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Clinical Chemistry 53: 2152-2159, 2007. First published October 19, 2007; 10.1373/clinchem.2007.088930
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(Clinical Chemistry. 2007;53:2152-2159.)
© 2007 American Association for Clinical Chemistry, Inc.

Endocrinology and Metabolism

Association of C-Reactive Protein with Surrogate Measures of Insulin Resistance among Nondiabetic US Adults: Findings from National Health and Nutrition Examination Survey 1999–2002

Yuan-Xiang Meng1,a, Earl S. Ford2, Chaoyang Li2, Alexander Quarshie3, Ahmad M. Al-Mahmoud3, Wayne Giles2, Gary H. Gibbons4 and Gregory Strayhorn1

1 Department of Family Medicine, Morehouse School of Medicine, Atlanta, GA.
2 Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA.
3 Clinical Research Center and 4 Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA.

aAddress correspondence to this author at: Yuan-Xiang Meng, Department of Family Medicine, Morehouse School of Medicine, 1513 East Cleveland Ave., Bldg. 100, Suite 300A, East Point, GA 30344. Fax 404-756-1229; e-mail ymeng{at}msm.edu.


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Background: Increased C-reactive protein (CRP) concentration and insulin resistance (IR) are associated with increased rates of adverse cardiovascular events. We sought to examine the relationship of CRP with surrogate measures of IR among nondiabetic adults in the US.

Methods: We conducted analyses using data from the National Health and Nutrition Examination Survey 1999–2002. We analyzed a nationally representative sample of 2514 men and nonpregnant women age ≥20 years who were non-Hispanic white, non-Hispanic black, or Mexican American.

Results: After adjustment for age, sex, race/ethnicity, smoking status, systolic blood pressure, and serum concentrations of HDL cholesterol, LDL cholesterol, and triglyceride, CRP was significantly associated with 10 IR measures (all P values <0.01). The strength of the association attenuated after further adjustment for waist circumference (change in adjusted regression coefficients ranging from 60.0% to 75.1%). The association of CRP with each IR surrogate was similar (standardized regression coefficient ranges from 0.06 to 0.09). The association of CRP (>3 vs <1 mg/L) with the homeostasis model for assessment of IR (≥75th vs <75th percentile) was statistically significant among people with a body mass index ≥30 kg/m2 (odds ratio, 2.6; 95% CI, 1.3–5.1) or with a body mass index <25 kg/m2 (odds ratio, 2.5; 95% CI, 1.5–4.2).

Conclusions: CRP was significantly associated with the surrogate measures of IR among nondiabetic adults. Obesity may play an important role in the association of CRP with IR in this nationally representative sample.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
Insulin resistance (IR)1 is a pathophysiological state characterized by a subnormal physiological response to insulin concentrations within reference intervals. Experimental and epidemiological studies have demonstrated a strong association of IR with many diseases or metabolic abnormalities, including coronary heart disease, stroke, type 2 diabetes, hypertension, dyslipidemia, systemic inflammation, and atherogenesis(1)(2).

It is estimated that approximately 20.6 million adults aged 20 years or older in the US have diabetes, with the majority of these having type 2 diabetes in 2005(3). The prevalence of hyperinsulinemia has increased by 35% among nondiabetic adults in the US in the past decade(4). It is believed that IR and subsequently compensatory hyperinsulinemia develop much earlier than β-cell dysfunction and may exist and progress years before even prediabetes would be diagnosed by the detection of impaired fasting glucose or impaired glucose tolerance. Several studies have demonstrated that early interventions, including lifestyle modification and pharmacological treatment, can effectively delay the onset of diabetes in prediabetic individuals and therefore decrease the incidence of cardiovascular disease and other diabetes-related chronic illnesses(5)(6). Thus, early identification of individuals who have developed IR is particularly important in clinical practice, but diagnosis is challenging.

Although IR can be determined by a variety of methods, they are difficult to apply in daily clinical practice, particularly in outpatient care settings. Evidence indicates that compensatory hyperinsulinemia is highly correlated with IR(7) and may offer a more useful way to identify insulin-resistant patients than measurements of glucose intolerance. However, analytic methods for insulin measurements are not standardized, and it is difficult to relate absolute values of plasma insulin concentrations from one laboratory to another(8).

C-reactive protein (CRP) is a marker for systemic subclinical inflammation and may have prognostic value in identifying persons who are at an increased risk of developing type 2 diabetes and subsequent cardiovascular complications(9). Given its ease of measurement, biological stability, and improved high-sensitivity method, CRP may be useful as a clinical measure for identifying individuals at risk for IR(10). CRP has been associated with increased adiposity(11), but only a few studies have demonstrated that CRP is associated with IR independent of obesity(12).

Because it is not feasible to directly measure IR in large epidemiologic studies, surrogate measures using fasting insulin or the combination of fasting insulin with fasting glucose or triglyceride (TG) have been proposed(13)(14)(15)(16)(17)(18)(19)(20)(21). Little is known, however, about the association of CRP with different surrogate measures of IR. In this study, we used data from the National Health and Nutrition Examination Survey (NHANES) 1999–2002 to examine the association of CRP with a series of surrogate measures of IR.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
study design and participants
NHANES 1999–2002 is a stratified, multistage probability sample of the civilian noninstitutionalized US population. Trained interviewers, using a computer-assisted personal survey system, spoke to participants at home. Respondents were then asked to attend a mobile examination center, where they completed additional questionnaires, underwent various examinations, and provided blood samples. Details of the NHANES 1999–2002 surveys may be found elsewhere(22)(23). This analysis was limited to men and nonpregnant women who were ≥20 years old, were non-Hispanic white, non-Hispanic black, or Mexican-American Hispanics, did not use hormone therapy, had no diagnosed diabetes or hypoglycemia medications or fasting glucose ≥126 mg/dL (7.0 mmol/L), had morning blood drawn with fasting time ≥8 h, and had CRP concentration ≤10 mg/L (n = 4040).

procedures
Detailed descriptions of blood collection and processing have been previously provided(24). In brief, serum specimens were frozen at <–70 °C, shipped on dry ice, and stored at <–70 °C until analysis. CRP concentrations were quantified at the University of Washington Medical Center, Seattle, WA, by latex-enhanced nephelometry, a high-sensitivity assay, on a BN II nephelometer (Dade Behring). Control materials of 2 concentrations from Bio-Rad Laboratories were used for QC purposes, and CVs ranged from 4.93% to 7.84%.

Serum insulin concentration was measured using an RIA reagent set from Pharmacia Diagnostics. The cross-reactivity of Pharmacia insulin antibody with proinsulin is approximately 40%. All insulin assays for NHANES 1999–2002 were performed by the same laboratory at the University of Missouri-Columbia. Identical laboratory procedures for insulin assays and their QC were performed. The overall CVs were 3.3%–5.4% in NHANES 1999–2002. Plasma glucose concentration was measured using an enzymatic reaction. Serum TG concentration was measured enzymatically after hydrolyzation to glycerol, and HDL cholesterol (HDL-C) was measured after the precipitation of other lipoproteins with a heparin-manganese chloride mixture.

Up to 4 blood pressure readings were obtained in the mobile examination center. The mean of the last 2 measurements for participants who had 3 or 4 measurements, the last measurement for participants with 2 measurements, and the single measurement for participants who had only 1 measurement were used to establish blood pressure status. Body mass index [BMI = weight (kg)/height (m)2] was calculated using measured weight and height, and BMIs were categorized into 3 groups (1, <25; 2, 25–29.9; and 3, ≥30) according to WHO criteria(25). Waist circumference was measured with a steel measuring tape to the nearest 0.1 cm at the high point of the iliac crest at minimal respiration.

surrogate measures of ir
A total of 10 surrogate measures of IR were used in this study, including fasting insulin, homeostasis model assessment (HOMA) of IR, log (1/HOMA), empirical fasting IR index, fasting insulin:fasting glucose ratio, Raynaud index, Bennetts index, quantitative insulin sensitivity check index, Avignon index, and McAuley index (see Appendix ). Nine indices were derived from the measures of fasting insulin and fasting glucose or TG. The formulas for calculating these indices are provided in the Appendix . In addition, we included the C-peptide concentrations as a marker of insulin secretion because it is cosecreted with insulin in equimolar amounts but is not subject to hepatic clearance(26).


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  		Table 5. Appendix Formulas for calculating selected surrogate measures of IR1

statistical analysis
We assessed continuous variables for gaussian distribution and performed logarithm transformation for fasting insulin, CRP, and C-peptide to approximate a gaussian distribution. Means and Pearson correlation coefficients were calculated for all surrogate measures of IR and CRP. Multiple linear regression analyses were performed to assess the association of CRP with IR surrogate measures, adjusting for potential confounders including age, sex, race/ethnicity, smoking status, systolic blood pressure (SBP), and concentrations of HDL-C, LDL cholesterol (LDL-C), and TG. Additional adjustment for waist circumference was made to examine the role of central obesity in the association. Using the standardized score of dependent and independent variables, a standardized regression coefficient of CRP for each IR measure was calculated to facilitate the comparisons of association between CRP and the IR surrogate measures with different units.

Because no standardization of insulin assay is available, a universal cutoff value of fasting insulin or HOMA to define IR is not available. Therefore, we defined the HOMA-IR using the 75th percentile of HOMA generated among nondiabetic adults in NHANES III as a cutoff value according to the suggestion of the European Group for the Study of Insulin Resistance(27). In addition, we categorized CRP into 3 groups (<1, 1 to 3, and >3 mg/L)(28). To examine the role of obesity for the association of CRP with IR surrogate measures, odds ratios and 95% CI of CRP for HOMA-IR were estimated in multiple logistic regression models stratified by the 3 BMI categories. An {alpha} of 0.05 was used to define statistical significance for 2-sided tests. All analyses were conducted using SAS (version 8.2) and SUDAAN software (Release 9.0, Research Triangle Institute), to account for the complex sampling design.


   Results
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Among participants who met inclusion criteria for our analyses (n = 4040), we further excluded those with missing data for fasting insulin, glucose, C-peptide, TG, HDL-C, or LDL-C (n = 1392), and for smoking, SBP, weight, height, or waist circumference (n = 134). The final analytic sample (n = 2514; 62.3%) comprised 56.7% males, 79.7% whites, 12.0% blacks, and 8.3% Mexican Americans. The percentage of current smokers was 25.9%, 27.3%, and 23.7% for white, black, and Mexican-American respondents, respectively. As shown in Table 1 , black and Mexican-American participants were younger than whites (P <0.0001). Compared to white participants, blacks had higher SBP (P <0.01), fasting insulin (P <0.01), HDL-C (P <0.01), and body weight (P <0.01), but lower fasting glucose (P <0.01), C-peptide (P <0.01), TG (P <0.0001), and LDL-C (P <0.01). In contrast, Mexican Americans had lower SBP (P <0.01), HDL-C (P <0.01), LDL-C (P <0.01), and body weight (P <0.01), but higher fasting insulin (P <0.0001) than whites. There were no significant differences in geometric means of CRP (P = 0.38) and waist circumferences (P = 0.45) among race/ethnic groups. Black participants had higher mean fasting insulin:fasting glucose ratios and McAuley indices than whites.


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  		Table 1. Means or percentages1 of demographic characteristics, variables, and surrogate measures of IR by race or ethnicity among US adults (age ≥20 years), NHANES 1999–2002.

The unadjusted Pearson correlation coefficients of CRP with the 10 measures of IR ranged from 0.32 to 0.39 among whites (P <0.001), from 0.28 to 0.36 among blacks (P <0.01), and from 0.29 to 0.36 among Mexican Americans (P <0.001) (Table 2 ). The correlation coefficients were not statistically significant between CRP and log fasting insulin, HOMA, log (1/HOMA), empirical fasting IR index, and fasting insulin:fasting glucose ratio across the 3 racial or ethnic groups. In contrast, the correlation coefficients were statistically significant between CRP and Raynaud index, Bennetts index, quantitative insulin sensitivity check index, Avignon index, and McAuley index across the 3 racial or ethnic groups.


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  		Table 2. Pearson correlation coefficients1 between CRP and surrogate measures of IR among US adults (aged ≥20 years), NHANES 1999–2002.

Multiple linear regression analyses showed that CRP was significantly associated with all 10 measures of IR after adjustment for age, sex, race or ethnicity, smoking status, SBP, HDL-C, LDL-C, TG (except for the McAuley index) (model 1; Table 3 ). The associations were attenuated after further adjustment for waist circumference (model 2; Table 3 ). The change in adjusted regression coefficient of CRP on IR surrogate measures between model 1 and model 2 ranged from 60.0% to 75.1%. The partial R2 of CRP decreased after additional adjustment for waist circumference.


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  		Table 3. Association of CRP with surrogate measures of IR among US adults (age ≥20 years), NHANES 1999–2002.

The associations of CRP with the surrogate measures of IR were statistically significant among participants with BMI <25 kg/m2 (P ranged from <0.001 to 0.005) and among participants with BMI ≥30 kg/m2 (P <0.001 for all measures), but not statistically significant among participants whose BMI was between 25 and <30 kg/m2 (P ranged from 0.18 to 0.67) (Table 4 ).


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  		Table 4. Association of CRP with surrogate measures of IR by BMI categories among US adults (aged ≥20 years), NHANES 1999–2002.

Participants with CRP >3 mg/L and BMI ≥30 kg/m2 had the highest prevalence of IR (76.2%), whereas those with CRP <1 mg/L and BMI <25 kg/m2 had the lowest prevalence of IR (6.6%) (Fig. 1 ). After adjustment for age, sex, race/ethnicity, smoking status, SBP, serum concentrations of HDL-C, LDL-C, and TG, the association of CRP (>3 vs <1 mg/L) with HOMA-IR (≥75th vs <75th percentile) was statistically significant among people with a BMI ≥30 kg/m2 (odds ratio, 2.6; 95% CI, 1.3–5.1) or with a BMI <25 kg/m2 (odds ratio, 2.5; 95% CI, 1.5–4.2). The linear trend in the odds ratios of CRP (<1, 1 to 3, >3 mg/L) for HOMA IR was statistically significant among people with a BMI <25 kg/m2 (P = 0.0003) and with a BMI ≥30 kg/m2 (P = 0.01), but not statistically significant among people with a BMI between 25 and <30 kg/m2 (P = 0.77). There was a marginally significant interaction between CRP and BMI on HOMA-IR (P = 0.07).


Figure 1
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  		Figure 1. Age-adjusted prevalence of insulin resistance by the categories of CRP and body mass index.

Insulin resistance was defined using the 75th percentile of HOMA among nondiabetic adults in NHANES III.


   Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
Using the recent nationally representative sample, we found that CRP was significantly associated with all 10 surrogate measures of IR among nondiabetic US adults. This association appeared to be consistent across the 3 racial or ethnic groups. Approximately 60%–75% of the variance for the association of CRP with IR was explained by central obesity status as measured by waist circumference. Among obese participants with a high concentration of CRP (>3 mg/L), approximately 76% had IR as defined using the 75th percentile of HOMA. More importantly, our data showed that CRP was significantly associated with surrogate measures of IR among people with a BMI <25 kg/m2.

The unique results of our study are the consistent significant associations of CRP with all 10 surrogate IR measures and across the 3 racial or ethnic groups. Among the 10 surrogate measures of IR, CRP appeared to be more strongly associated with fasting insulin, Raynaud index, quantitative insulin sensitivity check index, and McAuley index. The common feature of these 4 IR indices is that they involve only fasting insulin or an addition of fasting glucose or TG, suggesting that the fasting glucose concentration does not always correctly reflect the status of IR or insulin action. Previous studies have shown that fasting insulin alone may be a simple and effective surrogate measure of IR(29). Our results are consistent with these studies in that fasting insulin was at least as good as other complex indices of IR in relation to CRP.

Our results were consistent with previous studies that have examined associations of some measures of IR, such as fasting insulin and HOMA, with CRP in diabetic patients(30) and nondiabetic Asians(31). It is interesting that although measurements of fasting glucose concentrations are necessary for the diagnosis of prediabetes and diabetes according to the current clinical guidelines, in nondiabetic individuals these measurements may not be necessary for identifying IR, whereas measurements of fasting insulin can be quite useful. Commercial laboratories should consider reporting not only the absolute insulin concentration but also information as to where the value falls within the laboratory’s frequency distribution of nondiabetic individuals.

As shown in our study, approximately 60%–75% of the variance for the association between CRP and IR can be explained by waist circumference, suggesting that central obesity plays an important role in the association of CRP with IR. Numerous studies have shown that both CRP and IR are related to obesity(28)(32)(33)(34). However, whether obesity plays a role as a moderator or mediator for the association of CRP with IR is still elusive. Recent studies have demonstrated that obesity was a major determinant for the association of CRP with metabolic syndrome among patients with type 2 diabetes(35) and in the general adult population(36). Our results add further support for the notion that central obesity as measured by waist circumference or overall obesity as measured by BMI could be a mediator for the association of CRP with IR among nondiabetic adults.

In addition, consistent with recent studies(34), we found that this relationship was also significant among people who had normal weight (BMI <25 kg/m2). Although the mechanism for the association between CRP and IR is not fully understood, study evidence suggests that low-grade, chronic inflammation state may lead to IR because of the role of inflammatory cytokines released from adipocytes(34). This and other previous studies(34) reporting the association of CRP with IR independent of obesity suggest that another pathway linking inflammation and IR among nonobese individuals is also plausible. In the high IR tertile, about 1 in 6 people was of normal weight(37); thus the search for clinically useful biomarkers of IR among this subpopulation is necessary. Although BMI, a less expensive and convenient measure, may serve as an indicator for IR among people with excessive weight, CRP could be 1 of the novel biomarkers for IR among people with normal weight.

One of the interesting findings in our study was the lack of association between CRP and surrogate measures of IR among people who were overweight (i.e., BMI between 25 and 29.9 kg/m2). The exact physiological and biochemical mechanisms for this observation are unknown, but this finding might be attributable to the poor discriminatory power of BMI for body fat and lean mass. A recent study demonstrated that a BMI ≥30 kg/m2 had good specificity but poor sensitivity, whereas a BMI ≥25 kg/m2 had good sensitivity but poor specificity to detect obesity as defined by body fat >25% in men and 35% in women(38). Small increases of BMI as seen in overweight people could be due to increases in body fat or increments in lean mass or both. Preserved and increased lean mass have been associated with better fitness and exercise capacity, whereas excessive body fatness has been associated with adverse metabolic profiles(39). Further studies are warranted to examine the mechanisms for the interrelations of CRP, IR, and body composition. Alternative methods might be needed to accurately characterize people who truly have excessive body fat vs those who have increased muscle mass, especially when their BMI is mildly increased.

Our study has several strengths. First, we used 10 surrogate measures of IR and C-peptide as a measurement of β-cell function(26) to examine the association of CRP with IR and insulin secretion. Our results showed that the association appeared to be consistent using any of these measures. Second, we used a large representative sample of US adults; therefore, we were able to conduct analyses stratified by race or ethnicity or body weight status. Our results indicated that the association of CRP with IR is potentially generalizable across different racial or ethnic groups because the association between CRP and some surrogate measures of IR (e.g., fasting insulin and HOMA) appeared to be similar by race/ethnicity.

There are several limitations in the present study. First, we used a cross-sectional design; therefore the observed association between CRP and surrogate measures of IR may not be assumed causal. It is likely that IR could contribute to the increase of CRP(33). Second, we did not have a direct measure of IR in our data; thus, we were unable to validate the surrogate measures. Previous evaluation studies have shown that simple indices, particularly fasting insulin, are valid and reliable surrogate measures of IR in large epidemiologic studies. The 3rd limitation was related to the use of a single insulin assay measured with the Pharmacia Insulin RIA reagent set. Because no standardization of insulin assays is available thus far, caution may be needed when comparing our results with other assays. The cross-reactivity of Pharmacia insulin antibody with proinsulin (approximately 40%) may overestimate the true insulin concentrations in the population. However, because proinsulin concentration is relatively low among people without diabetes(40), the impact of cross-reactivity between insulin and proinsulin on our results could be minimal.

In conclusion, because we are unable to routinely measure IR in clinical practice, efforts to find simple measures for IR are ongoing. One of the clinical challenges of identifying individuals with IR is the cumbersome nature of the assays. Obesity, particularly central obesity, plays an important role for the association between CRP and IR; however, it is conceivable that the use of CRP in clinical settings could facilitate an earlier identification of IR among people who may not achieve certain clinical thresholds for measures of adiposity or fasting glucose. In addition, by providing a simple and cost-effective way to identify high-risk individuals with IR, the measurement of CRP concentration might facilitate preventive interventions in patients who are at greatest risk of developing diabetes, the metabolic syndrome, or cardiovascular complications.


   Acknowledgments
 
Grant/funding support: Y.-X.M. was a Clinical Research Education and Career Development fellow partially supported by National Institutes of Health Grant 1R25RR017694-01.

Financial disclosures: None declared.

Acknowledgments: We thank the Morehouse School of Medicine Master of Science in Clinical Research Program and Department of Family Medicine.


   Footnotes
 
The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.

1 Nonstandard abbreviations: IR, insulin resistance; CRP, C-reactive protein; TG, triglyceride; HDL-C, HDL cholesterol; BMI, body mass index; HOMA, homeostasis model assessment; SBP, systolic blood pressure; LDL-C, LDL cholesterol.


   References
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 References
 

  1. Kernan WN, Inzucchi SE, Viscoli CM, Brass LM, Bravata DM, Horwitz RI. Insulin resistance and risk for stroke. Neurology 2002;59:809-815.[Abstract/Free Full Text]
  2. Reaven GM. Banting Lecture 1988. Role of insulin resistance in human disease. Diabetes 1988;37:1595-1607.[Abstract]
  3. . Centers for Disease Control and Prevention. National diabetes fact sheet: general information and national estimates on diabetes in the United States, 2005 2005 U.S. Department of Health and Human Services, Center for Disease Control and Prevention Atlanta, GA. .
  4. Li C, Ford ES, McGuire LC, Mokdad AH, Little RR, Reaven GM. Trends in hyperinsulinemia among nondiabetic adults in the U.S. Diabetes Care 2006;29:2396-2402.[Abstract/Free Full Text]
  5. Esposito K, Pontillo A, Di Palo C, Giugliano G, Masella M, Marfella R, et al. Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA 2003;289:1799-1804.[Abstract/Free Full Text]
  6. Lindstrom J, Ilanne-Parikka P, Peltonen M, Aunola S, Eriksson JG, Hemio K, et al. Sustained reduction in the incidence of type 2 diabetes by lifestyle intervention: follow-up of the Finnish Diabetes Prevention Study. Lancet 2006;368:1673-1679.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  7. Yeni-Komshian H, Carantoni M, Abbasi F, Reaven GM. Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers. Diabetes Care 2000;23:171-175.[Abstract]
  8. Robbins DC, Andersen L, Bowsher R, Chance R, Dinesen B, Frank B, et al. Report of the American Diabetes Association’s Task Force on standardization of the insulin assay. Diabetes 1996;45:242-256.[Abstract]
  9. Ridker PM, Koenig W, Fuster V. C-reactive protein and coronary heart disease. N Engl J Med 2004;351:295-298.[CrossRef][Medline] [Order article via Infotrieve]
  10. Ridker PM, Wilson PW, Grundy SM. Should C-reactive protein be added to metabolic syndrome and to assessment of global cardiovascular risk?. Circulation 2004;109:2818-2825.[Abstract/Free Full Text]
  11. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Elevated C-reactive protein levels in overweight and obese adults. JAMA 1999;282:2131-2135.[Abstract/Free Full Text]
  12. Taniguchi A, Nagasaka S, Fukushima M, Sakai M, Okumura T, Yoshii S, et al. C-reactive protein and insulin resistance in non-obese Japanese type 2 diabetic patients. Metabolism 2002;51:1578-1581.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  13. Avignon A, Boegner C, Mariano-Goulart D, Colette C, Monnier L. Assessment of insulin sensitivity from plasma insulin and glucose in the fasting or post oral glucose-load state. Int J Obes Relat Metab Disord 1999;23:512-517.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  14. Duncan MH, Singh BM, Wise PH, Carter G, Alaghband-Zadeh J. A simple measure of insulin resistance. Lancet 1995;346:120-121.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  15. Hanson RL, Pratley RE, Bogardus C, Narayan KM, Roumain JM, Imperatore G, et al. Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol 2000;151:190-198.[Abstract/Free Full Text]
  16. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 2000;85:2402-2410.[Abstract/Free Full Text]
  17. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-419.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  18. McAuley KA, Williams SM, Mann JI, Walker RJ, Lewis-Barned NJ, Temple LA, et al. Diagnosing insulin resistance in the general population. Diabetes Care 2001;24:460-464.[Abstract/Free Full Text]
  19. Garcia-Estevez DA, Araujo-Vilar D, Fiestras-Janeiro G, Saavedra-Gonzalez A, Cabezas-Cerrato J. Comparison of several insulin sensitivity indices derived from basal plasma insulin and glucose levels with minimal model indices. Horm Metab Res 2003;35:13-17.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  20. Raynaud E, Perez-Martin A, Brun JF, Benhaddad AA, Mercier J. Revised concept for the estimation of insulin sensitivity from a single sample. Diabetes Care 1999;22:1003-1004.[Web of Science][Medline] [Order article via Infotrieve]
  21. Anderson RL, Hamman RF, Savage PJ, Saad MF, Laws A, Kades WW, et al. Exploration of simple insulin sensitivity measures derived from frequently sampled intravenous glucose tolerance (FSIGT) tests. The Insulin Resistance Atherosclerosis Study. Am J Epidemiol 1995;142:724-732.[Abstract/Free Full Text]
  22. Centers for Disease Control and Prevention. NHANES 1999–2000 public data release file documentation.http://www.cdc.gov/nchs/about/major/nhanes/nhanes99–00.htm (accessed December 15, 2005)..
  23. Centers for Disease Control and Prevention. NHANES 2001–2002 public data release file documentation.http://www.cdc.gov/nchs/about/major/nhanes/nhanes01–02.htm (accessed December 15, 2005)..
  24. . Centers for Disease Control and Prevention. The Third National Health and Nutrition Examination Survey (NHANES III 1988–94) reference manuals and reports 1996 National Center for Health Statistics Bethesda, MD. [CD-ROM].
  25. . World Health Organization. Physical Status: The Use and Interpretation of Anthropometry. Report of a WHO Expert Committee. Technical Report Series No. 854 1995 World Health Organization Geneva, Switzerland. .
  26. Polonsky KS, Pugh W, Jaspan JB, Cohen DM, Karrison T, Tager HS, et al. C-peptide and insulin secretion: relationship between peripheral concentrations of C-peptide and insulin and their secretion rates in the dog. J Clin Invest 1984;74:1821-1829.[Web of Science][Medline] [Order article via Infotrieve]
  27. Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 1999;16:442-443.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  28. Ridker PM. Clinical application of C-reactive protein for cardiovascular disease detection and prevention. Circulation 2003;107:363-369.[Free Full Text]
  29. Laakso M. How good a marker is insulin level for insulin resistance?. Am J Epidemiol 1993;137:959-965.[Abstract/Free Full Text]
  30. Iwasaki T, Nakajima A, Yoneda M, Terauchi Y. Relationship between the serum concentrations of C-reactive protein and parameters of adiposity and insulin resistance in patients with type 2 diabetes mellitus. Endocr J 2006;53:345-356.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  31. Nakanishi N, Shiraishi T, Wada M. Association between C-reactive protein and insulin resistance in a Japanese population: the Minoh Study. Intern Med 2005;44:542-547.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  32. Ford ES. Body mass index, diabetes, and C-reactive protein among U.S. adults. Diabetes Care 1999;22:1971-1977.[Abstract/Free Full Text]
  33. McLaughlin T, Abbasi F, Lamendola C, Liang L, Reaven G, Schaaf P, et al. Differentiation between obesity and insulin resistance in the association with C-reactive protein. Circulation 2002;106:2908-2912.[Abstract/Free Full Text]
  34. Yudkin JS, Stehouwer CD, Emeis JJ, Coppack 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;19:972-978.[Abstract/Free Full Text]
  35. Kahn SE, Zinman B, Haffner SM, O’Neill MC, Kravitz BG, Yu D, et al. Obesity is a major determinant of the association of C-reactive protein levels and the metabolic syndrome in type 2 diabetes. Diabetes 2006;55:2357-2364.[Abstract/Free Full Text]
  36. Santos AC, Lopes C, Guimaraes JT, Barros H. Central obesity as a major determinant of increased high-sensitivity C-reactive protein in metabolic syndrome. Int J Obes 2005;29:1452-1456.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  37. McLaughlin T, Allison G, Abbasi F, Lamendola C, Reaven G. Prevalence of insulin resistance and associated cardiovascular disease risk factors among normal weight, overweight, and obese individuals. Metabolism 2004;53:495-499.[CrossRef][Web of Science][Medline] [Order article via Infotrieve]
  38. Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ, Squires RW, et al. Diagnostic performance of body mass index to detect obesity in patients with coronary artery disease. Eur Heart J 2007;28:2087-2093.[Abstract/Free Full Text]
  39. Goldberg AP, Busby-Whitehead MJ, Katzel LI, Krauss RM, Lumpkin M, Hagberg JM. Cardiovascular fitness, body composition, and lipoprotein lipid metabolism in older men. J Gerontol A Biol Sci Med Sci 2000;55:M342-M349.[Abstract/Free Full Text]
  40. Reaven GM, Chen YD, Hollenbeck CB, Sheu WH, Ostrega D, Polonsky KS. Plasma insulin, C-peptide, and proinsulin concentrations in obese and nonobese individuals with varying degrees of glucose tolerance. J Clin Endocrinol Metab 1993;76:44-48.[Abstract]



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