult Treatment Plan III criteria.

Statistical analysis

Adiponectin, VAT, SAT, pericardial fat, and intrathoracic fat volumes were approximately normally distributed. Resistin was logarithmically transformed to normalize the distribution. All analyses were a priori performed specific to sex due to strong sex interactions we had previously observed with SAT and VAT (1). Sex-specific and age-adjusted Pearson correlation coefficients were calculated to determine the correlation between each fat compartment, adiponectin, and log resistin.

Sex-specific multivariable linear regression was used to evaluate the associations of adiponectin and log resistin (dependent variables; separate models for each) with fat depots (independent variables), after adjustment for age, smoking, alcohol use, menopausal status (women only), and hormone replacement therapy (women only). VAT, SAT, pericardial fat, and intrathoracic fat were standardized to a means ± SD of 0 ± 1; the covariate-adjusted average change in adiponectin and log resistin per SD of adipose tissue volume was estimated. Using this approach, the multivariable regression results for each fat compartment could be more directly compared. The multivariable models for VAT, pericardial fat, and intrathoracic fat were then additionally adjusted for BMI and waist circumference in order to assess whether relations were maintained after adjusting for measures of generalized adiposity. As secondary analyses, multivariable models for VAT, SAT, pericardial fat, and intrathoracic fat were also additionally adjusted for physical activity and education status.

Multivariable linear and logistic regressions were used to relate VAT and SAT (independent variables) to cardiometabolic risk factors (dependent variables). Models for systolic blood pressure, fasting plasma glucose, log triglycerides, HDL cholesterol, and metabolic syndrome were evaluated (separate model for each risk factor). Age, smoking, alcohol use, menopausal status (women only), hormone replacement therapy (women only), and treatment of hypertension, dyslipidemia, and diabetes were entered as covariates in each of the models. Each multivariable model was also adjusted for either adiponectin or log resistin (separate models for each adjustment) to assess the extent of attenuation of the association of VAT and SAT with each cardiometabolic risk factor upon adjustment for these adipokines. All analyses were performed using SAS version 9.1. A two-tailed value of P < 0.05 was considered statistically significant.

Study sample characteristics

Overall, 916 participants were available for analysis. The mean age was 59 years (Table 1), and slightly more than one-quarter were obese. The majority of women were postmenopausal.

Age-adjusted correlations between fat depots and adipokines

VAT, SAT, pericardial fat, and intrathoracic fat were negatively correlated with adiponectin (r = −0.19 to −0.34, P < 0.001 [women]; r = −0.15 to −0.26, P < 0.01 [men] except SAT) and positively correlated with resistin (r = 0.16–0.21, P < 0.001 [women]; r = 0.11–0.14, P < 0.05 [men] except VAT) (Table 2).

Multivariable-adjusted regressions with fat depots and adiponectin

In women, adiponectin concentration was 2.1 ± 0.3 μg/ml lower per SD increase of VAT, 1.2 ± 0.3 μg/ml lower per SD increase of SAT, 1.2 ± 0.3 μg/ml lower per SD increase of pericardial fat, and 1.2 ± 0.3 μg/ml lower per SD increase of intrathoracic fat (P < 0.001) in multivariable-adjusted models (Table 3). The regression coefficients for adiponectin concentration were larger per SD of VAT than per SD of SAT (P < 0.001). In men, the multivariable-adjusted effect sizes of VAT, SAT, pericardial fat, and intrathoracic fat on adiponectin concentration were weaker but still significant in nearly all cases (Table 3). A sex-by-VAT interaction was observed for adiponectin (P < 0.001), as well as a sex-by-SAT (P = 0.001) and sex–by–pericardial fat interaction (P = 0.03).

In women, VAT remained associated with adiponectin after additional adjustment for BMI and waist circumference (P = <0.001); pericardial fat and intrathoracic fat did not remain associated with adiponectin (P = 0.15 [pericardial fat], P = 0.5 [intrathoracic fat]). In contrast, among men, VAT, pericardial fat, and intrathoracic fat were associated with adiponectin after further adjustment for BMI and waist circumference (all P < 0.02).

Covariates (age, sex, smoking, alcohol use, menopausal status, and hormone replacement therapy) explained 2% of the circulating variability in adiponectin concentration in women and 4% in men; adding VAT to this model increased the model R2 to 13% in women and 10% in men (Table 3). Multivariable-adjusted models for SAT, pericardial fat, and intrathoracic fat accounted for a smaller portion of the variation in adiponectin concentration (R2 range 4–8%).

Multivariable-adjusted regressions with fat depots and resistin

VAT, SAT, pericardial fat, and intrathoracic fat were associated with resistin. Sex interactions were not observed (all P > 0.1). In women, additional adjustment for BMI and waist circumference attenuated associations between VAT, pericardial fat, intrathoracic fat, and resistin (all P > 0.2). Results were similar for men, except intrathoracic fat remained associated with resistin after additional adjustment for BMI and waist circumference (P = 0.02). None of the multivariable-adjusted models explained a substantial portion of the variability in resistin concentration (R2 range 3–6%).

Secondary analyses

When physical activity and education status were included as additional covariates, relations between adipose tissue volumes, adiponectin, and resistin were not materially different (data not shown).

Multivariable-adjusted regressions with VAT, SAT, and cardiometabolic risk factors additionally adjusted for adipokines

VAT was associated with all cardiometabolic risk factors in both sexes as previously reported (1), except for systolic blood pressure in men (online appendix Tables 1A and 1B [available at http://care.diabetesjournals.org/cgi/content/full/dc08-1733/DC1]).

In women, we observed some attenuation of the effect size of VAT on log triglycerides and HDL cholesterol after additional adjustment for adiponectin, but strong associations were still observed. After multivariable adjustment, HDL cholesterol was 5.1 ± 0.7 mg/dl (0.13 ± 0.02 mmol/l) lower per SD increase of VAT. After further adjustment for adiponectin, HDL cholesterol was 3.1 ± 0.6 mg/dl (0.08 ± 0.02 mmol/l) lower per SD increase of VAT. Fasting plasma glucose and diabetes did not appreciably change after adjustment for adiponectin. For SAT, similar results were observed.

In men, there was more attenuation of the effect size of VAT on fasting plasma glucose, but not for diabetes, after additional adjustment for adiponectin. The effect size of VAT on HDL cholesterol was minimally weakened after additional adjustment for adiponectin. Relations between VAT, log triglycerides, and metabolic syndrome, as well as between SAT and cardiometabolic risk factors, did not appreciably change after adjustment for adiponectin in men. None of the associations between VAT, SAT, and cardiometabolic risk factors appreciably changed upon adjustment for resistin (online appendix Tables 1A and 1B).

In the community-based Framingham offspring cohort, VAT, SAT, pericardial fat, and intrathoracic fat were negatively correlated with adiponectin but positively correlated with resistin. For both sexes, the strongest correlation was between VAT and adiponectin, but the correlation coefficients were still weak. Furthermore, the addition of VAT only increased the model R2 for adiponectin from 2–4% to 10–13%. VAT is not a primary determinant of systemic total adiponectin concentration, and circulating levels of adiponectin cannot be used as a surrogate for VAT. Adjustment for adiponectin attenuated relations between VAT, SAT, and cardiometabolic risk factors, but adiponectin did not fully account for the strong relations between VAT and cardiometabolic risk factors. Easily obtainable anthropometric measures (BMI and waist circumference) did not explain the associations between VAT and adiponectin in women or men and between pericardial fat, intrathoracic fat, and adiponectin in men. Therefore, ectopic fat depots may provide some additional insight about systemic adiponectin concentrations not apparent with measures of general adiposity.

The modest inverse association between VAT and adiponectin has been observed previously (6,8,11). We observed strong sex interactions in the effect size of VAT on systemic adiponectin concentration. Other studies have demonstrated significant sex interactions in the associations between VAT and cardiometabolic risk factors (1,7). VAT may have higher lipolytic activity in women compared with men (12). Sex differences in free fatty acid delivery to the liver may account for the observed sex interactions, but the mechanisms are still not clear.

The associations between fat depots and resistin have not been extensively studied. The role of resistin in human obesity is controversial, but a recent candidate gene study (13) suggests an association between resistin and regional fat distribution and metabolic complications in HIV lipodystrophy. We were able to observe small but significant associations between fat depots and resistin.

The strong relations between VAT, diabetes, dyslipidemia, hypertension, and metabolic syndrome have been observed previously. The results of the present study reveal that adiponectin does not fully explain the relations between VAT, systolic blood pressure, diabetes, and metabolic syndrome. The associations between VAT, log triglycerides, HDL cholesterol, and fasting plasma glucose were attenuated, but not fully accounted for, by adiponectin. These findings contrast with prior work in the Health, Aging, and Body Composition Cohort, where the association between VAT and diabetes in a multivariable model was minimally attenuated by adjustment for combination of adiponectin, leptin, and plasminogen activator inhibitor-1 (11). However, PAI-1 was the only adipokine found to be independently associated with incident diabetes. Given the modest correlations between VAT, SAT, and adiponectin, it is not surprising that we did not observe more attenuation in these models. Adjustment for resistin did not weaken any of the associations between VAT and cardiometabolic risk factors.

The differential associations between VAT, SAT, and adiponectin may be explained by differential adipocyte adipokine production and regulatory feedback of local inflammatory signals. Several small studies in lean and obese nondiabetic participants have shown that adiponectin mRNA levels are lower in VAT compared with SAT (14,15), although these findings are not universal (16). VAT is associated with several inflammatory markers, even after adjustment for BMI and waist circumference (17). Tumor necrosis factor-α, interleukin-6, and C-reactive protein can suppress adiponectin gene expression. In addition, adiponectin binds to complement factors and injured vessel walls (18); therefore, adiponectin concentrations may be further reduced in the presence of a high VAT volume and systemic inflammation. In humans, resistin is primarily derived from macrophages that have infiltrated adipose tissue (19). A rodent study (20) revealed that macrophage content is higher in VAT compared with SAT in db/db mice but had similar concentrations in control mice.

Given the modest correlations between VAT and adiponectin, adiponectin does not appear to be a good serum proxy for VAT. Adjustment for adiponectin affected relations between VAT and fasting plasma glucose in men, HDL cholesterol, and log triglycerides, albeit minimally. These findings suggest that additional circulating biomarkers and hormones may mediate the relations between VAT and cardiometabolic risk factors. It is also possible that high molecular weight adiponectin, rather than total adiponectin, may be a more potent mediator of the associations between VAT and cardiometabolic risk factors.

The strengths of this study include a well-characterized study sample and precise volumetric quantification of adipose tissue. The limitations include the cross-sectional design of the study, lack of ethnic diversity, noncontemporaneous CT and adipokine concentrations, and adiponectin assay. Given the cross-sectional, observational nature of our study design, causality cannot be inferred. Because the sample is comprised of primarily white participants, generalizability to other ethnic groups is uncertain. Adipokine concentrations, as well as glucose, cholesterol, and anthropometric measurements, were obtained several years before the CTs were performed. Serum adiponectin levels and cardiometabolic risk factors may have changed at the time of the CTs, and regional fat depot measurements may have been different at the time of adipokine concentrations. However, adipokine measurements have been shown to be stable over a 1-year period (9). Nonetheless, we cannot rule out misclassification based on the 4-year time lag between the adipokine and CT measurements that may have affected our results. Any minor variation in adipokine measurements should not impact the relative association among VAT, SAT, pericardial fat, intrathoracic fat, and adipokines, which is the primary focus of this study. We measured systemic levels and not local adipokine concentrations, and systemic levels may not reflect local production and release by all regional fat depots. Adipokines produced by VAT are released into portal circulation, where hormones can be metabolized. Therefore, systemic adiponectin concentration may underestimate adiponectin concentration that is actually secreted by VAT. However, a recent study did not demonstrate a difference between portal and systemic adiponectin concentrations (21); therefore, this is unlikely to explain our results. Lastly, high molecular weight adiponectin is more strongly associated with regional adiposity and metabolic diseases than total adiponectin (22). Thus, our results may have been stronger if we had measured high molecular weight adiponectin instead of total adiponectin. Adiponectin and resistin are correlated with fat depots cross-sectionally, but none of these adipokines can serve as surrogates for these fat depots. Relations among VAT, SAT, and cardiometabolic risk factors were not fully explained by adiponectin or resistin concentrations.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This work was supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195), the National Institutes of Health, National Center for Research Resources, General Clinical Research Centers Program (grant no. M01-RR-01066), and by a Career Development Award from the American Diabetes Association (to J.B.M.). J.B.M. was further supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) K24 DK080140. R.S.V. is supported in part by 2K24HL04334 (National Heart, Lung, and Blood Institute, National Institutes of Health) and RO1 DK080739 (NIDDK).

No potential conflicts of interest relevant to this article were reported.

Parts of this study were presented in abstract form at the 69th Scientific Sessions of the American Diabetes Association, San Francisco, California, 6–10 June 2008.

1.
Fox
CS
,
Massaro
JM
,
Hoffmann
U
,
Pou
KM
,
Maurovich-Horvat
P
,
Liu
CY
,
Vasan
RS
,
Murabito
JM
,
Meigs
JB
,
Cupples
LA
,
D'Agostino
RBS
,
O'Donnell
CJ
:
Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the framingham heart study
.
Circulation
2007
; 
116
:
39
48
2.
Vega
GL
,
Adams-Huet
B
,
Peshock
R
,
Willett
D
,
Shah
B
,
Grundy
SM
:
Influence of body fat content and distribution on variation in metabolic risk
.
J Clin Endocrinol Metab
2006
; 
91
:
4459
4466
3.
Hayashi
T
,
Boyko
EJ
,
Leonetti
DL
,
McNeely
MJ
,
Newell-Morris
L
,
Kahn
SE
,
Fujimoto
WY
:
Visceral adiposity is an independent predictor of incident hypertension in japanese americans
.
Ann Intern Med
2004
; 
140
:
992
1000
4.
de Vos
AM
,
Prokop
M
,
Roos
CJ
,
Meijs
MF
,
van der Schouw
YT
,
Rutten
A
,
Gorter
PM
,
Cramer
MJ
,
Doevendans
PA
,
Rensing
BJ
,
Bartelink
ML
,
Velthuis
BK
,
Mosterd
A
,
Bots
ML
:
Peri-coronary epicardial adipose tissue is related to cardiovascular risk factors and coronary artery calcification in post-menopausal women
.
Eur Heart J
2008
; 
29
:
777
783
5.
Rosito
GA
,
Massaro
JM
,
Hoffmann
U
,
Ruberg
FL
,
Mahabadi
AA
,
Vasan
RS
,
O'Donnell
CJ
,
Fox
CS
:
Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study
.
Circulation
2008
; 
117
:
605
613
6.
Cnop
M
,
Havel
PJ
,
Utzschneider
KM
,
Carr
DB
,
Sinha
MK
,
Boyko
EJ
,
Retzlaff
BM
,
Knopp
RH
,
Brunzell
JD
,
Kahn
SE
:
Relationship of adiponectin to body fat distribution, insulin sensitivity and plasma lipoproteins: Evidence for independent roles of age and sex
.
Diabetologia
2003
; 
46
:
459
469
7.
Kanaya
AM
,
Harris
T
,
Goodpaster
BH
,
Tylavsky
F
,
Cummings
SR
:
Health, Aging, and Body Composition (ABC) Study
.
Adipocytokines attenuate the association between visceral adiposity and diabetes in older adults
.
Diabetes Care
2004
; 
27
:
1375
1380
8.
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
9.
Lee
SA
,
Kallianpur
A
,
Xiang
YB
,
Wen
W
,
Cai
Q
,
Liu
D
,
Fazio
S
,
Linton
MF
,
Zheng
W
,
Shu
XO
:
Intra-individual variation of plasma adipokine levels and utility of single measurement of these biomarkers in population-based studies
.
Cancer Epidemiol Biomarkers Prev
2007
; 
16
:
2464
2470
10.
Maurovich-Horvat
P
,
Massaro
J
,
Fox
CS
,
Moselewski
F
,
O'Donnell
CJ
,
Hoffmann
U
:
Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography
.
Int J Obes (Lond)
2007
; 
31
:
500
506
11.
Kanaya
AM
,
Wassel Fyr
C
,
Vittinghoff
E
,
Harris
TB
,
Park
SW
,
Goodpaster
BH
,
Tylavsky
F
,
Cummings
SR
:
Adipocytokines and incident diabetes mellitus in older adults: the independent effect of plasminogen activator inhibitor 1
.
Arch Intern Med
2006
; 
166
:
350
356
12.
Nielsen
S
,
Guo
Z
,
Johnson
CM
,
Hensrud
DD
,
Jensen
MD
:
Splanchnic lipolysis in human obesity
.
J Clin Invest
2004
; 
113
:
1582
1588
13.
Ranade
K
,
Geese
WJ
,
Noor
M
,
Flint
O
,
Tebas
P
,
Mulligan
K
,
Powderly
W
,
Grinspoon
SK
,
Dube
MP
:
Genetic analysis implicates resistin in HIV lipodystrophy
.
AIDS
2008
; 
22
:
1561
1568
14.
Hernandez-Morante
JJ
,
Milagro
FI
,
Larque
E
,
Lujan
J
,
Martinez
JA
,
Zamora
S
,
Garaulet
M
:
Relationship among adiponectin, adiponectin gene expression and fatty acids composition in morbidly obese patients
.
Obes Surg
2007
; 
17
:
516
524
15.
Perrini
S
,
Laviola
L
,
Cignarelli
A
,
Melchiorre
M
,
De Stefano
F
,
Caccioppoli
C
,
Natalicchio
A
,
Orlando
MR
,
Garruti
G
,
De Fazio
M
,
Catalano
G
,
Memeo
V
,
Giorgino
R
,
Giorgino
F
:
Fat depot-related differences in gene expression, adiponectin secretion, and insulin action and signalling in human adipocytes differentiated in vitro from precursor stromal cells
.
Diabetologia
2008
; 
51
:
155
164
16.
Yang
WS
,
Chen
MH
,
Lee
WJ
,
Lee
KC
,
Chao
CL
,
Huang
KC
,
Chen
CL
,
Tai
TY
,
Chuang
LM
:
Adiponectin mRNA levels in the abdominal adipose depots of nondiabetic women
.
Int J Obes Relat Metab Disord
2003
; 
27
:
896
900
17.
Pou
KM
,
Massaro
JM
,
Hoffmann
U
,
Vasan
RS
,
Maurovich-Horvat
P
,
Larson
MG
,
Keaney
JF
 Jr
,
Meigs
JB
,
Lipinska
I
,
Kathiresan
S
,
Murabito
JM
,
O'Donnell
CJ
,
Benjamin
EJ
,
Fox
CS
:
Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham Heart Study
.
Circulation
2007
; 
116
:
1234
1241
18.
Peake
PW
,
Shen
Y
,
Walther
A
,
Charlesworth
JA
:
Adiponectin binds C1q and activates the classical pathway of complement
.
Biochem Biophys Res Commun
2008
; 
367
:
560
565
19.
Curat
CA
,
Wegner
V
,
Sengenes
C
,
Miranville
A
,
Tonus
C
,
Busse
R
,
Bouloumie
A
:
Macrophages in human visceral adipose tissue: increased accumulation in obesity and a source of resistin and visfatin
.
Diabetologia
2006
; 
49
:
744
747
20.
Okamoto
Y
,
Arita
Y
,
Nishida
M
,
Muraguchi
M
,
Ouchi
N
,
Takahashi
M
,
Igura
T
,
Inui
Y
,
Kihara
S
,
Nakamura
T
,
Yamashita
S
,
Miyagawa
J
,
Funahashi
T
,
Matsuzawa
Y
:
An adipocyte-derived plasma protein, adiponectin, adheres to injured vascular walls
.
Horm Metab Res
2000
; 
32
:
47
50
21.
Fontana
L
,
Eagon
JC
,
Trujillo
ME
,
Scherer
PE
,
Klein
S
:
Visceral fat adipokine secretion is associated with systemic inflammation in obese humans
.
Diabetes
2007
; 
56
:
1010
1013
22.
Araki
S
,
Dobashi
K
,
Kubo
K
,
Asayama
K
,
Shirahata
A
:
High molecular weight, rather than total, adiponectin levels better reflect metabolic abnormalities associated with childhood obesity
.
J Clin Endocrinol Metab
2006
; 
91
:
5113
5116
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

Supplementary data