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American Journal of Epidemiology Advance Access published online on June 25, 2008
American Journal of Epidemiology, doi:10.1093/aje/kwn132
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Original Contribution

Epidemiology of Cytokines

The Women On the Move through Activity and Nutrition (WOMAN) Study

Eric Wong1, Matthew Freiberg1,2, Russell Tracy3 and Lewis Kuller1

1 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
2 Department of Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA
3 Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Colchester, VT

Correspondence to Dr. Lewis H. Kuller, Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, 130 North Bellefield Avenue, Room 550, Pittsburgh, PA 15213 (e-mail: kullerl{at}edc.pitt.edu).

Received for publication February 12, 2008. Accepted for publication April 21, 2008.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Using multiplex technology, the authors investigated the laboratory and biologic variation of a panel of cytokines (interleukin (IL)-1a, IL-1 receptor antagonist, IL-4, IL-6, IL-8, IL-10, interferon-inducible protein-10, monocyte chemoattractant protein-1, and tumor necrosis factor-{alpha}) over 18 months and their relations to cardiovascular disease risk factors, hormone therapy, and weight loss. Data were obtained from the Woman On the Move through Activity and Nutrition (WOMAN) Study, a randomized clinical trial investigating the effect of nonpharmacologic interventions on subclinical atherosclerosis among overweight, postmenopausal women in Pennsylvania. The present analysis (February 2002–August 2005) comprised 290 women aged 52–62 years (mean age = 57 years). Most of the cytokines were detectable in a majority of the samples, and the between-individual biologic variation was greater than the within-individual biologic and laboratory variation. There was little association between use of hormone therapy at baseline or change in hormone therapy by 18 months and cytokine levels. Weight loss was associated with a decrease in levels of IL-1 receptor antagonist, IL-6, and C-reactive protein. The results suggest that a wide panel of cytokines may be measured simultaneously from one sample. There is large unexplained variability in cytokine levels that is probably due to genetic-environmental associations.

cytokines; hormones; inflammation; obesity; weight loss; women

Abbreviations: CV, coefficient of variation; HOMA-IR, homeostasis model assessment of insulin resistance; IP-10, interferon-inducible protein-10; MCP-1, monocyte chemoattractant protein-1; TNF-{alpha}, tumor necrosis factor-{alpha}; WOMAN, Women On the Move through Activity and Nutrition


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Accumulating evidence suggests that inflammation is an integral part of many long-incubation chronic diseases, such as rheumatoid arthritis, diabetes, and atherosclerosis (1–4).

In 1974, Cohen introduced the term cytokine (3). In 1989, Balkwill and Burke (5) defined cytokines according to Cohen, as "one group of protein cell regulators variously called lymphokines, monokines, interleukins, interferons and chemokines produced by a wide variety of cells in the body that play an important role in many physiologic responses" (3, p. 518). Cytokines are involved in the pathophysiology of a broad range of diseases and also have therapeutic potential (4, 6). The cytokines consist of more than 40 secreted factors involved in intercellular communication (3, 7).

Multiplexed proteomics is the newest technique being employed to measure cytokines. In the present study, our first goal was to examine the biologic variability of a panel of cytokines using the Bio-Plex Luminex-100 platform and the correlations between these cytokines and other inflammatory markers among postmenopausal women in the Women On the Move through Activity and Nutrition (WOMAN) Study (8, 9). Weak reproducibility, either within or between laboratories, has led to spurious conclusions regarding biomarkers and disease (10). Our second goal in this study was to evaluate the relation between use of hormone therapy by postmenopausal women, obesity, and weight loss and cytokine levels and changes in cytokine levels over 18 months.

Coronary heart disease is the leading cause of death in women (11, 12). Initial epidemiologic, experimental, and animal studies indicated that hormone therapy could reduce the risk of coronary heart disease; however, investigators from the Women's Health Initiative, the largest randomized controlled trial to date to have examined hormone therapy and the risk of incident cardiovascular disease, reported an increased risk of cardiovascular events among postmenopausal women using hormone therapy (13–15). Hormone therapy exerts a wide range of effects, including modification of lipoprotein metabolism, vascular and endothelial function, and stimulation and inhibition of inflammation (16–21). Furthermore, some data suggest that hormone therapy alters plasma levels of various inflammatory markers and cytokines, including C-reactive protein (22–28), cell adhesion molecules (23–25), monocyte chemoattractant protein-1 (MCP-1) (29–31), tumor necrosis factor-{alpha} (TNF-{alpha}) (27, 32), and interleukin-6 (22, 25–27).

Increased fat cell mass (i.e., obesity) is associated with higher levels of some cytokines, such as interleukin-6 (33) and C-reactive protein (34). Obesity is considered an "inflammatory" condition. Macrophages within the fat mass probably secrete cytokines. The cytokines, such as interleukin-6, may increase production of blood levels of C-reactive protein (35). Adipokines produced by the fat cells may also contribute both to the risk of cardiovascular disease and to the inflammatory response. In the Framingham Heart Study, Pou et al. (36) reported positive correlations between C-reactive protein, fibrinogen, interleukin-6, and MCP-1 and body mass index (weight (kg)/height (m)2), visceral adipose tissue, subcutaneous tissue, and waist circumference. Levels of MCP-1, a cytokine, are increased in obesity. MCP-1 has further been shown to effect macrophage infiltration into adipose tissue. Inflammation related to obesity may be related to the increased risk of coronary heart disease among obese persons (37).


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Study population
We selected participants for the present analysis from the WOMAN Study (9), a randomized clinical trial (National Institutes of Health identifier NCT00023543 [ClinicalTrials.gov] (clinicaltrials.gov)) combining behavioral, dietary, and physical activity interventions that was designed to modify cardiovascular disease risk factors as assessed by noninvasive measures of atherosclerosis. A complete description of the design, objectives, sampling strategies, and examination techniques of the WOMAN Study has been previously published (9). The WOMAN Study is an ongoing study consisting of 508 postmenopausal women aged 52–62 years who were recruited by direct mailing in selected zip codes throughout Allegheny County, Pennsylvania, from April 2002 through October 2003. The women were then randomized to either a health education group or a lifestyle change group (23). Initial entry criteria for the WOMAN Study required prospective participants to have used hormone therapy for at least 2 years. Shortly after recruitment for the WOMAN Study was begun, results of the Women's Health Initiative (13, 15) demonstrated an increased risk of coronary heart disease associated with use of hormone therapy. This prompted modification of the WOMAN Study recruitment criteria. The modified criteria resulted in the inclusion of women with a recent history of use of hormone therapy, where their median time off of hormone therapy was 7 months prior to entry, and a recommendation to discontinue hormone therapy for women already recruited into the trial within a few months after randomization. Additional inclusion criteria included a waist circumference greater than 80 cm, a body mass index of 25–39.9, blood pressure less than 140/90 mmHg regardless of medication use, a low density lipoprotein cholesterol level of 100–160 mg/dl and no current use of cholesterol-lowering medication, no diagnosis of diabetes or use of diabetic medication, a Beck Depression Inventory score less than 20, no diagnosis of a psychotic disorder, successful completion of a 400-m corridor walk test (heart rate 40–135 beats/minute throughout), and a willingness to be randomized.

A total of 290 participants from the WOMAN Study were tested for inflammatory markers and cytokines at baseline and at 18 months. The sample included 87.7 percent (222 of 253) of participants in the lifestyle change (i.e., intervention) group of the trial and 27 percent (68 of 255) of those in the health education (i.e., control) group. All of the women from the control group had been on hormone therapy at baseline but stopped using it by 18 months (68 of 70 eligible women).

At 18 months, there were substantial differences in weight loss between the lifestyle change group (18 pounds (8.2 kg)) and the health education group (4 pounds (1.8 kg)) (38). The weight loss was due to a combination of increased exercise and reduced intake of total and fat calories. The study was approved by the institutional review board of the University of Pittsburgh, and informed consent was obtained from each participant at every visit.

Clinical and laboratory measurements
Multianalyte profiling was performed on the Bio-Plex Luminex system (Bio-Rad Laboratories, Inc., Hercules, California) at the University of Vermont (Dr. Russell Tracy). Calibration microspheres for classification and reporter readings as well as sheath fluid were purchased from Bio-Rad Laboratories. Plasma concentrations of interleukin-1a, interleukin-1 receptor antagonist, interleukin-4, interleukin-8, interleukin-10, interferon-inducible protein-10 (IP-10), TNF-{alpha}, and MCP-1 were determined using a Millipore (Linco Research, St. Charles, Missouri) cytokine eight-plex panel. Acquired fluorescence data were analyzed by Bio-Plex Manager software (version 4.1; Bio-Rad Laboratories). All analyses were performed according to the manufacturer's protocol. The sensitivity level for all measured cytokines was 0.64 pg/ml. The intraassay laboratory coefficients of variation (CVs) ranged from 4.4 percent to 44.7 percent.

Statistical analysis
All participants were categorized and analyzed by hormone therapy group ("hormone therapy on"—use of hormone therapy at both baseline and 18 months (n = 69); "hormone therapy off"—using hormone therapy at baseline but off of hormone therapy by 18 months (n = 142); "hormone therapy none"—no use of hormone therapy at baseline or 18 months (n = 79)). Significant differences by hormone therapy group were assessed by analysis of variance or the Kruskal-Wallis test, depending on the normality of the distributions.

Data on traditional cardiovascular disease risk factors and demographic factors at baseline are reported as mean values with standard deviations or as percentages. Data for inflammatory parameters are reported as median values and interquartile ranges, and because of their nonnormal distributions, they were log-transformed before subsequent analysis. Untransformed values are reported for ease of interpretation. Baseline values among hormone therapy groups were compared using analysis of variance, whereas 18-month and change values were compared using analysis of covariance, with the baseline inflammatory value, change in weight, age, race/ethnicity, and current smoking status included as covariates. Post-hoc comparisons were made with the Tukey-Kramer test. Inflammatory marker values that fell outside the range of two standard deviations were examined. In the majority of cases, a chronic inflammatory condition (e.g., fibromyalgia) or an inflammatory risk factor (e.g., acute illness or recent surgery) was found prior to blood drawing. These values were excluded from subsequent analyses for each specific cytokine: interleukin-1a, baseline (n = 16) and 18 months (n = 13); interleukin-1 receptor antagonist, baseline (n = 6) and 18 months (n = 3); interleukin-4, baseline (n = 17) and 18 months (n = 15); interleukin-6, baseline (n = 11) and 18 months (n = 9); interleukin-8, baseline (n = 17) and 18 months (n = 17); interleukin-10, baseline (n = 6) and 18 months (n = 2); IP-10, baseline (n = 11) and 18 months (n = 9); MCP-1, baseline (n = 9) and 18 months (n = 14); TNF-{alpha}, baseline (n = 8) and 18 months (n = 3); and C-reactive protein, baseline (n = 23) and 18 months (n = 6).

We calculated the coefficient of analytic variation (CVa), that is, variation within the Vermont laboratory, as the intraassay CV (CV = (standard deviation/mean) x 100). Biologic variation was calculated as the within-subject CV over time and the between-subject CV (CVw and CVb, respectively). We calculated analytic and biologic variation using the baseline and 18-month values and separating the corresponding variances with a nested analysis of variance. The index of individuality was defined as CVw/CVb (10).

Relations between baseline cytokines and 18-month cytokines (e.g., correlation between interleukin-1a and 18-month interleukin-1a) were examined using Spearman correlation coefficients. Furthermore, relations between the individual cytokines were examined using Spearman correlation coefficients, where the baseline and 18-month values were averaged in order to reduce the effect of analytic and biologic variation.

The relations between traditional cardiovascular disease risk factors and the inflammatory markers were examined with simple linear regression; subgroup analyses for hormone therapy groups were also performed (data not shown). Predictors included age, waist circumference, systolic blood pressure, heart rate, log-transformed homeostasis model assessment of insulin resistance (HOMA-IR) (fasting glucose x fasting insulin), high density lipoprotein cholesterol, low density lipoprotein cholesterol, log-transformed triglycerides, and current smoking status.

The effect of weight loss on inflammatory parameters was examined with analysis of covariance using the baseline inflammatory value, hormone therapy group, age, body mass index, and waist circumference as covariates. As a secondary analysis, this was rerun for the lifestyle change group only, excluding the health education group, and the results were similar to those for the total sample. Significant linear trends were represented graphically. As a secondary analysis, this was rerun using percentage of body fat (dual-energy x-ray absorptiometry) instead of weight loss, and the results were similar. For all analyses, the statistical significance level was p < 0.05. All analyses were conducted using SAS, version 9.1 (SAS Institute, Inc., Cary, North Carolina). For persons with cytokine levels that were not detectable, the minimal level of detection was used as a dummy variable in the analysis.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Sixty-nine women (24 percent) were on hormone therapy at both baseline and the 18-month study visit, whereas 142 (49 percent) were on hormone therapy at baseline but not at 18 months. Nearly all of these participants discontinued hormone therapy within the first 6 months after randomization, in response to the release of the Women's Health Initiative report on the cardiovascular disease risk associated with hormone therapy (13). The remaining 27 percent (n = 79) were not on hormone therapy at either time point. However, nearly all of the women had previously used hormone therapy, usually within 6 months of randomization. Regardless of hormone therapy status, the mean age of participants was 57 years; 91 percent were Caucasian. The mean body mass index was 30.7, and the average high density lipoprotein cholesterol level was 59.3 mg/dl (table 1).


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  		TABLE 1. Distribution of data on traditional cardiovascular disease risk factors according to use of hormone therapy at baseline and at 18 months, Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005

 
The distributions of cytokine levels and changes in cytokine levels are shown in table 2. There were few consistent differences in the cytokine measures at baseline or at the 18-month visit between the three hormone therapy groups (see Web table 1, which is posted on the Journal's website (http://www.aje.oxfordjournals.org)). There were relatively small changes in median levels of cytokines between baseline and 18 months. There was a decrease in C-reactive protein level in all three groups (table 2).


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  		TABLE 2. Distribution of cytokines and inflammatory markers at baseline and 18 months (n = 290), Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005*

 
We compared changes in cytokine levels for women who had stopped using hormone therapy by 18 months between the lifestyle change group and the health education group, comparing both mean and median changes. There were no differences for any of the cytokines.

Analytic and biologic variability
Analytic variation, biologic variation, and the index of individuality (CVw/CVb) for the entire cohort are shown in table 3. The analytic variability (CVa) represents the laboratory variation between samples, and the biologic variation represents both the within-individual variation over time (CVw) and the between-individual variation (CVb). Interleukin-10 had the highest CVa (44.7), while IP-10 and MCP-1 had the lowest CVa's (5.5 and 4.4, respectively). Interleukin-1a, interleukin-4, and interleukin-10 had the highest CVb's, while IP-10, MCP-1, and TNF-{alpha} had the lowest CVb's. Interleukin-10 had the highest CVw in the cohort, and IP-10, MCP-1, and TNF-{alpha} had the lowest CVw's in the cohort. As noted, a high coefficient for the index of individuality—that is, CVw/CVb ≥ 0.6—suggests that the within-individual variation over time is approaching the between-individual variation. Determination of the factors associated with within-individual variation over time, assuming low laboratory within-assay variation, is likely to be a more productive approach. The CVw/CVb ratio was less than 0.6 for interleukin-1a, interleukin-4, interleukin-8, interleukin-10, and TNF-{alpha}, and it was 0.6 or more for interleukin-1 receptor antagonist, IP-10, MCP-1, and TNF-{alpha}.


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  		TABLE 3. Analytic and biologic variation of cytokine levels as determined using the Bio-Plex Luminex-100 system,* Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005

 
Cytokines that are detectable using the Bio-Plex Luminex-100 system
There were substantial differences in the percentage of women with nondetectable levels of the specific cytokines at baseline, at 18 months, or both (table 4). IP-10 and MCP-1 were detectable in all samples at both baseline and 18 months. Interleukin-1a, interleukin-4, and interleukin-10 were the least detectable cytokines both at baseline and at 18 months.


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  		TABLE 4. Percentages of cytokines that were nondetectable at baseline and at 18 months, Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005

 
Correlation coefficients for the correlation between baseline and 18-month cytokine levels (autocorrelation) are reported in table 5. Interleukin-1a, interleukin-4, and interleukin-8 had the strongest autocorrelations (0.93, 0.91, and 0.90, respectively), while interleukin-6, IP-10, and MCP-1 had the weakest autocorrelations (0.54, 0.64, and 0.63, respectively), which is consistent with the between- versus within-individual variations in table 3 (also see Web table 2 (http://www.aje.oxfordjournals.org)).


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  		TABLE 5. Spearman correlation coefficients (r) for correlations between baseline and 18-month cytokine levels, Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005

 
Interrelations between cytokines
The strongest correlations between cytokines were for interleukin-1a and interleukin-4, interleukin-1a and interleukin-8, and interleukin-4 and interleukin-8 (0.88, 0.76, and 0.72, respectively). C-reactive protein was significantly and positively correlated with both interleukin-1 receptor antagonist and interleukin-6 (table 6).


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  		TABLE 6. Spearman correlation coefficients (r) for correlations between cytokine levels for the entire cohort, Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005*

 
Traditional coronary heart disease risk factors
Traditional coronary heart disease risk factors were found to be predictive of levels of interleukin-1 receptor antagonist, interleukin-6, IP-10, and TNF-{alpha} but not other cytokines (table 7). Waist circumference, high density lipoprotein cholesterol, triglycerides, and HOMA-IR (standardized β = –0.22, –0.17, 0.24, and 0.24, respectively) were predictive of levels of interleukin-1 receptor antagonist. Waist circumference, systolic blood pressure, low density lipoprotein cholesterol, and HOMA-IR (standardized β = 0.26, 0.13, 0.09, and 0.35) were predictive of interleukin-6. Age, triglycerides, and HOMA-IR (standardized β = 0.20, 0.14, and 0.12) were predictive of IP-10. Age, high density lipoprotein cholesterol, triglycerides, and HOMA-IR (standardized β = 0.10, –0.16, 0.16, and 0.15) were predictive of TNF-{alpha}. Waist circumference, triglycerides, and HOMA-IR (standardized β = 0.20, 0.16, and 0.20) were predictive of C-reactive protein.


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  		TABLE 7. Effect of cardiovascular disease risk factors (one-standard-deviation increase) on cytokine levels (change in standard deviation), Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005*

 
Weight loss
Changes in levels of cytokines and other risk factors over 18 months were determined according to weight change (table 8). Weight change was divided into four categories corresponding to quartiles in the original study, from weight loss of more than 18.8 pounds (>8.5 kg) (n = 88) to weight gain. Weight loss was also associated with significant decreases in interleukin-1 receptor antagonist, interleukin-6, and C-reactive protein (see table 8 and the Web figure (http://www.aje.oxfordjournals.org)). Results were similar when we substituted change in body fat for dual-energy x-ray absorptiometry. The relation of weight loss to other risk factors is shown in Web table 3 (http://www.aje.oxfordjournals.org).


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  		TABLE 8. Change{dagger} in cytokine levels (pg/ml) from baseline to 18 months, by weight change, Woman On the Move through Activity and Nutrition (WOMAN) Study, February 2002–August 2005

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
In order to study the relation between a value, such as the level of a cytokine, and disease risk factors or outcomes, the laboratory, within-individual, and between-individual variability of that value should be determined in the populations of interest. A ratio of within-individual to between-individual variability that is greater than or equal to 0.6 should be of concern when comparing the levels of biologic variables (i.e., cytokines) among individuals (table 3). When the within-individual variability is very high, a single measurement is unlikely to represent the true value for that subject. Taking multiple measurements of the variable over time and averaging the levels may provide a better estimate of the individual level in relation to the population average. The effect of substantial within-individual variability of a cytokine on blood levels will also adversely affect efforts to evaluate the effect of genotype (39). The classification of each individual's level in relation to his or her genotype will be very imprecise. The weak association of genotype with cytokine inflammatory marker levels may be due, in part, to substantial within-individual variability of measurement. Very few epidemiologic investigators have reported the biologic variation of a particular analyte over a period of time. In the present study, there was a very large and consistent variation in most of the cytokines between individuals that was not explained by hormone therapy or the traditional risk factors measured (38). It is unlikely that these variations were due to autoantibodies. Patients with either known rheumatologic conditions or elevated cytokine levels (more than two standard deviations) were excluded from analysis. Whether these variations in cytokine levels are due to specific genetic attributes (40–45), to other environmental variables such as nutrition (46), or to undetected subclinical disease is unknown. In this study, hormone therapy appeared to have only a relatively small effect on most cytokine measurements and on changes occurring over 18 months (table 2).

Cytokines are often classified with respect to structural homology, site of action, or functional activity. They are produced by a variety of cell types (e.g., macrophages and endothelial cells). The cytokines involved with inflammation and atherosclerosis are often further characterized according to their proinflammatory versus antiinflammatory effects and their proatherogenic and antiatherogenic effects. In the present study, proinflammatory cytokines included interleukin-1a, interleukin-6, interleukin-8, IP-10, MCP-1, and TNF-{alpha}. Antiinflammatory cytokines included interleukin-1 receptor antagonist, interleukin-4, and interleukin-10. The atherogenic classification scheme is similar to the inflammatory grouping, with the exceptions that interleukin-6 has been noted to have both antiatherogenic (47) and proatherogenic (48, 49) effects and interleukin-4 has been shown to have predominantly proatherogenic effects (50) despite some of its antiinflammatory characteristics.

In this cohort, the strongest correlations were consistently found between interleukin-1a, interleukin-4, and interleukin-8 (table 6). These associations are not surprising given that these cytokines are found to be proatherogenic and contribute to similar inflammatory pathways within the vascular wall. Interleukin-1a plays a major role in vascular wall inflammation by activating endothelial cells, activating monocytes, inducing cytokines, chemokines, and growth factors, and supporting proliferation of smooth muscle cells (51), all of which contribute to atherogenesis. The chemokine (chemoattractant cytokine) interleukin-8 is produced by endothelial cells and induces the proliferation and migration of vascular smooth muscle cells, contributing to the inflammatory process within the vascular wall (52). Interleukin-4, produced by T cells, participates in this inflammatory milieu by inducing the up-regulation of vascular cell adhesion molecule-1 (53), an adhesion molecule that promotes atherogenesis through the selective adhesion of mononuclear cells to the vascular endothelium (54, 55). Inflammatory processes are regulated by a complex dynamic system involving many factors. The level of a specific cytokine is correlated with the levels of a number of other cytokines regardless of their proinflammatory or antiinflammatory nature. This suggests that instead of focusing on an absolute level of a specific cytokine out of context from the overall inflammatory profile, it may be more useful to examine the relative associations between the proinflammatory and antiinflammatory systems (56, 57).

Levels of cytokines such as interleukin-6 were predicted by abdominal obesity (waist circumference) and insulin resistance (HOMA-IR), while levels of both TNF-{alpha} and C-reactive protein were predicted by triglycerides and insulin resistance. The relation of interleukin-6 to waist circumference was expected, given that abdominal fat (or macrophages within fat) is a large source of this cytokine. The relation to insulin sensitivity is also well documented (58–60). Recent evidence suggests that SOCS (suppressor of cytokine signaling) proteins disrupt both leptin and insulin receptor signaling and may provide a common pathway for leptin and insulin resistance (60). Furthermore, interleukin-6 has been shown to down-regulate IRS (insulin receptor substrate) proteins and up-regulate SOCS-3 protein in hepatocytes, impairing insulin receptor signaling (33).

Greater weight loss was associated with a greater decline in interleukin-1 receptor antagonist, interleukin-6, and C-reactive protein. The results were similar when the effect of a decrease in percentage of body fat was analyzed instead of weight loss. Whether the reduction in these levels with weight loss is associated with an independent effect on CV risk has not yet been determined by long-term weight loss trials (34, 61, 62).

Other cytokines such as interleukin-1 receptor antagonist and IP-10 were also found to be related to traditional cardiovascular disease risk factors. There is limited literature on the association between the chemokine IP-10 and traditional cardiovascular disease risk factors, but a recent study showed that increased levels of IP-10 are related to increased risk of coronary heart disease (63, 64). Cells within atherosclerotic plaque such as macrophages, endothelial cells, and smooth muscle cells have also been shown to express IP-10 (65). In the current study, IP-10 was positively associated with age, triglycerides, and insulin resistance.

The results of this study are limited to postmenopausal women. Only a single blood sample was collected at each time point, so short-term within-day variation could not be measured. An unmeasured variable that determined whether women continued or stopped hormone therapy could have affected changes in cytokine levels.

In summary, in this prospective study of postmenopausal women, we demonstrated that a wide panel of cytokines can be simultaneously and serially measured over time and that sustained weight loss can lower levels of interleukin-1 receptor antagonist, interleukin-6, and C-reactive protein.

It may be unlikely that a single specific cytokine is uniquely related to the risk of disease (65–67). Even in diseases in which specific therapy for modulating cytokine levels has been beneficial—for example, the use of anti-TNF-{alpha} receptor treatment in rheumatoid arthritis—other cytokines are clearly also important, and medications for modulating levels or receptors are currently being evaluated. There are more than 50 different cytokines.

Levels of many cytokines are correlated with each other, and the cytokines function together with regard to a specific biologic response. There are also important genetic and environmental determinants of each of the cytokines. It is likely that investigators will identify different cytokines of interest in relation to specific diseases. The significance of the observation may be a function of the laboratory and within- versus between-individual variation and not necessarily of clinical relevance. More studies are showing that a combination of cytokines predicts disease. Focusing on one cytokine, unless it can be clearly documented that it is the key to the pathophysiology of the disease, may not enhance our understanding of human biology or risk prediction (68–70). It may be preferable in epidemiologic studies to define the extent of the inflammatory response in relation to levels of multiple cytokines measured at the same time. Future investigators should also identify the determinants of the cytokine response and its relation to specific T-cell subtypes and to such factors as endothelial cell function and thrombogenesis.

The use of solution phase multiplexed proteomics to measure cytokines is an emerging technique in epidemiologic studies. This technology will be useful in large epidemiologic and clinical studies, because a wide array of proteins may now be measured using a much smaller volume of plasma. Further work is needed to improve detection limits, to increase correlation with standard enzyme-linked immunosorbent assay methods for analytes with very low plasma concentrations, and to develop more efficient antibody capture and detection techniques, especially in "healthy populations" as compared with persons with clinical diseases such as cancer, rheumatoid arthritis, diabetes, and coronary heart disease (71). To our knowledge, this was the first study to assess the biologic variability in and determinants of a panel of cytokines over a long period of time using solution phase multiplexed proteomics in a "normal" population not selected for specific diseases. The results of this study support the continued utilization and refinement of this technique.


   ACKNOWLEDGMENTS
 
This research was funded by National Heart, Lung, and Blood Institute contract R01-HL-66468.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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