Relationship Between Adiponectin and Glycemic Control, Blood Lipids, and Inflammatory Markers in Men With Type 2 Diabetes

The Health Professionals Follow-up Study is a prospective cohort study of 51,529 U.S. male health professionals (dentists, veterinarians, pharmacists, optometrists, osteopathic physicians, and podiatrists) aged 40–75 years at study initiation in 1986. This cohort is followed through biennially mailed questionnaires focusing on different lifestyle factors and health outcomes. In addition, between 1993 and 1994, 18,159 study participants provided blood samples by overnight courier. Among participants who returned blood samples, 741 had a diagnosis of type 2 diabetes at the time of blood draw and reported their weight on the preceding questionnaire. The study was approved by the institutional review boards at the Harvard School of Public Health and the Brigham and Women’s Hospital. Completion of the self-administered questionnaire was considered to imply informed consent.

We sent supplementary questionnaires to all men who reported a diagnosis of diabetes on any of the regular biennial questionnaires to confirm this self-report. In accordance with the criteria of the National Diabetes Data Group (10), confirmation of diabetes required at least one of the following: 1) an elevated plasma glucose concentration (fasting plasma glucose ≥7.8 mmol/l, random plasma glucose ≥11.1 mmol/l, and/or plasma glucose ≥11.1 mmol/l after ≥2 h during an oral glucose tolerance test) plus at least one classic symptom (excessive thirst, polyuria, weight loss, or hunger); 2) no symptoms, but at least two elevated plasma glucose concentrations (by the above criteria) on different occasions; or 3) treatment with hypoglycemic medication (insulin or oral hypoglycemic agent). We used the National Diabetes Data Group criteria to define diabetes because the majority of our subjects were diagnosed before the release of the American Diabetes Association criteria in 1997 (11). The validity of self-reported diabetes using the supplementary questionnaire has been documented in a subsample of 71 men from the Health Professionals Follow-up Study cohort. Of these, 12 had incomplete medical records, whereas the diagnosis of type 2 diabetes was confirmed in 57 (97%) of the remaining 59 men (12).

Participants provided information on their age, weight, smoking status, aspirin use, and physical activity. If the weight was missing, we used the weight reported on the preceding questionnaire instead. We calculated BMI as the ratio of weight (in kilograms) to height (in meters squared). Self-reports of body weight have been shown (13) to be highly correlated with technician-measured weights (r = 0.97) in this cohort. Physical activity was computed as metabolic equivalents per week using the duration per week of various forms of exercise, weighting each activity by its intensity level (14). History of cardiovascular disease (angina pectoris, myocardial infarction, coronary bypass surgery or coronary angioplasty, and stroke), high blood pressure, high blood cholesterol, and cancer were determined from self-reports preceding the blood collection. Alcohol intake was estimated with a dietary questionnaire in 1994.

Each interested participant was sent a blood collection kit containing instructions and needed supplies (blood tubes, tourniquet, gauze, Band-Aids, and needles). The participants made arrangements for the blood to be drawn. Blood samples were collected in three 10-ml liquid EDTA blood tubes, placed on ice packs stored in Styrofoam containers, and returned to our laboratory via overnight courier. Over 95% of the samples arrived within 24 h. After receipt, the chilled blood was centrifuged, aliquoted into plasma, erythrocytes, and buffy coat, and stored in continuously monitored nitrogen freezers at a temperature not higher than −130°C. We requested information on the date and time of the blood sample drawing and the time elapsed since the preceding meal to identify nonfasting (<8 h) subjects.

All biomarker assays were carried out on a Hitachi 911 analyzer (Roche Diagnostics, Indianapolis, IN). Adiponectin was measured by a competitive radioimmunoassay using a commercial reagent set from Linco Research (St. Louis, MO) with a day-to-day variability at adiponectin concentrations of 3, 6, and 15 ng/ml of 9.2, 6.9, and 9.2%, respectively. HbA_1c determination was based on turbidimetric immunoinhibition using hemolyzed whole blood or packed red cells. The day-to-day variability at HbA_1c concentrations of 5.5 and 9.1% was 1.9 and 3.0%, respectively. The determination of total cholesterol, triglycerides, and HDL cholesterol concentrations was simultaneously performed using reagents and calibrators from Roche Diagnostics (Indianapolis, IN); coefficients of variation for these measurements were <1.8%. LDL cholesterol was measured by a homogenous direct method from Genzyme (Cambridge, MA). The day-to-day variability at LDL cholesterol concentrations of 2.33, 2.74, and 3.34 mmol/l was <3.1%. Measurement of apoB₁₀₀ was based on the immunonephelometric assay using reagents and calibrators from Wako Chemicals (Richmond, VA), with a day-to-day variability of <5%. We calculated non-HDL cholesterol as the difference between total and HDL cholesterol. CRP was measured via an immunoturbidimetric assay using reagents and calibrators from Denka Seiken (Niigata, Japan). The day-to-day variability of the assay at concentrations of 0.91, 3.07, and 13.38 mg/l were 2.8, 1.6, and 1.1%, respectively. Fibrinogen was measured with an immunoturbidimetric assay using reagents and calibrators from Kamiya Biomedical (Seattle, WA). The day-to-day variability of the assay at concentrations of 4.92, 9.51, and 16.29 μmol/l was 0.9, 1.1, and 1.5%, respectively. Soluble intracellular cell adhesion molecules (sICAMs), soluble vascular cell adhesion molecules (sVCAMs), and plasma soluble fractions of tumor necrosis factor (TNF)-α receptor 2 (sTNFR2s) were measured by enzyme-linked immunosorbent assay from R&D Systems (Minneapolis, MN). The day-to-day variabilities of the assays at concentrations of 64.2, 117, 290, and 453 ng/ml sICAM and at concentrations of 9.8, 24.9, and 49.6 ng/ml sVCAM were 10.1, 7.4, 6.0, and 6.1% and 10.2, 8.5, and 8.9%, respectively. The day-to-day variability of the sTNFR2 assay at concentrations of 89.9, 197, and 444 pg/ml was 5.1, 3.5, and 3.6%, respectively.

Spearman correlations and scatter plots were used to evaluate bivariate relationships between plasma levels of adiponectin and of lipids and inflammatory markers. Multivariate linear regression analyses with robust variance were performed to evaluate the association between adiponectin and biomarkers without the need of normal distribution assumptions (15). We adjusted for age, BMI, activity (quartiles of metabolic equivalents), smoking (never, past, and current), aspirin use, history of cardiovascular disease, history of high blood pressure, history of high blood cholesterol, history of cancer, fasting status, alcohol intake (0.0, 0.1–4.9, 5.0–9.9, 10.0–14.9, and ≥15.0 g/day), and insulin use. We furthermore tested for effect modifications by obesity status, with adjustment for potential confounders. All statistical analyses were performed using SAS statistical software version 8 (SAS Institute, Cary, NC).

Among the study population of 741 diabetic men, higher plasma adiponectin levels were associated with higher age, higher physical activity, higher alcohol consumption, and lower BMI (Table 1). Furthermore, men with relatively high adiponectin levels were more likely to use insulin and were less likely to have a history of high blood cholesterol. Although HDL and LDL cholesterol were positively associated with adiponectin levels, the opposite was observed for HbA_1c, triglycerides, non-HDL cholesterol, apoB₁₀₀, CRP, and fibrinogen. Adiponectin levels were not significantly associated with creatinine levels. One hundred twenty-three subjects (26%) had a history of cardiovascular disease. One-half of the subjects reported aspirin use, and 31% used at least six aspirin tablets per week. We calculated Spearman correlation coefficients to further elucidate the associations between adiponectin and blood lipids and inflammatory markers (Table 2). Adiponectin was positively correlated with HDL cholesterol (r = 0.42) and negatively correlated with triglycerides (r = −0.38). Significant correlations (at P < 0.01) were also found between adiponectin and HbA_1c, apoB₁₀₀, CRP, and fibrinogen, although correlation coefficients were relatively weak (<0.20).

Figure 1 presents scatter plots between log-transformed adiponectin and HDL cholesterol, triglycerides, apoB₁₀₀, HbA_1c, CRP, and fibrinogen levels. We observed a positive association between adiponectin and HDL cholesterol and a negative association between adiponectin and triglycerides, apoB₁₀₀, HbA_1c, CRP, and fibrinogen. All biomarkers monotonically increased or decreased with increasing adiponectin, thus no indications for nonmonotonic associations between these variables were present.

We used multivariate linear regression to estimate the change in blood lipids and inflammatory markers required for an increase in adiponectin by 10 μg/ml (61% compared with the mean level of 16.5 μg/ml), adjusting for age, physical activity, smoking, and other covariates, including BMI (Table 3). A 10-μg/ml increase in plasma adiponectin was associated with a 0.21% point decrease in HbA_1c, a 0.39-mmol/l decrease in triglycerides (−18% as compared with the mean level), a 0.13-mmol/l increase in HDL cholesterol (14%), and significant reductions in apoB₁₀₀ (−0.04 g/l, −4%), CRP (−0.51 mg/l, −15%), fibrinogen (−0.53 μmol/l, −4%), and sICAM (−7.56 ng/ml, −2%). These associations remained significant after further adjustment for the waist-to-hip ratio in a subset of men who reported their waist and hip circumferences in 1996 (n = 667), except in the case for sICAM. Furthermore, adjustment for HDL cholesterol, CRP, and fibrinogen only slightly attenuated the association for triglycerides (β = −0.22, P < 0.001). Adiponectin remained positively associated with HDL cholesterol after additional adjustment for CRP and fibrinogen (β = 0.13, P < 0.001). The association between adiponectin and apoB₁₀₀ was only slightly attenuated after controlling for HDL cholesterol (β = 0.04, P < 0.001). Similarly, adiponectin remained significantly associated with CRP and fibrinogen, controlling for HDL cholesterol and HbA_1c. Regression parameter estimates (β) for an increase in adiponectin of 10 μg/ml predicting CRP and fibrinogen were −0.42 (P = 0.019) and −0.36 (P = 0.035), respectively.

We further evaluated whether the observed associations were modified by obesity status. The associations were relatively consistent across obesity strata, and interaction terms were all nonsignificant (CRP, P = 0.191; fibrinogen, P = 0.657; triglycerides, P = 0.966; apoB₁₀₀, P = 0.221; and HDL cholesterol, P = 0.999).

We found that among men with type 2 diabetes, high plasma levels of adiponectin were positively associated with HDL cholesterol and inversely associated with HbA_1c, triglycerides, apoB₁₀₀, CRP, and fibrinogen. These associations were independent of lifestyle factors and of anthropometric characteristics. Associations between adiponectin and CRP as well as fibrinogen were furthermore independent of HbA_1c and HDL cholesterol, which suggests that adiponectin may have anti-inflammatory properties in diabetic subjects that are not mediated by its potential effects on glycemic control and blood lipids.

Our observation that adiponectin levels may be associated with the status of glycemic control among diabetic men is supported by previous studies that found adiponectin to be a major modulator of insulin action and resistance. Plasma levels of adiponectin are significantly lower in humans with insulin resistance or type 2 diabetes (5) and predict the development of type 2 diabetes (6–8). Strong correlations between adiponectin and measures of insulin sensitivity have been well established in humans (4). The connection between adiponectin levels and insulin resistance have been furthermore confirmed by data obtained from animal models, wherein adiponectin treatment reversed insulin resistance in lipoatrophic mice (16), increased hepatic glucose uptake (17), and stimulated muscular fatty acid oxidation (18). Furthermore, thiazolidinedione treatment increased plasma adiponectin levels in normal and diabetic subjects (19–22), which supports the thought that adiponectin plays a role in the systemic insulin-sensitizing and anti-inflammatory properties of the thiazolidinediones. Similarly, increased plasma adiponectin may underlie the improvement of insulin resistance with sulfonylureas (23).

Our data suggest that decreased adiponectin levels may be associated with an atherogenic lipoprotein profile. A 10-μg/ml increase in adiponectin was associated with substantially higher HDL cholesterol and lower triglycerides and apoB₁₀₀. Similar associations between adiponectin and HDL and triglycerides were observed among nondiabetic (24–31) and diabetic (32,33) subjects. Inverse associations between adiponectin and apoB₁₀₀ were also observed in one (24) but not in another (25) study. The mechanisms by which adiponectin may affect blood lipids are largely unknown. Effects of adiponectin on hepatic lipase activity, which is increased in central obesity and insulin resistance, are suspected (25). However, our observed associations were largely independent from BMI and waist-to-hip ratio. Similarly, adiponectin was found to be associated with HDL cholesterol and triglycerides independent from abdominal fat area measured by computed tomography scan (25), visceral and extremity fat (26), BMI and body fat mass (24,29–32), or waist circumference (33). This suggests that although adiponectin might in part mediate effects of body fat on lipoproteins, other unrecognized pathways controlling its production might be important as well. Furthermore, the associations were largely independent of fasting insulin concentration (32,33) and indexes of insulin resistance assessed by the homeostasis model assessment (29,31), the euglycemic-hyperinsulinemic clamp, and the oral glucose tolerance test (30). This suggests that mechanisms other than effects on insulin resistance and hepatic lipase activity most likely mediate the association between adiponectin and blood lipids.

Our finding that plasma adiponectin levels may be negatively associated with levels of CRP and fibrinogen is supported by previous reports (28,29,34–37) that indicate potential anti-inflammatory properties of adiponectin. Anti-inflammatory effects of adiponectin might be mediated indirectly by its effects on glycemic control and blood lipids. Hyperglycemia acutely increases circulating cytokine concentrations (38,39). HDL cholesterol downregulates expression of adhesive molecules on the surface of vascular endothelium (40) and inhibits platelet aggregation (41) and thus has anti-inflammatory and antithrombotic properties. However, adjustment for HDL cholesterol and HbA_1c in our study only slightly attenuated the association between adiponectin and CRP and fibrinogen. In vitro studies suggest several other mechanisms, which might explain the observed associations. Adiponectin was found to affect monocyte adhesion to the endothelium (42) and to suppress macrophage-to-foam cell transformation (43). Furthermore, adiponectin inhibits the production and action of TNF-α (42,44,45), which may influence interleukin-6 and CRP production. Therefore, adiponectin may affect CRP levels in plasma and adipose tissue through modulating the inflammatory cascades (35). Interestingly, adiponectin was not associated with sTNFR2 levels in our study. Similarly, in a previous study (36), adiponectin was associated with CRP and interleukin-6, but no association with TNF-α was observed. It has also been suggested (37) that the association between adiponectin and TNF-α may not be dose dependent, but may rather involve a threshold effect; however, our data do not support this hypothesis. The lack of association between adiponectin and sICAM and sVCAM in our study suggests that adiponectin may play a less important role in monocyte adhesion, although it is critical in subsequent stages of atherosclerosis, e.g., macrophage cytokine production and macrophage-to-foam cell transformation (4).

A major limitation of our study is its cross-sectional nature, which does not allow for inferring causality from our results. In particular, although our results are suggestive of a role of adiponectin in carbohydrate and lipid metabolism and inflammatory processes, we were not able to determine whether decreased adiponectin levels are a cause or an effect of the metabolic state of high glycemia, dyslipidemia, and inflammation. However, these relationships are consistent with a large body of experimental evidence (16,38,39) on the role of adiponectin in glucose metabolism and inflammation.

Although associations between adiponectin and metabolic markers may be modified by the presence and type of cholesterol-lowering and diabetes medications, we did not collect detailed medication data at the time of blood draw in our cohort and were therefore unable to evaluate this. The cross-sectional study design would, however, complicate the interpretation of such an analysis with respect to causality.

In conclusion, our study supports the hypothesis that increased adiponectin levels may be associated with lower hyperglycemia and dyslipidemia, as well as with a lower inflammatory state in diabetic subjects. Lifestyle and pharmaceutical approaches that increase adiponectin levels might be valuable in decreasing the atherosclerotic risk present in type 2 diabetes.

This study was supported by research grants (HL65582, HL35464, and CA55075) from the National Institutes of Health. M.B.S. was also supported by a fellowship within the Postdoc Program of the German Academic Exchange Service. F.B.H.’s research is partially supported by an American Heart Association Established Investigator Award.