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Original Investigation |

A Prospective Study of Inflammatory Cytokines and Diabetes Mellitus in a Multiethnic Cohort of Postmenopausal Women FREE

Simin Liu, MD, ScD; Lesley Tinker, PhD, RD; Yiqing Song, MD, ScD; Nader Rifai, PhD; Denise E. Bonds, MD; Nancy R. Cook, ScD; Gerardo Heiss, MD; Barbara V. Howard, PhD; Gokhan S. Hotamisligil, MD, PhD; Frank B. Hu, MD, PhD; Lewis H. Kuller, MD, DrPH; JoAnn E. Manson, MD, DrPH
[+] Author Affiliations

Author Affiliations: Departments of Epidemiology (Dr Liu) and Medicine (Dr Liu), University of California, Los Angeles; Division of Preventive Medicine (Drs Liu, Song, and Manson) and Channing Laboratory (Drs Hu and Manson), Department of Medicine, Brigham and Women's Hospital, and Department of Laboratory Medicine, Children's Hospital (Dr Rifai), Harvard Medical School, and Departments of Epidemiology (Drs Liu, Cook, Hu, and Manson) and Genetics and Complex Diseases (Dr Hotamisligil), Harvard School of Public Health, Boston, Massachusetts; Public Health Division, Fred Hutchinson Cancer Research Center, Seattle, Washington (Dr Tinker); Departments of Public Health Sciences (Dr Bonds) and Medicine (Dr Bonds), University of Virginia, Charlottesville; Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill (Dr Heiss); MedStar Research Institute, Washington, DC (Dr Howard); and Department of Epidemiology, University of Pittsburgh, School of Public Health, Pittsburgh, Pennsylvania (Dr Kuller).


Arch Intern Med. 2007;167(15):1676-1685. doi:10.1001/archinte.167.15.1676.
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Background  Inflammatory cytokines, including tumor necrosis factor α, IL-6 (interleukin 6), and high-sensitivity C-reactive protein (hsCRP), have been related to both insulin resistance and type 2 diabetes mellitus. However, prospective studies that comprehensively assess their roles in the development of type 2 diabetes are few, especially in minority populations.

Methods  Among 82 069 postmenopausal women aged 50 to 79 years without cardiovascular disease or diabetes mellitus who participated in the Women's Health Initiative Observational Study, we prospectively examined the relationships of plasma levels of tumor necrosis factor α receptor 2, IL-6, and hsCRP to diabetes risk. During a median follow-up period of 5.9 years, 1584 women who had clinical diabetes were matched by age, ethnicity, clinical center, time of blood draw, and duration of follow-up to 2198 study participants who were free of the disease.

Results  After adjustment for matching factors and known diabetes risk factors, all 3 markers were significantly associated with increased diabetes risk; the estimated relative risks comparing the highest with the lowest quartiles were 1.47 (95% confidence interval [CI], 1.10-1.97) for tumor necrosis factor α receptor 2, 3.08 (95% CI, 2.25-4.23) for IL-6, and 3.46 (95% CI, 2.50-4.80) for hsCRP (P for trend, <.01 for all biomarkers). When mutually adjusted, IL-6 and hsCRP remained significant in each ethnic group. While no statistically significant interactions were observed between ethnicity and these biomarkers on diabetes risk, there were consistent trends for the associations of hsCRP and IL-6 with increased diabetes risk in all ethnic groups.

Conclusion  These prospective data showed that elevated levels of IL-6 and hsCRP were consistently and significantly associated with an increased risk of clinical diabetes in postmenopausal women.

Figures in this Article

Low-grade chronic inflammation, as reflected by elevated circulating levels of inflammatory cytokines,13 may promote insulin resistance in liver, skeletal muscle, and vascular endothelium,4,5 ultimately leading to the clinical expression of both type 2 diabetes mellitus and cardiovascular disease (CVD).6 To date, cross-sectional studies have associated plasma levels of high-sensitivity C-reactive protein (hsCRP),716 IL-6 (interleukin 6),7,8,15,17,18 and soluble tumor necrosis factor α (TNF-α) receptors79,15 with obesity and insulin resistance. Similar relationships have also been observed between these cytokines and other metabolic risk factors such as dyslipidemia and hypertension among nondiabetic individuals. In several prospective studies, however, the relationships of these inflammatory markers to risk of clinical diabetes have been inconsistent.1933 Moreover, while TNF-α receptors, IL-6, and hsCRP may each arguably serve as a sensitive marker of an underlying inflammatory state, previous studies have mainly focused on hsCRP.1921,2326,28,30,32,33 Furthermore, whereas ethnic differences for levels of inflammatory markers such as hsCRP have been reported,20,34 to our knowledge no studies to date have comprehensively assessed the relationships between these biomarkers and risk of type 2 diabetes according to ethnicity.

We therefore prospectively examined the associations between baseline levels of inflammatory cytokines and risk of clinical diabetes in apparently healthy women aged 50 to 79 years from the Women's Health Initiative Observational Study (WHIOS), an ethnically diverse cohort of postmenopausal women, including whites, blacks, Hispanics, and Asians/Pacific Islanders.

STUDY POPULATION

The WHIOS is an ongoing longitudinal study that was designed to examine the association between clinical, socioeconomic, behavioral, and dietary factors and subsequent health outcomes. Details of the rationale, eligibility, and other design aspects have been published elsewhere.35,36 In brief, between September 1994 and December 1998, the WHIOS enrolled a total of 93 676 women aged 50 to 79 years at 40 clinical centers throughout the United States. At baseline, women completed screening and enrollment questionnaires, underwent a physical examination, and provided a fasting blood specimen. Of the 93 676 postmenopausal women enrolled into the WHIOS cohort, 82 069 had no history of diabetes or CVD. The study was reviewed and approved by human subjects review committees at each participating institution, and signed informed consent was obtained from all women enrolled.

ASSESSMENT OF BASELINE VARIABLES

The methods of data collection and validation have been reported previously.35,36 Baseline information was collected using a standardized protocol implemented by centrally trained clinic staff, with a quality assurance program for uniformity across the entire study population. Self-administered questionnaires at study entry included information on age, ethnicity, education, income, reproductive history, detailed history of hormone replacement therapy and/or use of oral contraceptives, family history of CVD and diabetes, current prescription and over-the-counter medication use, use of vitamin and mineral supplements, smoking status, physical activity, alcohol use, and a detailed dietary assessment using a standardized food frequency questionnaire. During the initial screening visit, anthropometric measurements, blood pressure levels, and fasting blood specimens were also obtained from the participants. The women were instructed to fast overnight for at least 12 hours, to avoid taking any oral medications other than those used for diabetes, and to avoid vigorous exercise and smoking before the blood draw.

Ethnicity was determined by self-report and identified as white, not of Hispanic origin; black/African American; Hispanic/Latino; American Indian or Alaskan Native; Asian/Pacific Islander; or unknown (none of the above). Family history of diabetes was defined by self-report of diabetes in a first-degree relative. Smoking status (nonsmoker, former smoker, or current smoker) was determined from lifetime smoking of at least 100 cigarettes, current daily cigarette smoking, and smoking cessation. Physical activity was quantified by the number of weekly episodes of mild, moderate, or strenuous recreational physical activity. Alcohol intake habits were assessed, and alcohol consumption was computed from a food frequency questionnaire. Weight was measured to the nearest 0.1 kg on a balance beam scale, with the participant dressed in indoor clothing without shoes. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.

ASCERTAINMENT OF DIABETES MELLITUS

Throughout the entire WHIOS, diabetes was consistently defined a priori as self-report of clinical cases for women reporting diagnosis when they were not pregnant and those reporting diabetes treatments by diet, oral hypoglycemic medications, or insulin. Participants were followed up annually with self-administered questionnaires (medical history and exposure updates) and an additional clinical center visit for physical measurements 3 years after enrollment. They completed an annual medical history update form on which they indicated the occurrence of any hospitalization and a wide variety of outcomes, including diabetes. Incident diabetes cases were identified based on postbaseline self-report of first-time use of hypoglycemic medication (oral hypoglycemic agents or insulin), or hospitalization for previously unreported diabetes. Eligible cases were WHIOS participants who provided adequate blood specimens, who were free of reported CVD or diabetes at baseline, and who subsequently reported new diabetes treatment with oral hypoglycemic drugs or insulin or hospitalization for diabetes during a median follow-up period of 5.9 years (mean, 5.5 years). Following the principles of risk-set sampling,37 for each new case controls were selected randomly from women who remained free of CVD and/or diabetes at the time the case was identified during follow-up. Identical exclusion or inclusion criteria were applied to eligible controls who were WHIOS participants, who were free of reported CVD and/or diabetes at baseline, and who provided baseline blood specimens. Controls were further matched to the cases by age (±2.5 years), racial/ethnic group (white, black, Hispanic, and Asian/Pacific Islander), clinical center (geographic location), time of blood draw (±0.10 hours), and length of follow-up. In the current study, 968 cases involving white women were randomly chosen and matched with 1 control subject each. Among 616 incident cases involving ethnic minority women, 366 women were black, 152 were Hispanic, and 98 were Asian/Pacific Islander. The 1:2 matching ratio was used for minorities to increase statistical power. We estimated that we would have greater than 80% statistical power to detect a relative risk (RR) equal to or greater than 1.5 among whites or blacks and an RR equal to or greater than 2.0 among other racial or ethnic groups.

MEASUREMENT OF BIOCHEMICAL VARIABLES

According to a standardized protocol, fasting blood specimens were collected from each participant at baseline and processed locally into separate aliquots containing serum, plasma, and buffy coat. The aliquots were frozen and then shipped to a central repository, where they were kept for long-term storage at −70°C. All biochemical assays were carried out by laboratory staff blinded to case/control status (in Dr Rifai's laboratory). Blood samples from cases and their matched controls were handled identically, shipped in the same batch, and assayed in random order in the same analytical run to reduce systematic bias and interassay variation. Tumor necrosis factor α receptor 2 (TNF-α–R2) was measured by an enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, Minnesota). Interleukin 6 (IL-6) was measured by an ultrasensitive enzyme-linked immunosorbent assay (R&D Systems). High-sensitivity C-reactive protein (hsCRP) was measured on a chemistry analyzer (Hitachi 911; Roche Diagnostics, Indianapolis, Indiana) using an immunoturbidimetric assay with reagents and calibrators (Denka Seiken Co Ltd, Niigata, Japan). The coefficients of variation were 3.5% for TNF-α–R2, 7.6% for IL-6, and 1.61% for hsCRP.

STATISTICAL ANALYSIS

Mixed-effects regression was used to examine mean differences in baseline variables, with case-control cluster modeled as a random effect. A matched χ2 test was used to assess differences in proportions. As a surrogate measure of insulin resistance, the homeostasis model assessment—the product of basal fasting glucose (in millimoles per milliliter) and insulin (microinternational units per milliliter) levels divided by 22.5—was used to estimate baseline insulin resistance (HOMA-IR). For TNF-α–R2, IL-6, hsCRP, and HOMA-IR with markedly skewed distributions, we made logarithmic transformations to enhance compliance with normality assumption for estimating age- and ethnicity-adjusted Pearson partial correlation coefficients.

We divided participants into quartiles according to levels of inflammatory markers among controls and applied conditional logistic regression to estimate the RR and 95% confidence interval (CI) for clinical diabetes using the lowest quartile as the reference. To test for linear trend, we used the median levels of inflammatory markers within quartiles in the controls as a continuous variable. We also estimated the RR per standard deviation increment for each marker assuming a linear relationship (per 791 pg/mL for TNF-α–R2, per 4.24 pg/mL for IL-6, and per 5.3 mg/L for hsCRP). We used conditional logistic regression to adjust for matching factors such as age, ethnicity, clinical center, and time of blood draw. In multivariable analyses, we adjusted for BMI (modeled as a continuous covariate), family history of diabetes (yes or no), smoking (never, past, and current smokers), alcohol intake (never, past, and current drinkers), physical activity (quintiles), and postmenopausal hormone use (yes or no). Further, we included TNF-α–R2, IL-6, and hsCRP simultaneously in the same multivariable model to determine their relative significance after mutual adjustment. To examine whether the associations between inflammatory biomarkers and risk of diabetes differ by ethnicity, we examined these markers in relation to diabetes risk stratified by ethnicity. A log likelihood ratio test was used to test the statistical significance of interactions.

To evaluate the joint relationships between any combinations of 2 biomarkers for the risk of diabetes, we divided the study population into 9 groups according to the following cut points: TNF-α–R2 (tertiles: <2077, 2077-2697, and >2697 pg/mL), IL-6 (tertiles: <1.14, 1.14-2.17, and >2.17 pg/mL), and hsCRP (clinical cut points: <1, 1-3, and >3 mg/L). Compared with women who had the lowest levels for both markers, each subgroup-specific RR was estimated using the same conditional logistic regression analysis. To examine whether the associations between inflammatory biomarkers and diabetes were modified by obesity, we also conducted subgroup analyses stratified by BMI (<25 and ≥25) and waist circumference (<89 and ≥89 cm). The likelihood ratio test was performed to test the statistical significance of interactions.

Because women with clinical diabetes who were treated by hypoglycemic drugs or insulin may represent only 1 specific phenotype (more severe cases) whose pathogenesis may be different from that of other diabetes phenotypes (including the asymptomatic cases), we also modified our definition of diabetes post hoc based on fasting glucose levels obtained at baseline and conducted sensitivity analyses excluding those women who had fasting glucose levels greater than or equal to 126 mg/dL (to convert to millimoles per liter, multiply by 0.0555). To further assess the robustness of our findings, we excluded clinical cases reported during the first year of follow-up. All P values were 2-tailed, and P values less than .05 were considered to indicate statistical significance. All analyses were performed with SAS software (version 9.1; SAS Institute Inc, Cary, North Carolina).

Compared with controls, cases had a greater proportion of traditional risk factors at baseline (Table 1), including a significantly higher BMI, waist circumference, and waist-hip ratio and a lower frequency of physical activity and of moderate alcohol intake. Cases were also more likely to have a family history of diabetes than controls. Similar patterns were observed in each ethnic group. In particular, women with diabetes had significantly higher median levels of TNF-α–R2, IL-6, and hsCRP at baseline compared with controls (TNF-α–R2, 2633 pg/mL vs 2361 pg/mL; IL-6, 2.59 pg/mL vs 1.54 pg/mL; and hsCRP, 4.0 mg/L vs 2.1 mg/L [all P values <.001]).

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Study Population According to Ethnicity and Diabetes Status a

Levels of inflammatory markers varied across ethnic groups, however (Table 1). On average, the highest relative difference in TNF-α–R2 levels between cases and controls was in whites (10.4%), followed by Hispanics (9.7%), blacks (5.9%), and Asians/Pacific Islanders (2.1%). The relative differences in IL-6 levels were also highest in whites (45%) but lowest in blacks (31%), with similar results for Hispanics (37%) and Asians/Pacific Islanders (37%). The hsCRP levels were higher among whites, blacks, and Hispanics than among Asians/Pacific Islanders in cases and controls, but the relative differences in hsCRP levels between cases and controls did not vary considerably across ethnic groups, with the highest percentages found among whites (52%) and Asians/Pacific Islanders (52%).

Table 2 shows the RRs across quartiles of inflammatory marker levels. In analyses adjusted for matching factors, circulating levels of TNF-α–R2, IL-6, and hsCRP were each positively and significantly associated with diabetes risk. After further adjustment for BMI and other traditional risk factors, the RRs of diabetes in the highest quartile compared with the lowest quartile were 1.47 for TNF-α–R2 (95% CI, 1.10-1.97; P for trend, .003), 3.08 for IL-6 (95% CI, 2.25-4.23; P for trend, <.001), and 3.46 for hsCRP (95% CI, 2.50-4.80; P for trend <.001). Additional adjustment for fasting glucose levels, fasting insulin levels, or HOMA-IR attenuated but did not eliminate these associations with levels of IL-6 and hsCRP. When these 3 inflammatory markers were mutually adjusted, TNF-α–R2 levels were not significantly associated with diabetes risk (Table 2). When we further excluded those cases (n = 737) and controls (n = 27) with fasting glucose levels equal to or greater than 126 mg/dL measured once at baseline, results changed little; the RRs were 1.11 (95% CI, 0.76-1.61; P for trend, .48) for TNF-α–R2, 3.08 (95% CI, 2.06-4.60; P for trend <.001) for IL-6, and 2.56 (95% CI, 1.68-3.89; P for trend, <.001) for hsCRP (Table 2). The associations remained similar when we further excluded clinical cases that developed in the first year of follow-up (n = 36).

Table Graphic Jump LocationTable 2. Adjusted Relative Risk (RR) Estimates According to Plasma Levels of Inflammatory Cytokines Among Postmenopausal Women

In subgroup analyses stratified by ethnicity, levels of hsCRP and IL-6 showed consistent associations with diabetes risk in all ethnic groups, while TNF-α–R2 was significantly related to diabetes only among Hispanic women (Table 3). Although only a small number of participants were Asian/Pacific Islander, a significant trend toward increased risk of diabetes associated with elevated levels of IL-6 was still apparent. We found that increasing levels of hsCRP or IL-6 were monotonically associated with increasing multivariable RRs of diabetes across the full range of plasma values of TNF-α–R2, indicating that incremental changes in hsCRP and IL-6 levels remain independently associated with diabetes risk irrespective of TNF-α–R2 levels. The increased diabetes risk tended to be additive when levels of both hsCRP and IL-6 were evaluated jointly in multivariable-adjusted models (Figure). We observed no statistically significant multiplicative interactions of TNF-α–R2, hsCRP, or IL-6 with BMI (<25 and ≥25) and waist circumference (<89 and ≥89 cm) in relation to diabetes risk, although positive associations appeared more evident among women with a BMI greater than or equal to 25 for these 3 markers (Table 4).

Place holder to copy figure label and caption
Figure.

Multivariate relative risk (RR) of diabetes according to joint classifications of plasma levels of high-sensitivity C-reactive protein (hsCRP [to convert C-reactive protein to nanomoles per liter, multiply by 9.524]), tumor necrosis factor α receptor 2 (TNF-α–R2), and IL-6 (interleukin 6). Plasma levels of TNF-α–R2 and IL-6 were categorized into tertiles, respectively. Ranges of less than 1, 1 to 3, and greater than or equal to 3 mg/L were used for hsCRP levels. The adjusted RR of type 2 diabetes among women in each category is shown relative to women with the lowest tertiles for both biomarkers. The model was adjusted for matching factors, body mass index, alcohol intake, level of physical activity, cigarette smoking status, use or nonuse of postmenopausal hormone therapy, and presence or absence of family history of diabetes. A, Joint effect of TNF-α–R2 and IL-6. B, Joint effect of TNF-α–R2 and hsCRP. C, Joint effect of IL-6 and hsCRP.

Graphic Jump Location
Table Graphic Jump LocationTable 3. Ethnicity-Specific Relative Risk (RR) Estimates for Clinical Diabetes According to Plasma Levels of Inflammatory Cytokines a
Table Graphic Jump LocationTable 4. Interactions Between Inflammatory Markers and BMI and Waist Circumference on Diabetes Risk

In this large, prospective cohort of American women aged 50 to 79 years who were followed up for 5.9 years, we found that high circulating levels of hsCRP, IL-6, and TNF-α–R2 at baseline were significantly associated with increased diabetes risk. These associations were independent of traditional risk factors, including several measures for obesity and insulin resistance. When all 3 biomarkers were mutually adjusted, hsCRP and IL-6 levels remained significantly associated with diabetes risk in all ethnic groups.

These findings support the hypothesis that chronic inflammation plays an important role in the development of type 2 diabetes in women. Although substantial variability has been observed for different inflammatory markers in relation to diabetes risk, accumulating evidence supported an independent role of inflammatory markers in affecting type 2 diabetes.1925,2733 Levels of hsCRP (ie, elevated levels of CRP well below the conventional clinical upper limit of 10 mg/L [to convert C-reactive protein to nanomoles per liter, multiply by 9.524] as measured by high-sensitivity assays) has been the main focus of previous work. Of 11 prospective studies,1921,2326,28,30,32,33 7 reported a significant positive association between hsCRP levels and diabetes risk independent of BMI,19,2325,28,30,33 waist-hip ratio,23,25,28,33 or measures of insulin resistance.19,2325,33 The RRs were in the range of 1.30 to 5.5 when the highest category of hsCRP levels was compared with the lowest category of hsCRP levels. Consistent with reports of these previous studies, hsCRP was a significant and strong risk marker for diabetes in our cohort of postmenopausal women. Human CRP is primarily synthesized by hepatocytes and regulated by inflammatory cytokines (mostly TNF-α and IL-638); however, whether hsCRP itself directly influences insulin resistance or diabetes remains speculative. Measurement of hsCRP levels is sensitive and robust compared with other inflammatory markers (with a long plasma half-life of 18 to 20 hours, a relatively low degree of intraindividual variability, and no circadian variation). Interleukin 6 is produced in a variety of tissues, including activated leukocytes, adipocytes, and endothelial cells.39,40 Our prospective data also indicate that IL-6 levels may predict risk of type 2 diabetes in these postmenopausal women.

Tumor necrosis factor α has been shown to directly inhibit insulin signaling and may thus be a critical mechanism whereby adiposity induces peripheral insulin resistance. Plasma levels of TNF-α–R2 are increasingly used as a reliable surrogate marker for TNF-α because soluble TNF-α–R2 is easily and reliably measured in frozen plasma with greater sensitivity and reliability than TNF-α alone. Prospective data directly linking TNF-α to diabetes risk are scarce. One previous study found that elevated levels of TNF-α were associated with increased diabetes risk but were not independent of BMI or waist-hip ratio.31 In a small study of Pima Indians, TNF-α was not related to diabetes risk.26 We observed a moderately increased diabetes risk associated with elevated levels of soluble TNF-α–R2, although this association was no longer significant when hsCRP and IL6 were simultaneously adjusted for. In the current study, a positive association between TNF-α–R2 and diabetes risk was more evident in Hispanic women than in other ethnic groups, although this finding could have been caused by chance, and a formal test for multiplicative interaction showed no significance. The reasons for such ethnicity-specific differences are unknown and warrant consideration in further studies.

In the past decade, considerable progress has been made in our understanding of the mechanisms underlying the relationship of systemic inflammation with the development of type 2 diabetes. In vitro studies have shown that hsCRP can induce endothelial dysfunction by down-regulating the expression of endothelial nitric oxide synthase,41,42 eliciting overproduction of endothelial adhesion molecules that promote insulin resistance.43,44 As the principal downstream mediator of the acute phase response, hsCRP may account for the integrated effects of both TNF-α and IL-6. Inhibition of insulin receptor signaling is a central mechanism through which obesity-related metabolic and inflammatory stresses induce insulin resistance.4,5 Alternatively, reverse causation may also be possible. An insulin resistance state may facilitate hepatic hsCRP production because insulin has anti-inflammatory effects and resistance to this effect would then lead to increased synthesis of CRP.45 It is also possible that mildly impaired glucose status without clinical diagnosis may elicit oxidative stress and production of free fatty acids that may raise levels of hsCRP. The ultimate resolution of the temporality of these interrelationships among inflammation, endothelial dysfunction, and insulin resistance will come from experiments that are designed to examine the temporal sequence of events at the molecular levels. Nevertheless, if systemic inflammation is causally linked to diabetes development, anti-inflammatory treatment may be of benefit. Indeed, accumulating evidence indicates that interventions aimed at improving insulin resistance, such as pharmacologic treatments with salicylates, 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, angiotensin-converting enzyme inhibitors, and peroxisome proliferator-activated receptor γ agonists, and lifestyle interventions, such as exercise and weight loss, may reduce hsCRP levels.46 These observations offer insights into the therapeutic potential of targeting systemic inflammation for prevention and/or treatment of insulin resistance and clinical diabetes.

Several issues regarding our study design need to be recognized. First, potential biases due to the inclusion of undiagnosed diabetes in the controls must be considered. As a continuous progressive metabolic disorder, the asymptomatic pathologic process of type 2 diabetes may begin years before clinical diagnosis is possible. Because the choice of timing for clinical diagnosis is defined by a somewhat arbitrary standard, underdiagnosed diabetes is inevitable (as in any large population studies of chronic diseases). To avoid potential bias associated with the timing of outcome definition, we took careful steps to ensure comparable follow-up by (1) using standard protocols to define cases and controls and (2) matching each case-control pair for age, clinical center, and follow-up time. This stringent strategy applied in a well-defined prospective setting, coupled with multiple sensitivity analysis, makes it highly unlikely that underdiagnosis may differ according to markers of our interest. Furthermore, while our focus on clinical cases with uses of diabetic medications may lead to misclassification of diabetics in the nondiabetic groups, it nevertheless serves to minimize false-positive diagnoses. It can be shown that if the specificity of diagnosis is 100% (ie, all the cases defined are true cases), underdiagnosis would have little impact on the RR measure of associations between exposures and disease. Our results were altered little when baseline fasting glucose and insulin levels were controlled for (through exclusion or adjustment in multivariable models). Although we cannot exclude the possibility of residual confounding by incompletely measured or unmeasured covariates (eg, even after careful adjustment for BMI and waist-hip ratio), it seems unlikely that more complete statistical adjustment would completely eliminate the associations that were consistently observed across diverse subgroups.

In summary, elevated levels of hsCRP and IL-6 were significantly associated with increased diabetes risk in this cohort of multiethnic postmenopausal women. These prospective data support the notion that inflammatory markers can be used in identifying individuals with greater risk of developing diabetes and may have therapeutic implications for diabetes prevention and treatment.

Correspondence: Simin Liu, MD, ScD, Departments of Epidemiology and Medicine, University of California, Los Angeles, School of Public Health and David Geffen School of Medicine, Box 951772, 650 Charles E. Young Dr S, Los Angeles, CA 90095-1772 (siminliu@ucla.edu).

Accepted for Publication: April 11, 2007.

Author Contributions:Study concept and design: Liu, Howard, Hotamisligil, Hu, and Manson. Acquisition of data: Liu, Rifai, Bonds, Heiss, and Manson. Analysis and interpretation of data: Liu, Tinker, Song, Cook, Hu, Kuller, and Manson. Drafting of the manuscript: Liu. Critical revision of the manuscript for important intellectual content: Liu, Tinker, Song, Rifai, Bonds, Cook, Heiss, Howard, Hotamisligil, Hu, Kuller, and Manson. Statistical analysis: Liu, Song, Cook, and Kuller. Obtained funding: Liu and Manson. Administrative, technical, and material support: Liu, Tinker, Rifai, Howard, and Manson. Study supervision: Liu, Howard, Hotamisligil, Kuller, and Manson.

Financial Disclosure: Dr Manson is listed as a coinventor on a pending patent held by Brigham and Women’s Hospital that relates to inflammatory biomarkers in diabetes prediction.

Funding/Support: The Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, US Department of Health and Human Services (for a short list of the WHI Investigators, see below). This study was supported by National Institute of Diabetes and Digestive and Kidney Diseases R01 grant DK062290 from the National Institutes of Health.

Box Section Ref ID

Short List of WHI Investigators

Program Office

National Heart, Lung, and Blood Institute, Bethesda, Maryland: Elizabeth Nabel, Jacques Rossouw, Shari Ludlam, Linda Pottern, Joan McGowan, Leslie Ford, and Nancy Geller.

Clinical Coordinating Centers

Fred Hutchinson Cancer Research Center, Seattle, Washington: Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E. Patterson, Anne McTiernan; Wake Forest University School of Medicine, Winston-Salem, North Carolina: Sally Shumaker; Medical Research Labs, Highland Heights, Kentucky: Evan Stein; University of California at San Francisco: Steven Cummings.

Clinical Centers

Albert Einstein College of Medicine, Bronx, New York: Sylvia Wassertheil-Smoller; Baylor College of Medicine, Houston, Texas: Jennifer Hays; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts: JoAnn Manson; Brown University, Providence, Rhode Island: Annlouise R. Assaf; Emory University, Atlanta, Georgia: Lawrence Phillips; Fred Hutchinson Cancer Research Center: Shirley Beresford; George Washington University Medical Center, Washington, DC: Judith Hsia; Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, California: Rowan Chlebowski; Kaiser Permanente Center for Health Research, Portland, Oregon: Evelyn Whitlock; Kaiser Permanente Division of Research, Oakland, California: Bette Caan; Medical College of Wisconsin, Milwaukee: Jane Morley Kotchen; MedStar Research Institute–Howard University, Washington, DC: Barbara V. Howard; Northwestern University, Chicago/Evanston, Illinois: Linda Van Horn; Rush Medical Center, Chicago, Illinois: Henry Black; Stanford Prevention Research Center, Stanford, California: Marcia L. Stefanick; State University of New York at Stony Brook: Dorothy Lane; The Ohio State University, Columbus: Rebecca Jackson; University of Alabama at Birmingham: Cora E. Lewis; University of Arizona, Tucson/Phoenix: Tamsen Bassford; University at Buffalo, Buffalo, New York: Jean Wactawski-Wende; University of California at Davis, Sacramento: John Robbins; University of California at Irvine: F. Allan Hubbell; University of California at Los Angeles: Howard Judd; University of California at San Diego, LaJolla/Chula Vista: Robert D. Langer; University of Cincinnati, Cincinnati, Ohio: Margery Gass; University of Florida, Gainesville/Jacksonville: Marian Limacher; University of Hawaii, Honolulu: David Curb; University of Iowa, Iowa City/Davenport: Robert Wallace; University of Massachusetts–Fallon Clinic, Worcester: Judith Ockene; University of Medicine and Dentistry of New Jersey, Newark: Norman Lasser; University of Miami, Miami, Florida: Mary Jo O’Sullivan; University of Minnesota, Minneapolis: Karen Margolis; University of Nevada, Reno: Robert Brunner; University of North Carolina, Chapel Hill: Gerardo Heiss; University of Pittsburgh, Pittsburgh, Pennsylvania: Lewis Kuller; University of Tennessee, Memphis: Karen C. Johnson; University of Texas Health Science Center, San Antonio: Robert Brzyski; University of Wisconsin, Madison: Gloria E. Sarto; Wake Forest University School of Medicine: Denise Bonds; Wayne State University School of Medicine–Hutzel Hospital, Detroit, Michigan: Susan Hendrix.

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Weyer  CYudkin  JSStehouwer  CDSchalkwijk  CGPratley  RETataranni  PA Humoral markers of inflammation and endothelial dysfunction in relation to adiposity and in vivo insulin action in Pima Indians. Atherosclerosis 2002;161 (1) 233- 242
PubMed Link to Article
Festa  AD'Agostino  R  JrHoward  GMykkanen  LTracy  RPHaffner  SM Chronic subclinical inflammation as part of the insulin resistance syndrome: the Insulin Resistance Atherosclerosis Study (IRAS). Circulation 2000;102 (1) 42- 47
PubMed Link to Article
Fröhlich  MImhof  ABerg  G  et al.  Association between C-reactive protein and features of the metabolic syndrome: a population-based study. Diabetes Care 2000;23 (12) 1835- 1839
PubMed Link to Article
Ridker  PMBuring  JECook  NRRifai  N C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation 2003;107 (3) 391- 397
PubMed Link to Article
Rutter  MKMeigs  JBSullivan  LMD'Agostino  RB  SrWilson  PW C-reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation 2004;110 (4) 380- 385
PubMed Link to Article
Yudkin  JSStehouwer  CDEmeis  JJCoppack  SW C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue? Arterioscler Thromb Vasc Biol 1999;19 (4) 972- 978
PubMed Link to Article
Temelkova-Kurktschiev  TSiegert  GBergmann  S  et al.  Subclinical inflammation is strongly related to insulin resistance but not to impaired insulin secretion in a high risk population for diabetes. Metabolism 2002;51 (6) 743- 749
PubMed Link to Article
Hak  AEPols  HAStehouwer  CD  et al.  Markers of inflammation and cellular adhesion molecules in relation to insulin resistance in nondiabetic elderly: the Rotterdam study. J Clin Endocrinol Metab 2001;86 (9) 4398- 4405
PubMed Link to Article
Müller  SMartin  SKoenig  W  et al.  Impaired glucose tolerance is associated with increased serum concentrations of interleukin 6 and co-regulated acute-phase proteins but not TNF-alpha or its receptors. Diabetologia 2002;45 (6) 805- 812
PubMed Link to Article
Barzilay  JIAbraham  LHeckbert  SR  et al.  The relation of markers of inflammation to the development of glucose disorders in the elderly: the Cardiovascular Health Study. Diabetes 2001;50 (10) 2384- 2389
PubMed Link to Article
Duncan  BBSchmidt  MIPankow  JS  et al.  Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 2003;52 (7) 1799- 1805
PubMed Link to Article
Festa  AD'Agostino  R  JrTracy  RPHaffner  SM Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes 2002;51 (4) 1131- 1137
PubMed Link to Article
Ford  ES Leukocyte count, erythrocyte sedimentation rate, and diabetes incidence in a national sample of US adults. Am J Epidemiol 2002;155 (1) 57- 64
PubMed Link to Article
Freeman  DJNorrie  JCaslake  MJ  et al.  C-reactive protein is an independent predictor of risk for the development of diabetes in the West of Scotland Coronary Prevention Study. Diabetes 2002;51 (5) 1596- 1600
PubMed Link to Article
Han  TSSattar  NWilliams  KGonzalez-Villalpando  CLean  MEHaffner  SM Prospective study of C-reactive protein in relation to the development of diabetes and metabolic syndrome in the Mexico City Diabetes Study. Diabetes Care 2002;25 (11) 2016- 2021
PubMed Link to Article
Hu  FBMeigs  JBLi  TYRifai  NManson  JE Inflammatory markers and risk of developing type 2 diabetes in women. Diabetes 2004;53 (3) 693- 700
PubMed Link to Article
Krakoff  JFunahashi  TStehouwer  CD  et al.  Inflammatory markers, adiponectin, and risk of type 2 diabetes in the Pima Indian. Diabetes Care 2003;26 (6) 1745- 1751
PubMed Link to Article
Nakanishi  NYoshida  HMatsuo  YSuzuki  KTatara  K White blood-cell count and the risk of impaired fasting glucose or type II diabetes in middle-aged Japanese men. Diabetologia 2002;45 (1) 42- 48
PubMed Link to Article
Pradhan  ADManson  JERifai  NBuring  JERidker  PM C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 2001;286 (3) 327- 334
PubMed Link to Article
Schmidt  MIDuncan  BBSharrett  AR  et al.  Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study. Lancet 1999;353 (9165) 1649- 1652
PubMed Link to Article
Snijder  MBDekker  JMVisser  M  et al.  Prospective relation of C-reactive protein with type 2 diabetes: response to Han et al. Diabetes Care 2003;26 (5) 1656- 1657
PubMed Link to Article
Spranger  JKroke  AMohlig  M  et al.  Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Diabetes 2003;52 (3) 812- 817
PubMed Link to Article
Thorand  BLowel  HSchneider  A  et al.  C-reactive protein as a predictor for incident diabetes mellitus among middle-aged men: results from the MONICA Augsburg cohort study, 1984-1998. Arch Intern Med 2003;163 (1) 93- 99
PubMed Link to Article
Laaksonen  DENiskanen  LNyyssonen  K  et al.  C-reactive protein and the development of the metabolic syndrome and diabetes in middle-aged men. Diabetologia 2004;47 (8) 1403- 1410
PubMed Link to Article
Albert  MAGlynn  RJBuring  JRidker  PM C-reactive protein levels among women of various ethnic groups living in the United States (from the Women's Health Study). Am J Cardiol 2004;93 (10) 1238- 1242
PubMed Link to Article
Women's Health Initiative Study Group, Design of the Women's Health Initiative clinical trial and observational study. Control Clin Trials 1998;19 (1) 61- 109
PubMed Link to Article
Anderson  GLManson  JWallace  R  et al.  Implementation of the Women's Health Initiative study design. Ann Epidemiol 2003;13 (9) ((suppl)) S5- S17
PubMed Link to Article
Prentice  RLMoolgavkar  SHFarewell  VT Biostatistical issues and concepts in epidemiologic research. J Chronic Dis 1986;39 (12) 1169- 1183
PubMed Link to Article
Black  SKushner  ISamols  D C-reactive protein. J Biol Chem 2004;279 (47) 48487- 48490
PubMed Link to Article
Mohamed-Ali  VGoodrick  SRawesh  A  et al.  Subcutaneous adipose tissue releases interleukin-6, but not tumor necrosis factor-alpha, in vivo. J Clin Endocrinol Metab 1997;82 (12) 4196- 4200
PubMed
Yudkin  JSKumari  MHumphries  SEMohamed-Ali  V Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link? Atherosclerosis 2000;148 (2) 209- 214
PubMed Link to Article
Venugopal  SKDevaraj  SYuhanna  IShaul  PJialal  I Demonstration that C-reactive protein decreases eNOS expression and bioactivity in human aortic endothelial cells. Circulation 2002;106 (12) 1439- 1441
PubMed Link to Article
Verma  SWang  CHLi  SH  et al.  A self-fulfilling prophecy: C-reactive protein attenuates nitric oxide production and inhibits angiogenesis. Circulation 2002;106 (8) 913- 919
PubMed Link to Article
Pasceri  VWillerson  JTYeh  ET Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation 2000;102 (18) 2165- 2168
PubMed Link to Article
Pinkney  JHStehouwer  CDCoppack  SWYudkin  JS Endothelial dysfunction: cause of the insulin resistance syndrome. Diabetes 1997;46 ((suppl 2)) S9- S13
PubMed Link to Article
Bloomgarden  ZT Inflammation, atherosclerosis, and aspects of insulin action. Diabetes Care 2005;28 (9) 2312- 2319
PubMed Link to Article
Pickup  JC Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 2004;27 (3) 813- 823
PubMed Link to Article

Figures

Place holder to copy figure label and caption
Figure.

Multivariate relative risk (RR) of diabetes according to joint classifications of plasma levels of high-sensitivity C-reactive protein (hsCRP [to convert C-reactive protein to nanomoles per liter, multiply by 9.524]), tumor necrosis factor α receptor 2 (TNF-α–R2), and IL-6 (interleukin 6). Plasma levels of TNF-α–R2 and IL-6 were categorized into tertiles, respectively. Ranges of less than 1, 1 to 3, and greater than or equal to 3 mg/L were used for hsCRP levels. The adjusted RR of type 2 diabetes among women in each category is shown relative to women with the lowest tertiles for both biomarkers. The model was adjusted for matching factors, body mass index, alcohol intake, level of physical activity, cigarette smoking status, use or nonuse of postmenopausal hormone therapy, and presence or absence of family history of diabetes. A, Joint effect of TNF-α–R2 and IL-6. B, Joint effect of TNF-α–R2 and hsCRP. C, Joint effect of IL-6 and hsCRP.

Graphic Jump Location

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Study Population According to Ethnicity and Diabetes Status a
Table Graphic Jump LocationTable 2. Adjusted Relative Risk (RR) Estimates According to Plasma Levels of Inflammatory Cytokines Among Postmenopausal Women
Table Graphic Jump LocationTable 3. Ethnicity-Specific Relative Risk (RR) Estimates for Clinical Diabetes According to Plasma Levels of Inflammatory Cytokines a
Table Graphic Jump LocationTable 4. Interactions Between Inflammatory Markers and BMI and Waist Circumference on Diabetes Risk

References

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Wajchenberg  BL Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 2000;21 (6) 697- 738
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Wellen  KEHotamisligil  GS Inflammation, stress, and diabetes. J Clin Invest 2005;115 (5) 1111- 1119
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Abbatecola  AMFerrucci  LGrella  R  et al.  Diverse effect of inflammatory markers on insulin resistance and insulin-resistance syndrome in the elderly. J Am Geriatr Soc 2004;52 (3) 399- 404
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You  TRyan  ASNicklas  BJ The metabolic syndrome in obese postmenopausal women: relationship to body composition, visceral fat, and inflammation. J Clin Endocrinol Metab 2004;89 (11) 5517- 5522
PubMed Link to Article
Moon  YSKim  DHSong  DK Serum tumor necrosis factor-alpha levels and components of the metabolic syndrome in obese adolescents. Metabolism 2004;53 (7) 863- 867
PubMed Link to Article
Weyer  CYudkin  JSStehouwer  CDSchalkwijk  CGPratley  RETataranni  PA Humoral markers of inflammation and endothelial dysfunction in relation to adiposity and in vivo insulin action in Pima Indians. Atherosclerosis 2002;161 (1) 233- 242
PubMed Link to Article
Festa  AD'Agostino  R  JrHoward  GMykkanen  LTracy  RPHaffner  SM Chronic subclinical inflammation as part of the insulin resistance syndrome: the Insulin Resistance Atherosclerosis Study (IRAS). Circulation 2000;102 (1) 42- 47
PubMed Link to Article
Fröhlich  MImhof  ABerg  G  et al.  Association between C-reactive protein and features of the metabolic syndrome: a population-based study. Diabetes Care 2000;23 (12) 1835- 1839
PubMed Link to Article
Ridker  PMBuring  JECook  NRRifai  N C-reactive protein, the metabolic syndrome, and risk of incident cardiovascular events: an 8-year follow-up of 14 719 initially healthy American women. Circulation 2003;107 (3) 391- 397
PubMed Link to Article
Rutter  MKMeigs  JBSullivan  LMD'Agostino  RB  SrWilson  PW C-reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation 2004;110 (4) 380- 385
PubMed Link to Article
Yudkin  JSStehouwer  CDEmeis  JJCoppack  SW C-reactive protein in healthy subjects: associations with obesity, insulin resistance, and endothelial dysfunction: a potential role for cytokines originating from adipose tissue? Arterioscler Thromb Vasc Biol 1999;19 (4) 972- 978
PubMed Link to Article
Temelkova-Kurktschiev  TSiegert  GBergmann  S  et al.  Subclinical inflammation is strongly related to insulin resistance but not to impaired insulin secretion in a high risk population for diabetes. Metabolism 2002;51 (6) 743- 749
PubMed Link to Article
Hak  AEPols  HAStehouwer  CD  et al.  Markers of inflammation and cellular adhesion molecules in relation to insulin resistance in nondiabetic elderly: the Rotterdam study. J Clin Endocrinol Metab 2001;86 (9) 4398- 4405
PubMed Link to Article
Müller  SMartin  SKoenig  W  et al.  Impaired glucose tolerance is associated with increased serum concentrations of interleukin 6 and co-regulated acute-phase proteins but not TNF-alpha or its receptors. Diabetologia 2002;45 (6) 805- 812
PubMed Link to Article
Barzilay  JIAbraham  LHeckbert  SR  et al.  The relation of markers of inflammation to the development of glucose disorders in the elderly: the Cardiovascular Health Study. Diabetes 2001;50 (10) 2384- 2389
PubMed Link to Article
Duncan  BBSchmidt  MIPankow  JS  et al.  Low-grade systemic inflammation and the development of type 2 diabetes: the atherosclerosis risk in communities study. Diabetes 2003;52 (7) 1799- 1805
PubMed Link to Article
Festa  AD'Agostino  R  JrTracy  RPHaffner  SM Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes 2002;51 (4) 1131- 1137
PubMed Link to Article
Ford  ES Leukocyte count, erythrocyte sedimentation rate, and diabetes incidence in a national sample of US adults. Am J Epidemiol 2002;155 (1) 57- 64
PubMed Link to Article
Freeman  DJNorrie  JCaslake  MJ  et al.  C-reactive protein is an independent predictor of risk for the development of diabetes in the West of Scotland Coronary Prevention Study. Diabetes 2002;51 (5) 1596- 1600
PubMed Link to Article
Han  TSSattar  NWilliams  KGonzalez-Villalpando  CLean  MEHaffner  SM Prospective study of C-reactive protein in relation to the development of diabetes and metabolic syndrome in the Mexico City Diabetes Study. Diabetes Care 2002;25 (11) 2016- 2021
PubMed Link to Article
Hu  FBMeigs  JBLi  TYRifai  NManson  JE Inflammatory markers and risk of developing type 2 diabetes in women. Diabetes 2004;53 (3) 693- 700
PubMed Link to Article
Krakoff  JFunahashi  TStehouwer  CD  et al.  Inflammatory markers, adiponectin, and risk of type 2 diabetes in the Pima Indian. Diabetes Care 2003;26 (6) 1745- 1751
PubMed Link to Article
Nakanishi  NYoshida  HMatsuo  YSuzuki  KTatara  K White blood-cell count and the risk of impaired fasting glucose or type II diabetes in middle-aged Japanese men. Diabetologia 2002;45 (1) 42- 48
PubMed Link to Article
Pradhan  ADManson  JERifai  NBuring  JERidker  PM C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 2001;286 (3) 327- 334
PubMed Link to Article
Schmidt  MIDuncan  BBSharrett  AR  et al.  Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study. Lancet 1999;353 (9165) 1649- 1652
PubMed Link to Article
Snijder  MBDekker  JMVisser  M  et al.  Prospective relation of C-reactive protein with type 2 diabetes: response to Han et al. Diabetes Care 2003;26 (5) 1656- 1657
PubMed Link to Article
Spranger  JKroke  AMohlig  M  et al.  Inflammatory cytokines and the risk to develop type 2 diabetes: results of the prospective population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study. Diabetes 2003;52 (3) 812- 817
PubMed Link to Article
Thorand  BLowel  HSchneider  A  et al.  C-reactive protein as a predictor for incident diabetes mellitus among middle-aged men: results from the MONICA Augsburg cohort study, 1984-1998. Arch Intern Med 2003;163 (1) 93- 99
PubMed Link to Article
Laaksonen  DENiskanen  LNyyssonen  K  et al.  C-reactive protein and the development of the metabolic syndrome and diabetes in middle-aged men. Diabetologia 2004;47 (8) 1403- 1410
PubMed Link to Article
Albert  MAGlynn  RJBuring  JRidker  PM C-reactive protein levels among women of various ethnic groups living in the United States (from the Women's Health Study). Am J Cardiol 2004;93 (10) 1238- 1242
PubMed Link to Article
Women's Health Initiative Study Group, Design of the Women's Health Initiative clinical trial and observational study. Control Clin Trials 1998;19 (1) 61- 109
PubMed Link to Article
Anderson  GLManson  JWallace  R  et al.  Implementation of the Women's Health Initiative study design. Ann Epidemiol 2003;13 (9) ((suppl)) S5- S17
PubMed Link to Article
Prentice  RLMoolgavkar  SHFarewell  VT Biostatistical issues and concepts in epidemiologic research. J Chronic Dis 1986;39 (12) 1169- 1183
PubMed Link to Article
Black  SKushner  ISamols  D C-reactive protein. J Biol Chem 2004;279 (47) 48487- 48490
PubMed Link to Article
Mohamed-Ali  VGoodrick  SRawesh  A  et al.  Subcutaneous adipose tissue releases interleukin-6, but not tumor necrosis factor-alpha, in vivo. J Clin Endocrinol Metab 1997;82 (12) 4196- 4200
PubMed
Yudkin  JSKumari  MHumphries  SEMohamed-Ali  V Inflammation, obesity, stress and coronary heart disease: is interleukin-6 the link? Atherosclerosis 2000;148 (2) 209- 214
PubMed Link to Article
Venugopal  SKDevaraj  SYuhanna  IShaul  PJialal  I Demonstration that C-reactive protein decreases eNOS expression and bioactivity in human aortic endothelial cells. Circulation 2002;106 (12) 1439- 1441
PubMed Link to Article
Verma  SWang  CHLi  SH  et al.  A self-fulfilling prophecy: C-reactive protein attenuates nitric oxide production and inhibits angiogenesis. Circulation 2002;106 (8) 913- 919
PubMed Link to Article
Pasceri  VWillerson  JTYeh  ET Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation 2000;102 (18) 2165- 2168
PubMed Link to Article
Pinkney  JHStehouwer  CDCoppack  SWYudkin  JS Endothelial dysfunction: cause of the insulin resistance syndrome. Diabetes 1997;46 ((suppl 2)) S9- S13
PubMed Link to Article
Bloomgarden  ZT Inflammation, atherosclerosis, and aspects of insulin action. Diabetes Care 2005;28 (9) 2312- 2319
PubMed Link to Article
Pickup  JC Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 2004;27 (3) 813- 823
PubMed Link to Article

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