Elevated Plasma SPARC Levels Are Associated with Insulin Resistance, Dyslipidemia, and Inflammation in Gestational Diabetes Mellitus

Abstract

Objective

Recent studies suggested that secreted protein acidic and rich in cysteine (SPARC), a novel adipokine, is a key player in the pathology of obesity and type 2 diabetes. We aimed to determine whether concentrations of SPARC were altered in patients with gestational diabetes mellitus (GDM) compared to normal glucose tolerance (NGT) controls and to investigate the relationships between SPARC and metabolic parameters in pregnant women.

Design/Methods

Cross-sectional study of 120 pregnant women with GDM and 60 controls with NGT, in a university hospital setting. Plasma levels of SPARC, adiponectin, fibroblast growth factor 21 (FGF21), insulin and proinsulin were determined by ELISA.

Results

GDM women had higher SPARC and lower adiponectin than NGT subjects; no difference was found in FGF21. SPARC levels were the lowest in subjects in the third tertile of insulin sensitivity index (ISIOGTT) and correlated positively with pre-pregnant BMI, insulin and 3 h glucose during 100-g OGTT, HOMA-IR, fasting proinsulin, hsCRP and white blood cells count, and negatively with ISIOGTT, when adjusting for gestational age. Triglyceride (TG), Apolipoprotein A1, apolipoprotein B and lipoprotein (a) correlated with SPARC in partial Pearson correlation. Correlations between SPARC with adiponectin, systolic blood pressure and TG were marginally significant in partial Spearman correlation analysis. In multivariate regression analysis, SPARC was an independent negative indicator of ISIOGTT.

Conclusions

SPARC levels are correlated significantly with inflammation and may also be correlated with dyslipidemia and represent an independent determinant of insulin resistance in late pregnancy, indicating a potential role of SPARC in the pathophysiology of GDM.

About the Authors:

Lu Xu

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Fan Ping

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Jinhua Yin

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Xinhua Xiao

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Hongding Xiang

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Christie M. Ballantyne

Affiliations Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America, Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart Center, Houston, Texas, United States of America

Huaizhu Wu

Affiliation: Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas, United States of America

Ming Li

* E-mail: [email protected]

Affiliation: Department of Endocrinology, Key Laboratory of Endocrinology, Ministry of Health, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, China

Introduction

Gestational diabetes mellitus (GDM) has been defined as any degree of glucose intolerance with onset or first recognition during pregnancy [1]. GDM prevalence has increased by ∼10–100% in several race/ethnicity groups during the past 20 years [2]. A true increase in the prevalence of GDM, aside from its adverse consequences for infants in the newborn period, might also reflect or contribute to the current patterns of increasing diabetes and obesity, especially in the offspring [3].

Although the mechanisms for the development of GDM are unclear, similar underlying pathophysiology have been proposed for GDM and type 2 diabetes, including insulin resistance and relative insulin deficiency due to failure of pancreatic beta cells [4]. Importantly, in the past decade, a growing body of evidence has identified two pathologic changes implicated in insulin resistance and beta cell dysfunction: inflammation of adipose tissue and dysregulation of adipokine [5], [6]. For example, hypoadiponectinemia and increased levels of leptin, IL-6 and TNF-α have each been found to contribute to insulin resistance or beta cell failure in GDM [7], [8]._ENREF_8_ENREF_9.

Recently, secreted protein acidic and rich in cysteine (SPARC), has been suggested as a key player in the pathology of obesity and type 2 diabetes. SAPRC, also known as osteonectin or BM40 [9], is a widely expressed profibrotic protein with pleiotropic functions. As a modulator of cell-surface interaction, SPARC modulates tissue physiology by altering cell–ECM interactions, cell proliferation and migration [10]. Lately, it was found that adipocytes are the major source of circulating SPARC and SPARC inhibits adipogenesis and promotes adipose tissue fibrosis [11], [12]. Moreover, increased SPARC expression in adipose tissue is associated with insulin resistance. Clinical studies also revealed an association of increased SPARC levels with T2DM and diabetic retinopathy and nephropathy [13], [14]. Animal studies with Sparc-knockout mice showed amelioration of T2DM and its complications in the absence of SPARC [15], supporting a causal role of SPARC in the development of T2DM and its complications. Another novel adipokine is fibroblast growth factor 21 (FGF21), which has been reported to have beneficial effects on glucose homeostasis and insulin sensitivity in animal studies [16], [17], and reports on FGF21 in GDM were limited and controversial [18], [19].

Unlike other adipokines such as adiponectin and leptin, circulating levels of SPARC have not been reported so far in GDM. Based on this fact and the roles of SPARC in insulin resistance and obesity-related disease, we sought to examine whether circulating levels of this adipokine are altered in GDM women compared to control subjects and to investigate a potential link between levels of SPARC with clinical and biochemical measures of glucose, lipid metabolism, insulin sensitivity and inflammation in these subjects.

Subjects and Methods

Ethics Statement

Informed written consent was obtained from each participant and the study protocol was approved by the Institutional Review Board of Peking Union Medical College Hospital (PUMCH).

Subjects

Study subjects were recruited from the PUMCH Pregnant Cohort as described elsewhere [20]. Briefly, all pregnant women in outpatient were subjected to a standardized 50 g glucose challenge test (GCT) between the 24th to 28th gestational weeks. Subjects with 1 h plasma glucose level <7.8 mmol/L were defined GCT negative (GCT–); Subjects with 1 h plasma glucose level ≥7.8 mmol/L were considered GCT positive (GCT+) and underwent an additional oral glucose tolerance test (OGTT) with 100 g glucose 1 week later. Diagnosis of GDM was made based on the ADA criteria[1], i.e., if ≥1 of the following were present, GDM was diagnosed: the plasma glucose values ≥5.3 mmol/L on fasting; 10.0 mmol/L at 1 h; 8.6 mmol/L at 2 h; and 7.8 mmol/L at 3 h after oral glucose challenge. Normal glucose tolerance (NGT) was diagnosed when plasma glucose levels were below the values at all time points. We randomly selected 120 GDM patients (from a total of 723 women with GDM) and 60 age–matched normal controls (from a total of 582 women with NGT), with a total of 180 pregnant women, for this study. Patients with pre-gestational diabetes mellitus, treatment with steroid hormones, serious systemic disorders such as lupus, congenital cardiopathy, chronic hepatitis and nephrosis were excluded.

Anthropometric measurement

Anthropometric indices of the patients were collected at the first prenatal examination between the 13th and 15th weeks of gestation and included age, height, pre-pregnancy weight, history of gravidity and parity, systolic and diastolic blood pressure, past history, family history of diabetes (first degree relatives). Pre-pregnancy body mass index (pre-BMI) was calculated as weight (kg)/height (m)2.

Blood samples and biochemical analysis

After overnight fast, venous blood samples were collected from each subject by direct venipuncture into tubes with or without EDTA as anticoagulant. Serum and plasma were obtained by centrifugation at 1000 x g for 20 min. White blood cells (WBC) count and serum levels of creatinine (Cr) and alanine transaminase(ALT) were measured using standard laboratory procedures at the first prenatal examination between the 13th and 15th weeks of gestation. In the second trimester of pregnancy (24–28th weeks of gestation), additional blood chemistry were obtained as follows: plasma glucose levels were determined by the glucose oxidize method; serum levels of triglyceride (TG) and total cholesterol (TC) were determined using standard enzymatic method; serum levels of high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) were measured by direct method using the Hitachi 7060C Automatic Biochemistry Analysis System (Tokyo, Japan). Serum apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) (Lp(a)) and hypersensitive C reaction protein (hsCRP) were measured using Beckman's turbidimetric immunoassay method(CA, USA). Plasma glycosylated hemoglobin (HbA1c) levels were determined by high performance liquid chromatography (HPLC) method using Bio-Rad's VariantIITURBO analyzer (CA, USA).

Oral glucose tolerance test (OGTT)

OGTTs were performed in the second trimester of pregnancy. The gestational age when OGTTs were performed was presented and used for data analysis. After overnight fast, the subjects took 100 g glucose orally. At 0 h, 1 h, 2 h, and 3 h after glucose intake, plasma glucose (mmol/L) and specific insulin (uIU/mL) concentrations were measured as described previously [20]. Insulin resistance was assessed by homeostasis model assessment (HOMA) (HOMA-IR = [fasting insulin]×[fasting glucose]/22.5) and insulin sensitivity was assessed by Matsuda and DeFronzo's insulin sensitivity index (ISIOGTT = 10,000/√([fasting plasma glucose×fasting plasma insulin]×[mean glucose×mean insulin]) [21]. In an earlier validation study in pregnant women ISOGTT exhibited better correlation with insulin sensitivity derived using the euglycaemic–hyperinsulinaemic clamp technique than did the HOMA-IR [22].

Determination of plasma levels of SPARC, adiponectin, FGF21 and proinsulin

Fasting plasma samples collected in the second trimester of pregnancy were frozen at −80°C for later adipokines analysis. Plasma levels of proinsulin, adiponectin, SPARC and FGF21 were measured by sandwich ELISA. Proinsulin and adiponectin were measured as described previously with intra- and interassay coefficient of variations (CVs) of <8.9%, <11.2, <5.4 and <8.5%, respectively [23]–[25]. FGF21 was measured by an ELISA kit (Phoenix Pharmaceuticals, Burlingame, CA, USA) with intra- and interassay CVs of <6.0% and <8.6% respectively. For SPARC assay, mouse anti-human SPARC monoclonal antibody ON1-1 (Invitrogen, CA, USA) at 0.025 ug/well and biotinylated polyclonal goat anti-human SPARC antibody EYR01 (R&D Systems, MN, USA) at 0.015 ug/well were used. The plasma samples were diluted at 1∶20 with 10% Block Ace (Dainippon Pharmaceutical, Osaka, Japan). In this ELISA system, human SPARC HON-3030 (Haematological Technologies Inc., Vermont, USA) was used to generate the standard curve to quantify the SPARC protein based on absorbance data. The intra- and interassay CVs were 5.2% and 9.1%, respectively. All samples were tested in duplicates in a blinded manner.

Statistical analysis

SPSS software version 13.0 (SPSS, Chicago, IL) and SAS software version 9.2 (SAS Institute Inc, Cary, NC) were used for statistical analyses. To evaluate the differences of the characteristics between two groups, independent sample Student t test was applied. General linear model was used to adjust pre-BMI in comparison. Univariate correlations between SPARC and metabolic variables in pregnancy were assessed both by partial Pearson and partial Spearman correlation analysis with adjustment of gestational age. To identify independent relationships between parameters and dependent variable of Ln ISIOGTT, multivariate stepwise linear regression analysis was performed. Variables that were associated with Ln ISIOGTT in univariate correlation analyses were selected into the model as independent variables, including pre-BMI, gestational age, systolic blood pressure, triglycerides, SPARC, adiponectin, proinsulin, hsCRP, WBC count and ALT. Distribution was tested for normality using Shapiro-Wilk W test, and non-normally distributed parameters were logarithmically transformed, where necessary. A p value < 0.05 was considered statistically significant in all analyses.

Results

Plasma levels of SPARC and FGF21 in subjects with NGT and GDM

Table 1 showed the baseline demographic, clinical and metabolic characteristics of the study population in pregnancy, stratified into two groups: NGT (n = 60) and GDM (n = 120). Women in the two groups were matched for age and had similar pre-BMI, gestational age and blood pressures.

[Figure omitted. See PDF.]

Table 1. Anthropometric and metabolic characteristics of the subjects.

https://doi.org/10.1371/journal.pone.0081615.t001

GDM women had higher levels of glucose and insulin at all time points during 100 g OGTT than NGT subjects (p<0.05). As glucose tolerance status worsened, HOMA-IR and fasting plasma level of proinsulin increased in GDM patients compared with NGT women(p = 0.007, p = 0.012, respectively). Similarly, some lipids (TG, p = 0.040; ApoB, p<0.001) and inflammatory markers (hsCRP, p = 0.033; WBC, p = 0.030) were higher in GDM than in NGT. Values of ISIOGTT and ApoA1 showed the opposite pattern, being lower in GDM than in NGT women (p<0.001 for both). Serum levels of ALT, Cr, TC, HDL-C, LDL-C and Lp(a) exhibited no differences between the two groups.

Plasma concentrations of SPARC were significantly increased (p = 0.040) and adiponectin were markedly decreased (p<0.001) in GDM patients compared to NGT subjects. The group differences of SPARC remained significant after adjustment of pre-BMI (p = 0.022, data not shown). We failed to find a difference in plasma FGF21 levels between the two groups.

When patients were divided by extent of insulin sensitivity into ISIOGTT tertiles, plasma levels of SPARC were lower in the second and third tertile as compared with the first tertile, and the differences between the third and the first tertile (p = 0.003) as well as the third and the second tertile (p = 0.033; Fig. 1A) were significant. Accordingly, SPARC levels increased progressively from the first to the third tertile of HOMA-IR (p = 0.001, T3 vs T1; p = 0.017, T3 vs T2; Fig. 1B).

[Figure omitted. See PDF.]

Figure 1. A. LnSPARC in ISIOGTT tertiles; B. LnSPARC in HOMA-IR tertile.

Data were shown mean±SEM. SPARC were logarithmically transformed for comparison. Differences between groups were assessed by One-way ANOVA. SPARC plasma levels are decreased from first to third tertile of ISIOGTT and increased from first to third tertile of HOMA-IR. A. * p = 0.003, T3 vs T1; p = 0.033, T3 vs T2; B. *p = 0.001 T3 vs T1; p = 0.017, T3 vs T2.

https://doi.org/10.1371/journal.pone.0081615.g001

Univariate correlations

As SPARC is expressed and secreted by placental tissue we adjusted for gestational age in the univariate correlation analysis. Partial Pearson correlation analysis after adjustment showed that plasma SPARC concentrations were positively associated with pre-BMI (r = 0.153, p = 0.040), insulin resistance: 3-hour glucose (r = 0.222, p = 0.003), 0-,1-,3 hours insulin during 100-g OGTT (r = 0.220, p = 0.003; r = 0.175, p = 0.018; r = 0.177, p = 0.017; respectively), HOMA-IR (r = 0.219, p = 0.003), fasting plasma levels of proinsulin (r = 0.182, p = 0.015), lipids including TG (r = 0.151, p = 0.045), ApoB (r = 0.153, p = 0.040) and Lp(a) (r = 0.168, p = 0.025), and inflammatory markers such as hsCRP (r = 0.175, p = 0.018) and WBC count(r = 0.203,p = 0.007; Table 2); whereas significant negative correlations existed between SPARC and ISIOGTT (r = −0.236, p = 0.001), SPARC and ApoA1(r = −0.159, p = 0.034). In contrast, circulating SPARC levels were not significantly correlated with age, blood pressure, 0-, 1-, 2-hours glucose and 2-hour insulin during 100-g OGTT, ALT, Cr, TC, HDL and LDL cholesterol and plasma adiponectin and FGF21 (Table 2) in Partial correlation analysis.

[Figure omitted. See PDF.]

Table 2. Univariate correlations between SPARC and metabolic parameters.

https://doi.org/10.1371/journal.pone.0081615.t002

In accordance with the results of partial Pearson analysis, in partial Spearman correlation analysis adjusted for gestation age, SPARC levels positively correlated with pre-BMI (r = 0.167, 0.024; Table 2), 3-hour glucose (r = 0.229, p = 0.002), 0-,1-,3 hours insulin during 100-g OGTT(r = 0.259, p<0.001; r = 0.193, p = 0.009; r = 0.191, p = 0.011; respectively), HOMA-IR (r = 0.251, p = 0.001), fasting plasma levels of proinsulin (r = 0.185, p = 0.014), inflammatory markers including hsCRP (r = 0.189, p = 0.011) and WBC count(r = 0.236,p = 0.002); and negatively related to ISIOGTT (r = −0.245, p = 0.001). Differently from results of Pearson correlation analysis, SPARC lost its correlations with lipoprotein ApoA1, ApoB and Lp (a) in Spearman analysis, but showed marginally significant correlation with adiponectin (r = −0.126, p = 0.090), systolic blood pressure (r = 0.147, p = 0.064) and TG (r = 0.129, p = 0.086).

Multiple regression analysis

Having demonstrated that GDM is characterized by elevated level of SPARC, which in turn related to insulin resistance, we next queried whether the relationship between SPARC and insulin resistance is independent of the other metabolic parameters. Multivariate stepwise regression analysis with logarithmically transferred ISIOGTT as a dependent variable was applied to investigate the relationship. SPARC (β = −0.156, p = 0.030), adiponectin (β = 0.295, p<0.001), fasting proinsulin (β = −0.365, p<0.001), hsCRP (β = −0.236, p = 0.001) and TG (β = −0.162, p = 0.032) were independently associated with insulin sensitivity as estimated by ISIOGTT (Table 3).

[Figure omitted. See PDF.]

Table 3. Multivariate stepwise linear regression analysis with Ln insulin sensitivity index (ISIOGTT) as dependent variable.

https://doi.org/10.1371/journal.pone.0081615.t003

Discussion

In our study, we first determined circulating levels of SPARC in pregnant women and found that SPARC levels were elevated significantly in GDM group compared with NGT group and correlated significantly with insulin resistance. We also demonstrated the associations between SPARC with inflammatory markers, TG, and adiponectin. Importantly, the key finding of the current study was the demonstration that SPARC in pregnancy was an independent indicator of insulin resistance. In agreement with this, SPARC level was significantly lower in subjects in the third tertiles of ISIOGTT as compared with the first tertile. Therefore, our findings implicate a potential role of SPARC in the pathophysiology of insulin resistance in GDM and provide insights on both risk stratification and modification in this patient population.

So far there are limited studies on SPARC and diabetic subjects, and the findings are consistent with our results: levels of SPARC are significantly elevated in T2DM patients compared with normal controls in Chinese and Japanese populations [14], [26]. A recent study showed that human placenta villi could express and secrete SPARC [27], suggesting that levels of SPARC may be affected by gestational age. However, in our study the subjects in GDM group and control group had similar gestational age, around 26 weeks of gestation and it has been shown that circulating levels of SPARC mainly come from adipose tissue [11], therefore, the differences in circulating levels of SPARC in GDM patients and controls may not be caused by gestational age. To avoid the potentially confounding effect of gestational age, we still adjusted for it in our univariate and multivariate correlations.

In the above studies about diabetic subjects, SPARC positively correlate with BMI, the percentage of fat, fasting insulin and 2 h insulin in OGTT, HOMA-IR and triglyceride in T2DM and normal controls [14], [26]. The correlations of levels of SPARC and levels of insulin at different time points during OGTT in these studies and our study may be explained by the interaction between SPARC and insulin. On the one hand, insulin may increase SPARC production, as evidenced by in vitro study where insulin promotes SPARC expression of visceral adipose tissue explants in a dose-dependent manner [11]. On the other hand, over-expression of human SPARC in INS-1 cells increases glucose-stimulated insulin secretion [28]. The influence of SPARC in insulin resistance may also arise from its role in obesity-induced adipose tissue fibrosis and associated metabolic dysfunction by regulating extracellular matrix (ECM) composition and inhibiting adipogenesis [29], [30]. Fibrosis of subcutaneous adipose tissue may reduce the ability of adipocytes to store triglycerides, which then overspill into the circulation, resulting in systemic hyperlipidemia and, ultimately, lipid infiltration into other organs such as skeletal muscle and liver leading to ectopic lipid deposition and insulin resistance in these organs. The implication of SPARC in dysfunction of adipose tissue could also explain our findings that SPARC were positively related to level of triglycerides but negatively with adiponectin. However, the correlation between SPARC and ApoA1, ApoB and Lp (a) were not consistent in Pearson correlation analysis and Spearman correlation analysis in our study, indicating that SPARC and these TG rich lipoprotein maybe linear related rather than monotonic related. Taken together, the association of SPARC and insulin resistance may be mediated by dysfunction of adipose tissue and related dyslipidemia. As increasing evidence has shown that levels of ApoB, especially when combined with apoAI to form the ApoB/A1 ratio, have higher sensitivity and specificity than cholesterol as predictors of atherosclerotic cardiovascular disease [31], and TG is an important component of metabolic syndrome [32], our results also suggested risk of elevated SPARC in GDM for future cardiometabolic disorders.

A growing body of evidence has recently reported that women with GDM exhibit evidence of sub-clinical inflammation. For example, increased CRP levels and leukocyte count in the first trimester have been demonstrated to independently predict the subsequent development of GDM later in pregnancy [33], [34]. In our study, for the first time we showed that circulating SPARC levels in the second trimester positively correlated with hsCRP levels and also associated with WBC count that was detected in the earlier trimester of pregnancy. A previous study also indentified strong correlation between serum hsCRP and SPARC expression in adipose tissue [11]. SPARC may link inflammation and glucose intolerance by excessive synthesis of ECM components [35]. A proinflammatory environment up-regulates expression of SPARC which then promotes the synthesis of ECM components and drives adipose tissue fibrosis and subsequent insulin resistance. The co-existence of high SPARC level and elevated inflammation markers in GDM and their close relevance with each other and with insulin resistance suggest that their interaction may play an important role in the development and progression of GDM.

In the current study, GDM was diagnosed based on the criteria of the American Diabetes Association as long as glucose value at any time point during OGTT exceeds its threshold. In central Europe, the use of the more stringent criteria for the diagnosis of GDM women detects more large-for-gestational age neonates with hypoglycemia and mothers with impaired postpartum glucose metabolism than the World Health Organization criteria [36]. Furthermore, Kautzky-Willer and coworkers [36] demonstrated convincingly that women with 1 abnormal glucose value during OGTT do not differ from those with 2 abnormal values in their obstetric outcome. According to this stricter criteria used in our study, adiponectin levels were significantly different in GDM group compared with NGT group, and low adiponectin emerged as an independent contributor to insulin resistance in pregnant women. Low adiponectin has been repeatedly associated with insulin resistance, consistent with the known insulin-sensitizing bioactivity of the protein [37]. This inverse relationship between adiponectin and insulin resistance has also been observed in GDM [38]. Thus, our results on adiponectin were in accordant with the previous findings of its protected role in development of insulin resistance in pregnancy, indicating its beneficial effects in Chinese pregnant women.

We did not find a significant difference in FGF21 between GDM and NGT women. So far studies on FGF21 in GDM were limited and controversial. Stein et al. assessed the levels of FGF21 in German GDM women at mid-pregnancy (24–28th week of gestation) and showed that serum FGF21 levels were not significantly different between subjects with GDM and healthy pregnant controls [18]. While another recent study in UK in GDM women at 39–40 weeks of gestation reported that GDM women had significantly higher plasma levels of FGF21 than controls [19]. In regard to the role of FGF21 in lipid metablism and insulin resistance during pregnancy, the current study highlights the need for future study in this population of different races and ethnics.

Our current study included some limitations. Firstly, given the cross-sectional nature of our study, temporal associations were not determine. Secondly, due to the complicated procedures of hyperinsulinemic-euglycemic clamp, it is impractical for use in large populations. Therefore, we did not use this method in the evaluation of insulin resistance in our study, but we used multiple simple indices including HOMA-IR, fasting proinsulin and ISIOGTT, which consistently shown the correlation between SPARC and insulin resistance. Finally, because of the difficulty in obtaining informed consent, we could not detect circulating SPARC levels in women with normal 50 g OGTT and compare SPARC levels between GCT– subjects and GDM subjects.

In conclusion, our results revealed that elevated plasma level of SPARC in GDM correlated with inflammatory markers and dyslipidemia and more importantly, was independently associated with insulin resistance in pregnant women, suggesting a potential role of SPARC in the pathogenesis of GDM, and hence may be relevant to strategies for both risk stratification and risk modifications in pregnant women.

Acknowledgments

We are indebted to the pregnant women for agreeing to participate in this study.

Author Contributions

Conceived and designed the experiments: ML XX HX. Performed the experiments: LX FP JY. Analyzed the data: LX ML FP JY XX HX HW CMB. Contributed reagents/materials/analysis tools: ML LX FP HW CMB. Wrote the paper: LX ML HW.

Citation: Xu L, Ping F, Yin J, Xiao X, Xiang H, Ballantyne CM, et al. (2013) Elevated Plasma SPARC Levels Are Associated with Insulin Resistance, Dyslipidemia, and Inflammation in Gestational Diabetes Mellitus. PLoS ONE 8(12): e81615. https://doi.org/10.1371/journal.pone.0081615

References

1. American Diabetes Associateion (2007) Diagnosis and classification of diabetes mellitus. Diabetes Care 30 Suppl 1S42–47.

2. Ferrara A (2007) Increasing prevalence of gestational diabetes mellitus: a public health perspective. Diabetes Care 30 Suppl 2S141–146.

3. Carpenter MW (2007) Gestational diabetes, pregnancy hypertension, and late vascular disease. Diabetes Care 30 Suppl 2S246–250.

4. Wiznitzer A, Mayer A, Novack V, Sheiner E, Gilutz H, et al.. (2009) Association of lipid levels during gestation with preeclampsia and gestational diabetes mellitus: a population-based study. Am J Obstet Gynecol 201 : 482 e481–488.

5. Greenberg AS, McDaniel ML (2002) Identifying the links between obesity, insulin resistance and beta-cell function: potential role of adipocyte-derived cytokines in the pathogenesis of type 2 diabetes. Eur J Clin Invest 32 Suppl 324–34.

6. Calle MC, Fernandez ML (2012) Inflammation and type 2 diabetes. Diabetes Metab 38: 183–191.

7. Miehle K, Stepan H, Fasshauer M (2012) Leptin, adiponectin and other adipokines in gestational diabetes mellitus and pre-eclampsia. Clin Endocrinol (Oxf) 76: 2–11.

8. Ategbo JM, Grissa O, Yessoufou A, Hichami A, Dramane KL, et al. (2006) Modulation of adipokines and cytokines in gestational diabetes and macrosomia. J Clin Endocrinol Metab 91: 4137–4143.

9. Termine JD, Kleinman HK, Whitson SW, Conn KM, McGarvey ML, et al. (1981) Osteonectin, a bone-specific protein linking mineral to collagen. Cell 26: 99–105.

10. Bornstein P (1995) Diversity of function is inherent in matricellular proteins: an appraisal of thrombospondin 1. J Cell Biol 130: 503–506.

11. Kos K, Wong S, Tan B, Gummesson A, Jernas M, et al. (2009) Regulation of the fibrosis and angiogenesis promoter SPARC/osteonectin in human adipose tissue by weight change, leptin, insulin, and glucose. Diabetes 58: 1780–1788.

12. Lee DE, Kehlenbrink S, Lee H, Hawkins M, Yudkin JS (2009) Getting the message across: mechanisms of physiological cross talk by adipose tissue. Am J Physiol Endocrinol Metab 296: E1210–1229.

13. Munjal ID, McLean NV, Grant MB, Blake DA (1994) Differences in the synthesis of secreted proteins in human retinal endothelial cells of diabetic and nondiabetic origin. Curr Eye Res 13: 303–310.

14. Kanauchi M, Nishioka M, Dohi K (2000) Secreted protein acidic and rich in cysteine (SPARC) in patients with diabetic nephropathy and tubulointerstitial injury. Diabetologia 43: 1076–1077.

15. Taneda S, Pippin JW, Sage EH, Hudkins KL, Takeuchi Y, et al. (2003) Amelioration of diabetic nephropathy in SPARC-null mice. J Am Soc Nephrol 14: 968–980.

16. Kharitonenkov A, Shiyanova TL, Koester A, Ford AM, Micanovic R, et al. (2005) FGF-21 as a novel metabolic regulator. J Clin Invest 115: 1627–1635.

17. Wente W, Efanov AM, Brenner M, Kharitonenkov A, Koster A, et al. (2006) Fibroblast growth factor-21 improves pancreatic beta-cell function and survival by activation of extracellular signal-regulated kinase 1/2 and Akt signaling pathways. Diabetes 55: 2470–2478.

18. Stein S, Stepan H, Kratzsch J, Verlohren M, Verlohren HJ, et al. (2010) Serum fibroblast growth factor 21 levels in gestational diabetes mellitus in relation to insulin resistance and dyslipidemia. Metabolism 59: 33–37.

19. Tan BK, Sivakumar K, Bari MF, Vatish M, Randeva HS (2013) Lower cerebrospinal fluid/plasma fibroblast growth factor 21 (FGF21) ratios and placental FGF21 production in gestational diabetes. PLoS One 8: e65254.

20. Ping F, Xiang HD, Li M, Li W, Liu JT, et al. (2012) Effects of variation in retinol binding protein 4 gene and adipose specific expression of gestational diabetes in Beijing, China. Diabetes Res Clin Pract 97: 283–289.

21. Matsuda M, DeFronzo RA (1999) Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22: 1462–1470.

22. Kirwan JP, Huston-Presley L, Kalhan SC, Catalano PM (2001) Clinically useful estimates of insulin sensitivity during pregnancy: validation studies in women with normal glucose tolerance and gestational diabetes mellitus. Diabetes Care 24: 1602–1607.

23. Ming L, Jing Y, Congyuan W, Kui Z (1999) A monoclonal antibody-based sandwich enzymeimmunoassay for human serum proinsulin. Chinese Journal of Diabetes 3: 157–159.

24. Li M, Fisette A, Zhao XY, Deng JY, Mi J, et al. (2009) Serum resistin correlates with central obesity but weakly with insulin resistance in Chinese children and adolescents. Int J Obes (Lond) 33: 424–439.

25. Xu L, Li M, Yin J, Cheng H, Yu M, et al. (2012) Change of Body Composition and Adipokines and Their Relationship with Insulin Resistance across Pubertal Development in Obese and Nonobese Chinese Children: The BCAMS Study. Int J Endocrinol 2012: 389108

26. Wu D, Li L, Yang M, Liu H, Yang G (2011) Elevated plasma levels of SPARC in patients with newly diagnosed type 2 diabetes mellitus. Eur J Endocrinol 165: 597–601.

27. Jiang Y, Zhu Y, Shi Y, He Y, Kuang Z, et al. (2013) Downregulation of SPARC expression inhibits the invasion of human trophoblast cells in vitro. PLoS One 8: e69079.

28. Harries LW, McCulloch LJ, Holley JE, Rawling TJ, Welters HJ, et al. (2013) A role for SPARC in the moderation of human insulin secretion. PLoS One 8: e68253.

29. Khan T, Muise ES, Iyengar P, Wang ZV, Chandalia M, et al. (2009) Metabolic dysregulation and adipose tissue fibrosis: role of collagen VI. Mol Cell Biol 29: 1575–1591.

30. Nie J, Sage EH (2009) SPARC inhibits adipogenesis by its enhancement of beta-catenin signaling. J Biol Chem 284: 1279–1290.

31. Gotto AM Jr, Whitney E, Stein EA, Shapiro DR, Clearfield M, et al. (2000) Relation between baseline and on-treatment lipid parameters and first acute major coronary events in the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS). Circulation 101: 477–484.

32. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, et al. (2005) Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112: 2735–2752.

33. Wolf M, Sandler L, Hsu K, Vossen-Smirnakis K, Ecker JL, et al. (2003) First-trimester C-reactive protein and subsequent gestational diabetes. Diabetes Care 26: 819–824.

34. Wolf M, Sauk J, Shah A, Vossen Smirnakis K, Jimenez-Kimble R, et al. (2004) Inflammation and glucose intolerance: a prospective study of gestational diabetes mellitus. Diabetes Care 27: 21–27.

35. Henegar C, Tordjman J, Achard V, Lacasa D, Cremer I, et al. (2008) Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity. Genome Biol 9: R14.

36. Kautzky-Willer A, Bancher-Todesca D, Weitgasser R, Prikoszovich T, Steiner H, et al. (2008) The impact of risk factors and more stringent diagnostic criteria of gestational diabetes on outcomes in central European women. J Clin Endocrinol Metab 93: 1689–1695.

37. Li S, Shin HJ, Ding EL, van Dam RM (2009) Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 302: 179–188.

38. Retnakaran R, Connelly PW, Maguire G, Sermer M, Zinman B, et al. (2007) Decreased high-molecular-weight adiponectin in gestational diabetes: implications for the pathophysiology of Type 2 diabetes. Diabet Med 24: 245–252.