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Plasma Adiponectin Concentrations in Early Pregnancy and Subsequent Risk of Gestational Diabetes Mellitus

Center for Perinatal Studies (M.A.W., C.Q., M.M.-R., S.V., T.S.), Swedish Medical Center, Seattle, Washington 98122; Department of Epidemiology (M.A.W.), University of Washington, School of Public Health and Community Medicine, Seattle, Washington 98195; and Obstetrix Medical Group (D.A.L.), Seattle, Washington 98122

Address all correspondence and requests for reprints to: Dr. Michelle A. Williams, Center for Perinatal Studies (Suite 4 North), Swedish Medical Center, 747 Broadway, Seattle, Washington 98122. E-mail: mwilliam{at}u.washington.edu.

	Abstract

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Low plasma adiponectin has been identified as a risk factor for type 2 diabetes. Our objective was to determine the extent to which low maternal plasma adiponectin is predictive of gestational diabetes mellitus (GDM), a condition that is biochemically and epidemiologically similar to type 2 diabetes. We used a prospective, nested case-control study design to compare maternal plasma adiponectin concentrations in 41 cases with 70 controls. Subjects were selected from a population of 968 women who provided blood samples in early pregnancy. Plasma adiponectin was determined using an ELISA. Adiponectin concentrations were statistically significantly lower in women with GDM than controls (4.4 vs. 8.1 µg/ml, P < 0.001). Approximately 73% of women with GDM, compared with 33% of controls, had adiponectin concentrations less than 6.4 µg/ml. After adjusting for confounding, women with adiponectin concentrations less than 6.4 µg/ml experienced a 4.6-fold increased risk of GDM, as compared with those with higher concentrations (95% confidence interval, 1.8–11.6). Our findings are consistent with other reports suggesting an association between hypoadiponectemia and risk of type 2 diabetes. Our findings extend the literature to include GDM. Studies designed to examine the effect of dietary and pharmacological mediators of adiponectin concentrations in pregnant and nonpregnant subjects are warranted.

	Introduction

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ADIPONECTIN, A 30-kDa protein with structural homology to collagen VIII, X, complement C1q, and TNF-, is an adipocyte-derived polypeptide that has insulin sensitizing properties and is abundantly present in peripheral circulation (1, 2, 3). Unlike other adipocytokines, such as leptin and TNF-, plasma adiponectin is inversely correlated with body mass index (BMI), intraabdominal fat, and indices of insulin resistance (4, 5). Plasma adiponectin concentrations are lower in smokers (6), patients with coronary artery disease (6, 7), and women with polycystic ovary syndrome (8). Moreover, investigators recently reported that plasma adiponectin concentrations are reduced with weight gain (9, 10) and are increased with weight loss achieved by gastric partition surgery (10) or by low-calorie diet (9, 11). Results from animal studies suggest that adiponectin is likely to play an important role in regulating insulin action (12, 13). Hotta et al. (13) reported that a decline in adiponectin concentration coincides with the development of hyperinsulinemia and insulin resistance in rhesus monkeys. Results from cross-sectional and prospective cohort studies support an association between plasma adiponectin and type 2 diabetes risk. In a cross-sectional study, Hotta et al. (14) reported that plasma adiponectin concentrations were statistically significantly reduced in patients with type 2 diabetes as compared with controls. Recently, investigators reported that low plasma adiponectin concentrations, measured at baseline, were associated with an increased risk of incident type 2 diabetes in Pima Indians (15). This observation was recently corroborated by Spranger et al. (16), who designed a nested, case-control study within the population-based European Prospective Investigation into Cancer and Nutrition Potsdam cohort. Taken together, results from available animal studies, as well as those from clinical and population-based epidemiological studies, suggest that adiponectin is likely to play a role in the pathogenesis of insulin resistance and type 2 diabetes.

On the basis of these observations, and given that gestational diabetes mellitus (GDM) is biochemically and epidemiologically similar to type 2 diabetes in nonpregnant adults (17, 18, 19), we used available data from a prospective cohort study to evaluate the extent to which plasma adiponectin concentrations in early pregnancy are associated with the subsequent risk of GDM. We hypothesized that lower plasma adiponectin concentrations would be associated with an increased risk of GDM.

	Subjects and Methods

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The population for the present analysis was drawn from participants of the ongoing OMEGA study, which is designed to examine maternal dietary and lifestyle risk factors of adverse pregnancy outcomes including preeclampsia and GDM. The study population is comprised of women attending prenatal care clinics affiliated with Swedish Medical Center and Tacoma General Hospital in Seattle and Tacoma, WA, respectively. Women who initiated prenatal care before 16 wk gestation were eligible for the study. Women were ineligible if they were younger than 18 yr of age, did not speak and read English, did not plan to carry the pregnancy to term, did not plan to deliver at either of the two research hospitals, or were past 16 wk gestation. Participants were invited to provide blood and urine samples and to participate in an in-person interview. Maternal and infant records were reviewed, and data were abstracted. The procedures used in this study were in agreement with the protocols approved by the Institutional Review Boards of Swedish Medical Center and Tacoma General Hospital, respectively. All participants provided written informed consent.

The study population for this report is from the first 1000 participants who were enrolled in the OMEGA study during the period of 1996–2000. During this period, 1219 eligible women were approached, and 1000 (approximately 82%) agreed to participate. A total of 968 participants provided blood samples. Women found to have chronic hypertension (n = 45) and pregestational diabetes mellitus (n = 4), based on self-report and confirmed with medical records information, were excluded. Women who experienced a spontaneous abortion or who had an induced abortion were excluded (n = 22). Also excluded were those women for whom the outcome of pregnancy was unknown due to any of the following reasons: moved, delivered elsewhere, and/or missing medical records (n = 46); hence, a cohort of 851 women without pregestational diabetes, without essential hypertension, and who completed their pregnancies and were eligible and available for selection for the present study. After assigning computer-generated random numbers to the 851 subjects eligible to serve as controls for this study, we randomly selected 70 control subjects for adiponectin testing. This number of controls was derived to provide a control to case ratio greater than 1.5 and to be compatible with the availability of adiponectin immunoassay reagents at the time of laboratory analyses. Given that there were no published reports concerning determinants of plasma adiponectin concentrations in pregnant women, we elected a priori to use a nonmatched case-control study design. As noted below, potential confounding was assessed using standardized statistical analytical methods. These standard analyses were supplemented with sensitivity (subgroup) analyses that included restricting some analyses to nonobese subjects.

From structured questionnaire and medical records, we obtained covariate information including maternal age, educational attainment, height, prepregnancy weight, reproductive and medical histories, and medical histories of first-degree family members. We also collected information on annual household income and maternal smoking before and during pregnancy. Prepregnancy BMI was calculated as weight in kilograms divided by height in meters squared. Maternal medical records were reviewed to collect detailed information concerning antepartum, labor, and delivery characteristics (e.g. gestational age-specific weight, blood pressures, and medical complications). Maternal BMI at the time of blood collection was calculated as described above. We also abstracted laboratory results from participants’ 50-g 1-h oral glucose tolerance tests (OGTT) and from the diagnostic 100-g 3-h OGTT. Women were diagnosed with GDM if two or more of the four OGTT glucose levels exceeded the American Diabetes Association criteria (20): fasting, more than 5.3 mmol/liter; 1-h, more than 10.0 mmol/liter; 2-h, more than 8.6 mmol/liter; 3-h, more than 7.8 mmol/liter. We selected all 41 women who developed GDM according to the criteria described above; and using a random sampling algorithm, we selected 70 women who remained normoglycemic throughout pregnancy to serve as controls.

Maternal nonfasting blood samples, collected in 10-ml Vacutainer tubes at 13 wk gestation, were frozen at –80 C until analysis. Plasma adiponectin concentrations were measured using an ELISA (B-Bridge International, Inc., San Jose, CA) with the intra- and interassay coefficients of variation both less than 8%. All assays were performed without knowledge of case-control status.

We examined frequency distributions of maternal sociodemographic characteristics and medical and reproductive histories according to case-control status. We defined a woman as overweight if her prepregnancy BMI was at least 25 kg/m²; lean women were those with a prepregnancy BMI of less than 25 kg/m². We compared median concentrations between cases and controls using the Mann-Whitney two sample statistics. Multivariable least-square regression procedures were used to estimate mean case-control differences in plasma adiponectin concentrations after adjusting for potential confounders. Associations of maternal adiponectin concentrations with prepregnancy and early pregnancy BMI, as well as maternal age, were estimated with Spearman correlation coefficients. We categorized a priori each subject according to tertiles determined by the distribution of adiponectin among control subjects. Because only 11 of the 41 cases were distributed across the upper 2 tertiles of adiponectin concentrations, we collapsed the upper two groups into one category. Hence, maternal adiponectin concentrations were eventually categorized as a binary variable, i.e. less than 6.4 µg/ml (the lowest tertile) vs. 6.4 µg/ml or more (the upper 2 tertiles). We used logistic regression with GDM as the binary outcome variable to derive relative risk estimates [i.e. odds ratios (OR) and 95% confidence intervals (CI)]. To assess confounding, we added additional covariates into the logistic regression model with adiponectin, one at a time, and we compared the adjusted and unadjusted ORs (21). Multivariate logistic regression models included covariates that altered unadjusted OR by at least 10%, as well as maternal first-degree family history of type 2 diabetes, prepregnancy BMI, and age. We explored the possibility of a nonlinear relation between plasma adiponectin concentrations and GDM risk using generalized additive modeling procedures (22). In an effort to further examine the independent relationship between maternal plasma adiponectin and GDM risk, we calculated the adiponectin:early-pregnancy BMI ratio as a means of expressing individual adiposity-adjusted adiponectin concentration. We then repeated logistic regression analyses.

All analyses were performed using Stata 7.0 statistical software (Stata, College Station, TX). All continuous variables are presented as mean ± SE. All reported CI values were calculated at the 95% level. All reported P-values are two-tailed.

	Results

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Table 1 summarizes the characteristics of the study population. Women who developed GDM were more likely to be of advanced maternal age, overweight, and have a family history of type 2 diabetes mellitus than controls. Other characteristics, including marital status, race/ethnicity, and gestational age at blood collection, were similar for cases and controls.

We next examined the association between maternal plasma adiponectin concentrations, prepregnancy, and early pregnancy BMI for GDM cases and controls, respectively (Table 2). Among control subjects, early pregnancy adiponectin concentrations were not correlated with maternal prepregnancy BMI (r = 0.08, P = 0.49) or BMI in early pregnancy (r = –0.06, P = 0.60). Notably, correlation coefficients between adiponectin concentrations and indices of maternal adiposity among GDM cases were considerably stronger than those estimated for control subjects. Among GDM cases, adiponectin concentrations were inversely correlated with maternal prepregnancy BMI (r = –0.44, P < 0.01), and BMI in early pregnancy (r = –0.42, P < 0.01). Figure 1 summarizes the distributions of maternal plasma adiponectin concentrations according to early pregnancy BMI for GDM cases and controls, respectively. Maternal plasma adiponectin concentrations were not correlated with maternal age or gestational age at blood collection.

View larger version (21K): [in this window] [in a new window]   FIG. 1. Distribution of maternal adiponectin concentrations in early pregnancy (13 wk gestation on average) in relation to BMI in 41 women who subsequently developed GDM and in 70 women who remained normoglycemic throughout pregnancy.

In post hoc analyses, we noted a considerable case-control imbalance in the proportion of women with early pregnancy BMI of at least 30 kg/m² (see Fig. 1). Hence, we performed another sensitivity analysis that restricted the study population to 25 GDM cases and 66 controls with similar BMI distributions. After adjusting for maternal age, family history of type 2 diabetes, and BMI at blood collection, we noted that women with adiponectin concentrations less than 6.4 µg/ml experienced a 3.2-fold increased risk of GDM as compared with those who had higher concentrations (OR = 3.2; 95% CI, 1.2–8.6) (data not shown). Hence, using different multivariable modeling strategies and subgroup analytical strategies, we noted that the association between maternal adiponectin and GDM risk was independent of maternal adiposity.

We modeled the risk of GDM in relation to maternal plasma adiponectin concentrations expressed as a continuous variable using a generalized additive model. From these analyses, we noted a linear-component in the relationship between GDM risk and plasma adiponectin (figure not shown). On the basis of this observation, we modeled plasma adiponectin concentrations expressed as a continuous variable. From this analysis, we noted that a 1-µg/ml decrease in plasma adiponectin concentration was associated with a 20% increase in GDM risk (adjusted OR = 1.2; 95% CI, 1.1–1.4) after adjusting for maternal age, first-degree family history of type 2 diabetes, and BMI at the time of blood collection.

We next examined the independent and joint associations of maternal plasma adiponectin concentrations and prepregnancy overweight status with risk of GDM. As shown in Table 4, lean women with low adiponectin concentrations (<6.4 µg/ml), as compared with lean women with adiponectin concentrations of at least 6.4 µg/ml (i.e. the referent group), experienced a 2.4-fold increased risk of GDM (95% CI, 0.9–6.5). Women who were overweight and who had low adiponectin concentrations experienced a 11-fold increased risk of GDM (95% CI, 2.0–61.6). There were too few overweight women with adiponectin concentrations of at least 6.4 µg/ml to calculate an OR. Although these analyses were hindered by our relatively small sample size, it appears that low adiponectin concentrations in early pregnancy may be an independent predictor of GDM in lean women, and that the risk of GDM may be markedly elevated in overweight women with low adiponectin concentrations in early pregnancy.

	Discussion

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Adiponectin is known to stimulate fatty acid oxidation, reduce plasma triglycerides, and improve glucose metabolism by increasing insulin sensitivity. Notably, the diabetes-susceptibility locus that has been mapped to human chromosome 3q27 (23) is also the site of the adiponectin gene (APM1) (24). The gene is thought to be expressed exclusively in adipose tissue. IGF-1 and peroxisome proliferator-activated receptors (PPAR) are known to stimulate the synthesis of adiponectin, whereas TNF- has been shown to inhibit synthesis (25, 26). Maeda et al. (12) reported recently that adiponectin gene expression is up-regulated and that plasma adiponectin concentrations are increased in obese mice and in insulin-resistant obese humans by the PPAR- agonist, thiazolidinediones.

To our knowledge, there have been no published studies on maternal plasma adiponectin and GDM. Our findings, however, are generally consistent with reports demonstrating associations between plasma adiponectin and risk of type 2 diabetes (13, 15, 16).

Our present study has several important strengths. First, determination of adiponectin concentrations using plasma collected in early pregnancy served to define the temporal relationship between reduced maternal plasma adiponectin concentrations and subsequent risk of GDM. Hence, our results suggest that alternations in maternal plasma adiponectin concentrations precede the clinical diagnosis of GDM. Second, the high follow-up rate (>95%) also minimized possible selection bias.

Several limitations also merit discussion and consideration. For instance, although we excluded women with a medical diagnosis of pregestational diabetes, we cannot, with absolute certainty, exclude the possibility that some subjects in our study had undiagnosed diabetes or other abnormalities of glucose tolerance before or in early pregnancy. Universal glucose tolerance testing in early pregnancy is not part of the standard obstetric care (20). However, several observations serve to attenuate concerns about undiagnosed diabetes in this study population. First, we noted that 95% of study subjects reported having a regular medical physical exam within a 24-month period before the index pregnancy. Second, the cumulative incidence of GDM in our study cohort is consistent with observations in other settings (20).

A single measurement of plasma adiponectin is not likely to provide a time-integrated measure of maternal adiponectin status during the study pregnancy. Available data suggest that adiponectin concentrations in pregnant and lactating mice are associated with changes in estrogen and PRL (27). Hence, it is plausible that in pregnant women, adiponectin concentrations are likely under complex hormonal control. Longitudinal studies with serial measurements of maternal plasma adiponectin concentrations and indices of insulin sensitivity are needed to elucidate the mechanisms and pathophysiological consequences of hypoadiponectinemia during pregnancy. Last, our relatively small number of GDM cases hindered inferences from some of our analyses, particularly those summarized in Table 4.

The pathophysiology of hypoadiponectinemia and type 2 diabetes and GDM remains unknown, though investigators speculate that the insulin-sensitizing effect of adiponectin may result from four different mechanisms: 1) increased lipid oxidation; 2) direct improvement of insulin signaling at the receptor/post-receptor level; 3) inhibition of gluconeogenesis, and 4) inhibition of TNF- signaling in adipose tissue (26). Clearly, more studies are needed to further elucidate the mechanisms for the potential role adiponectin plays in regulating glucose metabolism in pregnant and nonpregnant individuals.

In summary, after adjusting for possible confounding factors, we noted that low maternal plasma adiponectin concentrations in early pregnancy were predictive of a 4.6-fold increased risk of GDM. Further work is needed to provide a better understanding of the determinants of plasma adiponectin concentrations in pregnant women. Increased knowledge from such studies may yield strategies for identifying women at highest risk of developing GDM, and may help to identify strategies for lowering the occurrence of GDM among overweight women who are at particularly high risk of developing the disorder.

	Acknowledgments

 
The authors are indebted to the staff of the Center for Perinatal Studies for their expert technical assistance.

	Footnotes

 
This research was supported by an award from the National Institutes of Health, National Institute of Child Health and Human Development (HD-R01–32562).

Abbreviations: BMI, Body mass index; CI, confidence interval(s); GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance tests; OR, odds ratio(s); PPAR, peroxisome proliferator-activated receptors.

Received July 11, 2003.

Accepted January 23, 2004.

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This Article Abstract Full Text (PDF) Submit a related Letter to the Editor Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Request Copyright Permission Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Williams, M. A. Articles by Luthy, D. A. Search for Related Content PubMed PubMed Citation Articles by Williams, M. A. Articles by Luthy, D. A.