OBJECTIVE—Previous studies have yielded conflicting results on the association of adiponectin levels and inflammation. Low systemic concentrations of adiponectin, as well as elevated levels of immune mediators, represent risk factors for the development of type 2 diabetes and coronary artery disease. The major aim of this cross-sectional study was to investigate the interdependence of hypoadiponectinemia and low-grade systemic inflammation.

RESEARCH DESIGN AND METHODS—The study sample consisted of 606 participants aged 55–74 years (244 with normal glucose tolerance, 242 with impaired glucose tolerance, and 120 with newly diagnosed type 2 diabetes) of the population-based KORA S4 (Cooperative Health Research in the Region of Augsburg Survey 4; 1999–2001). Systemic concentrations of adiponectin and a wide range of anthropometric, metabolic, and inflammatory variables were available for analyses. The association of adiponectin with 15 immunological markers, including leukocyte count, acute-phase proteins, cytokines, cytokine receptors, and chemokines, was assessed using univariable and multivariable models.

RESULTS—No evidence for a significant correlation between adiponectin and all immunological parameters except eotaxin could be found after multivariable adjustments, whereas multiple strong correlations with obesity and metabolic factors were present.

CONCLUSIONS—From these data, we conclude that hypoadiponectinemia and a proinflammatory state are largely independent from each other.

Hypoadiponectinemia is associated with insulin resistance, whereas an increase of circulating adiponectin concentrations improves glucose levels and increases fatty acid oxidation (1–4). Adiponectin may be considered a biomarker for insulin sensitivity, and in prospective studies, hypoadiponectinemia predicts the incidence of type 2 diabetes (5,6) and coronary artery disease (7). In addition to the insulin-sensitizing properties of adiponectin, the link between adiponectin and chronic subclinical inflammation, which is characteristic of obesity, type 2 diabetes, and cardiovascular disease, has also been investigated. In vitro, adiponectin inhibits adhesion molecule expression on endothelial cells (8), suppresses nuclear factor-κB–mediated signaling (9), impairs macrophage functions (10,11), and downregulates cytokine secretion from adipocytes (12).

However, in vivo data concerning the possible systemic anti-inflammatory effect of adiponectin are more ambiguous. Some clinical studies have reported significant inverse correlations between the systemic concentrations of adiponectin and circulating immune mediators (13–24), whereas others found no evidence for a significant correlation (14–18,21–24). Most of these reports are based on univariable analyses and do not take into account the potential modification of these relationships by variables that are related to metabolic syndrome.

Since obesity is associated with hypoadiponectinemia and with increased circulating levels of various immunological markers, which are both major risk factors for the development of type 2 diabetes and cardiovascular disease, it would be important to know whether hypoadiponectinemia and systemic low-grade inflammation are independent or whether inflammatory markers can be considered “as surrogate markers of hypoadiponectinemia,” as recently proposed (17). The main aim of this study was therefore to assess the association of serum adiponectin levels with 15 immunological variables compared with anthropometric and metabolic parameters in a population-based sample and to test in multivariable regression analyses for significant associations of immune mediators with adiponectin, which are independent of potentially confounding anthropometric and metabolic factors.

The population-based Cooperative Health Research in the Region of Augsburg Survey 4 (KORA S4) study population has been described extensively (25–29). In the age range 55–74 years, 120 individuals with newly diagnosed type 2 diabetes (and thus without antidiabetic treatment, which could interfere with adiponectin and immune marker levels) and 242 subjects with impaired glucose tolerance (IGT) were available for analysis. Control subjects with normal glucose tolerance (NGT; n = 244) were randomly selected after frequency matching for age and sex.

The measurement of anthropometric parameters, metabolic and immunological variables (from fasting blood samples), blood pressure, and the assessment of alcohol intake, smoking, and physical activity has been described previously (25–29). Hypertension was defined as use of antihypertensive treatment or systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg. In the NGT group, 42 men and 31 women were on antihypertensive treatment. In the IGT/type 2 diabetic group, 89 men and 85 women received antihypertensive treatment. Serum adiponectin concentrations were determined using the human adiponectin radioimmunoassay from Linco Research (St. Charles, MO). Mean intra- and interassay variations in control sera were 5.5 and 9.2%, respectively.

Statistical analyses

Adiponectin concentrations were compared between men and women using Wilcoxon’s test and between the different glucose tolerance status groups using the Kruskal-Wallis test (three groups), and in case of significance, Wilcoxon tests for each pairwise group comparison. The associations of anthropometric, metabolic, and immunological variables with adiponectin were estimated after adjustment for age and sex or after adjustment for age, sex, IGT, type 2 diabetes, BMI, hypertension, total cholesterol, HDL cholesterol, and uric acid using linear regression models with log values of adiponectin as dependent variables and the aforementioned variables and one of the immunological parameters as independent variables. Immune markers entered the linear regression models as dummy variables based on quartiles of normoglycemic subjects. P < 0.05 was considered statistically significant. Calculations were carried out using the SAS statistical package version 8.2 TS2M0.

Clinical, anthropometric, metabolic, and immunological characteristics of the study sample are summarized in Tables 1 and 2. Serum adiponectin concentrations were highly dependent on sex and on glucose tolerance status. Adiponectin levels (median [25th–75th percentile]) were significantly higher in women than in men (10.6 [7.6–13.6] vs. 6.6 μg/ml [4.8–9.0]; P < 0.0001). Adiponectin concentrations in women with NGT, IGT, or type 2 diabetes were 12.2 (9.5–15.0), 9.7 (7.2–12.5), and 9.0 μg/ml (6.9–11.7), respectively. In men, adiponectin levels in subjects with NGT, IGT, or type 2 diabetes were 7.8 (5.4–9.7), 6.1 (4.6–8.5), and 5.9 μg/ml (4.3–7.8), respectively. In both women and men, adiponectin levels of normoglycemic subjects were significantly higher than in subjects with IGT or with type 2 diabetes, whereas adiponectin concentrations between subjects with IGT and type 2 diabetes did not differ statistically significantly.

First, the associations of adiponectin with leukocyte count, acute-phase proteins, cytokines, cytokine receptors, and chemokines were evaluated in subjects with NGT or with disturbed glucose metabolism (IGT/type 2 diabetes) after adjustment for age and sex (Table 1). In the NGT group only, we found a positive correlation between adiponectin and tumor necrosis factor-α (TNF-α; P = 0.023). However, there was no evidence for a significant correlation between adiponectin and any of the other immunological markers in both NGT and IGT/type 2 diabetic groups.

In contrast, there were strong positive correlations between adiponectin and age or HDL cholesterol and strong negative correlations with fasting insulin, homeostasis model assessment of insulin resistance, HbA1c (A1C), and triglycerides in both groups and additionally with fasting glucose, uric acid, and serum albumin in the IGT/type 2 diabetic group (Table 2). Adiponectin levels were also significantly associated with indexes of obesity and body fat distribution (e.g., inverse associations with BMI, waist circumference, and waist-to-hip ratio [WHR]) (data not shown), but these associations did not remain significant after adjustment for glucose tolerance status, sex, and age (Table 2).

Furthermore, we looked for associations between adiponectin and immunological variables in the total study sample using multivariable linear regression analyses (Table 3). After adjustment for sex, IGT, and type 2 diabetes as major modulators of adiponectin levels, only eotaxin was significantly associated with adiponectin (P = 0.015), unlike any of the other immunological markers (P = 0.123 for TNF-α). In a second model, adjusting for sex, IGT, and type 2 diabetes, as well as for other variables that were associated with either adiponectin or immune marker concentrations (age, BMI, hypertension, total cholesterol, HDL cholesterol, and uric acid), the positive correlation between adiponectin and eotaxin persisted (P = 0.004), whereas all other associations remained nonsignificant (Table 3).

In the present study, serum adiponectin levels in general were not associated with a wide panel of immunological markers that were chosen to represent different aspects of immunity. In particular, there was no statistically significant association between adiponectin and proinflammatory immune mediators such as C-reactive protein (CRP), interleukin (IL)-6, and IL-8 in both univariable and multivariable analyses, although an inverse association could be assumed if adiponectin were a potent anti-inflammatory protein. However, there was some evidence for positive correlations of TNF-α and eotaxin with adiponectin.

TNF-α was associated with adiponectin levels in the age- and sex-adjusted model in individuals with NGT but not in the IGT/type 2 diabetic group or in the multivariable models. Most studies on the relationship between both proteins showed mutually antagonistic effects: adiponectin suppresses TNF-α expression or TNF-α–induced gene expression (9), while TNF-α reduces the expression of adiponectin (30). In addition, adipose tissue mRNA levels and plasma concentrations of TNF-α and adiponectin have been reported to be negatively correlated in nondiabetic subjects (14). Given the fact that some studies also found a somewhat surprising upregulation of TNF-α by adiponectin in their in vitro systems (31,32), the biological relevance of our finding remains to be elucidated.

Regarding the relation of eotaxin with adiponectin, there were positive but nonsignificant trends in the first analysis, which stratified the sample into subjects with NGT and IGT/type 2 diabetes. Both the sex- and glucose tolerance status–adjusted model and the fully adjusted model (also including age, BMI, hypertension, total cholesterol, HDL cholesterol, and uric acid) demonstrated a positive association between eotaxin and adiponection in the total study population. Eotaxin represents T helper 2 activity and is mostly expressed during allergic reactions (33). The authors are not aware of studies demonstrating a link between eotaxin and adiponectin expression. Eotaxin is also released from adipose tissue, but unlike adiponectin, stromal-vascular cells appear to be the major source of this chemokine (34). Since several recent studies did not find significant associations between eotaxin and type 2 diabetes (28) or cardiovascular disease (35), the meaning of our finding is difficult to assess.

To control the biological relevance of the adiponectin dataset, we performed additional analyses in the same study population and found, as expected, that adiponectin levels were significantly inversely correlated with variables that define the metabolic syndrome and strongly positively correlated with HDL cholesterol, another component of the metabolic syndrome. In the unadjusted analysis, we also found the previously described modulation of adiponectin levels by factors such as age, sex, glucose tolerance status, BMI, waist circumference, and WHR. The associations with indexes of obesity were considerably attenuated by adjusting for age and sex and by adjusting for glucose tolerance status. This finding may be attributable to our study sample, which exhibited a relatively small BMI or WHR range, since most of the elderly study participants were overweight or obese.

The overall lack of interdependence of adiponectin and inflammation was surprising since both hypoadiponectinemia and elevated leukocyte counts, as well as increased levels of CRP, serum amyloid A, fibrinogen, IL-6, sIL-6R, TNF-α (data for subgroup in 27), IL-8, RANTES (regulated on activation, normal T-cell expressed and secreted) (28), and macrophage-migration inhibitory factor (29) were associated with IGT and/or type 2 diabetes in the KORA S4 participants. Previous studies consistently demonstrated an inverse correlation between adiponectin and CRP (albeit weaker than the correlation between adiponectin and metabolic markers), which was not significant in our study population. However, there was some controversy about the association of adiponectin with IL-6, TNF-α, and fibrinogen (15–17,19–22,36), and other studies also reported no evidence for an association with sTNFR60, sTNFR80, serum amyloid A, and IL-8 (15,21–24). Some of these discrepancies may be explained by the widely different characteristics of the study populations regarding age, sex, glucose tolerance status, or degree of obesity; therefore, results from univariable analyses cannot be compared directly. Most analyses published thus far did not adjust for potential confounders, so data from multivariable regression, as performed in our study, are not currently available. There is some evidence that metabolic factors could have a major impact on the association of adiponectin with immune mediators since two studies reported that the inverse association between adiponectin and IL-6 (16) and CRP (18) disappeared after adjusting for obesity-related variables or for sex and BMI only, respectively.

In vitro approaches to characterize adiponectin functions suggested that adiponectin exerts anti-inflammatory properties in the interaction with leukocytes, endothelial cells, and adipocytes (8,9,10,11,12). These studies used a recombinant low–molecular weight form of adiponectin, which was derived from Escherichia coli and, thus, lacked posttranslational modifications and multimerization sites, whereas endogenous human adiponectin consists largely of middle–and high–molecular weight forms (37). This difference in adiponectin structure may explain the discrepancy of our in vivo data with previous in vitro studies. Given the frequent anatomical vicinity of adipocytes, lymph nodes, and blood vessels, it is conceivable that these in vitro data on the regulation of cytokines and chemokines by adiponectin represent paracrine effects that are not reflected by systemic levels in the circulation. Clinical studies to investigate the anti-inflammatory, antidiabetic, and antiatherogenic potency of the different molecular-weight forms of endogenous adiponectin would be highly desirable.

The cross-sectional design of the current study allows for the analysis of associations between systemic concentrations of adiponectin and immunological variables but not for causal investigation of the impact of adiponectin levels on disease risk or how an increase or decrease of adiponectin directly affects the markers of interest and vice versa. In addition, the selection of the study participants for this case-control study led to an overrepresentation of subjects with IGT and type 2 diabetes.

However, there are several strengths of the study that are relevant for the investigation of possible interdependence of adiponectin and immune markers. First, we used a well-characterized study population with extensive phenotype data covering many different aspects of systemic immunity and metabolism and allowing for multivariable analyses. Second, the oral glucose tolerance tests performed during the survey identified individuals with IGT and type 2 diabetes; therefore, the glucose tolerance status, as a potential confounder of relationships between adiponectin and immune markers, could be taken into account.

In conclusion, although adiponection levels exhibited strong negative correlations with several factors defining metabolic syndrome and a strong positive association with HDL cholesterol, there was no evidence for associations of adiponectin with a wide range of immunological parameters except eotaxin. We therefore conclude that hypoadiponectinemia and low-grade inflammation are independent and distinct factors and not just “two sides of the same coin.”

Table 1—

Characteristics of the study sample and correlations between log adiponectin and inflammatory variables depending on glucose tolerance status (adjusted for age and sex)

VariableNGTIGT/type 2 diabetesNGT: association with log adiponectin
IGT/type 2 diabetes: association with log adiponectin
βPβP
WBC (×10−3/μl) 5.7 (4.9–6.8) 6.2 (5.3–7.3)* 0.003 0.901 −0.043 0.064 
CRP (mg/l) 1.3 (0.7–3.0) 2.4 (1.2–4.6)* −0.013 0.600 −0.021 0.401 
SAA (mg/l) 3.4 (2.1–5.6) 4.1 (2.6–7.0)* −0.002 0.952 0.010 0.666 
Fibrinogen (g/l) 2.8 (2.5–3.2) 3.0 (2.6–3.4)† −0.027 0.266 −0.014 0.534 
IL-6 (pg/ml) 1.7 (0.7–2.9) 2.5 (1.4–3.8)* 0.025 0.317 0.003 0.901 
sIL-6R (ng/ml) 126 (91–167) 140 (103–183)‡ 0.003 0.915 0.013 0.573 
TNF-α (pg/ml) 0.2 (0.1–1.0) 0.5 (0.1–1.7)* 0.050 0.023 0.007 0.720 
sTNFR60 (ng/ml) 2.3 (1.7–3.1) 2.1 (1.6–2.9)† 0.017 0.497 0.001 0.974 
sTNFR80 (ng/ml) 8.2 (6.4–10.6) 8.2 (6.2–10.7) 0.011 0.665 0.028 0.215 
MIF (ng/ml) 5.0 (2.4–8.5) 9.0 (5.9–13.8)* 0.025 0.316 −0.031 0.303 
IL-8 (pg/ml) 13.5 (10.0–16.9) 14.3 (10.9–18.6)† 0.000 0.999 −0.004 0.844 
RANTES (ng/ml) 19.9 (14.2–29.3) 26.1 (18.1–39.7)* −0.046 0.063 −0.018 0.457 
IP-10 (pg/ml) 281 (194–438) 310 (208–438) 0.000 0.996 0.005 0.842 
Eotaxin (pg/ml) 74 (41–107) 76 (45–112) 0.035 0.153 0.043 0.059 
MCP-1 (pg/ml) 311 (214–433) 306 (229–392) −0.028 0.257 0.011 0.667 
VariableNGTIGT/type 2 diabetesNGT: association with log adiponectin
IGT/type 2 diabetes: association with log adiponectin
βPβP
WBC (×10−3/μl) 5.7 (4.9–6.8) 6.2 (5.3–7.3)* 0.003 0.901 −0.043 0.064 
CRP (mg/l) 1.3 (0.7–3.0) 2.4 (1.2–4.6)* −0.013 0.600 −0.021 0.401 
SAA (mg/l) 3.4 (2.1–5.6) 4.1 (2.6–7.0)* −0.002 0.952 0.010 0.666 
Fibrinogen (g/l) 2.8 (2.5–3.2) 3.0 (2.6–3.4)† −0.027 0.266 −0.014 0.534 
IL-6 (pg/ml) 1.7 (0.7–2.9) 2.5 (1.4–3.8)* 0.025 0.317 0.003 0.901 
sIL-6R (ng/ml) 126 (91–167) 140 (103–183)‡ 0.003 0.915 0.013 0.573 
TNF-α (pg/ml) 0.2 (0.1–1.0) 0.5 (0.1–1.7)* 0.050 0.023 0.007 0.720 
sTNFR60 (ng/ml) 2.3 (1.7–3.1) 2.1 (1.6–2.9)† 0.017 0.497 0.001 0.974 
sTNFR80 (ng/ml) 8.2 (6.4–10.6) 8.2 (6.2–10.7) 0.011 0.665 0.028 0.215 
MIF (ng/ml) 5.0 (2.4–8.5) 9.0 (5.9–13.8)* 0.025 0.316 −0.031 0.303 
IL-8 (pg/ml) 13.5 (10.0–16.9) 14.3 (10.9–18.6)† 0.000 0.999 −0.004 0.844 
RANTES (ng/ml) 19.9 (14.2–29.3) 26.1 (18.1–39.7)* −0.046 0.063 −0.018 0.457 
IP-10 (pg/ml) 281 (194–438) 310 (208–438) 0.000 0.996 0.005 0.842 
Eotaxin (pg/ml) 74 (41–107) 76 (45–112) 0.035 0.153 0.043 0.059 
MCP-1 (pg/ml) 311 (214–433) 306 (229–392) −0.028 0.257 0.011 0.667 

Data are median (25th–75th percentile) unless otherwise indicated. Data entered the linear regression models as discrete values based on quartiles of normoglycemic subjects, and datasets for all variables were complete or almost complete with no more than four or six values missing in the NGT and IGT/type 2 diabetic groups, respectively.

*

P < 0.001,

†

P < 0.05,

‡

P < 0.01 compared with NGT. IP-10, interferon-inducible protein 10; MCP-1, monocyte chemoattractant protein 1; MIF, macrophage-migration inhibitory factor; SAA, serum amyloid A; WBC, white blood cell.

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Table 2—

Characteristics of the study sample and correlations between log adiponectin and anthropometric and metabolic variables depending on glucose tolerance status (adjusted for age and sex)

VariableNGTIGT/type 2 diabetesNGT: association with log adiponectin
IGT/type 2 diabetes: association with log adiponectin
βPβP
Sex (M/F)* 137/107 202/160 −0.488 <0.001 −0.392 <0.001 
Age (years)* 65.3 ± 5.3 65.2 ± 5.3 0.017 0.001 0.011 0.026 
BMI (kg/m2) 27.4 ± 3.7 29.8 ± 4.0† −0.011 0.147 −0.007 0.271 
Waist circumference (cm) 94 ± 11 100 ± 11† −0.003 0.282 −0.002 0.464 
Hip circumference (cm) 104 ± 7 108 ± 8† −0.003 0.481 0.000 0.989 
WHR 0.90 ± 0.08 0.93 ± 0.07† −0.625 0.247 −0.678 0.197 
Body fat (kg) 27.3 ± 6.6 31.0 ± 7.0† −0.004 0.376 −0.002 0.531 
Body fat (%) 35.8 ± 5.5 38.0 ± 5.5† −0.001 0.829 0.004 0.522 
Fat-free mass (kg) 48.7 ± 8.7 50.3 ± 8.4‡ −0.010 0.057 −0.009 0.057 
Fasting glucose (mg/dl) 96 ± 7 114 ± 25† −0.003 0.440 −0.003 0.002 
Fasting insulin (mU/l)§ 8.4 (5.7–12.2) 12.5 (9.0–19.2)† −0.143 0.001 −0.140 <0.001 
HOMA-IR§ 2.0 (1.4–3.0) 3.4 (2.3–5.6)† −0.139 0.001 −0.146 <0.001 
A1C (%) 5.5 ± 0.3 5.8 ± 0.7† −0.197 0.019 −0.132 <0.001 
Cholesterol (mmol/l) 6.3 ± 1.2 6.2 ± 1.1 0.011 0.669 0.004 0.870 
LDL cholesterol (mmol/l) 4.0 ± 1.1 3.9 ± 1.0 −0.025 0.318 −0.033 0.189 
HDL cholesterol (mmol/l) 1.5 ± 0.4 1.4 ± 0.4† 0.384 <0.001 0.381 <0.001 
Fasting triglycerides (mg/dl)§ 106 (80–144) 131 (102–182)† −0.253 <0.001 −0.203 <0.001 
Uric acid (mg/dl) 5.5 ± 1.3 6.1 ± 1.5† −0.032 0.218 −0.041 0.038 
Serum albumin (g/l)§ 37.8 (35.6–40.6) 38.4 (35.9–41.3)‡ −0.376 0.172 −0.581 0.016 
Systolic blood pressure (mmHg)‖ 122 ± 12 125 ± 11 0.001 0.810 0.002 0.732 
Diastolic blood pressure (mmHg)‖ 74 ± 7 76 ± 7 −0.007 0.217 0.009 0.185 
VariableNGTIGT/type 2 diabetesNGT: association with log adiponectin
IGT/type 2 diabetes: association with log adiponectin
βPβP
Sex (M/F)* 137/107 202/160 −0.488 <0.001 −0.392 <0.001 
Age (years)* 65.3 ± 5.3 65.2 ± 5.3 0.017 0.001 0.011 0.026 
BMI (kg/m2) 27.4 ± 3.7 29.8 ± 4.0† −0.011 0.147 −0.007 0.271 
Waist circumference (cm) 94 ± 11 100 ± 11† −0.003 0.282 −0.002 0.464 
Hip circumference (cm) 104 ± 7 108 ± 8† −0.003 0.481 0.000 0.989 
WHR 0.90 ± 0.08 0.93 ± 0.07† −0.625 0.247 −0.678 0.197 
Body fat (kg) 27.3 ± 6.6 31.0 ± 7.0† −0.004 0.376 −0.002 0.531 
Body fat (%) 35.8 ± 5.5 38.0 ± 5.5† −0.001 0.829 0.004 0.522 
Fat-free mass (kg) 48.7 ± 8.7 50.3 ± 8.4‡ −0.010 0.057 −0.009 0.057 
Fasting glucose (mg/dl) 96 ± 7 114 ± 25† −0.003 0.440 −0.003 0.002 
Fasting insulin (mU/l)§ 8.4 (5.7–12.2) 12.5 (9.0–19.2)† −0.143 0.001 −0.140 <0.001 
HOMA-IR§ 2.0 (1.4–3.0) 3.4 (2.3–5.6)† −0.139 0.001 −0.146 <0.001 
A1C (%) 5.5 ± 0.3 5.8 ± 0.7† −0.197 0.019 −0.132 <0.001 
Cholesterol (mmol/l) 6.3 ± 1.2 6.2 ± 1.1 0.011 0.669 0.004 0.870 
LDL cholesterol (mmol/l) 4.0 ± 1.1 3.9 ± 1.0 −0.025 0.318 −0.033 0.189 
HDL cholesterol (mmol/l) 1.5 ± 0.4 1.4 ± 0.4† 0.384 <0.001 0.381 <0.001 
Fasting triglycerides (mg/dl)§ 106 (80–144) 131 (102–182)† −0.253 <0.001 −0.203 <0.001 
Uric acid (mg/dl) 5.5 ± 1.3 6.1 ± 1.5† −0.032 0.218 −0.041 0.038 
Serum albumin (g/l)§ 37.8 (35.6–40.6) 38.4 (35.9–41.3)‡ −0.376 0.172 −0.581 0.016 
Systolic blood pressure (mmHg)‖ 122 ± 12 125 ± 11 0.001 0.810 0.002 0.732 
Diastolic blood pressure (mmHg)‖ 74 ± 7 76 ± 7 −0.007 0.217 0.009 0.185 

Data are means ± SD, median (25th–75th percentile), or absolute numbers. Datasets for all variables except blood pressure were complete or almost complete with no more than 6 or 11 values missing for the NGT and IGT/type 2 diabetic groups, respectively.

*

Sex adjusted for age only, age adjusted for sex only.

§

Shown here as median and 25th–75th percentiles, but log values were used in regression models.

‖

Subjects with blood pressure >140/90 mmHg or on antihypertensive treatment were excluded (remaining n: 115 NGT, 87 IGT/type 2 diabetes).

‡

P < 0.05,

†

P < 0.001 compared with NGT. HOMA-IR, homeostasis model assessment for insulin resistance.

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Table 3—

Association between log adiponectin and inflammatory mediators: multivariable regression analysis for the total study population

VariableModel 1
Model 2
βPβP
WBC −0.021 0.219 −0.012 0.469 
CRP −0.016 0.371 −0.017 0.337 
SAA 0.009 0.602 −0.003 0.845 
Fibrinogen −0.014 0.402 −0.002 0.923 
IL-6 0.019 0.283 0.016 0.363 
sIL-6R 0.022 0.204 0.013 0.449 
TNF-α 0.023 0.123 0.017 0.233 
sTNFR60 0.015 0.387 0.011 0.535 
sTNFR80 0.027 0.103 0.027 0.090 
MIF 0.001 0.942 0.010 0.605 
IL-8 0.006 0.722 −0.006 0.736 
RANTES −0.032 0.065 −0.017 0.317 
IP-10 0.008 0.632 −0.005 0.769 
Eotaxin 0.041 0.015 0.046 0.004 
MCP-1 −0.006 0.751 −0.012 0.474 
VariableModel 1
Model 2
βPβP
WBC −0.021 0.219 −0.012 0.469 
CRP −0.016 0.371 −0.017 0.337 
SAA 0.009 0.602 −0.003 0.845 
Fibrinogen −0.014 0.402 −0.002 0.923 
IL-6 0.019 0.283 0.016 0.363 
sIL-6R 0.022 0.204 0.013 0.449 
TNF-α 0.023 0.123 0.017 0.233 
sTNFR60 0.015 0.387 0.011 0.535 
sTNFR80 0.027 0.103 0.027 0.090 
MIF 0.001 0.942 0.010 0.605 
IL-8 0.006 0.722 −0.006 0.736 
RANTES −0.032 0.065 −0.017 0.317 
IP-10 0.008 0.632 −0.005 0.769 
Eotaxin 0.041 0.015 0.046 0.004 
MCP-1 −0.006 0.751 −0.012 0.474 

All inflammatory mediators entered the linear regression models as discrete values based on quartiles of normoglycemic subjects. Model 1: adjusted for sex, IGT, and type 2 diabetes. Model 2: adjusted for sex, IGT, type 2 diabetes, age, BMI, hypertension, total cholesterol, HDL cholesterol, and uric acid. MCP-1, monocyte chemoattractant protein 1; MIF, macrophage-migration inhibitory factor; RANTES, regulated on activation, normal T-cell expressed and secreted; SAA, serum amyloid A; WBC, white blood cell.

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The KORA research platform was initiated and financed by the National Research Center for Environment and Health (GSF), which is funded by the German Federal Ministry of Education and Research and by the State of Bavaria. The work was supported by the Deutsche Forschungsgemeinschaft; the European Foundation for the Study of Diabetes; the German Federal Ministry of Health and Social Security; the German Federal Ministry of Education, Science, Research and Technology; the Ministry of Science and Research of the State North Rhine-Westphalia; and the Department of Internal Medicine II-Cardiology at the University of Ulm.

The KORA S4 and, in particular, the diabetes substudy (oral glucose tolerance test) was coinitiated and cofinanced by the German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, which is funded by the German Federal Ministry of Health and Social Security and the Ministry of Science and Research of the State North Rhine-Westphalia.

We thank Petra Weskamp, Philipp Rating, and Gerlinde Trischler for expert technical assistance. We appreciate the voluntary contribution of all study participants.

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A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.

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DIABETES CARE
2006