Arch Gynecol Obstet DOI 10.1007/s00404-013-3070-y
MATERNAL-FETAL MEDICINE
Circulating apelin levels are associated with cardiometabolic risk factors in women with previous gestational diabetes Baris Akinci • Aygul Celtik • Sunay Tunali • Sinan Genc • Faize Yuksel • Mustafa Secil • Mehmet Ali Ozcan • Firat Bayraktar
Received: 4 September 2012 / Accepted: 24 October 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Purpose Women with previous gestational diabetes mellitus (pGDM) are at high risk for type 2 diabetes and cardiovascular disorders. In this study, we aimed to compare plasma apelin levels between women with and without pGDM, and to investigate the possible association of apelin with cardiometabolic risk factors. Methods Among 252 consecutive Caucasian women with GDM being included in a prospective postpartum followup protocol, 141 women eligible for the study protocol were enrolled. Control group consisted of 49 age- and body mass index-matched healthy women without pGDM. Circulating apelin, IL-6 and plasminogen activator inhibitor levels, and carotid intima media thickness (IMT) were measured. To evaluate carbohydrate intolerance, 75-g oral glucose tolerance test was performed. Fasting insulin and
lipids were measured, and homeostasis model assessment index was calculated. Results Plasma apelin levels were reduced in women with pGDM (p \ 0.001). In multiple regression analysis, apelin was negatively associated with fasting (r2 0.090, b -0.273, p = 0.001) and post-load glucose (r2 0.061, b -0.187, p = 0.022), serum IL-6 (r2 0.082, b -0.234, p = 0.002), and carotid IMT (r2 0.057, b -0.168, p = 0.033). Conclusions Our results suggested that suppressed apelin levels were associated with increased cardiovascular risk in women with pGDM. Keywords Apelin Atherosclerosis Carotid IMT Previous gestational diabetes
Introduction B. Akinci (&) Department of Endocrinology and Metabolism, Izmir University, Izmir, Turkey e-mail: [email protected] B. Akinci F. Bayraktar Division of Endocrinology and Metabolism, Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey A. Celtik Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey S. Tunali F. Yuksel M. A. Ozcan Division of Hematology, Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey S. Genc M. Secil Department of Radiology, Dokuz Eylul University, Izmir, Turkey
The prevalence of gestational diabetes mellitus (GDM) is increasing in line with the increasing prevalence of type 2 diabetes and obesity over the world [1]. It has been demonstrated that maternal hyperglycaemia in women with GDM leads to increased risk for perinatal morbidity including fetal hyperinsulinemia, excessive fetal growth, neonatal hypoglycemia and hyperbilirubinemia [2, 3]. Women with GDM are at high risk for maternal complications as well. Studies have suggested that perinatal and maternal morbidity rates may be reduced with the intensive care of patients with GDM [3–5]. In addition, GDM is associated with an increased risk of developing diabetes in the postpartum period. Women with a previous history of GDM (pGDM) are also at increased risk of developing cardiovascular disorders, hypertension, dyslipidemia and metabolic syndrome [3, 6].
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Endothelial dysfunction is believed to be the initiating factor in the development of atherosclerosis [7]. Like circulating levels of systemic inflammation markers and homeostatic parameters, the levels of several adipocytokines have been associated with endothelial dysfunction and atherosclerosis. Endothelial dysfunction in asymptomatic individuals can be demonstrated by non-invasive techniques such as flow-mediated dilation, ankle–arm index and carotid intima media thickness (IMT). Carotid IMT has been associated with coronary atherosclerosis, and is also proposed as a predictor for vascular events in the future [8]. Carotid IMT is increased in patients with carbohydrate disturbance, either in patients with type 2 diabetes or patients with impaired glucose tolerance (IGT) [9]. Several studies have demonstrated increased carotid IMT in women with pGDM [10, 11] ENREF_6. Apelin is a recently discovered adipocytokine identified as an endogenous ligand of the G protein-coupled receptor APJ [12]. Apelin, first isolated from bovine stomach extracts, has been shown to be produced by the adipose tissue and also expressed in the brain, lung, heart, gastrointestinal tract, pancreas and kidney. Apelin, a 77 aminoacid prepropeptide, cleaves to various length peptides, apelin-12, -13, -16, -17, -19 and -36. At least 12 C-terminal amino acid residues are essential for the biological activity. Being localized in human vascular and endocardial endothelial cells, apelin has been proposed to play a role in the cardiovascular system [13, 14]. In the current study, we aimed to compare plasma apelin levels between women with and without pGDM, and to investigate the possible association between plasma apelin levels and cardiometabolic risk factors.
Materials and methods Two hundred and fifty-two consecutive Caucasian women, who were diagnosed with GDM between January 2002 and January 2008, were included in a prospective follow-up protocol. GDM was diagnosed by screening with a 50-g 1-h glucose challenge test at 24–28th week of gestation, followed by a 100-g 3-h oral glucose tolerance test (OGTT) at Dokuz Eylul University Hospital. Women with severe obesity, prior history of GDM or delivery of large-forgestational-age-infant, diagnosis of polycystic ovary syndrome, and strong family history for type-2 diabetes were screened at their initial prenatal visits because of the high risk for developing GDM. Carpenter and Coustan criteria [15] were used for GDM diagnosis (equal to or exceeding 95, 180, 155, and 140 mg/dL, with at least two elevated values considered abnormal). Control group was composed of age- and body mass index (BMI)-match hospital staff, in whom GDM was excluded by a 50 g screening test between
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24 and 28th week of gestation at the same period of time. Between June 2008 and June 2009, subjects were invited for the current study protocol. A total of 141 women with previous GDM and 49 healthy controls, who were eligible for the study protocol and gave an informed consent, were enrolled. Exclusion criteria were known cardiovascular disorders, type 1 or type 2 diabetes (diagnosed before the index pregnancy), familial hyperlipidemia, hypertension, acute infection, chronic inflammatory disease, coagulation disorders and other systemic diseases. Subjects were not included if they were on peri- or postmenopausal period at the time of sampling. The study was approved by the local ethics committee of Dokuz Eylul University. Height (m) and weight (kg) were measured under fasting conditions with subjects in light clothing and without shoes. BMI was calculated as body weight divided by square height. Waist circumference was measured from the mid-level between iliac crest and the lowest rib. Blood pressure was measured using a sphygmomanometer in the sitting position after 5-min rest. Subjects with a blood pressure level higher than 130 mmHg for systolic and 80 mmHg for diastolic at the examination or patients using antihypertensive drugs were defined as hypertensive and excluded from the study. Blood was taken from the cannulated antecubital vein between 8:00 and 9:00 a.m. after 10-h overnight fasting. Smokers were asked to refrain from smoking for 24-h before sampling. To evaluate carbohydrate intolerance, 75-g OGTT was performed. Type 2 diabetes, IGT and impaired fasting glucose (IFG) were defined according to American Diabetes Association (ADA) recommendations [3]. Blood samples were transferred into tubes containing fluoride for plasma glucose assay, buffered citrate for plasminogen activator inhibitor-1 (PAI-1) assay, and tubes suitable for serum separation. Blood samples for apelin assay were kept in ethylenediaminetetraacetic acid-containing tubes. Tubes were centrifuged at 3,000g for 10 min (Nuve, Ankara, Turkey). Serum and plasma were extracted, aliquoted, and stored at -80 °C until analysis. Glucose levels were measured by a colorimetric method with the Roche/Hitachi D/P Modular System Autoanalyzer (Roche Diagnostics, Basel, Switzerland). Triglycerides, total cholesterol and high-density lipoprotein (HDL) cholesterol were measured by Roche/Hitachi D/P Modular System Autoanalyzer (Roche Diagnostics, Basel, Switzerland). Low density lipoprotein (LDL) cholesterol was calculated by the Friedewald’s equation method. Insulin levels were measured by a chemiluminescent method using an automatic immunoanalyzer (Roche Diagnostics, Mannheim, Germany). Homeostasis model assessment (HOMA-IR) score, an estimate of insulin resistance, was calculated as fasting serum insulin (mU/L) 9 fasting plasma glucose (mmol/L)/22.5.
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Apelin was measured with enzyme-linked immunosorbent assay (ELISA) according to the manufacturer’s instructions (Phoenix Pharmaceuticals, Belmont, CA, USA). The intra- and inter-assay coefficients of variations for apelin-12 were 12 and 8 %, respectively. Interleukin-6 (IL-6) levels were measured with an ELISA kit (Invitrogen, Grand Island, NY, USA). Experiments were performed according to the manufacturer’s instructions. Intra- and inter-assay coefficients of variations for IL-6 were 7.2 and 5.4 %, respectively. PAI-1 antigen was measured by ELISA (Bender Medical Systems, Vienna, Austria). Intraassay and inter-assay coefficients of variations were 4.7 and 5.0 %, respectively. Ultrasounds for carotid IMT were performed after 10-h overnight fasting between 08:00 and 10:00 a.m. using a high-resolution Doppler ultrasound (Philips HDI 5000, Bothell, WA, USA) with a 7–12 MHz linear array transducer. Images were recorded and evaluated by a radiologist. Ultrasonographic images of the right and the left common carotid arteries (CCA) of each case at the lower 1/3 cervical region proximally and 1 cm above the carotid bulb distally in longitudinal plane were obtained. Measurements of the proximal and distal CCA posterior wall were done manually by the provided distance measurement system of the ultrasonography device after magnification of the images. Three measurements were made in a nonneighboring fashion within an approximately 1-cm segment from both left and right CCA proximal and distal portions. IMT thickness values were then calculated by obtaining the arithmetic means of the measured values. Table 1 Characteristics of patients with pGDM and healthy controls
The sample size was calculated using Power and Precision software (Biostat, Englewood, NJ, USA). The criterion for significance (a) has been set at 0.05. With the proposed sample size for the groups, sample size determination used a significance level of 0.05 for a two-tailed test with 80 % power. This effect was selected as the smallest effect that would be important to detect, in the sense that any smaller effect would not be of clinical or substantive significance. Sample size determination was based on substantive knowledge and previous research. To be able to generate subgroups with adequate number of subjects in each subgroup, the study group was composed of a rather large number of subjects. Variable distributions were assessed by the Kolmogorov–Smirnov normality test. Independent sample t test was used to compare variables of patients. One-way ANOVA with Bonferroni correction was applied for comparison of subgroups. Mann–Whitney U and Kruskal–Wallis tests were used for comparison of non-normally distributed variables. Categorical variables were compared by the Chi-square test. Correlation analyses were performed using Pearson’s coefficients. Spearman’s test was used when necessary. Regression analysis was employed to assess correlations between studied parameters. Statistical analysis was performed using Statistical Package of Social Science (SPSS Inc, Chicago, IL, USA), version 15.0 for Windows. Data were expressed as mean ± standard deviation (SD). For variables that require log transformation, median values were also provided in parentheses. A p value \0.05 was accepted as statistically significant.
pGDM (n = 141)
Healthy controls (n = 49)
p value
Age (years)
35.18 ± 5.55
34.89 ± 5.64
0.757
Postpartum duration (years)
3.36 ± 1.87
3.47 ± 1.76
0.725
Smoking (n, %) BMI (kg/m2)
28 (19.9 %) 26.82 ± 4.25
9 (18.4 %) 26.5 ± 2.66
0.82 0.62
Waist (cm)
90.31 ± 11.68
87.45 ± 8.93
0.119
OGTT (mmol/L)
Data are shown as mean ± SD. Results are unadjusted. For variables that require log transformation, median values are also provided in parentheses
0 min
5.65 ± 1.39
4.38 ± 0.48
\0.001
120 min
7.57 ± 3.18
4.77 ± 0.87
\0.001
Total cholesterol (mmol/L)
5.07 ± 0.98
4.59 ± 0.82
Triglyceride (mmol/L)
1.35 ± 0.75 (1.12)
1.01 ± 0.65 (0.84)
0.002 \0.001
HDL cholesterol (mmol/L)
1.34 ± 0.34
1.49 ± 0.39
0.008
LDL cholesterol (mmol/L)
3.11 ± 0.85
2.63 ± 0.71
\0.001
Insulin (mU/L)
7.44 ± 5.51 (6.49)
6.06 ± 3.26 (6.13)
0.339
HOMA
1.92 ± 1.59 (1.57)
1.19 ± 0.68 (1.12)
0.004
PAI-1 (ng/mL)
133.88 ± 45.28 (130.71)
117.64 ± 40.77 (120.47)
0.012
IL-6 (pg/mL)
2.81 ± 1.39 (2.49)
2.36 ± 0.94 (2.25)
0.067
Apelin (ng/mL
0.33 ± 0.18 (0.31)
0.56 ± 0.29 (0.5)
Carotid IMT (mm)
0.56 ± 0.08 (0.55)
0.53 ± 0.07 (0.52)
\0.001 0.014
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Results Table 1 shows characteristics of patients and healthy controls. There was no significant difference in terms of age, BMI, waist circumference, postpartum duration and the proportion of smokers between two groups. Women with pGDM had higher fasting and post-load glucose levels on OGTT than healthy controls. Women with pGDM had more atherogenic lipid profiles. HOMA was significantly higher in the pGDM group. Women with pGDM had significantly higher levels of plasma PAI-1. Although not significant, IL-6 levels were slightly elevated in the pGDM group. On the other hand, plasma apelin levels were significantly reduced in the pGDM group. There was a slight, but statistically significant, difference in carotid IMT between two groups. Seventy-four women (52.5 %) with pGDM had normal carbohydrate tolerance, whereas 46 of those (32.6 %) had IFG and/or IGT and 21 patients (14.9 %) had newly diagnosed diabetes. All patients in the control group had normal glucose regulation at follow-up. Table 2 shows the comparison of parameters between subgroups concerning postpartum carbohydrate intolerance among women with pGDM. As expected, age was higher in women with diabetes, in line with the increased postpartum follow-up
duration. There was no significant difference in smoking habits. Women with IFG/IGT had increased BMI, waist circumference and fasting insulin levels than pGDM women with normal carbohydrate tolerance. Women with diabetes also had increased BMI, waist circumference and fasting insulin levels; however, the difference was not statistically significant probably due to the small number of patients developing diabetes. HOMA was increased both in IFG/IGT and diabetes subgroups. Women with diabetes had significantly higher triglyceride, IL-6 and PAI-1 levels and carotid IMT. Although point estimates indicate that women with diabetes had more depressed apelin levels compared to pGDM women with normal glucose tolerance or IFG/IGT, the difference was quite small and was not statistically significant (p [ 0.05). Plasma apelin levels were negatively correlated with fasting (r = -0.258, p \ 0.001) and post-load glucose (r = -0.161, p = 0.027), serum IL-6 (r = -0.151, p = 0.038) levels, and carotid IMT (r = -0.150, p = 0.039). Plasma apelin was positively correlated with HDL cholesterol (r = 0.203, p = 0.005). Plasma apelin was not correlated with BMI, waist and HOMA. In multiple regression analysis, fasting glucose, post-load glucose, carotid IMT and IL-6 levels were found to be associated with plasma apelin when the data was controlled
Table 2 Characteristics of women with pGDM stratified by current glucose regulation category (i.e., normal, IFG/IGT, or diabetes) Normal (n = 74)
IFG/IGT (n = 46)
Diabetes (n = 21)
Age (years)
34.45 ± 5.39
35.19 ± 5.15
37.76 ± 6.33
Postpartum duration (years) ,à
2.82 ± 1.71
3.57 ± 1.83
4.81 ± 1.49
Smoking (n, %)
17 (23 %)
8 (17.4 %)
3 (14.3 %)
BMI (kg/m2)*
25.52 ± 3.72
28.49 ± 4.08
27.74 ± 4.97
Waist (cm)*
87.49 ± 11.94
94.26 ± 10.25
91.66 ± 11.41
0 min ,à,*
4.85 ± 0.39
5.96 ± 0.48
7.78 ± 2.25
120 min .à,*
5.69 ± 1.13
7.88 ± 1.73
13.51 ± 3.03
5.05 ± 0.95
4.95 ± 0.94
5.39 ± 1.14
Triglyceride (mmol/L)
1.25 ± 0.67 (0.99)
1.35 ± 0.78 (1.11)
1.68 ± 0.89 (1.64)
HDL cholesterol (mmol/L)*
1.41 ± 0.35
1.21 ± 0.26
1.39 ± 0.38
OGTT (mmol/L)
Total cholesterol (mmol/L)
LDL cholesterol (mmol/L)
3.07 ± 0.81
3.13 ± 0.78
3.23 ± 1.11
Insulin (mU/L)* HOMA ,*
6.37 ± 5.13 (5.37) 1.38 ± 1.13 (1.14)
8.97 ± 6.21 (8.06) 2.38 ± 1.63 (2.16)
7.91 ± 4.56 (7.66) 2.82 ± 2.18 (2.31)
PAI-1 (ng/ml)
129.89 ± 42.09 (123.01)
128.37 ± 35.51 (127)
159.99 ± 64.8 (144.78)
,à
2.37 ± 0.96 (2.25)
2.74 ± 1.33 (2.35)
4.52 ± 1.51 (4.28)
Apelin (ng/mL)
0.33 ± 0.19 (0.35)
0.34 ± 0.18 (0.31)
0.3 ± 0.09 (0.31)
Carotid IMT (mm) ,à
0.54 ± 0.07 (0.54)
0.55 ± 0.07 (0.54)
0.63 ± 0.09 (0.62)
IL-6 (pg/mL)
Data are shown as mean ± SD. Results are unadjusted. For variables that require log transformation, median values are also provided in parentheses * p \ 0.05 IFG/IGT vs. normal p \ 0.05 diabetes vs. normal
à
p \ 0.05 diabetes vs. IFG/IGT
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Arch Gynecol Obstet Table 3 Cardiometabolic risk factors associated with apelin levels 2
Model r
b
p
Fasting glucose
0.090
-0.273
0.001
Post-load glucose
0.061
-0.187
0.022
Carotid IMT
0.057
-0.168
0.033
IL-6
0.082
-0.234
0.002
Separate models run for each analysis along with age, postpartum duration, smoking, BMI, waist circumference and HOMA index. Analysis performed in the whole study population
for age, postpartum duration, smoking, BMI, waist and HOMA index (Table 3).
Discussion Our results indicate that plasma apelin levels were reduced in women with pGDM. The decrease in apelin level was associated with an increase in fasting and post-load glucose levels, serum IL-6 and carotid IMT. In addition, apelin level was positively correlated with HDL cholesterol. Considering the association of reduced apelin levels with the cardiovascular risk markers studied, we may suggest that the alteration in apelin levels might be associated with cardiovascular disorders in women with pGDM. However, we should also emphasize that, although statistically significant, the difference between PGDM and healthy women was small and, like most of the studies performed in diabetic cohorts, our study had a cross-sectional design. To the best of our knowledge, our study is first to investigate the relationship between apelin and cardiovascular risk in women with pGDM. Recently, a trend to lower concentrations of apelin has been reported in lactating women with pGDM compared to lactating healthy women [16]. Diminished endothelium-dependent vasodilatation as a result of decreased apelin levels might promote endothelial dysfunction in women with pGDM. In addition to adipocytes, apelin has been shown to be expressed in the heart, coronary arteries and endothelial cells. Recent reports have indicated that apelin is associated with endothelium-dependent vasodilatation [17, 18]. Apelin leads to vasodilatation due to NO release [19]. Beyond NO-related mechanisms, a possible role of apelin as counter-regulator against the action of angiotensin II has been proposed [20]. Apelin has been shown to protect the heart against ischemia–reperfusion injury [21]. In a previous study, Li et al. [22] reported reduced plasma apelin levels in patients with stable angina pectoris. They also demonstrated a significant inverse correlation with plasma apelin levels and Gensini score, a measurement to estimate the extent of coronary artery disease. In the same way, Malyszko et al. [23] reported reduced apelin levels in kidney allograft recipients
with coronary artery disease. Plasma apelin levels have been found to be decreased in non-obese, non-diabetic and normotensive patients with elevated LDL cholesterol [24]. In a prospective study, Tasci et al. [25] showed that lipidlowering treatment with either therapeutic life style change or statins were associated with a significant increase in plasma apelin levels. On the other hand, in a very recent study, Rittig et al. [26] reported no association of apelin with early atherosclerosis in a population what they called as ‘‘a diabetes prone population’’. However, this study lacks generalizability due to a variety of factors. First of all, it is not clear how this ‘‘diabetes prone population’’ was defined. Rather than being overweight, those young subjects presented no common risk factor for developing diabetes. They were far from being insulin resistant according to the insulin sensitivity data the authors reported, and they had very similar mean flow-mediated dilatation and intima media thickness values to what had been reported previously in healthy people [27, 28]. Finally, since there was no control group included in the study, the statement of that apelin had no value on cardiovascular risk stratification lacks for proper evidence. In our study, there was a negative relationship between apelin and glucose levels in women with pGDM. Similar to our data, Erdem et al. [29] reported reduced plasma apelin levels in newly diagnosed drug-naive patients with type 2 diabetes. In a further study, Zhang et al. [30] confirmed decreased plasma apelin levels in patients with newly diagnosed and untreated type 2 diabetes compared to healthy control subjects. Both studies reported negative correlations between fasting glucose levels and plasma apelin levels [29, 30]. In a recent comprehensive study, Zhong et al. [31] showed that both mRNA and protein levels of apelin receptor were diminished in the aorta of diabetic mice. Despite a clear relationship between apelin and glucose levels, we failed to demonstrate any significant relationship between apelin and the measures of obesity and insulin resistance. In the same way, Telejko et al. [32] reported no association between circulating apelin and indices of insulin resistance in pregnant women with GDM. However, controversial results have been reported concerning the effect of obesity and insulin resistance on plasma apelin levels. Obesity is a well-known cause of cardiovascular disorders which links insulin resistance and type 2 diabetes. Apelin has been demonstrated to be produced and secreted by adipocytes [33, 34]. Like other mediators released from adipocytes, apelin concentrations were found to be increased in obese animal models [35]. Adipocyte apelin content was observed to be higher in obese mice than normal weight mice [34, 35]. A positive correlation between plasma insulin levels and adipocyte apelin mRNA expression has been shown [34]. Moreover, it has been
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demonstrated that adipocyte apelin expression was stimulated by insulin [13, 34]. In humans, morbid obesity was found to be associated with higher plasma apelin levels. Castan-Laurell et al. [36] found that hypocaloric diet associated with weight reduction resulted in a decrease in plasma apelin levels. In morbid obese patients with diabetes or IFG, bariatric surgery was observed to result in a significant decrease in apelin levels. On the other hand, Heinonen et al. [37] observed no significant changes in plasma apelin after diet-induced weight loss in men and women with the metabolic syndrome. They proposed that apelin was not strongly correlated with the fat mass. In another very recent study, Chang et al. [38] reported lower serum apelin levels in women with polycystic ovary syndrome, a prototype of insulin resistance. Lack of correlation between circulating apelin levels and the measures of obesity/insulin resistance suggests that obesity, per se, is probably not the main determinant of plasma apelin concentrations. There were some limitations of our study. Based on the evidence from the Hyperglycemia and Adverse Pregnancy Outcomes study, the International Association of Diabetes and Pregnancy Study Groups (IADPSG) has released a new set of recommendations for the diagnosis of GDM [39]. This new set of recommendations has also been widely confirmed by the ADA [3]. However, we used previous recommendations of ADA (Carpenter and Coustan criteria) for GDM diagnosis, as our study was designed before the IADPSG recommendations were available. Further studies investigating cardiovascular risk in women with previous GDM, who were previously categorized as normal would be necessary. Prospective studies that examine whether apelin levels predict development of type 2 diabetes, IMT progression or cardiovascular events are needed. The effects of ethnic differences on the diagnosis and course of GDM should be also considered. In addition, the control group was made up of hospital staff with a negative GDM screening during index pregnancy. One can argue that the staff might be systematically different than the patient population with respect to socio-economic status, likelihood to breastfeed, or other factors that may impact cardiometabolic risk.
Conclusion We suggest that decreased apelin levels might take a part in the development of cardiovascular disorders in women with pGDM. Further studies are required to elucidate the underlying mechanism of the association between apelin and cardiovascular risk in women with pGDM. Conflict of interest The authors declare that they have no competing financial interests.
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31. Zhong JC, Yu XY, Huang Y, Yung LM, Lau CW, Lin SG (2007) Apelin modulates aortic vascular tone via endothelial nitric oxide synthase phosphorylation pathway in diabetic mice. Cardiovasc Res 74(3):388–395 32. Telejko B, Kuzmicki M, Wawrusiewicz-Kurylonek N et al (2010) Plasma apelin levels and apelin/APJ mRNA expression in patients with gestational diabetes mellitus. Diabetes Res Clin Pract 87(2):176–183 33. Castan-Laurell I, Dray C, Attane C, Duparc T, Knauf C, Valet P (2011) Apelin, diabetes, and obesity. Endocrine 40(1):1–9 34. Boucher J, Masri B, Daviaud D et al (2005) Apelin, a newly identified adipokine up-regulated by insulin and obesity. Endocrinology 146(4):1764–1771 35. Rayalam S, Della-Fera MA, Krieg PA, Cox CM, Robins A, Baile CA (2008) A putative role for apelin in the etiology of obesity. Biochem Biophys Res Commun 368(3):815–819 36. Castan-Laurell I, Vitkova M, Daviaud D et al (2008) Effect of hypocaloric diet-induced weight loss in obese women on plasma apelin and adipose tissue expression of apelin and APJ. Eur J Endocrinol 158(6):905–910 37. Heinonen MV, Laaksonen DE, Karhu T et al (2009) Effect of diet-induced weight loss on plasma apelin and cytokine levels in individuals with the metabolic syndrome. Nutr Metab Cardiovasc Dis 19(9):626–633 38. Chang CY, Tsai YC, Lee CH, Chan TF, Wang SH, Su JH (2011) Lower serum apelin levels in women with polycystic ovary syndrome. Fertil Steril 95(8):2520–2523 39. Metzger BE, Gabbe SG, Persson B et al (2010) International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care 33(3):676–682
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MATERNAL-FETAL MEDICINE
Circulating apelin levels are associated with cardiometabolic risk factors in women with previous gestational diabetes Baris Akinci • Aygul Celtik • Sunay Tunali • Sinan Genc • Faize Yuksel • Mustafa Secil • Mehmet Ali Ozcan • Firat Bayraktar
Received: 4 September 2012 / Accepted: 24 October 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Purpose Women with previous gestational diabetes mellitus (pGDM) are at high risk for type 2 diabetes and cardiovascular disorders. In this study, we aimed to compare plasma apelin levels between women with and without pGDM, and to investigate the possible association of apelin with cardiometabolic risk factors. Methods Among 252 consecutive Caucasian women with GDM being included in a prospective postpartum followup protocol, 141 women eligible for the study protocol were enrolled. Control group consisted of 49 age- and body mass index-matched healthy women without pGDM. Circulating apelin, IL-6 and plasminogen activator inhibitor levels, and carotid intima media thickness (IMT) were measured. To evaluate carbohydrate intolerance, 75-g oral glucose tolerance test was performed. Fasting insulin and
lipids were measured, and homeostasis model assessment index was calculated. Results Plasma apelin levels were reduced in women with pGDM (p \ 0.001). In multiple regression analysis, apelin was negatively associated with fasting (r2 0.090, b -0.273, p = 0.001) and post-load glucose (r2 0.061, b -0.187, p = 0.022), serum IL-6 (r2 0.082, b -0.234, p = 0.002), and carotid IMT (r2 0.057, b -0.168, p = 0.033). Conclusions Our results suggested that suppressed apelin levels were associated with increased cardiovascular risk in women with pGDM. Keywords Apelin Atherosclerosis Carotid IMT Previous gestational diabetes
Introduction B. Akinci (&) Department of Endocrinology and Metabolism, Izmir University, Izmir, Turkey e-mail: [email protected] B. Akinci F. Bayraktar Division of Endocrinology and Metabolism, Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey A. Celtik Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey S. Tunali F. Yuksel M. A. Ozcan Division of Hematology, Department of Internal Medicine, Dokuz Eylul University, Izmir, Turkey S. Genc M. Secil Department of Radiology, Dokuz Eylul University, Izmir, Turkey
The prevalence of gestational diabetes mellitus (GDM) is increasing in line with the increasing prevalence of type 2 diabetes and obesity over the world [1]. It has been demonstrated that maternal hyperglycaemia in women with GDM leads to increased risk for perinatal morbidity including fetal hyperinsulinemia, excessive fetal growth, neonatal hypoglycemia and hyperbilirubinemia [2, 3]. Women with GDM are at high risk for maternal complications as well. Studies have suggested that perinatal and maternal morbidity rates may be reduced with the intensive care of patients with GDM [3–5]. In addition, GDM is associated with an increased risk of developing diabetes in the postpartum period. Women with a previous history of GDM (pGDM) are also at increased risk of developing cardiovascular disorders, hypertension, dyslipidemia and metabolic syndrome [3, 6].
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Endothelial dysfunction is believed to be the initiating factor in the development of atherosclerosis [7]. Like circulating levels of systemic inflammation markers and homeostatic parameters, the levels of several adipocytokines have been associated with endothelial dysfunction and atherosclerosis. Endothelial dysfunction in asymptomatic individuals can be demonstrated by non-invasive techniques such as flow-mediated dilation, ankle–arm index and carotid intima media thickness (IMT). Carotid IMT has been associated with coronary atherosclerosis, and is also proposed as a predictor for vascular events in the future [8]. Carotid IMT is increased in patients with carbohydrate disturbance, either in patients with type 2 diabetes or patients with impaired glucose tolerance (IGT) [9]. Several studies have demonstrated increased carotid IMT in women with pGDM [10, 11] ENREF_6. Apelin is a recently discovered adipocytokine identified as an endogenous ligand of the G protein-coupled receptor APJ [12]. Apelin, first isolated from bovine stomach extracts, has been shown to be produced by the adipose tissue and also expressed in the brain, lung, heart, gastrointestinal tract, pancreas and kidney. Apelin, a 77 aminoacid prepropeptide, cleaves to various length peptides, apelin-12, -13, -16, -17, -19 and -36. At least 12 C-terminal amino acid residues are essential for the biological activity. Being localized in human vascular and endocardial endothelial cells, apelin has been proposed to play a role in the cardiovascular system [13, 14]. In the current study, we aimed to compare plasma apelin levels between women with and without pGDM, and to investigate the possible association between plasma apelin levels and cardiometabolic risk factors.
Materials and methods Two hundred and fifty-two consecutive Caucasian women, who were diagnosed with GDM between January 2002 and January 2008, were included in a prospective follow-up protocol. GDM was diagnosed by screening with a 50-g 1-h glucose challenge test at 24–28th week of gestation, followed by a 100-g 3-h oral glucose tolerance test (OGTT) at Dokuz Eylul University Hospital. Women with severe obesity, prior history of GDM or delivery of large-forgestational-age-infant, diagnosis of polycystic ovary syndrome, and strong family history for type-2 diabetes were screened at their initial prenatal visits because of the high risk for developing GDM. Carpenter and Coustan criteria [15] were used for GDM diagnosis (equal to or exceeding 95, 180, 155, and 140 mg/dL, with at least two elevated values considered abnormal). Control group was composed of age- and body mass index (BMI)-match hospital staff, in whom GDM was excluded by a 50 g screening test between
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24 and 28th week of gestation at the same period of time. Between June 2008 and June 2009, subjects were invited for the current study protocol. A total of 141 women with previous GDM and 49 healthy controls, who were eligible for the study protocol and gave an informed consent, were enrolled. Exclusion criteria were known cardiovascular disorders, type 1 or type 2 diabetes (diagnosed before the index pregnancy), familial hyperlipidemia, hypertension, acute infection, chronic inflammatory disease, coagulation disorders and other systemic diseases. Subjects were not included if they were on peri- or postmenopausal period at the time of sampling. The study was approved by the local ethics committee of Dokuz Eylul University. Height (m) and weight (kg) were measured under fasting conditions with subjects in light clothing and without shoes. BMI was calculated as body weight divided by square height. Waist circumference was measured from the mid-level between iliac crest and the lowest rib. Blood pressure was measured using a sphygmomanometer in the sitting position after 5-min rest. Subjects with a blood pressure level higher than 130 mmHg for systolic and 80 mmHg for diastolic at the examination or patients using antihypertensive drugs were defined as hypertensive and excluded from the study. Blood was taken from the cannulated antecubital vein between 8:00 and 9:00 a.m. after 10-h overnight fasting. Smokers were asked to refrain from smoking for 24-h before sampling. To evaluate carbohydrate intolerance, 75-g OGTT was performed. Type 2 diabetes, IGT and impaired fasting glucose (IFG) were defined according to American Diabetes Association (ADA) recommendations [3]. Blood samples were transferred into tubes containing fluoride for plasma glucose assay, buffered citrate for plasminogen activator inhibitor-1 (PAI-1) assay, and tubes suitable for serum separation. Blood samples for apelin assay were kept in ethylenediaminetetraacetic acid-containing tubes. Tubes were centrifuged at 3,000g for 10 min (Nuve, Ankara, Turkey). Serum and plasma were extracted, aliquoted, and stored at -80 °C until analysis. Glucose levels were measured by a colorimetric method with the Roche/Hitachi D/P Modular System Autoanalyzer (Roche Diagnostics, Basel, Switzerland). Triglycerides, total cholesterol and high-density lipoprotein (HDL) cholesterol were measured by Roche/Hitachi D/P Modular System Autoanalyzer (Roche Diagnostics, Basel, Switzerland). Low density lipoprotein (LDL) cholesterol was calculated by the Friedewald’s equation method. Insulin levels were measured by a chemiluminescent method using an automatic immunoanalyzer (Roche Diagnostics, Mannheim, Germany). Homeostasis model assessment (HOMA-IR) score, an estimate of insulin resistance, was calculated as fasting serum insulin (mU/L) 9 fasting plasma glucose (mmol/L)/22.5.
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Apelin was measured with enzyme-linked immunosorbent assay (ELISA) according to the manufacturer’s instructions (Phoenix Pharmaceuticals, Belmont, CA, USA). The intra- and inter-assay coefficients of variations for apelin-12 were 12 and 8 %, respectively. Interleukin-6 (IL-6) levels were measured with an ELISA kit (Invitrogen, Grand Island, NY, USA). Experiments were performed according to the manufacturer’s instructions. Intra- and inter-assay coefficients of variations for IL-6 were 7.2 and 5.4 %, respectively. PAI-1 antigen was measured by ELISA (Bender Medical Systems, Vienna, Austria). Intraassay and inter-assay coefficients of variations were 4.7 and 5.0 %, respectively. Ultrasounds for carotid IMT were performed after 10-h overnight fasting between 08:00 and 10:00 a.m. using a high-resolution Doppler ultrasound (Philips HDI 5000, Bothell, WA, USA) with a 7–12 MHz linear array transducer. Images were recorded and evaluated by a radiologist. Ultrasonographic images of the right and the left common carotid arteries (CCA) of each case at the lower 1/3 cervical region proximally and 1 cm above the carotid bulb distally in longitudinal plane were obtained. Measurements of the proximal and distal CCA posterior wall were done manually by the provided distance measurement system of the ultrasonography device after magnification of the images. Three measurements were made in a nonneighboring fashion within an approximately 1-cm segment from both left and right CCA proximal and distal portions. IMT thickness values were then calculated by obtaining the arithmetic means of the measured values. Table 1 Characteristics of patients with pGDM and healthy controls
The sample size was calculated using Power and Precision software (Biostat, Englewood, NJ, USA). The criterion for significance (a) has been set at 0.05. With the proposed sample size for the groups, sample size determination used a significance level of 0.05 for a two-tailed test with 80 % power. This effect was selected as the smallest effect that would be important to detect, in the sense that any smaller effect would not be of clinical or substantive significance. Sample size determination was based on substantive knowledge and previous research. To be able to generate subgroups with adequate number of subjects in each subgroup, the study group was composed of a rather large number of subjects. Variable distributions were assessed by the Kolmogorov–Smirnov normality test. Independent sample t test was used to compare variables of patients. One-way ANOVA with Bonferroni correction was applied for comparison of subgroups. Mann–Whitney U and Kruskal–Wallis tests were used for comparison of non-normally distributed variables. Categorical variables were compared by the Chi-square test. Correlation analyses were performed using Pearson’s coefficients. Spearman’s test was used when necessary. Regression analysis was employed to assess correlations between studied parameters. Statistical analysis was performed using Statistical Package of Social Science (SPSS Inc, Chicago, IL, USA), version 15.0 for Windows. Data were expressed as mean ± standard deviation (SD). For variables that require log transformation, median values were also provided in parentheses. A p value \0.05 was accepted as statistically significant.
pGDM (n = 141)
Healthy controls (n = 49)
p value
Age (years)
35.18 ± 5.55
34.89 ± 5.64
0.757
Postpartum duration (years)
3.36 ± 1.87
3.47 ± 1.76
0.725
Smoking (n, %) BMI (kg/m2)
28 (19.9 %) 26.82 ± 4.25
9 (18.4 %) 26.5 ± 2.66
0.82 0.62
Waist (cm)
90.31 ± 11.68
87.45 ± 8.93
0.119
OGTT (mmol/L)
Data are shown as mean ± SD. Results are unadjusted. For variables that require log transformation, median values are also provided in parentheses
0 min
5.65 ± 1.39
4.38 ± 0.48
\0.001
120 min
7.57 ± 3.18
4.77 ± 0.87
\0.001
Total cholesterol (mmol/L)
5.07 ± 0.98
4.59 ± 0.82
Triglyceride (mmol/L)
1.35 ± 0.75 (1.12)
1.01 ± 0.65 (0.84)
0.002 \0.001
HDL cholesterol (mmol/L)
1.34 ± 0.34
1.49 ± 0.39
0.008
LDL cholesterol (mmol/L)
3.11 ± 0.85
2.63 ± 0.71
\0.001
Insulin (mU/L)
7.44 ± 5.51 (6.49)
6.06 ± 3.26 (6.13)
0.339
HOMA
1.92 ± 1.59 (1.57)
1.19 ± 0.68 (1.12)
0.004
PAI-1 (ng/mL)
133.88 ± 45.28 (130.71)
117.64 ± 40.77 (120.47)
0.012
IL-6 (pg/mL)
2.81 ± 1.39 (2.49)
2.36 ± 0.94 (2.25)
0.067
Apelin (ng/mL
0.33 ± 0.18 (0.31)
0.56 ± 0.29 (0.5)
Carotid IMT (mm)
0.56 ± 0.08 (0.55)
0.53 ± 0.07 (0.52)
\0.001 0.014
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Results Table 1 shows characteristics of patients and healthy controls. There was no significant difference in terms of age, BMI, waist circumference, postpartum duration and the proportion of smokers between two groups. Women with pGDM had higher fasting and post-load glucose levels on OGTT than healthy controls. Women with pGDM had more atherogenic lipid profiles. HOMA was significantly higher in the pGDM group. Women with pGDM had significantly higher levels of plasma PAI-1. Although not significant, IL-6 levels were slightly elevated in the pGDM group. On the other hand, plasma apelin levels were significantly reduced in the pGDM group. There was a slight, but statistically significant, difference in carotid IMT between two groups. Seventy-four women (52.5 %) with pGDM had normal carbohydrate tolerance, whereas 46 of those (32.6 %) had IFG and/or IGT and 21 patients (14.9 %) had newly diagnosed diabetes. All patients in the control group had normal glucose regulation at follow-up. Table 2 shows the comparison of parameters between subgroups concerning postpartum carbohydrate intolerance among women with pGDM. As expected, age was higher in women with diabetes, in line with the increased postpartum follow-up
duration. There was no significant difference in smoking habits. Women with IFG/IGT had increased BMI, waist circumference and fasting insulin levels than pGDM women with normal carbohydrate tolerance. Women with diabetes also had increased BMI, waist circumference and fasting insulin levels; however, the difference was not statistically significant probably due to the small number of patients developing diabetes. HOMA was increased both in IFG/IGT and diabetes subgroups. Women with diabetes had significantly higher triglyceride, IL-6 and PAI-1 levels and carotid IMT. Although point estimates indicate that women with diabetes had more depressed apelin levels compared to pGDM women with normal glucose tolerance or IFG/IGT, the difference was quite small and was not statistically significant (p [ 0.05). Plasma apelin levels were negatively correlated with fasting (r = -0.258, p \ 0.001) and post-load glucose (r = -0.161, p = 0.027), serum IL-6 (r = -0.151, p = 0.038) levels, and carotid IMT (r = -0.150, p = 0.039). Plasma apelin was positively correlated with HDL cholesterol (r = 0.203, p = 0.005). Plasma apelin was not correlated with BMI, waist and HOMA. In multiple regression analysis, fasting glucose, post-load glucose, carotid IMT and IL-6 levels were found to be associated with plasma apelin when the data was controlled
Table 2 Characteristics of women with pGDM stratified by current glucose regulation category (i.e., normal, IFG/IGT, or diabetes) Normal (n = 74)
IFG/IGT (n = 46)
Diabetes (n = 21)
Age (years)
34.45 ± 5.39
35.19 ± 5.15
37.76 ± 6.33
Postpartum duration (years) ,à
2.82 ± 1.71
3.57 ± 1.83
4.81 ± 1.49
Smoking (n, %)
17 (23 %)
8 (17.4 %)
3 (14.3 %)
BMI (kg/m2)*
25.52 ± 3.72
28.49 ± 4.08
27.74 ± 4.97
Waist (cm)*
87.49 ± 11.94
94.26 ± 10.25
91.66 ± 11.41
0 min ,à,*
4.85 ± 0.39
5.96 ± 0.48
7.78 ± 2.25
120 min .à,*
5.69 ± 1.13
7.88 ± 1.73
13.51 ± 3.03
5.05 ± 0.95
4.95 ± 0.94
5.39 ± 1.14
Triglyceride (mmol/L)
1.25 ± 0.67 (0.99)
1.35 ± 0.78 (1.11)
1.68 ± 0.89 (1.64)
HDL cholesterol (mmol/L)*
1.41 ± 0.35
1.21 ± 0.26
1.39 ± 0.38
OGTT (mmol/L)
Total cholesterol (mmol/L)
LDL cholesterol (mmol/L)
3.07 ± 0.81
3.13 ± 0.78
3.23 ± 1.11
Insulin (mU/L)* HOMA ,*
6.37 ± 5.13 (5.37) 1.38 ± 1.13 (1.14)
8.97 ± 6.21 (8.06) 2.38 ± 1.63 (2.16)
7.91 ± 4.56 (7.66) 2.82 ± 2.18 (2.31)
PAI-1 (ng/ml)
129.89 ± 42.09 (123.01)
128.37 ± 35.51 (127)
159.99 ± 64.8 (144.78)
,à
2.37 ± 0.96 (2.25)
2.74 ± 1.33 (2.35)
4.52 ± 1.51 (4.28)
Apelin (ng/mL)
0.33 ± 0.19 (0.35)
0.34 ± 0.18 (0.31)
0.3 ± 0.09 (0.31)
Carotid IMT (mm) ,à
0.54 ± 0.07 (0.54)
0.55 ± 0.07 (0.54)
0.63 ± 0.09 (0.62)
IL-6 (pg/mL)
Data are shown as mean ± SD. Results are unadjusted. For variables that require log transformation, median values are also provided in parentheses * p \ 0.05 IFG/IGT vs. normal p \ 0.05 diabetes vs. normal
à
p \ 0.05 diabetes vs. IFG/IGT
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Model r
b
p
Fasting glucose
0.090
-0.273
0.001
Post-load glucose
0.061
-0.187
0.022
Carotid IMT
0.057
-0.168
0.033
IL-6
0.082
-0.234
0.002
Separate models run for each analysis along with age, postpartum duration, smoking, BMI, waist circumference and HOMA index. Analysis performed in the whole study population
for age, postpartum duration, smoking, BMI, waist and HOMA index (Table 3).
Discussion Our results indicate that plasma apelin levels were reduced in women with pGDM. The decrease in apelin level was associated with an increase in fasting and post-load glucose levels, serum IL-6 and carotid IMT. In addition, apelin level was positively correlated with HDL cholesterol. Considering the association of reduced apelin levels with the cardiovascular risk markers studied, we may suggest that the alteration in apelin levels might be associated with cardiovascular disorders in women with pGDM. However, we should also emphasize that, although statistically significant, the difference between PGDM and healthy women was small and, like most of the studies performed in diabetic cohorts, our study had a cross-sectional design. To the best of our knowledge, our study is first to investigate the relationship between apelin and cardiovascular risk in women with pGDM. Recently, a trend to lower concentrations of apelin has been reported in lactating women with pGDM compared to lactating healthy women [16]. Diminished endothelium-dependent vasodilatation as a result of decreased apelin levels might promote endothelial dysfunction in women with pGDM. In addition to adipocytes, apelin has been shown to be expressed in the heart, coronary arteries and endothelial cells. Recent reports have indicated that apelin is associated with endothelium-dependent vasodilatation [17, 18]. Apelin leads to vasodilatation due to NO release [19]. Beyond NO-related mechanisms, a possible role of apelin as counter-regulator against the action of angiotensin II has been proposed [20]. Apelin has been shown to protect the heart against ischemia–reperfusion injury [21]. In a previous study, Li et al. [22] reported reduced plasma apelin levels in patients with stable angina pectoris. They also demonstrated a significant inverse correlation with plasma apelin levels and Gensini score, a measurement to estimate the extent of coronary artery disease. In the same way, Malyszko et al. [23] reported reduced apelin levels in kidney allograft recipients
with coronary artery disease. Plasma apelin levels have been found to be decreased in non-obese, non-diabetic and normotensive patients with elevated LDL cholesterol [24]. In a prospective study, Tasci et al. [25] showed that lipidlowering treatment with either therapeutic life style change or statins were associated with a significant increase in plasma apelin levels. On the other hand, in a very recent study, Rittig et al. [26] reported no association of apelin with early atherosclerosis in a population what they called as ‘‘a diabetes prone population’’. However, this study lacks generalizability due to a variety of factors. First of all, it is not clear how this ‘‘diabetes prone population’’ was defined. Rather than being overweight, those young subjects presented no common risk factor for developing diabetes. They were far from being insulin resistant according to the insulin sensitivity data the authors reported, and they had very similar mean flow-mediated dilatation and intima media thickness values to what had been reported previously in healthy people [27, 28]. Finally, since there was no control group included in the study, the statement of that apelin had no value on cardiovascular risk stratification lacks for proper evidence. In our study, there was a negative relationship between apelin and glucose levels in women with pGDM. Similar to our data, Erdem et al. [29] reported reduced plasma apelin levels in newly diagnosed drug-naive patients with type 2 diabetes. In a further study, Zhang et al. [30] confirmed decreased plasma apelin levels in patients with newly diagnosed and untreated type 2 diabetes compared to healthy control subjects. Both studies reported negative correlations between fasting glucose levels and plasma apelin levels [29, 30]. In a recent comprehensive study, Zhong et al. [31] showed that both mRNA and protein levels of apelin receptor were diminished in the aorta of diabetic mice. Despite a clear relationship between apelin and glucose levels, we failed to demonstrate any significant relationship between apelin and the measures of obesity and insulin resistance. In the same way, Telejko et al. [32] reported no association between circulating apelin and indices of insulin resistance in pregnant women with GDM. However, controversial results have been reported concerning the effect of obesity and insulin resistance on plasma apelin levels. Obesity is a well-known cause of cardiovascular disorders which links insulin resistance and type 2 diabetes. Apelin has been demonstrated to be produced and secreted by adipocytes [33, 34]. Like other mediators released from adipocytes, apelin concentrations were found to be increased in obese animal models [35]. Adipocyte apelin content was observed to be higher in obese mice than normal weight mice [34, 35]. A positive correlation between plasma insulin levels and adipocyte apelin mRNA expression has been shown [34]. Moreover, it has been
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demonstrated that adipocyte apelin expression was stimulated by insulin [13, 34]. In humans, morbid obesity was found to be associated with higher plasma apelin levels. Castan-Laurell et al. [36] found that hypocaloric diet associated with weight reduction resulted in a decrease in plasma apelin levels. In morbid obese patients with diabetes or IFG, bariatric surgery was observed to result in a significant decrease in apelin levels. On the other hand, Heinonen et al. [37] observed no significant changes in plasma apelin after diet-induced weight loss in men and women with the metabolic syndrome. They proposed that apelin was not strongly correlated with the fat mass. In another very recent study, Chang et al. [38] reported lower serum apelin levels in women with polycystic ovary syndrome, a prototype of insulin resistance. Lack of correlation between circulating apelin levels and the measures of obesity/insulin resistance suggests that obesity, per se, is probably not the main determinant of plasma apelin concentrations. There were some limitations of our study. Based on the evidence from the Hyperglycemia and Adverse Pregnancy Outcomes study, the International Association of Diabetes and Pregnancy Study Groups (IADPSG) has released a new set of recommendations for the diagnosis of GDM [39]. This new set of recommendations has also been widely confirmed by the ADA [3]. However, we used previous recommendations of ADA (Carpenter and Coustan criteria) for GDM diagnosis, as our study was designed before the IADPSG recommendations were available. Further studies investigating cardiovascular risk in women with previous GDM, who were previously categorized as normal would be necessary. Prospective studies that examine whether apelin levels predict development of type 2 diabetes, IMT progression or cardiovascular events are needed. The effects of ethnic differences on the diagnosis and course of GDM should be also considered. In addition, the control group was made up of hospital staff with a negative GDM screening during index pregnancy. One can argue that the staff might be systematically different than the patient population with respect to socio-economic status, likelihood to breastfeed, or other factors that may impact cardiometabolic risk.
Conclusion We suggest that decreased apelin levels might take a part in the development of cardiovascular disorders in women with pGDM. Further studies are required to elucidate the underlying mechanism of the association between apelin and cardiovascular risk in women with pGDM. Conflict of interest The authors declare that they have no competing financial interests.
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