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Special Feature |
Division of Endocrinology (R.R., A.J.G.H., B.Z.), University of Toronto, Toronto M5G 1X5, Ontario, Canada; Leadership Sinai Centre for Diabetes (A.J.G.H., N.R., B.Z.), and Division of Obstetrics and Gynecology (M.S.), Mount Sinai Hospital, Toronto M5G 1X5, Ontario, Canada; and Department of Laboratory Medicine and Pathobiology, Univeristy of Toronto, Toronto M5G 1X5, Ontario, Canada
Address all correspondence and requests for reprints to: Dr. Bernard Zinman, Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, 60 Murray Street, Suite 5024, Toronto, Ontario, Canada M5G 1X5. E-mail: zinman{at}mshri.on.ca.
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Abstract |
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Introduction |
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Gestational diabetes mellitus (GDM), like type 2 DM, is characterized by the metabolic defects of ß-cell dysfunction and insulin resistance (16). A potential pathophysiologic relationship between GDM and type 2 DM is further supported by the significantly elevated lifetime risk of type 2 DM in women with a history of previous GDM (17). As such, it has been hypothesized that GDM may represent the transient unmasking of a latent metabolic syndrome, one that may become clinically apparent later in life as type 2 DM (18). Interestingly, a recent study demonstrated that total sialic acid, an integrated marker of the acute phase cascade, is elevated in women with a history of previous GDM, thereby potentially implicating inflammation as a factor in the link between previous GDM and type 2 DM (19). To our knowledge, however, the hypothesis that GDM, in itself, may be a state of subclinical inflammation has not been directly evaluated. Therefore, in the present study, we investigated the relation of the inflammatory marker CRP to GDM. We further evaluated the association of CRP with 1) GDM risk factors such as prepregnancy obesity, ethnicity, and family history and 2) metabolic features of GDM such as insulin resistance.
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Subjects and Methods |
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Study participants consisted of 180 healthy pregnant women attending outpatient obstetrics clinics, who had been referred for a 100-g oral glucose tolerance test (OGTT) following an abnormal result on a screening 50-g glucose challenge test [plasma glucose 
7.8 mmol/liter at 1 h after meals (pc)]. Exclusion criteria included: 1) preexisting chronic medical conditions that may affect acute phase markers, including DM, polycystic ovarian syndrome, collagen vascular diseases, inflammatory bowel disease, and chronic inflammatory conditions or 2) current use of corticosteroids. Participants were recruited before undergoing the 100-g OGTT. The study protocol was approved by the Research Ethics Board at Mount Sinai Hospital, and all subjects gave written informed consent.
Baseline evaluation
On the day of the OGTT, demographic and historical information was collected by interviewer-administered questionnaire. Data collected included: 1) patient demographics; 2) information regarding current pregnancy including illnesses, infections, and medications; 3) personal medical, obstetrical, and smoking history; and 4) family history. Specific GDM risk factors were assessed including age, ethnicity, prepregnancy weight, weight gain during pregnancy, personal history of GDM, previous delivery of macrosomic infant, and family history of GDM or macrosomic infant or type 2 DM. Anthropometric measurements of height (measured to nearest 0.5 cm) and weight (measured to nearest 0.1 kg) were obtained using a medical scale.
Laboratory measurements
The 100-g OGTT was performed in the morning after overnight fast. Venous blood samples were drawn at baseline and 60, 120, and 180 min following ingestion of a standard 100-g glucose load. Fasting CRP concentration was determined using the Behring BN100 and the N high-sensitivity CRP reagent (Dade-Behring, Mississauga, Ontario, Canada) (20). Specific insulin was measured at each of the four time points using Elecsys 1010 (Roche Diagnostics, Basel, Switzerland) immunoassay analyzer and the electrochemiluminescence immunoassay. This assay shows 0.05% cross-reactivity to intact human proinsulin and the primary circulating split form (Des 31, 32).
Glycemic status
The OGTT stratified participants into three glycemic tolerance groups: 1) GDM, as defined by the National Diabetes Data Group (NDDG) criteria [requires at least two of the following: fasting glucose, >5.8; 1 h pc glucose, >10.6; 2 h pc, >9.2; or 3 h pc, >8.1]; 2) impaired glucose tolerance (IGT), as defined by NDDG criteria (requires one of above GDM criteria); and 3) normal glucose tolerance (NGT), defined as subjects not meeting any of the GDM or IGT criteria (21).
Statistical analysis
All analyses were conducted using the Statistical Analysis System (SAS, version 8.02, SAS Institute, Cary, NC). Participants were stratified into four groups: 1) NGT lean [prepregnancy body mass index (BMI) < 25 kg/m2]; 2) NGT overweight (prepregnancy BMI 25 kg/m2); 3) IGT; and 4) GDM. Means and SDs or proportions were presented by group, and ANOVA and chi-square tests were used to assess univariate differences between continuous and categorical variables, respectively. The distributions of fasting insulin and CRP were substantially skewed, and thus medians and interquartile ranges were presented for these variables (Table 1
). In addition, the natural logarithmic transformations of fasting insulin and CRP were used in subsequent multivariate analyses, with back-transformed results presented in tables and figures. Parity was defined as the sum of term and premature infants. Smoking exposure was defined as never smoker, remote smoking exposure (former smokers who had not smoked in at least 10 months), and recent smoking exposure (those who reported that they were either currently smoking or had quit within the last 10 months). Univariate associations of CRP with continuous measures of adiposity, glucose, and insulin were assessed with Spearman correlation analysis. Analysis of covariance was used to test differences in CRP concentration across categories of glucose tolerance after adjustment for covariates including prepregnancy BMI, weight gain in pregnancy, age, parity, ethnicity, smoking history, previous GDM or delivery of an infant 10 lb, family history of GDM or type 2 DM, and fasting insulin. Furthermore, we investigated these differences within subgroups of prepregnancy BMI (< vs. 50th percentile of prepregnancy BMI) and tested for effect modification by including interaction terms in the models. Multiple linear regression analysis was used to determine which factors were significantly and independently associated with variation in log CRP (Table 3) and log fasting insulin (Table 4). Finally, we used analysis of covariance to assess differences in CRP across tertiles of prepregnancy BMI in unadjusted analysis and analysis adjusted for age, ethnicity, smoking history, previous GDM, family history of type 2 DM or GDM, weight gain in pregnancy, fasting insulin, and glucose intolerance.
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Results |
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25 kg/m2); 3) IGT; and 4) GDM. Demographic and metabolic characteristics of the four groups are shown in Table 1
. As expected, prepregnancy BMI differed significantly among groups, with the overweight NGT group (mean, 29.3 kg/m2) much higher than GDM (mean, 24.7 kg/m2), IGT (mean, 24.1 kg/m2), and lean NGT (mean, 21.5 kg/m2) (overall P value from ANOVA < 0.0001). There were proportionally more multiparas in the overweight NGT group (overall P = 0.0390). GDM patients were more likely to have a history of previous GDM or previous delivery of a macrosomic infant, although at borderline statistical significance (overall P = 0.0505). Otherwise, there were no significant differences across the groups in age, weeks gestation, weight gain during pregnancy, parity, smoking exposure, ethnicity, and family history of GDM or type 2 DM. Glycemic parameters of fasting and 2 h pc blood glucose showed the anticipated progression from NGT to IGT to GDM. Interestingly, median CRP level was highest in the overweight NGT subjects (8.8 mg/liter), followed by GDM (5.5 mg/liter), IGT (4.4 mg/liter), and lean NGT (4.4 mg/liter) (overall P = 0.0297). GDM patients had the highest fasting insulin levels followed, in turn, by overweight NGT, IGT, and lean NGT (overall P = 0.0007).
CRP was most strongly associated with prepregnancy BMI in univariate Spearman correlation analysis (r = 0.38, P < 0.0001) (Table 2). CRP was also significantly correlated with fasting insulin and fasting blood glucose, although not with 2 h pc insulin or glucose. Nevertheless, there was no significant relationship between CRP and glucose tolerance status (Fig 1A). Adjustment for prepregnancy BMI, weight gain in pregnancy, age, parity, ethnicity, smoking history, previous GDM, family history of GDM or type 2 DM, and fasting insulin also did not reveal an underlying association between CRP and glucose intolerance (Fig 1B).
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), a model consisting of GDM risk factors (prepregnancy BMI, weight gain in pregnancy, age, ethnicity, personal history of previous GDM or macrosomic infant, and family history of GDM or type 2 DM), parity, smoking history, fasting insulin, and glucose intolerance accounted for 21.13% of the variance in logarithmically transformed CRP. Prepregnancy BMI was again the most important factor (P = 0.0018). Asian background was inversely related to CRP (P = 0.0405), whereas fasting insulin was associated at borderline statistical significance (P = 0.0563).
To further evaluate the relationship between CRP and adiposity, study participants were stratified into tertiles based on prepregnancy BMI. A step-wise increase in CRP per tertile of prepregnancy BMI was noted (trend P < 0.0001) (Fig. 2A). Adjustment for age, ethnicity, parity, smoking history, previous GDM, family history of type 2 DM or GDM, weight gain in pregnancy, fasting insulin, and glucose intolerance attenuated the magnitude of the relationship slightly, although the association remained statistically significant (trend P = 0.0158) (Fig. 2B).
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). A model consisting of age, prepregnancy BMI, weight gain, parity, ethnicity, glucose intolerance, and CRP reconciled 28.30% of the variance in fasting insulin. Prepregnancy BMI and weight gain were the most important factors (both P < 0.0001), whereas ethnicity (Asian or South Asian), glucose intolerance, and CRP were also independently related to fasting insulin (all P < 0.05). |
Discussion |
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Several studies have firmly established the strong association between obesity and elevated inflammatory markers, leading to the recognition of obesity as a state of chronic low-grade inflammation (4, 23, 24). Circulating levels of CRP correlate highly with several measures of body fat, including BMI, waist circumference, waist-to-hip ratio, fat-free mass, and adipose body mass as assessed by bioelectrical impedance (25). Given the demonstrated value of CRP in predicting incident type 2 DM and cardiovascular disease, chronic subclinical inflammation has been proposed as a pathophysiologic mechanism linking adiposity with an increased risk of metabolic and atherosclerotic disease. This hypothesis is consistent with the observation from several studies that the inclusion of measures of adiposity in multivariate models partially attenuates the relationship between baseline CRP and incident diabetes (12, 13, 14). In fact, in the Insulin Resistance Atherosclerosis Study, the independent association of CRP with incident type 2 DM was abrogated by adjustment for either body mass index or waist circumference (15).
In pregnancy, limited data to date suggest that similar inflammatory implications of obesity are present. Ramsay et al. (22) recently demonstrated microvascular endothelial dysfunction, metabolic perturbation (including fasting hyperinsulinemia), and low-grade inflammation, as manifested by elevated CRP and IL-6, in normoglycemic obese pregnant women, compared with lean pregnant controls. The findings of the present study further support an independent association between maternal obesity and CRP while extending the relationship over different strata of glycemic tolerance. The failure of glucose intolerance and GDM risk factors to significantly attenuate the graded increase in CRP per tertile of prepregnancy BMI supports a dominant upstream role for adiposity in CRP regulation in pregnancy. On the other hand, the independent relationship of both adiposity (prepregnancy BMI and weight gain in pregnancy) and CRP to fasting insulin on multiple linear regression analysis is potentially consistent with the notion of insulin resistance as a downstream complication of obesity and inflammation, as previously demonstrated in the nonpregnant state (4). Taken together, these data suggest a model of obesity-driven systemic inflammation, leading to insulin resistance.
Precedence for the concept of systemic inflammation linking adiposity with an obesity-related pregnancy complication can be found in recent thinking regarding the pathophysiology of preeclampsia (26). First-trimester elevation of CRP has been associated with increased risk of subsequent preeclampsia (27). However, this association is eliminated by adjustment for first-trimester BMI. As such, inflammation has been proposed as a factor in the pathway through which obesity predisposes women to an increased risk of preeclampsia (27).
Maternal obesity has been associated with the up-regulation of inflammatory markers in the first trimester prior to any observed glucose dysregulation (22). The findings of the present study suggest that the subsequent development of glucose intolerance later in pregnancy is not independently related to maternal CRP level. Furthermore, it is important to recognize that an overwhelming effect of maternal obesity on inflammatory markers is not obscuring an underlying relationship between CRP and GDM. Indeed, even within the leaner half of study participants (prepregnancy BMI, <50th percentile), in whom the effect of maternal obesity should be minimized, CRP was not predictive of glucose intolerance (data not shown). These findings are in contrast to the association between CRP and incident diabetes observed in nonpregnant individuals. Many factors may be contributing to this difference. First of all, the current study involves young, healthy women, in whom the effect of unrecognized intercurrent conditions that up-regulate inflammatory markers (such as subclinical coronary artery disease) should be minimal. A second possibility is that hormonal and metabolic factors specific to pregnancy may affect the relationship between inflammation and diabetogenesis.
Despite the lack of correlation with glucose intolerance, CRP was independently associated with insulin resistance in the present study. This association was weaker than that between measures of adiposity and insulin resistance and could reflect a type I error. Nevertheless, this observation demands further study. The classic paradigm that insulin resistance in pregnancy is due to placental hormones such as estrogen, progesterone, and human placental lactogen has recently been challenged. Kirwan et al. (28) demonstrated that TNF
was the strongest independent predictor of insulin resistance throughout pregnancy, whereas the placental reproductive hormones did not correlate with insulin sensitivity in late pregnancy. Previously implicated as a factor in obesity-related insulin resistance (29), TNF induces expression of IL-6, the principal determinant of hepatic CRP expression (30). As such, the role of TNF signaling in the association between CRP and insulin resistance in pregnancy warrants further study.
We recognize that the cross-sectional nature of this study limits the applicability of the findings to different stages of pregnancy. Nevertheless, the present study is the first investigation of the relationship between CRP, maternal obesity and glucose intolerance in pregnancy. Further prospective evaluation is now indicated.
In conclusion, we have demonstrated that maternal serum levels of CRP are not related to GDM but rather correlate significantly with prepregnancy obesity. An independent contribution of CRP to risk of GDM could not be confirmed. We propose a model wherein obesity mediates a systemic inflammatory response, with possible downstream metabolic sequelae including insulin resistance and glucose dysregulation. The longitudinal applicability of this model before, during and after pregnancy requires further prospective study.
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Acknowledgments |
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Footnotes |
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Received February 5, 2003.
Accepted April 25, 2003.
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is a predictor of insulin resistance in human pregnancy. Diabetes 51:2207–2213This article has been cited by other articles:
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