Adiponectin is a key component in multiple metabolic pathways. Studies evaluating associations of adiponectin with clinical outcomes in older adults have reported conflicting results. We investigated the association of adiponectin with mortality and cardiovascular disease (CVD) morbidity in a young, multiethnic adult population. We analyzed data from participants in the Dallas Heart Study without baseline CVD who underwent assessment of total adiponectin from 2000 to 2002. The primary outcome of all-cause mortality was assessed over median 10.4 years of follow-up using multivariable-adjusted Cox proportional hazards models. Secondary outcomes included CVD mortality, major adverse cardiovascular and cerebrovascular events (MACCE), and heart failure (HF). The study cohort included 3,263 participants, mean age 43.4 years, 44% women, and 50% black. There were 184 deaths (63 CVD), 207 MACCE, and 46 HF events. In multivariable models adjusted for age, gender, race, hypertension, diabetes, smoking, high-density lipoprotein cholesterol-C, hyperlipidemia, high-sensitivity C-reactive protein level, estimated glomerular filtration rate, and body mass index, increasing adiponectin quartiles were positively associated with all-cause mortality Q4 versus Q1 (hazard ratio [HR] = 2.27; 95% confidence interval [CI] 1.47, 3.50); CVD mortality Q4 versus Q1 (HR = 2.43; 95% CI 1.15, 5.15); MACCE Q4 versus Q1 (HR = 1.71; 95% CI 1.13, 2.60); and HF Q4 versus Q1 (HR = 2.95; 95% CI 1.14, 7.67). Findings were similar with adiponectin as a continuous variable and consistent across subgroups defined by age, gender, race, obesity, diabetes, metabolic syndrome, or elevated high-sensitivity C-reactive protein. In conclusion, higher adiponectin was associated with increased mortality and CVD morbidity in a young, multiethnic population. These findings may have implications for strategies aimed at lowering adiponectin to prevent adverse outcomes.
Adiponectin is a 247 amino acid peptide secreted by adipose tissue directly involved with multiple metabolic pathways. Plasma levels are inversely related to body weight, insulin resistance, and type 2 diabetes and reflect increased peroxisome proliferator–activated receptor γ activity. Adiponectin also has anti-inflammatory and anti-atherogenic properties. Considering these favorable associations with cardiovascular disease (CVD) risk factors, it is surprising that only a single study in humans found an inverse association with myocardial infarction (MI) risk in subjects without previous CVD. In contrast, multiple other studies found a positive association between adiponectin and several adverse outcomes in subjects with established CVD or at high risk for CVD. Higher adiponectin has been associated with increased mortality in patients with heart failure (HF) and with increased overall and CVD mortality, coronary artery disease, stroke, and HF in several large prospective trials in older adults. Importantly, few data are available from younger populations without established CVD. It is plausible that the association of adiponectin with clinical outcomes may differ in younger populations that would allow better delineation of a potential protective association of adiponectin with outcomes. Therefore, we examined the association of adiponectin with fatal and nonfatal CVD outcomes and with imaging biomarkers of subclinical CVD in participants from the Dallas Heart Study (DHS), a large, prospective, multi-ethnic population cohort. We also examined the consistency of associations across a spectrum of participants with different levels of clinical risk factors for cardiometabolic disease.
Methods
The DHS is a multiethnic, probability-based, population cohort study of Dallas County adults with deliberate oversampling of blacks. Detailed methods of the DHS have been described previously. Briefly, from 2000 to 2002, an initial cohort of 6,101 subjects participated in an in-home survey. Of these, 3,398 participants aged 30 to 65 years participated in a second visit to provide blood samples, and 3,072 subjects came to UT Southwestern Medical Center for a third visit where multitechnique imaging, including comprehensive assessments of subclinical CVD and body composition, were performed. For the present study, participants with prevalent CVD (defined as self-reported coronary heart disease, ischemic stroke or transient ischemic attack, or clinical HF) and those with missing adiponectin data were excluded, yielding a final sample size of 3,263. Participants provided written informed consent, and the protocol was approved by the Institutional Review Board of the University of Texas Southwestern Medical Center.
Weight and height were measured by standard scales. Body mass index (BMI) was calculated as weight (kg)/height 2 (m). Waist circumference was measured 1 cm above the iliac crest and hip circumference at the widest circumference of the buttocks at the area of the greater trochanters. Dual x-ray absorptiometry (Delphi W scanner; Hologic Inc., Bedford, Massachusetts, and Discovery software, version 12.2) was used to measure total body fat mass, lean mass, and lower body subcutaneous fat mass. Visceral adipose tissue and abdominal subcutaneous adipose tissue mass were measured by a 1.5-T magnetic resonance (MR) imaging system (Intera; Philips Medical Systems, Best, The Netherlands) using a prospectively designed and validated method of fat mass prediction from a single MR imaging slice at the L2-L3 intervertebral level. LV mass, end-systolic and diastolic volumes, and wall thickness were obtained from short-axis, breath-hold, electrocardiographic-gated cine cardiac MR images using the same 1.5-T system as previously described. Coronary artery calcium was measured as previously described. Liver fat was measured using 1.5 T 1 H MR spectroscopy and is reported as a percentage of signal from fat to total signal from fat and water.
Obesity was defined as a BMI ≥30 kg/m 2 . Race/ethnicity, history of CVD, and smoking status were self-reported. Variable definitions for hypertension, hypercholesterolemia, diabetes, and low-/high-density lipoprotein cholesterol have been previously described using conventional clinical definitions. The metabolic syndrome was defined by the National Cholesterol Education Program’s Adult Treatment Panel III report. Physical activity was derived using self-reported frequency and type of leisure time physical activity and a standard conversion for metabolic equivalence units. The homeostasis model assessment of insulin resistance index was calculated by fasting insulin (μIU/ml) × fasting glucose (mmol/L)/22.5. Estimated glomerular filtration rate (eGFR) was calculated by the modification of diet in renal disease formula.
Blood samples were obtained from participants after an overnight fast and collected in EDTA-containing tubes. Plasma aliquots were stored at −80°C until assays were performed. Total adiponectin levels were quantified using a commercially available sandwich enzyme-linked immunosorbent assay (Millipore, Billerica, Massachusetts) according to the manufacturer’s specifications. The measured intra-assay CVs were between 1.0% and 7.4% and the inter-assay CVs between 2.4% and 8.4%. Other biomarkers including leptin, high-sensitivity C-reactive protein (hs-CRP), interleukin-18, and N-terminal of the prohormone B-type natriuretic peptide (NT-proBNP) were measured as previously described.
The primary end point was all-cause mortality. Secondary end points included CV mortality, incident major adverse cardiovascular and cerebrovascular events (MACCE; a composite of CVD death, nonfatal MI, nonfatal stroke, or coronary revascularization by percutaneous coronary intervention or coronary artery bypass grafting), and incident HF. All nonfatal events were ascertained through December 31, 2011, using (1) a detailed health survey regarding interval cardiovascular events administered by the Data Coordinating Center during annual calls to study subjects and/or (2) for subjects providing informed consent (>90%), quarterly tracking of hospital admissions using the Dallas-Fort Worth Hospital Council Data Initiative Database that includes all hospital admission data for 70 of 72 hospitals in the Dallas-Fort Worth area. Primary clinical source documents were collected and reviewed for all suspected nonfatal events, and all nonfatal events were independently adjudicated by a blinded end point committee. Additionally, for all revascularization events, a 3-month blanking period was used to minimize the chance that information obtained during the study visit led to a revascularization procedure. Death events were ascertained through December 31, 2011, from the National Death Index and classified as cardiovascular if the primary cause was related to the cardiovascular system according to the International Statistical Classification of Diseases, 10th Revision , codes I00 to I99.
Demographic and clinical variables were compared across quartiles of adiponectin levels using the Jonckheere-Terpstra trend test. Associations between adiponectin levels and imaging markers of subclinical CVD were assessed by multivariable-adjusted linear regression. Adiponectin was modeled using standardized β coefficients (per 1 SD of the log-transformed adiponectin level) and as gender- and race-specific quartiles. Cox proportional hazards models were used to assess the association between adiponectin (both as a continuous measure and by gender- and race-specific quartiles) and the time to a first event, with associations reported as hazard ratio (HR) and 95% confidence interval. Multivariable models were adjusted for age, gender, race, hypertension, diabetes, smoking, low high-density lipoprotein cholesterol (HDL-C), hyperlipidemia, hs-CRP level, eGFR, and BMI. The proportional hazards assumption was met for all models. Additional analyses further adjusting for relevant covariates associated with both adiponectin and CVD prognosis in the literature (lean mass and natriuretic peptide levels) were also performed. We performed multivariable-adjusted subgroup analyses with stratification by age (<45 vs ≥45 years), gender (men vs women), race (black vs nonblack), BMI (obese vs nonobese), diabetes status (yes vs no), components of the metabolic syndrome (0, 1 to 2, ≥3), and elevated hs-CRP (≤3 vs >3 mg/L). To evaluate the models for overfitting, a shrinkage coefficient for each outcome was calculated by (likelihood model chi-square-p)/likelihood model chi-square, where p = number of covariates in the model; values close to 1 suggest minimal model overfitting. Linearity of the association between adiponectin and all-cause mortality was tested using adjusted cubic splines. A 2-sided p value <0.05 was considered to be statistically significant. All statistical analyses were performed using SAS software, version 9.3 (SAS Institute, Cary, NC, USA).
Results
The study cohort included 3,263 participants with mean age 43.4 years (SD ± 10); 44% were women and 50% were black. The median follow-up time was 10.4 years (IQR 10.0 to 10.8).
Demographic, clinical, laboratory, and imaging characteristics of the study population stratified by adiponectin quartiles are presented in Table 1 . Higher adiponectin quartiles were associated with a lower prevalence of male gender, black race, traditional cardiovascular risk factors, and generally more favorable adiposity and cardiovascular imaging profiles. The associations of log-transformed adiponectin levels with imaging markers of subclinical CVD are listed in Table 2 . After multivariable adjustment for age, gender, race, hypertension, diabetes, smoking, low HDL-C, hyperlipidemia, hs-CRP level, eGFR, and BMI, higher adiponectin levels remained significantly associated with lower LV wall thickness, higher left ventricular end-diastolic volumes indexed to body surface area, and lower liver fat content but not with other markers of subclinical CVD. Results were similar for adiponectin quartiles in multivariable analyses.
| Variable | Q1 N=815 (0.65 – 4.39 ng/mL) | Q2 N=816 (4.40 – 6.50 ng/mL) | Q3 N=816 (6.51 – 9.53 ng/mL) | Q4 N=816 (9.54 – 34.52 ng/mL) | P-trend |
|---|---|---|---|---|---|
| Age (years) | 42 (35, 51) | 43 (36, 50) | 42 (36, 50) | 45 (37, 53) | 0.003 |
| Men | 477 (58.5%) | 411 (50.4%) | 332 (40.7%) | 217 (26.6%) | <0.0001 |
| Black | 521 (63.9%) | 434 (53.2%) | 364 (44.6%) | 308 (37.7%) | <0.0001 |
| White | 138 (16.9%) | 199 (24.4%) | 280 (34.3%) | 373 (45.7%) | <0.0001 |
| Hispanic | 134 (16.4%) | 162 (19.9%) | 161 (19.7%) | 116 (14.2)% | 0.25 |
| Hypertension | 290 (36.0%) | 261 (32.7%) | 226 (27.9%) | 222 (27.8%) | <0.0001 |
| Diabetes | 136 (16.7%) | 90 (11.0%) | 65 (8.0%) | 46 (5.6%) | <0.0001 |
| Low HDL cholesterol | 436 (53.5%) | 392 (48.0%) | 317 (38.9%) | 194 (23.8%) | <0.0001 |
| Metabolic Syndrome | 361 (44.3%) | 305 (37.4%) | 232 (28.4%) | 145 (17.8%) | <0.0001 |
| Current Smoker | 244 (29.9%) | 226 (27.7%) | 241 (29.6%) | 216 (26.6%) | 0.25 |
| Physical Activity (METS x min/wk) | 120 (0, 540) | 133 (0, 540) | 120 (0, 549.5) | 213 (0, 660) | 0.04 |
| Total cholesterol (mg/dL) | 180 (153, 207) | 178 (158, 204) | 176 (152, 200) | 178 (155, 203) | 0.26 |
| HDL cholesterol (mg/dL) | 43 (36, 50) | 45 (38, 53) | 49 (42, 58) | 56 (47, 66) | <0.0001 |
| LDL cholesterol (mg/dL) | 108 (86, 133) | 109 (88, 130) | 103 (82, 125) | 101 (78, 123) | <0.0001 |
| Triglycerides (mg/dL) | 113 (78, 172) | 102 (71, 155) | 91 (66, 137) | 81 (58, 113) | <0.0001 |
| hs-C-Reactive Protein (mg/L) | 3.9 (1.8, 7.7) | 3 (1.2, 7.3) | 2.7 (1.0, 6.4) | 1.9 (0.7, 4.65) | <0.0001 |
| IL-18 (mg/dL) | 533.1 (372.2, 803.0) | 525.6 (372.0, 753.0) | 506.0 (340.8, 742.6) | 469.5 (331.7, 726.5) | 0.003 |
| NT-pro-BNP (pg/mL) | 16.5 (6.8, 38.9) | 22.6 (10.2, 42.9) | 30 (15.2, 60.2) | 43.55 (23.0, 86.1) | <0.0001 |
| Estimated GFR (mL/min per 1.73 m 2 ) | 101.0 (87.5, 114.2) | 98.8 (87.9, 113.2) | 98.9 (86.0, 112.2) | 95.84 (83.4, 109.7) | <0.0001 |
| Weight (kg) | 90.7 (78.9, 106.8) | 87.5 (74.6, 102.5) | 81.2 (68.9, 95.7) | 73.0 (63.1, 85.3) | <0.0001 |
| BMI (kg/m 2 ) | 30.54(27.1, 35.0) | 29.3 (25.7, 34.0) | 27.8 (24.4, 32.7) | 25.4 (22.4, 29.6) | <0.0001 |
| Waist/Hip ratio | 0.94 (0.89, 0.99) | 0.92 (0.86, 0.97) | 0.9 (0.85, 0.95) | 0.84 (0.79, 0.9) | <0.0001 |
| Total Fat Mass (kg) | 26.8 (19.7, 34.4) | 27.0 (19.3, 35.8) | 25.3 (18.2, 34.7) | 23.9 (16.8, 30.8) | <0.0001 |
| Total Lean Mass (kg) | 61.5 (52.8, 69.2) | 57.8 (48.9, 66.0) | 52.9 (44.8, 62.1) | 46.8 (41.2, 55.2) | <0.0001 |
| Abdominal Subcutaneous Fat (kg) | 4.7 (3.3, 6.8) | 4.5 (3.1, 6.9) | 4.1 (2.8, 6.3) | 3.6 (2.4, 5.1) | <0.0001 |
| Visceral Fat (kg) | 2.4 (1.9, 3.0) | 2.3 (1.7, 3.1) | 1.9 (1.4, 2.6) | 1.5 (1.1, 2.1) | <0.0001 |
| Lower Body Fat (kg) | 8.3 (6.0, 11.8) | 9.0 (6.3, 12.2) | 8.8 (6.2, 12.5) | 9.1 (6.7, 12.0) | 0.03 |
| Liver Fat (%) | 5.2 (2.9, 10.1) | 4.0 (2.5, 7.4) | 3.3 (2.0, 5.6) | 2.6 (1.6, 4.2) | <0.0001 |
| LV Mass/BSA (g/m 2 ) | 85.0 (75.1, 96.4) | 80.9 (70.6, 93.4) | 78.1 (69.0, 91.0) | 74.7 (66.6, 85.7) | <0.0001 |
| LV wall thickness (mm) | 12.2 (11.1, 13.5) | 11.6 (10.6, 12.7) | 11.2 (10.2, 12.3) | 10.6 (9.7, 11.7) | <0.0001 |
| LVEDV/BSA(ml/m 2 ) | 49.3 (43.6, 56.6) | 50.8 (45.8, 57.5) | 51.6 (45.2, 57.9) | 52.0 (45.8, 58.9) | 0.0001 |
| Coronary Artery Calcium (Agatston units) | 1.0 (0.0, 9.5) | 0.7 (0.0, 5.0) | 0.0 (0.0, 4.1) | 0.0 (0.0, 1.6) | <0.0001 |
| CAC >10 | 135 (22.2%) | 119 (18.9%) | 124 (19.3%) | 108 (16.2%) | 0.01 |
| Aortic wall thickness (mm) | 1.7 (1.5, 1.9) | 1.7 (1.5, 1.8) | 1.7 (1.5, 1.8) | 1.6 (1.5, 1.8) | 0.0002 |
| Subclinical CVD Marker | Standardized β-coefficient | P-value |
|---|---|---|
| Left ventricular wall thickness | ||
| Unadjusted | -0.32 | <0.0001 |
| Multivariable-adjusted † | -0.03 | 0.04 |
| Aortic wall thickness | ||
| Unadjusted | -0.08 | 0.0002 |
| Multivariable-adjusted † | 0.03 | 0.19 |
| Left ventricular end-diastolic volume indexed to body surface area | ||
| Unadjusted | 0.07 | 0.001 |
| Multivariable-adjusted † | 0.15 | <0.0001 |
| Left ventricular mass indexed to body surface area | ||
| Unadjusted | -0.19 | <0.0001 |
| Multivariable-adjusted † | 0.03 | 0.10 |
| Liver fat | ||
| Unadjusted | -0.28 | <0.0001 |
| Multivariable-adjusted † | -0.29 | <0.0001 |
| Coronary artery calcium (log-transformed) | ||
| Unadjusted | -0.09 | <0.0001 |
| Multivariable-adjusted † | 0.004 | 0.81 |
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