Figures
Abstract
Background
Recent studies have suggested that mast-cell activation and inflammation are important in obesity and diabetes. Plasma levels of mast cell proteases and the mast cell activator immunoglobulin E (IgE) may serve as novel inflammatory markers that associate with the risk of pre-diabetes and diabetes mellitus.
Methods and Results
A total of 340 subjects 55 to 75 years of age were grouped according to the American Diabetes Association 2003 criteria of normal glucose tolerance, pre-diabetes, and diabetes mellitus. The Kruskal-Wallis test demonstrated significant differences in plasma IgE levels (P = 0.008) among groups with different glucose tolerance status. Linear regression analysis revealed significant correlations between plasma levels of chymase (P = 0.030) or IgE (P = 0.022) and diabetes mellitus. Ordinal logistic regression analysis showed that IgE was a significant risk factor of pre-diabetes and diabetes mellitus (odds ratio [OR]: 1.674, P = 0.034). After adjustment for common diabetes risk factors, including age, sex, hypertension, body-mass index, cholesterol, homeostatic model assessment (HOMA) index, high-sensitivity C-reactive protein (hs-CRP), and mast cell chymase and tryptase, IgE remained a significant risk factor (OR: 1.866, P = 0.015). Two-variable ordinal logistic analysis indicated that interactions between hs-CRP and IgE, or between IgE and chymase, increased further the risks of developing pre-diabetes and diabetes mellitus before (OR: 2.204, P = 0.044; OR: 2.479, P = 0.033) and after (OR: 2.251, P = 0.040; OR: 2.594, P = 0.026) adjustment for common diabetes risk factors.
Citation: Wang Z, Zhang H, Shen X-H, Jin K-L, Ye G-f, Qian L, et al. (2011) Immunoglobulin E and Mast Cell Proteases Are Potential Risk Factors of Human Pre-Diabetes and Diabetes Mellitus. PLoS ONE 6(12): e28962. https://doi.org/10.1371/journal.pone.0028962
Editor: Yiqing Song, Brigham & Women's Hospital, and Harvard Medical School, United States of America
Received: July 13, 2011; Accepted: November 17, 2011; Published: December 16, 2011
Copyright: © 2011 Wang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by awards from the Huzhou Municipal Science and Technology Agency (2008GS09) (HZ), the Zhejiang Province Department of Health (2008B178) (ZW), and the Zhejiang Province Department of Education (20070470) (X-HS); by grants from the National Institutes of Health, HL60942, HL81090, HL88547 (G-PS); and by EIA award 0840118N from the American Heart Association (G-PS). The funders had no role in the study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The development of diabetes mellitus — and in particular, of type 2 diabetes mellitus (type 2 DM) — involves multiple stages and is complicated by multiple factors, such as obesity and cardiovascular disease. The exact mechanisms leading to diabetes mellitus, however, remain unknown. Pre-diabetes refers to the intermediate states between normal glucose tolerance (NGT) and type 2 DM and is the precursor of type 2 DM. Individuals with pre-diabetes often bear clusters of cardiovascular disease risk factors [1]. In recent years, studies have focused on the inflammatory risk factors of pre-diabetes and type 2 DM [1]–[6]. Elevated plasma levels of inflammatory high-sensitivity C-reactive protein (hs-CRP) were seen in two Asian-population–based cohorts of pre-diabetes [7]. Mast cells are essential components of asthma and allergic responses [8], [9], but recent studies have shown that these cells are important in diet-induced obesity and type 2 DM. Mice lacking mast cells or receiving the mast cell inhibitors cromolyn or ketotifen (Zaditor) are fully protected from developing type 2 DM [10]. By releasing the mast-cell–specific serine proteases chymase [11] and trypase [12], these “allergy cells” contribute to neovascularization and vascular-cell apoptosis.
One of the best-known mechanisms of mast cell activation is the binding of immunoglobulin E (IgE) to its high-affinity receptor FcεR1 on the mast cell surface. After IgE binding, mast cells release histamine, mast cell protease, proteoglycan, cytokines, and chemokines [13], [14]. Many of these inflammatory mediators associate with diabetes mellitus [15]–[17]. Plasma levels of mast cell chymase and tryptase are greatly elevated in patients with acute myocardial infarction (AMI) or unstable angina pectoris (UAP) [18]. We recently showed that human plasma IgE levels also associate with coronary artery intima thickness and rupture. IgE levels in patients with AMI were double those in subjects with stable angina pectoris (SAP) or without coronary heart disease (CHD) [19]. In human atherosclerotic lesions, IgE and its receptor FcεR1 are localized to macrophage-rich areas, as well as to smooth-muscle cells (SMCs) and endothelial cells (ECs). In atherosclerosis-prone apolipoprotein E–deficient (Apoe−/−) mice, the absence of FcεR1 α-subunit reduced atherosclerotic lesion sizes by 75% in the thoracic to abdominal aorta, and by more than 50% in the aortic arch. Mechanistically, we demonstrated that IgE activated macrophage, EC, and SMC signaling transduction, apoptosis, and cytokine and chemokine production [19] — all of which participate in metabolic diseases [10], [20], [21]. The current study is designed to examine whether IgE and mast cell proteases associate with infammation and diabetes status in a Chinese population from a pre-diabetes study.
Materials and Methods
Study population
This study is part of the Pre-diabetes Intervention Project (PDIP), which began in 2008 at the School of Medicine, Huzhou Teacher's College, Zhejiang, China. From July to August 2008, a total of 3163 volunteers 55 to 75 years of age, from three neighborhood communities in the city of Huzhou, were invited for pre-diabetes screening. After excluding subjects with known DM or cardiovascular disease, cerebrovascular disease, malignant disease, chronic liver disease, or kidney failure, and those taking medications, 1500 volunteers were invited for a fasting glucose test and a 2-hour oral glucose tolerance test (2h-OGTT) as part of the pre-diabetes screening. Of those, 1197 accepted the invitation and participated in both tests from September to December 2008. Among the subjects, 807 (67.42%) had normal glucose tolerance (NGT), 267 (22.30%) were diagnosed with pre-diabetes, and 123 (10.28%) were diagnosed with DM. One year after the initial visit, 267 pre-diabetes subjects, 123 DM subjects, and 100 randomly-selected NGT subjects were invited for anthropometric measurements and clinical tests. A total of 340 subjects participated in the final study, and among them, 71 had normal glucose tolerance (NGT), 189 had pre-diabetes, and 80 had DM. This study was approved by the Huzhou City Ethics Committee, and all subjects gave written, informed consent prior to participating.
Data collection
Demographic data, including age and sex, were collected at the registry. Anthropometric measurements (body height and weight, waist and hip circumference) were performed with the subjects wearing light underwear and without shoes. The biochemical parameters were measured in the Clinical Biochemistry Unit of Huzhou First Hospital, a teaching hospital of the School of Medicine. All participants were fasted for 10–12 hours to determine plasma glucose levels, lipid profiles, and insulin levels. To perform 2h-OGTT, participants were asked not to smoke or to partake in vigorous exercise during 2 hours of sample collection, and rested for a minimum of 15 minutes. An antecubital blood sample was drawn immediately before and 2 hours after glucose ingestion. The glucose oxidase method was used (Japanese OLYMPUS AV640 Automatic Biochemical Analyzer) to measure plasma glucose concentration. Plasma hs-CRP levels were determined by a high-sensitivity latex-enhanced nephelometric assay (Sysmex Wuxi Co., Ltd., Wuxi, China). Plasma insulin (Abbott Japan Co., Ltd., Tokyo, Japan) and IgE (DiaSys Diagnostic Systems GmbH, Holzheim, Germany) levels were measured using a radioimmunoassay. Plasma total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were determined by an enzymatic assay (Beijing Leadman Biochemistry Co., Ltd., Beijing, China). Plasma chymase and tryptase levels were determined as described previously [18].
Clinical criteria
Subjects with diabetes and pre-diabetes were grouped according to American Diabetes Association 2003 criteria [22]. Diabetes was classified with a fasting plasma glucose (FPG) ≥7.0 mmol/L or 2h-OGTT ≥11.1 mmol/L, or receiving hypoglycemic medication; whereas pre-diabetes was defined as FPG ≥5.6 and <7.0 mmol/L or 2h-OGTT ≥7.8 and <11.1 mmol/L. Subjects were classified as having a normal glucose profile if FPG <5.6 mmol/L and 2h-OGTT <7.8 mmol/L.
Based on China 2006 Blood Pressure Control Criteria and China Prevention and Treatment Classification Recommendation on Dyslipidemia [23], hypertension was defined as systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) ≥140/90 mmHg, or as receiving blood-pressure–lowering medications; high TG was defined as a fasting plasma TG ≥1.70 mmol/L, low HDL-C as a fasting HDL-C ≤0.9 mmol/L, high TC as TC ≥5.72 mmol/L, and low LDL-C as a fasting LDL-C ≤3.64 mmol/L. Based on the China Obesity Task Group Recommendation [24], general obesity was classified as body-mass index (BMI) ≥24 kg/m2; central obesity was defined as a waist circumference (WC) ≥80 cm in females or 85 cm in males. High waist-to-hip ratio (WHR) was classified as normal/abnormal according to upper quartile (P75 = 0.93). Homeostatic model assessment–insulin resistance (HOMA-IR = value of FPG × value of fasting insulin/22.5) was classified as normal/abnormal according to upper quartile (P75 = 2.58), and HOMA-β cell function [HOMA-β = 20× value of fasting insulin/(FPG-3.5)] was classified as normal/abnormal according to bottom quartile (P25 = 38.17). Hyperinsulinemia was classified as normal/abnormal according to upper quartile (P75 = 9.38 mIU/L).
Statistical analysis
We used the mean and standard deviation of continuous and normal distributional variables and the median and interquartile range of continuous but skewed distributional variables. Data were analyzed using one-way analysis of variance (ANOVA), chi-square test, linear regression test, Kruskal-Wallis test, ordinal logistic model, or binary logistic model. All statistical analyses were conducted using SPSS statistical software (version 11.0).
Results
Population distribution
Basic characteristics of all 340 participants are shown in Table 1. All data with normal distributions were expressed as mean ± SD (standard deviation); otherwise, data were expressed as median and interquartile ranges. Sex was expressed as the percentage of males, and plasma tryptase levels were expressed as percentages of those among the upper quartile (>2.74 ng/mL). Of the 340 participants — 96 men (average age, 67.84±5.35 years) and 244 women (average age, 64.30±6.02 years) — 71 (20.9%) were classified as NGT subjects, 189 (55.6%) as pre-diabetes subjects, and 80 (23.6%) as diabetes subjects. One-way ANOVA, Kruskal-Wallis test, or chi-square test demonstrated that FPG, 2h-OGTT, HOMA-β, HOMA-IR, TG, WC, AC, WHR, hs-CRP, and IgE levels were significantly higher in patients with pre-diabetes or diabetes mellitus than in the normal glucose group (NGG). Compared with patients in the NGG, levels of FPG, 2h-OGTT, fasting insulin, HOMA-β, HOMA-IR, SBP, WHR, hs-CRP, and IgE were significantly higher in the pre-diabetes group (PDG); levels of FPG, 2h-OGTT, fasting insulin, HOMA-β, HOMA-IR, TC, TG, BMI, WC, AC, WHR, hs-CRP, IgE, and chymase also were significantly higher in the diabetes mellitus group (DMG) (Table 1). Linear regression analysis revealed significant correlations in levels of FPG, 2h-OGTT, fasting insulin, HOMA-β, HOMA-IR, TC, TG, HDL-C, BMI, WC, AC, WHR, IgE, and chymase among the NGG, PDG, and DMG (Table 1).
Plasma IgE, chymase, and tryptase as independent risk factors of pre-diabetes and diabetes mellitus
Ordinal logistic regression analysis examined whether IgE and mast cell protease chymase and tryptase are significant risk factors for pre-diabetes and diabetes mellitus in this Chinese population, compared with variables from the NGG. Four different models of adjustment were used, based on selections of similar variables: Models one and three used HOMA-IR, whereas models two and four used HOMA-β. Models one and two used WC as the obesity standard, whereas models three and four used BMI (Table 2). Before adjustment for any of the variables listed in Table 2, hypertension, WC, WHR, HOMA-IR, HOMA-β, hs-CRP, and IgE were significant risk factors for pre-diabetes and diabetes mellitus. After adjustment in all four models, however, only hypertension and HOMA-IR (or HOMA-β) remained significant risk factors for pre-diabetes and diabetes mellitus. Among the remaining variables, IgE remained a significant pre-diabetes and diabetes mellitus risk factor in three models of adjustment. In contrast, mast cell chymase and tryptase were not significant risk factors for pre-diabetes and diabetes mellitus before or after adjustment for all common diabetes variables (Table 2).
Binary logistic regression analysis allowed us to examine risk factors for either pre-diabetes or diabetes mellitus, compared with variables from the NGG. Before adjustment, hypertension, WC, WHR, hs-CRP, and IgE were significant risk factors for pre-diabetes, while high hypertension, WC, WHR, BMI, HOMA-β index, HOMA-IR index, and hs-CRP levels were significant risk factors for diabetes mellitus (Table S1). After adjusting for age, sex, hypertension, BMI, TC, TG, hyperinsulinemia, hs-CRP, IgE, tryptase, and chymase, only hs-CRP (OR: 3.814 [1.588–9.162, 95% CI], P = 0.003) and IgE (OR: 3.367 [1.379–8.197, 95% CI], P = 0.008) remained significant risk factors for pre-diabetes, whereas hypertension, WHR, BMI, TC, HOMA-β, HOMA-IR, hs-CRP, and IgE became significant risk factors for diabetes mellitus (Table S1).
Plasma hs-CRP, IgE, chymase, and tryptase as dependent risk factors of pre-diabetes and diabetes mellitus
As discussed, the inflammatory biomarker hs-CRP associates with diabetes mellitus [7], [15]. When hs-CRP was considered as an interaction variable with all other variables, we found that age, sex, hypertension, WC (or WHR, or BMI), TG, HOMA-β, and IgE were significant risk factors for pre-diabetes and diabetes mellitus, compared with those from the NGG, in an ordinal logistic regression model. In contrast, TC, HDL, LDL, HOMA-IR, tryptase, and chymase were not significant risk factors for pre-diabetes and diabetes mellitus. After adjusting these risk factors using model three (Table 2), both age and sex lost their significance as pre-diabetes and diabetes mellitus risk factors, but other variables remained significant (Table 3). These observations suggest that high plasma hs-CRP, in combination with high plasma IgE, increased the risk of developing pre-diabetes and diabetes mellitus with an odds ratio (OR) of 2.204 (1.020–4.759, 95% CI) before adjustment and an OR of 2.251 (1.037–4.885, 95% CI) after adjustment. We obtained different observations, however, when we used a binary logistic regression model to compare variables from the NGG to those from the PDG or the DMG. Except age and sex, all tested variables (hypertension, WC, WHR, BMI, TC, TG, low HDL, high LDL, hyperinsulinemia, HOMA-β, HOMA-IR, IgE, tryptase, and chymase) were significant risk factors for pre-diabetes, when interacting with high plasma hs-CRP levels before or after adjustment (Table S2). The same sets of variables also were significant risk factors for diabetes mellitus before adjustment. But after adjustment, only hypertension, WHR, TC, high LDL, HOMA-β, HOMA-IR, IgE, and tryptase remained significant risk factors for diabetes mellitus (Table S2).
Plasma IgE significantly increases in AMI patients, compared with SAP patients or non-CHD patients [19]. In this Chinese population, the interaction of plasma IgE level with hypertension, WC, WHR, BMI, TG, HOMA-β, and chymase showed significant changes in OR, compared with those from the NGG before or after adjustment for all listed diabetes mellitus risk factors (Table 4). This ordinal logistic model of two-variable interactions is important, particularly under complicated circumstances. For example, IgE levels impact the risk of pre-diabetes and diabetes mellitus (OR: 1.674 [1.039–2.698, 95% CI], P = 0.034), but chymase alone did not affect the risk of pre-diabetes and diabetes mellitus significantly (OR: 1.387 [0.858–2.244, 95% CI], P = 0.182) (Table 2). Interactions of chymase and IgE, however, greatly increased the OR before adjustment (OR: 2.479 [1.079–5.778, 95% CI], P = 0.033) and after adjustment (OR: 2.594 [1.118–6.018, 95% CI], P = 0.026) for all common diabetes mellitus risk factors (Table 4), suggesting a dependent contribution of plasma chymase and IgE to pre-diabetes and diabetes mellitus.
When we performed binary logistic regression analysis, we found that interactions of high IgE with hypertension, WC, WHR, TC, low HDL, high LDL, hyperinsulinemia, HOMA-β, HOMA-IR, and chymase significantly increased the risk of having pre-diabetes before and after adjustment, compared with those from the NGG (Table S3). When we compared the variables between the DMG and the NGG, we found that interactions between IgE and hypertension, WC, WHR, BMI, TC, TG, HOMA-β, and HOMA-IR were significant risk factors for diabetes mellitus. After adjustment, while interactions between IgE and hypertension, WHR, BMI, TC, HOMA-β, and HOMA-IR remained significant risk factors for diabetes mellitus, the interactions of IgE with low HDL, high LDL, chymase, and tryptase significantly increased the risk of having diabetes mellitus (Table S3).
We made similar conclusions after analyzing interactions between chymase and several other diabetes mellitus factors, including hypertension, WC, WHR, TG, HOMA-β and HOMA-IR (Table 5). Under all methods tested, however, plasma tryptase alone did not correlate with plasma glucose levels (Table 1), nor did it affect the risk of pre-diabetes and diabetes mellitus (OR: 1.283 [0.794–2.074, 95% CI], P = 0.309) (Table 2). Its interactions with hs-CRP (OR: 1.853 [0.859–4.000, 95% CI], P = 0.116) (Table 3), IgE (2.091 [0.946–4.625, 95% CI], P = 0.068) (Table 4), or chymase (OR: 1.220 [0.566–2.629], 95% CI], P = 0.612) (Table 5) did not significantly influence the risk of developing pre-diabetes and diabetes mellitus. Interactions of plasma tryptase with hypertension, WC, WHR, TC, TG, HOMA-β and HOMA-IR did, however, increase the OR in an ordinal logistic model significantly (Table 6).
When the NGG was compared independently with the PDG in a binary logistic model, we found that interactions between chymase and hypertension, WC, WHR, BMI, LDL, hyperinsulinemia, and tryptase were significant risk factors for pre-diabetes. After adjustment, interactions between chymase and WC, WHR, or HOMA-IR index affected the risk of pre-diabetes significantly (Table S4). When the NGG was compared with the DMG in the same model, interactions between chymase and hypertension, WHR, BMI, HOMA-β, HOMA-IR, and tryptase were significant risk factors for diabetes mellitus before and after adjustment (Table S4). When interactions between tryptase and other variables were considered in the binary logistic regression analysis, WC was the only significant risk factor for pre-diabetes before adjustment, and it lost its significance after adjustment (Table S5). When the NGG was compared with the DMG, however, interactions between tryptase and obesity variables (WC, WHR, and BMI), HOMA-β, and HOMA-IR were significant risk factors for diabetes mellitus, and BMI, HOMA-β, and HOMA-IR remained significant risk factors for diabetes mellitus after adjustment (Table S5).
Discussion
Accumulating evidence indicates that age, sex, obesity, hypertension, dyslipidemia, insulin resistance, decreased β-cell sensitivity, hyperinsulinemia, HbA1c, and hs-CRP are related to pre-diabetes and diabetes mellitus [25]–[36]. Inflammatory cytokines are important to the pathogenesis of pre-diabetes and type 2 DM [37]–[39]. This study reveals that both IgE and chymase correlate with glucose tolerance in humans. Interactions with other diabetes risk factors greatly increased the impact of these mast-cell–related molecules on pre-diabetes and diabetes mellitus.
Mast cell chymase and tryptase are important proteases that have been implicated in cardiovascular diseases. Patients with atherosclerosis and patients with abdominal aortic aneurysms (AAAs) have elevated levels of these proteases [11], [12], [18]. Mechnistically, chymase contributes to angiogenesis, SMC apoptosis, and mast cell cysteinyl cathepsin expression. Mast cells lacking chymase or SMCs that are treated with chymase-deficient mast cells demonstrate reduced cysteinyl cathepsin expression and activties [11]. In contrast, mast cell tryptase contributes to EC chemokine expression, monocyte and mast cell cysteinyl cathepsin expression, leukocyte migration, and SMC apoptosis [12]. In patients with small AAAs, plasma levels of these proteases associate with abdominal aorta expansion rate, risks of later surgical repair, or overall mortality. Although a direct participation of chymase or tryptase in obesity or diabetes has not been tested in any experimental model, cysteinyl protease cathepsins — which are indirectly regulated by chymase and tryptase [11], [12] — have been confirmed in diet-induced obesity and diabetes and in genetically-generated obesity and diabetes. Mice deficient in cathepsin L or cathepsin K, or treated with their selective inhibitors, are leaner than control mice or have significantly improved glucose sensitivity [40], [41]. Both chymase and tryptase, therefore, may directly or indirectly participate in the pathogenesis of diabetes mellitus — a hypothesis that merits further investigation.
IgE often associates with allergic responses, but several small human population studies indicated an association between serum IgE levels and CHD. In the Helsinki Heart Study [42], a nested case-control design and logistic regression analysis of 135 patients with CHD and 135 control subjects, serum IgE levels were higher in CHD patients than in control subjects. In a group of 13 stable patients from Turkey who underwent elective coronary artery stenting, serum levels of IgE and hs-CRP reached their peaks at the second and third post-procedure days, but returned to the baseline at the end of the first month [43]. IgE, like hs-CRP, might serve as a biomarker of inflammation in CHD, as shown in a recent study from a group in Pakistan: 99 patients with a history of ischemic heart disease (IHD) and established atherosclerosis on angiography showed increased serum levels of oxidized LDL and IgE, compared with those in 101 age-matched and sex-matched healthy subjects without a known history of IHD [44]. We demonstrated in two independent Chinese populations (982 and 240 patients, respectively) that plasma IgE levels were highest among AMI patients, followed by UAP patients, SAP patients, and non-CHD volunteers [19]. Although our original study [19] tested the role of IgE on macrophages, SMCs, and ECs, one conventional function of IgE is the activation of mast cells — a cell type that plays critical roles in type 2 DM [10]. Our current study demonstrates that plasma IgE levels strongly correlate with glucose tolerance status (Table 1) and serves as a strong risk factor for pre-diabetes and diabetes mellitus, either independently or in their interactions with mast cell protease or many other diabetes risk factors (Table 2, Table S1). Combining these clinical data and our recent discoveries of IgE function in atherosclerosis [19], we conjecture that IgE participates directly in the pathogenesis of diabetes mellitus — a hypothesis that can be tested in animals in the future.
But this study also has limitations. When we enrolled these patients and volunteers, we did not exclude those who might have asthma or other allergic diseases, which might affect blood IgE or mast cell protease levels. Ideally, their circulating eosinophilic leukocyte contents would be measured. A larger and more complete population study therefore may be required to affirm our observations. Nevertheless, this study provided the first evidence that elevated plasma levels of mast cell proteases and IgE may serve as important risk factors for human type 2 DM, particularly when hs-CRP or other common diabetes mellitus risk factors are considered. These mast cell proteases or IgE also may serve as biomarkers for human type 2 DM.
Supporting Information
Table S1.
Influence of different variables on the relative risk of developing pre-diabetes and diabetes mellitus.
https://doi.org/10.1371/journal.pone.0028962.s001
(DOC)
Table S2.
Infuence of interactions between CRP and different variables on the relative risk of developing pre-diabetes and diabetes mellitus.
https://doi.org/10.1371/journal.pone.0028962.s002
(DOC)
Table S3.
Infuence of interactions between IgE and different variables on the relative risk of developing pre-diabetes and diabetes mellitus.
https://doi.org/10.1371/journal.pone.0028962.s003
(DOC)
Table S4.
Infuence of interactions between chymase and different variables on the relative risk of developing pre-diabetes and diabetes mellitus.
https://doi.org/10.1371/journal.pone.0028962.s004
(DOC)
Table S5.
Infuence of interactions between tryptase and different variables on the relative risk of developing pre-diabetes and diabetes mellitus.
https://doi.org/10.1371/journal.pone.0028962.s005
(DOC)
Acknowledgments
We thank all the volunteers who participated in this study and the clinic staff members from the Huzhou First Hospital, Huzhou, Zhejiang, China, for their assistance in clinical data collection. We also thank Ms. Sara Karwacki for her editorial assistance.
Author Contributions
Conceived and designed the experiments: G-PS ZW. Performed the experiments: ZW HZ X-HS K-LJ G-FY LQ BL Y-HZ. Analyzed the data: ZW G-PS. Contributed reagents/materials/analysis tools: ZW HZ X-HS K-LJ G-FY LQ BL Y-HZ G-PS. Wrote the paper: ZW G-PS.
References
- 1. Schmidt MI, Duncan BB, Sharrett AR, Lindberg G, Savage PJ, et al. (1999) Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study. Lancet 353: 1649–1652.
- 2. Barzilay JI, Abraham L, Heckbert SR, Cushman M, Kuller LH, et al. (2001) The relation of markers of inflammation to the development of glucose disorders in the elderly: the Cardiovascular Health Study. Diabetes 50: 2384–2389.
- 3. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM (2001) C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 286: 327–334.
- 4. Spranger J, Kroke A, Möhlig M, Hoffmann K, Bergmann MM, et al. (2003) 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 52: 812–817.
- 5. Hu FB, Meigs JB, Li TY, Rifai N, Manson JE (2004) Inflammatory markers and risk of developing type 2 diabetes in women. Diabetes 53: 693–700.
- 6. Meigs JB, Hu FB, Rifai N, Manson JE (2004) Biomarkers of endothelial dysfunction and risk of type 2 diabetes mellitus. JAMA 291: 1978–1986.
- 7. Sabanayagam C, Shankar A, Lim SC, Lee J, Tai ES, et al. (2011) Serum C-reactive protein level and prediabetes in two Asian populations. Diabetologia 54: 767–775.
- 8. Theoharides TC, Kalogeromitros D (2006) The critical role of mast cells in allergy and inflammation. Ann N Y Acad Sci 1088: 78–99.
- 9. Bradding P, Walls AF, Holgate ST (2006) The role of the mast cell in the pathophysiology of asthma. J Allergy Clin Immunol 117: 1277–1284.
- 10. Liu J, Divoux A, Sun J, Zhang J, Clément K, et al. (2009) Genetic deficiency and pharmacological stabilization of mast cells reduce diet-induced obesity and diabetes in mice. Nat Med 15: 940–945.
- 11. Sun J, Zhang J, Lindholt JS, Sukhova GK, Liu J, et al. (2009) Critical role of mast cell chymase in mouse abdominal aortic aneurysm formation. Circulation 120: 973–982.
- 12. Zhang J, Sun J, Lindholt JS, Sukhova GK, Sinnamon M, et al. (2011) Mast cell tryptase deficiency attenuates mouse abdominal aortic aneurysm formation. Circ Res 108: 1316–1327.
- 13. MacGlashan D Jr, Lavens-Phillips S, Katsushi M (1998) IgE-mediated desensitization in human basophils and mast cells. Front Biosci 3: d746–d756.
- 14. Gruber B (1989) Activation of rheumatoid synovial mast cells. Role of IgE-associated antiglobulins. Monogr Allergy 26: 120–134.
- 15. Retnakaran R, Hanley AJ, Raif N, Connelly PW, Sermer M, et al. (2003) C-reactive protein and gestational diabetes: the central role of maternal obesity. J Clin Endocrinol Metab 88: 3507–3512.
- 16. Pickup JC (2004) Inflammation and activated innate immunity in the pathogenesis of type 2 diabetes. Diabetes Care 27: 813–823.
- 17. Wellen KE, Hotamisligil GS (2005) Inflammation, stress, and diabetes. J Clin Invest 115: 1111–1119.
- 18. Xiang M, Sun J, Lin Y, Zhang J, Chen H, et al. (2011) Usefulness of serum tryptase level as an independent biomarker for coronary plaque instability in a Chinese population. Atherosclerosis 215: 494–499.
- 19. Wang J, Cheng X, Xiang MX, Alanne-Kinnunen M, Wang JA, et al. (2011) IgE stimulates human and mouse arterial cell apoptosis and cytokine expression and promotes atherogenesis in Apoe−/− mice. J Clin Invest 121: 3564–3577.
- 20. Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, et al. (2003) Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 112: 1796–1808.
- 21. Xu H, Barnes GT, Yang Q, Tan G, Yang D, et al. (2003) Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Invest 112: 1821–1830.
- 22. Mayor S (2007) International Diabetes Federation consensus on prevention of type 2 diabetes. Int J Clin Pract 61: 1773–1775.
- 23. Wu Y, Huxley R, Li L, Anna V, Xie G, et al. (2008) Prevalence, awareness, treatment, and control of hypertension in China: data from the China National Nutrition and Health Survey 2002. Circulation 118: 2679–2686.
- 24. Zhang P, Jiao S, Zhou Y (2007) Study on chronic disease related behaviour and lifestyle in adults in Beijing. Chin J Epidemiol 28: 1162–1166.
- 25. Haffner SM (2003) Pre-diabetes, insulin resistance, inflammation and CVD risk. Diabetes Res Clin Pract 61: Suppl 1S9–S18.
- 26. Ndumele CE, Pradhan AD, Ridker PM (2006) Interrelationships between inflammation, C-reactive protein, and insulin resistance. J Cardiometab Syndr 1: 190–196.
- 27. Haffner SM (2006) The metabolic syndrome: inflammation, diabetes mellitus, and cardiovascular disease. Am J Cardiol 97: 3A–11A.
- 28. Freeman DJ, Norrie J, Caslake MJ, Gaw A, Ford I, et al. (2002) C-reactive protein is an independent predictor of risk for the development of diabetes in the West of Scotland Coronary Prevention Study. Diabetes 51: 1596–1600.
- 29. Schulze MB, Hoffmann K, Manson JE, Willett WC, Meigs JB, et al. (2005) Dietary pattern, inflammation, and incidence of type 2 diabetes in women. Am J Clin Nutr 82: 675–684.
- 30. Walder K, Kantham L, McMillan JS, Trevaskis J, Kerr L, et al. (2002) Tanis: a link between type 2 diabetes and inflammation? Diabetes 51: 1859–1866.
- 31. Laakso M, Zilinskaite J, Hansen T, Boesgaard TW, Vänttinen M, et al. (2008) Insulin sensitivity, insulin release and glucagon-like peptide-1 levels in persons with impaired fasting glucose and/or impaired glucose tolerance in the EUGENE2 study. Diabetologia 51: 502–511.
- 32. Bock G, Dalla Man C, Campioni M, Chittilapilly E, Basu R, et al. (2006) Pathogenesis of pre-diabetes: mechanisms of fasting and postprandial hyperglycemia in people with impaired fasting glucose and/or impaired glucose tolerance. Diabetes 55: 3536–3549.
- 33. Abdul-Ghani MA, Jenkinson CP, Richardson DK, Tripathy D, DeFronzo RA (2006) Insulin secretion and action in subjects with impaired fasting glucose and impaired glucose tolerance: results from the Veterans Administration Genetic Epidemiology Study. Diabetes 55: 1430–1435.
- 34. Wasada T, Kuroki H, Katsumori K, Arii H, Sato A, et al. (2004) Who are more insulin resistant, people with IFG or people with IGT? Diabetologia 47: 758–759.
- 35. Meigs JB, Muller DC, Nathan DM, Blake DR, Andres R, et al. (2003) The natural history of progression from normal glucose tolerance to type 2 diabetes in the Baltimore Longitudinal Study of Aging. Diabetes 52: 1475–1484.
- 36. Festa A, D'Agostino R, Tracy RP, Haffner SM, Insulin Resistance Atherosclerosis Study (2002) Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes 51: 1131–1137.
- 37. Kim JK (2010) Inflammation and insulin resistance: an old story with new ideas. Korean Diabetes J 34: 137–145.
- 38. Sommer P, Sweeney G (2010) Functional and mechanistic integration of infection and the metabolic syndrome. Korean Diabetes J 34: 71–76.
- 39. Ikeoka D, Mader JK, Pieber TR (2010) Adipose tissue, inflammation and cardiovascular disease. Rev Assoc Med Bras 56: 116–121.
- 40. Yang M, Zhang Y, Pan J, Sun J, Liu J, et al. (2007) Cathepsin L activity controls adipogenesis and glucose tolerance. Nat Cell Biol 9: 970–977.
- 41. Yang M, Sun J, Zhang T, Liu J, Zhang J, et al. (2008) Deficiency and inhibition of cathepsin K reduce body weight gain and increase glucose metabolism in mice. Arterioscler Thromb Vasc Biol 28: 2202–2208.
- 42. Kovanen PT, Mänttäri M, Palosuo T, Manninen V, Aho K (1998) Prediction of myocardial infarction in dyslipidemic men by elevated levels of immunoglobulin classes A, E, and G, but not M. Arch Intern Med 158: 1434–1439.
- 43. Erdogan O, Altun A, Gul C, Ozbay G (2003) C-reactive protein and immunoglobulin-E response to coronary artery stenting in patients with stable angina. Jpn Heart J 44: 593–600.
- 44. Shahzad F, Tawwab S, Afzal N (2010) Association of interleukin-4 and IgE levels with LDL oxidation in atherosclerosis. Iran J Immunol 7: 109–116.