Pathophysiology of Preeclampsia: The Role of Adiposity and Serum Adipokines

Abstract

The goal of this study was to determine serum adiponectin, leptin, resistin, visfatin, and lipids in pregnant women during the first trimester and to examine the link between these biochemical markers and preeclampsia (PE). Changes in the levels of these adipokines occur in PE, hence this study looked into the possibility of employing these biomarkers to predict the disease. This study compared first-trimester serum biochemical and anthropometric markers in pregnant women with PE to the controls. After 20 weeks of pregnancy, blood pressure and urine protein were measured, and a PE diagnosis was made according to American Heart Association criteria. Generally, there were significant differences (p < 0.05) in the biochemical markers between the PEs and the controls. Even after correcting for body mass index (BMI) and family history of hypertension, analyses of area under the receiver operating characteristic curves (AUCs) for the adipokines revealed their capacity to reliably predict PE. After adjusting for BMI, it emerged that adiponectin, leptin, resistin, and visfatin were significant predictors of PE, with resistin being the best predictor. After controlling for BMI, age, parity, and family history of diabetes and preeclampsia, adiponectin was the greatest predictor.

1. Introduction

Pregnancy is a distinct situation marked by physiological insulin resistance that disappears after delivery. It is also marked by changes in the endocrine, metabolic, and circulatory systems, all of which are intended to supply energy and sustenance to the developing fetus [1]. Gestational diabetes (GDM) and pre-eclampsia (PE) may occur as complications during metabolic dysregulation in pregnancy. GDM is a type of glucose intolerance that develops or is first noticed during pregnancy [2]. A previous diagnosis of gestational or pre-diabetes, impaired fasting glycemia, a family history of type 2 diabetes mellitus (DM) in a first-degree relative, maternal age, ethnic background, being overweight, and a history of previous pregnancy resulting in a child with a high birth weight (>4 kg) are all risk factors for developing GDM [3].

The major goal of this study was to look at the relationship between adipokines, lipids, and preeclampsia, as well as the efficacy and accuracy of these markers in predicting PE [4]. PE is a pregnancy-specific illness in which women who were previously normotensive develop hypertension and proteinuria after 20 weeks of pregnancy [5]. PE affects between 2 and 5% of pregnancies and contributes significantly to fetal, neonatal, and maternal morbidity and mortality. In Ghana, the incidence rate is around 7% [6, 7], however, a prevalence of 8.3% was reported in a study at the Volt Regional Hospital, Ho [8]. PE can develop anywhere from 20 weeks post-conception to 6 weeks post-delivery, and it’s commonly considered early inception if it happens before 34 weeks. It shares some of the risk factors of metabolic syndrome, such as insulin resistance, subclinical inflammation, and obesity, and data suggests that women with PE are more likely to develop cardiovascular disease later in life [1].

1.1 Adiponectin

Adiponectin, also known as gelatin-binding protein of 28 kDa (GBP28), adipocyte complement-related protein of 30 kDa (ACRP30), adipoQ , adipose most abundant gene transcript 1 (apM1) is an adipocyte-specific secreted protein with roles in glucose and lipid metabolism [9]. The adiponectin gene is located on chromosome 3q27.3 and it is the most abundant protein released by adipose tissue and circulates in plasma as a low-molecular-weight trimer, a middle-molecular-weight hexamer, and a high-molecular-weight 12–18-mer [10, 11]. The biological activity of various variants varies, with HMW adiponectin being the most physiologically active [12]. The effects of adiponectin on glucose metabolism are mediated by two receptors, AdipoR1 and AdipoR2, respectively [13]. AdipoR2 is particularly abundant in the liver, whereas AdipoR1 is found in almost all bodily tissues [14]. Adiponectin activates adenosine monophosphate protein kinase (AMPK) and peroxisome proliferator-activated receptor alpha (PPAR-) by binding to its receptors AdipoR1 and AdipoR2, which leads to the activation of adenosine monophosphate protein kinase (AMPK) and peroxisome proliferator-activated receptor alpha (PPAR-α). In obesity-related insulin resistance, both adiponectin and its receptors are downregulated [13].

Adiponectin levels in the blood have a positive correlation with HDL cholesterol and a negative correlation with triglycerides [15]. Gender, age, and lifestyle all influence plasma adiponectin levels. Adiponectin gene expression is inhibited by β-adrenergic stimulation, glucocorticoids, and TNF-α [16, 17]. Type 2 diabetes, insulin resistance, obesity, hypertension, and left ventricular hypertrophy are all linked to low adiponectin levels in the blood [18].

Even in the absence of obesity, increased fat buildup in the body during pregnancy leads to a steady drop in adiponectin secretion [19]. Both adiponectin concentration and adiponectin mRNA are negatively correlated with fat mass hence with increased adipose tissue secretion during pregnancy, it’s possible that signals are sent to the adipose tissue, resulting in a decrease in adiponectin production even in the absence of obesity [19]. Despite the fact that some researchers have been unable to find adiponectin mRNA expression in the placenta [20, 21], studies show that it could be a source of the hormone [22].

A counterintuitive and considerable increase in adiponectin concentration has been found in several studies during pregnancy complicated with PE [23, 24]. Other researchers, on the other hand, discovered no significant differences in adiponectin mRNA expression in adipose tissue between PE patients and healthy controls [25].

1.2 Leptin

Leptin is a 16 kDa protein product of the ob gene located on chromosome 1p31 and was identified in 1994 [1]. The name “leptin” comes from the Greek word “leptos,” which means “thin,” because this protein causes increased energy expenditure and reduces calorie intake by acting on satiety signals in the hypothalamus [26]. Its amino acid sequence exhibits no major homologies to other proteins [27] and it’s made by differentiated adipocytes, but it’s also made in other tissues like the stomach fundus, skeletal muscle, the liver, and the placenta [28]. Leptin suppresses food intake and increases energy expenditure by acting on the hypothalamus [29]. It is also a pro-inflammatory protein and a member of the IL-6 super-family of cytokines [30]. Leptin enhances insulin sensitivity in the periphery and regulates pancreatic β-cell activity [13]. Despite a functioning leptin receptor and high leptin levels, leptin does not cause weight loss in the majority of cases of obesity. This reduced response to the anorexigenic and insulin-sensitizing effects of leptin is called “leptin resistance” [13].

During pregnancy, leptin modulates gonadotrophin-releasing hormone release and facilitates implantation [31]. It also boosts amino acid uptake, regulates placental growth, enhances mitogenesis, and induces human chorionic gonadotrophin synthesis in trophoblast cells [31]. Tumor necrosis factor (TNF) and interleukin (IL)-6 stimulate the synthesis of placental leptin mRNA [32]. Leptin levels begin to rise in the early stages of pregnancy, regardless of maternal weight gain [33], peaking approximately 28 weeks of pregnancy and then dropping to pre-gravid levels shortly after delivery [34]. The placenta, rather than maternal adipose tissue alone, appears to play a significant role in the rise in maternal leptin concentrations throughout pregnancy [35]. The presence of a distinct promoter region in the human placental leptin gene indicates that placental leptin is regulated differently from adipose-derived leptin [36]. The fetus itself contributes to leptin production starting early in the second trimester [37]. In comparison to the placenta, however, the fetus produces a modest amount of it. Furthermore, leptin concentrations in umbilical cord plasma correlate positively with birth weight of newborns [38].

Leptin levels are higher in pregnant women with PE [23] and they may be higher before the disease manifests itself clinically [39, 40], with peaks occurring around 28 weeks of gestation [34]. As a result, leptin may play a role in the disease’s pathogenesis. However, other authors have observed lowered [25] or unchanged [41] circulating levels in patients with PE.

1.3 Resistin

Resistin is a 12.5 kDa dimeric protein that circulates in human blood as two 92-amino-acid polypeptides connected by disulfide bridges at Cys-26 [42]. The signaling molecule resistin is found in monocytes, macrophages, and adipocytes [1]. The resistin gene is located on chromosome 19p13.3 and although the exact physiological role in humans is unknown, available evidence suggests that its presence in the blood is linked to a number of inflammatory indicators, including C-reactive protein, soluble TNF-receptor-2, IL-6, and lipoprotein-associated phospholipase A2 [43]. Coronary artery disease has been linked to high levels of resistin in the blood [43], and to severity of disease in sepsis and septic shock [44] and may be involved in the pathogenesis of rheumatoid arthritis [45].

Resistin concentration in PE has been reported by various researchers to remain unchanged [23] decreased [46] or increased [25]. The increased circulating resistin levels in PE could be related to the fact that its concentration in plasma is dependent on glomerular filtration, therefore as renal impairment progresses, resistin levels in plasma may rise [47].

1.4 Visfatin

Visfatin is a 52-kDa protein and is extensively produced in both human and mouse adipose tissue, and its plasma levels rise as obesity progresses [1]. Visfatin gene is located on chromosome 7q22.2 and is widely expressed in adipose tissue but can also be found in the placenta and fetal membranes [48] and myometrium [17]. It is also expressed in bone marrow, liver, muscle, heart, lung, and kidney [49] as well as by the lymphocyte. It is referred to as a pre-B cell colony enhancing factor because it enhances the maturation of B cell precursors [49]. Visfatin is released by amniotic epithelial cells during pregnancy [50] and has nicotinamide phosphoribosyltransferase activity [51].

Some contradictory results have been published on visfatin levels during pregnancy affected by preeclampsia. Some authors published increased visfatin levels in PE [52] while other investigators reported decreased levels [53] or values similar to normal pregnancy [54].

In a normal pregnancy, lipid profile changes are characterized by increases in total plasma cholesterol and triglyceride (TG) levels as a result of increased TG synthesis by the liver and very low-density lipoprotein-cholesterol (VLDL-C) synthesis in response to elevated estrogen levels [55]. The clearance of VLDL-C is reduced when the activity of lipoprotein lipase (LPL) is reduced due to estrogen-induced downregulation of LPL gene expression during pregnancy [56]. Women with PE had higher TG, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and very low-density lipoprotein cholesterol (VLDL-C), according to a study conducted in the Cape Coast metropolis in Ghana [57].

The differences in lipid profiles and abnormalities in certain adipokine metabolism described by different researchers warrant a closer look at their implications in the pathophysiology of PE. The main goal of this study was to see if the metabolism of adiponectin, leptin resistin, and visfatin are affected in the first trimester of pregnancy in pregnancies that go on to develop PE, and if these changes are significant enough in the prediction of PE to prompt interventions early enough to save the mother and baby.

2. Materials and methods

2.1 Study site and design

This case-control study was carried out at the Ho Teaching Hospital (HTH) located in the capital of Volta Region of Ghana between January and December 2016.

2.2 Criteria for selection

Pregnant women over the age of 18 with or without hypertension were included in the study (cases and controls, respectively). Pregnant women without dipstick proteinuria and blood pressures less than 140/90 mmHg were assigned to the control group, whereas those with hypertension and proteinuria were assigned to the case group. Pregnant women with renal disease, diabetes, cancer, multigravida, and pre-gestational hypertension were excluded.

2.3 Study population

We studied first trimester data in 90 pregnant women who later developed PE and 100 women who did not. Participants were chosen from a large prospective observational study of women attending the HTH prenatal clinic for early prediction of pregnancies that are prone to develop problems. Women with pregnancies between 11 and 13 weeks of gestation were invited to take part in the study. Participants’ maternal characteristics and medical histories were documented.

2.4 Anthropometric measurement

Participants wore light clothing and after removing their shoes, stood on a Bioimpedance analyzer (BIA; BSD01, Pure Pleasure, a division of the Stingray Group, Cape Town, South Africa) and their weights were recorded to the nearest 0.1 kg. The study participants were made to stand upright, heels together, head in the horizontal plane, and height was measured with a stadiometer to the nearest 0.5 cm without shoes. BMI was estimated as weight/height squared (kg/m²).

2.5 Blood pressure measurement

Each participant was instructed to sit comfortably, stretch her left arm on a table, and relax for 10 minutes. A mercury sphygmomanometer and stethoscope were used to take measurements from the left upper arm after the subjects had rested for at least 5 minutes. The mean blood pressure was recorded to the closest to 2.0 mmHg in triplicate, with at least 5 minutes of waiting time between tests following the American Heart Association’s standards [58].

2.6 Collection and preservation of samples

Five milliliters of blood were drawn between 7:00 and 8:00 a.m. during the first trimester and placed in serum separator tubes before being placed on ice packs. Within an hour, serum samples were separated and stored in aliquots at −80°C for biochemical analysis. Each participant was given a clean, dry, wide mouth, leak-proof container to collect 5 mL of urine sample after the 20th week of pregnancy.

2.7 Biochemical and urine analysis

Sandwich enzyme-linked immunosorbent assay technique (Elabscience Biotechnology Co. Ltd., Wu Han, People’s Republic of China) was used to analyze adiponectin, leptin, resistin, and visfatin in the baseline samples of both cases and controls, while the lipid profiles were performed using the Vitros dry chemistry analyzer (Ortho-Clinical Diagnostics, Johnson & Johnson, High Wycombe, UK). None of the samples in this investigation had been thawed and frozen before.

For less than 2 seconds, a urine strip was put into a urine sample up to the test area. To remove surplus urine, the strips’ margins were drawn around the brims of the vessels, ensuring that the test areas did not come into contact with them. To eliminate any residual urine, the strips were held vertically and tapped on absorbent papers [59]. Under bright light, the urine strip was horizontally held and compared to the color chart on the vial label.

The intensity of the blue-green color, which was related to the quantity of protein in the urine, was then used to determine the amount of protein. Proteinuria was defined as the presence of urine protein at concentrations of “+” or higher [60].

2.8 Study variables and outcome measurement

After the twentieth week of pregnancy, every pregnant woman in this hospital is screened for PE. PE occurrence (yes/no), as determined by PE diagnosis criteria, was the primary outcome. Urine protein was measured using the dip-stick qualitative/semi-quantitative method (Urit Medical Electronic Co., Ltd., Guangxi, People’s Republic of China) after 20 weeks of pregnancy. PE was diagnosed by a qualified Obstetrician/Gynecologist based on systolic and diastolic blood pressures of 140 mmHg or more on two occasions at least 4 hours apart (or both) in addition to proteinuria of + or more.

2.9 Statistical analysis

The SPSS software, version 20, and Graph Pad Prism, version 5.0, San Diego, California, USA, and Systat, Inc. Germany were used to analyze the data. The Shapiro-Wilk test was used to determine the normality of the variables under investigation, followed by a Mann-Whitney U-test to compare those with PE to those without. A value of p < 0.05 was considered significant in all of the statistical analyses. The AUC (area under the receiver operating characteristic (ROC) curve) is commonly used to assess a test’s/accuracy. When the AUC is less than 50%, the result is considered random guessing and thus not meaningful. This is represented by a diagonal line in the ROC plot [61]. The adipokines and lipids were evaluated for their accuracy (AUC 60%) in predicting preeclampsia-like pregnancies.

After correcting for potential confounding variables, multivariate analysis was performed on the individual adipokines as predictors of PE (age, BMI, relatives with hypertension, family history of diabetes mellitus, family history of preeclampsia, and parity). After correcting for confounders, the goal was to determine the independent contribution of each adipokine in predicting PE.

The parameters for the goodness of fit test for the models were −2Log (Likelihood), R2 (Cox and Snell), R2 (Nagelkerke), Akaike Information Criterion (AIC), and Correct Classification Rate (CCR).

The −2Log (Likelihood) statistic indicates how well a model predicts a certain occurrence, the lower the number, the better the model.

The coefficients of determination Cox and Snell R2 and Nagelkerke R2 are used to measure the amount of variation in the dependent variable that is explained by the independent variable. The Cox and Snell R2 has been modified to create the Nagelkerke R2. The AIC is also a relative quality estimator for statistical models. The better the model, the smaller the estimate. The Correct Classification Rate is another valuable metric for evaluating the utility of a logistic regression model (CCR).

3. Results

The baseline demographics, lipids, and adipokine characteristics of those with PE were compared to those without PE (Table 1). The mean age of those who acquired PE was significantly greater than that of those who did not (35.1 vs. 28.44 years; p < 0.0001), and their BMI was likewise significantly higher (32.63 vs. 24.99 kg/m²; P < 0.0001). Except for HDL, which was considerably lower in the PE group compared to those without PE (1.39 vs. 1.569, p = 0.043), the lipid profile parameters did not demonstrate any significant differences between the PE group and those without PE (Table 1). Leptin levels were statistically substantially higher in the PE group (39.26 vs. 18.46 ng/mL, P < 0.0001) than in the control group. Similarly, resistin and visfatin were considerably higher in PEs compared to normotensives (p < 0.0001), although adiponectin was significantly lower in PEs compared to non PEs (p < 0.0001).

Var	BMI		Age		ADP		LP		RTN		VF		TG		TC		HDL		LDL		VLDL
PE	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes	No	Yes
Min	18.9	27.1	16	24	17.7	15.2	2.7	9.9	1.3	4.0	0.3	0.5	0.5	0.5	2.4	2.6	0.1	0.1	0.2	0.2	0.2	0.2
Max	37.3	37.3	41	46	258.2	90.6	40.7	41	12.4	13.9	11.2	19.4	3.3	3.4	9.6	9.3	3.4	3.4	7.8	7.8	1.5	1.4
Mean	25.0	32.6	28.4	35.1	83.6	39.3	18.5	36.3	6.4	10.2	4.4	7.4	1.7	1.6	5.8	5.9	1.6	1.4	3.8	4.0	0.8	0.7
LB	24.2	32.0	27.4	34.0	77.3	35.3	16.8	34.7	6.0	9.8	3.9	6.7	1.5	1.5	5.5	5.5	1.4	1.2	3.5	3.6	0.7	0.6
UB	25.8	33.2	29.5	36.2	90.0	43.2	20.2	37.9	6.8	10.7	5.0	8.0	1.8	1.8	6.1	6.3	1.7	1.7	4.1	4.4	0.9	0.8
P value	<0.0001**		<0.0001**		<0.0001**		<0.0001**		<0.0001**		<0.0001**		0.86		0.826		0.043*		0.589		0.73

Table 1.

Mann-Whitney U test for base line biochemical markers and maternal characteristics for study participants.

^*

significant at p < 0.05

^**

significant at p < 0.01

Values in bold are significant at p < 0.05

ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin; TG, triglyceride; TC, total cholesterol; LDL, low density lipoprotein cholesterol; HDL, high density lipoprotein cholesterol; VLDL, very low density lipoprotein cholesterol.

Reproduced from: Ref. [62].

The ROC curves were used to assess the performance of the screening. Table 2 shows the areas under the ROC curve, the sensitivities and specificities, as well as the threshold points for detecting PE. The accuracy with which biochemical markers can differentiate on the condition of PE was tested in this study. As shown in Table 2, the adipokines leptin (92.0%), resistin (91.4%), and adiponectin (90.5%) have good accuracy levels, whereas visfastin (77.1%) has fair accuracy levels in diagnosing PE, according to Table 2 ratings. With a cut-off point of 50.55 ng/mL, adiponectin had a sensitivity and specificity of 87.8 and 86%, respectively, while leptin had a sensitivity and specificity of 92% with a threshold of 27 ng/mL. Furthermore, resistin had a sensitivity and specificity of 94 and 91%, respectively, with a cut-off point of around 9 ng/mL, whereas visfatin had a sensitivity and specificity of 69 and 83%, with a threshold of 6.67 ng/mL. This suggests that adiponectin, leptin, resistin, and visfatin are effective PE predictors (Table 2).

Variables	AUC (%)	Sensitivity (%)	Specificity (%)	Threshold point
ADP	90.5	87.8	86	≤50.552
LP	92	92.2	92	≥27.273
RTN	91.4	94.4	91.4	≥8.949
VF	77.1	68.9	83	≥6.667

Table 2.

AUC, sensitivity, specificity, and threshold point for the adipokines in the pregnant women.

ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin.

Reproduced from: Ref. [62].

Furthermore, a detailed examination of the ROC plots (Figure 1) reveals that they are all far from the diagonal line, which represents 50%, indicating that they are not random guesses but rather meaningful. This indicates that they are quite good at predicting pregnancies that are likely to result in PE. After adjusting for BMI, none of those in the normal BMI category had PE (Table 3); as a result, no AUC values for all of the adipokines studied were obtained. The overweight group, on the other hand, had greater AUCs, sensitivities, and specificities. Obese people, on the other hand, had lower sensitivities and specificities. These findings point to a possible influence of BMI on adiponectin, leptin, resistin, and visfatin, as well as a possible negative feedback mechanism in the metabolism of these adipocytokines during pregnancy. However, BMI does not appear to have an effect on the predictive ability of these PE signaling molecules.

Figure 1.

ROC curves for the adipokines. AUCs (%): ADP (95.0), LP (92.0), RTN (91.4), and VF (77.1). ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin.

Adipokine	BMI category	Prevalence (%)	AUC (%)	Sensitivity (%)	Specificity (%)	Threshold point
ADP	Normal weight	0	—	—	—	—
	Overweight	23	97.7	100	97.7	≤36.163
	Obese	89	83.2	71.4	90	≤44.980
LP	Normal weight	0	—	—	—	—
	Overweight	23	99.3	100	97.7	≥27.245
	Obese	89	70.7	61	80	≥38.482
RTN	Normal weight	0	—	—	—	—
	Overweight	23	95.5	100	90.9	≥8.949
	Obese	89	93.5	93.5	70	≥8.949
VF	Normal weight	0	—	—	—	—
	Overweight	23	89.5	84.6	90.9	≥6.628
	Obese	89	48.2	66.2	60	≥6.243

Table 3.

AUC, sensitivity, specificity, and threshold point for levels of the adipokines in the pregnant women controlling for BMI.

ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin. BMI classification: Normal Weight = (18.5–24.9 kg/m²), Overweight = (25.0–29.99 kg/m²), Obese = (Above 30.0 kg/m²)

Reproduced from: Ref. [62].

There were minor variations in the AUCs, sensitivities, specificities, and threshold points for predicting PE after controlling for family history of hypertension, which is a known confounding factor, but these variations were minor, and the overall effect of these adipocytokines’ predictive abilities remained intact (Table 4).

		Prevalence (%)	AUC (%)	Sensitivity (%)	Specificity (%)	Threshold point
	RWHP
ADP	YES	73	83.6	84.2	78.6	≤50.552
	NO	38	92.2	84.6	95.3	≤44.980
LP	YES	73	90.6	89.5	92.9	≥25.611
	NO	38	92.2	94.2	91.9	≥27.273
RTN	YES	73	89.8	89.5	85.7	≥9.012
	NO	38	91.6	94.2	93	≥8.949
VF	YES	73	72.5	78.9	78.6	≥6.349
	NO	38	75.1	61.5	83.7	≥6.667

Table 4.

AUC, sensitivity, specificity, and threshold point for the adipokines in the pregnant women controlling for those who have relatives with hypertension.

ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin; RWHP, relatives with hypertension; YES, those who have relatives with hypertension; NO, those who do not have relatives with hypertension.

Reproduced from: Ref. [62].

Table 5 shows a multivariate analysis of individual adipokines as PE predictors. Adiponectin, leptin, resistin, and visfatin were included as predictors in Models 1, 2, 3, and 4, respectively, while correcting for confounding factors such as age, parity, BMI, and relative with hypertension, and family history of diabetes and preeclampsia. Based on the criteria analyzed, Model 1 including adiponectin as a predictor was the best model. This means having the greatest Nagelkerke R2 and CCR values of 95 and 95.26%, respectively, implying that after correcting for confounders, adiponectin is the strongest predictor of PE. With Nagelkerke R2 and CCR statistics of 89 and 91.58%, respectively, Model 4 with visfatin as a predictor had the least predictive performance.

	Model 1 (ADP)			Model 2 (LP)			Model 3 (RTN)			Model 4 (VF)
	P-value	OR	OR CI (95%)	P-value	OR	OR CI (95%)	P-value	OR	OR CI (95%)	P-value	OR	OR CI (95%)
Intercept	0.50			<0.0001			<0.0001			<0.0001
ADP	<0.0001	0.93	(0.9, 0.96)
LP				<0.0001	1.15	(1.08, 1.22)
RTN							<0.0001	1.65	(1.3, 2.11)
VF										0.01	1.28	(1.07, 1.55)
Age Cat.
20–35 years
Above 35 years	0.04	8.86	(1.16, 67.68)	0.07	4.60	(0.88, 24.08)	0.01	6.25	(1.46, 26.69)	0.01	5.67	(1.41, 22.74)
Less than 20 years	0.89	1.39	(0.01, 135.01)	0.12	32.85	(0.41, 2604.47)	0.39	7.86	(0.07, 901.18)	0.12	27.92	(0.41, 1895.49)
BMI category
Overweight
Obese	<0.0001	44.24	(7.5, 260.91)	<0.0001	20.43	(4.75, 87.87)	<0.0001	19.88	(5.06, 78.17)	<0.0001	26.52	(7.73, 90.98)
Normal weight	0.16	0.12	(0.01, 2.29)	0.20	0.15	(0.01, 2.62)	0.10	0.08	(0, 1.67)	0.06	0.07	(0, 1.07)
Parity
1
2	0.84	1.18	(0.22, 6.41)	0.19	2.76	(0.6, 12.76)	0.08	4.00	(0.84, 19.14)	0.16	2.75	(0.67, 11.2)
3	0.02	11.34	(1.53, 84.29)	0.02	7.51	(1.29, 43.58)	0.13	3.73	(0.69, 20.11)	0.05	4.40	(0.99, 19.5)
>4	0.04	9.07	(1.03, 80.17)	<0.0001	17.35	(2.72, 110.49)	<0.0001	14.38	(2.45, 84.57)	<0.0001	12.19	(2.41, 61.59)
Family Hist. of Hyp.
No
Yes	0.19	2.93	(0.59, 14.48)	0.04	4.58	(0.97, 21.64)	0.10	3.84	(0.79, 18.77)	0.07	3.43	(0.89, 13.21)
Family Hist. of DM
No
Yes	0.40	2.23	(0.35, 14.34)	0.76	1.28	(0.26, 6.21)	0.72	0.75	(0.16, 3.53)	0.88	0.90	(0.21, 3.77)
Family Hist. of PE
No
Yes	0.79	0.82	(0.19, 3.52)	0.11	0.34	(0.09, 1.26)	0.22	0.45	(0.12, 1.62)	0.14	0.41	(0.12, 1.36)
Goodness of fit statistics
	Model 1			Model 2			Model 3			Model 4
−2Log (Likelihood)	27.186			37.24			40.66			54.11
R^2 (Cox and Snell)	0.71			0.70			0.69			0.67
R^2 (Nagelkerke)	0.95			0.93			0.92			0.89
AIC	51.19			61.24			64.66			78.11
CCR (%)	95.26			93.16			92.63			91.58

Table 5.

Multivariate analysis of clinical factors affecting preeclampsia.

Reproduced from: Ref. [62].

Values in bold are significant at p < 0.05.

In their respective models, adiponectin, leptin, resistin, and visfatin were found to be significant predictors of PE (P < 0.05). For each unit drop in adiponectin, the probabilities of PE increase by a factor of 1.1, according to the reciprocal of the odds ratio (Model 1). A one-unit increase in leptin increases the risk of PE by 1.15 times (Model 2). Additionally, a unit increase in resistin raises the probabilities of PE by 1.65 (Model 3), whereas visfatin increases the odds of PE by 1.28 (Model 4).

Obesity was revealed to be a significant confounder in all four models, with overweight as the reference category under BMI and parity of four or more with parity one as the reference category. In Models 1, 3, and 4, advanced maternal age above 35 years, with the age group 20–35 years as the reference category, was also found to be important.

Although adiponectin showed a mild positive link with HDL and a weak negative correlation with TG and VLDL, it did demonstrate a favorable correlation with HDL. Although leptin and resistin had minor negative relationships with HDL, visfatin had a strong negative link with HDL. Leptin, resistin, and visfatin all had negative correlations with adiponectin. Positive associations were found between leptin, resistin, and visfatin (Table 6). These links were weak in those of normal weight, but they were stronger in individuals who were overweight or obese (Table 7). This reemphasizes the link between adiposity and some of these adipokines. We had earlier examined the associations between maternal factors and PE in a report published in the International Journal of Women’s Health [63].

Variables	ADP	LP	RTN	VF	TC	HDL	LDL	VLDL	TG
ADP	1	−0.5403	−0.3807	−0.2399	0.0549	0.0531	0.0390	−0.0640	−0.0558
LP	−0.5403	1	0.6667	0.5460	0.0629	−0.1333	0.0963	−0.0016	0.0092
RTN	−0.3807	0.6667	1	0.4510	0.0911	−0.1248	0.0671	0.0227	0.0395
VF	−0.2399	0.5460	0.4510	1	0.1928	−0.1527	0.1699	0.1252	0.1437
TC	0.0549	0.0629	0.0911	0.1928	1	−0.3359	0.8891	0.5973	0.5912
HDL	0.0531	−0.1333	−0.1248	−0.1527	−0.3359	1	−0.3899	−0.1508	−0.1335
LDL	0.0390	0.0963	0.0671	0.1699	0.8891	−0.3899	1	0.3370	0.3385
VLDL	−0.0640	−0.0016	0.0227	0.1252	0.5973	−0.1508	0.3370	1	0.9794
TG	−0.0558	0.0092	0.0395	0.1437	0.5912	−0.1335	0.3385	0.9794	1

Table 6.

Correlation among adipokines and lipids.

Values in bold are different from 0 with a significance level alpha = 0.05.

Reproduced from: Ref. [62].

BMI category	Variables	ADP	LP	RTN	VF
	ADP	1	0.1259	0.0157	0.1568
Normal weight	LP	0.1259	1	−0.1357	0.305
	RTN	0.0157	−0.1357	1	−0.426
	VF	0.1568	0.305	−0.426	1
	ADP	1	−0.6234	−0.3162	−0.2106
Overweight	LP	−0.6234	1	0.5556	0.4342
	RTN	−0.3162	0.5556	1	0.3758
	VF	−0.2106	0.4342	0.3758	1
	ADP	1	−0.2559	−0.0167	0.031
Obese	LP	−0.2559	1	0.5853	0.497
	RTN	−0.0167	0.5853	1	0.4751
	VF	0.031	0.497	0.4751	1

Table 7.

Correlation of adipokines according to BMI category.

Values in bold are different from 0 with a significance level alpha = 0.05. ADP, adiponectin; LP, leptin; RTN, resistin; VF, visfatin. BMI classification: Normal Weight = (18.5–24.9 kg/m²), Overweight = (25.0–29.99 kg/m²), Obese = (Above 30.0 kg/m²).

Reproduced from: Ref. [62].

“We had earlier examined the associations between maternal factors and PE (Table 8) in a report published in the International Journal of Women’s Health [63]. That report indicated that those with PE had significantly higher number of miscarriages, number of previous pregnancies and number of children compared to those without PE.”

		Sum of squares	df	Mean square	F	Sig.
Age	Between groups	832.197	1	832.197	25.723	<0.0001
	Within groups	10029.175	310	32.352
	Total	10861.372	311
BMI	Between groups	1248.068	1	1248.068	76.461	<0.0001
	Within groups	5060.084	310	16.323
	Total	6308.152	311
RWHP	Between groups	0.378	1	0.378	1.771	0.184
	Within groups	66.084	310	0.213
	Total	66.462	311
NC	Between groups	3.721	1	3.721	6.182	0.013
	Within groups	186.584	310	0.602
	Total	190.304	311
MC	Between groups	2.856	1	2.856	6.199	0.013
	Within groups	142.808	310	0.461
	Total	145.663	311
SB	Between groups	0.416	1	0.416	3.216	0.074
	Within groups	39.855	308	0.129
	Total	40.271	309
CS	Between groups	0.003	1	0.003	0.026	0.871
	Within groups	28.607	300	0.095
	Total	28.609	301
NP	Between groups	13.344	1	13.344	8.634	0.004
	Within groups	479.105	310	1.545
	Total	492.449	311

Table 8.

Comparison of maternal characteristics and family history of respondents with those who developed PE (N = 312; PE = 26; Without PE = 286).

RWHP, relatives with hypertension; NC, number of children; MC, number of previous miscarriages; SB, number of previous stillbirths; CS, number of previous cesarean operations; NP, number of pregnancies.

Reproduced from: Ref. [63].

4. Discussion

The goal of this study was to estimate the levels of adiponectin, leptin, resistin, and visfatin, between 11 and 13 weeks of pregnancy and to see how successful it was to predict PE using first trimester levels of these biomarkers together with maternal factors.

Leptin levels were found to be considerably greater in those who developed PE later on compared to those who did not. This is in line with a previous study that found a rise in leptin levels several weeks before a clinical diagnosis of PE [8]. This observation is also consistent with another study that found an imbalance between adiponectin and leptin in the plasma of women with PE, resulting in raised leptin and decreased adiponectin levels; consequently, these two adipose-derived hormones may play a role in the pathogenesis of PE [64]. Similarly, as compared to normal pregnant controls, leptin levels were found to be 78% higher at 13 weeks of gestation in women who ultimately developed PE [65]. When comparing pregnant women whose first-trimester leptin levels were 25 ng/mL to pregnant women whose first-trimester leptin levels were 25 ng/mL, the risk of PE increased 18.8 fold [66]. Other studies have shown that leptin levels rise before the clinical beginning of the disease, and our findings support that theory [39, 40]. The findings of this study, together with prior research, suggest that leptin is involved in the pathophysiology of PE, rather than a rise in leptin as a result of impaired renal clearance. Hyperleptinemia has been shown to promote sodium reabsorption in the renal tubules, leading to water retention and elevated blood pressure [67]. Furthermore, tumor necrosis factor (TNF)-α and interleukin (IL)-6 upregulate placental leptin mRNA synthesis and increase the formation of endothelin, a vasoconstrictive peptide [68]. The constriction of the blood vessels leads to high blood pressure leading to PE.

Adiponectin levels in the first trimester were considerably lower in women with PE compared to the control group in this study. Other research has found that adiponectin levels are inversely proportional to coronary artery disease but not strongly related to blood pressure levels [69]. In another study, individuals with preeclampsia had lower median maternal high molecular weight and low molecular weight adiponectin concentrations than those with normal pregnancies [70]. Previous reports have demonstrated lower first-trimester adiponectin levels in women who subsequently developed PE compared to their peers [71, 72]. However, this study contradicts a publication that stated that circulation levels of adiponectin were higher in preeclamptic patients than in normal pregnant women [73, 74]. In another study, women with preeclampsia had approximately 50% greater third-trimester adiponectin levels than their normotensive counterparts [75]. In a similar study, women with preeclampsia had higher levels of circulating adiponectin [74]. The compensatory feedback mechanisms to the metabolically altered, anti-angiogenic, and pro-atherogenic condition of severe preeclampsia could explain these increases, which normally occur after the first trimester [74]. Hypoadiponectinemia in the first trimester of pregnant women who later developed PE implies that this adipocytokine is involved in PE etiology [16, 17]. Pregnancy is an inflammatory state associated with elevated plasma TNF-α, which could cause adiponectin levels to drop even further. An increase in TNF-α leads to an increase in endothelin levels [68] which constricts the blood vessels leading to high blood pressure [68]. Adiponectin appears to block the synthesis of angiotensin II, according to available evidence [76]. As adiponectin levels fall, angiotensin II levels rise, resulting in an increase in aldosterone levels. Hypertension results from a rise in aldosterone levels, which causes sodium and water retention.

When comparing pregnancies that resulted in PE to those that did not, this study discovered considerably greater resistin levels in PE pregnancies. A recent study found that preeclamptic pregnancies had higher levels of several adipokines, notably resistin, than healthy pregnant women [25]. Other studies, on the other hand, found no significant difference in resistin levels between preeclampsia patients and healthy pregnant women [77, 78]. Women with PE had significantly lower resistin levels than normotensive women of the same gestational age, according to some studies [46]. The involvement of resistin in the pathophysiology of PE is indicated by the rise in resistin levels months before the clinical diagnosis of PE. Resistin levels in the blood have been associated with coronary artery disease [43]. Resistin levels in the blood have been linked to a number of inflammatory indicators, including C-reactive protein, soluble TNF-α receptor-2, IL-6, and lipoprotein-associated phospholipase A2 [43]. Increased levels of endothelin result from increased TNF-α receptor-2 and IL-6 concentrations, resulting in high blood pressure [68].

Plasma visfatin levels were shown to be considerably higher during PE in our research. Visfatin levels rose during PE from the first trimester onwards, suggesting that visfatin may play a role in the disease’s development. Visfatin is widely expressed in adipose tissue, placenta, and fetal membranes [48]. Visfatin concentrations in the second and third trimesters of normal pregnancy have been found to be higher than those in the first trimester [79] indicating that this protein is produced by the placenta and fetal membrane. Thus, it’s probable that normal visfatin production is regulated to support the growing baby; yet, in some pregnancies, visfatin’s supporting role may be interrupted, resulting in PE. Our findings are consistent with one of similar research which showed greater visfatin levels in the PE compared to normal pregnancy [80]. One study found no significant differences between normal and preeclamptic pregnancies [54] while another found lower levels [53]. Different researchers’ reports on visfatin levels during pregnancy could be attributed to variances in sample procedures, ethnic or geographical differences, or the specific test methods used. This study’s findings imply that visfatin levels rise before preeclampsia develops.

Visfatin’s potential as a marker of preeclampsia, particularly in obese women, will need to be explored further with bigger sample size. Such research will add to the body of knowledge on how to predict this disease and how to start intervention programs to reduce maternal and fetal morbidity and mortality from PE.

The fact that the AUCs and respective sensitivities and specificities did not significantly change after controlling for family history of hypertension (Table 4) shows that these biomarkers can predict PE independently regardless of family history of hypertension. When maternal weight was taken into account (Table 3), these adipokines were found to be ineffective in predicting PE in women of normal weight (BMI 18.5–24.9 kg/m²). However, the fact that the overweight group (BMI 25–29.9 kg/m²) fared better in terms of predicting these adipokines than the obese group (BMI 30.0 kg/m²) implies a possible negative feedback mechanism that lowers plasma concentrations of these peptides as weight rises. To explain this occurrence, more research with bigger sample size is needed.

This study found that overweight pregnant women are more likely than normal-weight pregnant women to get PE during their pregnancy, corroborating an earlier study that found that the likelihood of developing PE increased by two to three times in women with a higher BMI [81] and also similar to another study, which associated higher maternal BMI to a number of pregnancy complications including PE [82]. In addition, this study backs up a recent analysis that showed that advanced maternal age, especially, 35 years or more was a risk factor for preeclampsia [83] as well as a BMI greater than 30 kg/m² [84]. Obesity may play a role in the development of PE, according to the findings of this study. Obesity affects nitric oxide production and causes endothelial dysfunction [85] therefore an excessive buildup of fat in a pregnant woman could lead to hypertension during pregnancy which could lead to PE.

With the exception of HDL cholesterol, which was considerably lower in the PE group (Table 1) compared to the normotensive group, this investigation found no significant differences in lipids between women who acquired PE and those who remained normotensive during pregnancy. This study contradicts a report by Brazilian researchers who found a substantial difference in TG-rich proteins (VLDL 1) and small dense lipoprotein (LDL III) in women with PE compared to normal pregnant women [86]. Our findings contrast with those published in the Cape Coast region of Ghana, where researchers found substantial dyslipidemia in women with PE compared to women without PE [57]. The variations could be related to the different stages of pregnancy during which the samples were taken. The samples for this study were taken before the commencement of PE, whereas the samples for the other investigations were taken after the disease had begun to manifest. The lack of a significant difference in first trimester lipids between those who got PE and those who did not show that the atherogenic lipid profile commonly seen in pregnant women as reported by other researchers may be insufficient in predicting the chance of getting PE. However, because lower HDL is a substantial risk factor for hypertension, it’s probable that the significantly lower HDL seen in individuals who went on to develop PE was linked to the disease’s etiology [87]. Adiponectin and resistin were found to be more significant and better predictors of PE than leptin and visfatin after correcting for these potential confounding variables (age, parity, BMI, family history of diabetes, and preeclampsia). Angiotensin II production is reduced by adiponectin [76] while resistin is linked to elevations in TNF-α receptor-2 and IL-6, and so promotes high blood pressure [43], leading to an increased level of endothelin which constricts blood vessels and raises high blood pressure [68]. A family history of PE has been linked to a threefold increase in the chance of developing PE [88, 89] however, we did not detect a significant link between PE and a family history of hypertension which is likely attributable to the fact that the data obtained from the participants in this study was focused on hypertension in general rather than PE.

According to the findings of this study, obesity may play a role in the development of PE. Obesity induces endothelial dysfunction by reducing nitric oxide production [85], hence if a pregnant woman has an excessive amount of fat on her body, she may develop hypertension and, as a result, PE. Obesity and having four or more children were discovered to be significant PE confounders.

4.1 Study limitations

The study’s limitations were limited sample size and insufficient information regarding the individuals’ nutritional state. Potassium is abundant in leafy greens like spinach and kale, as well as cherries and red beets. Potassium operates on the kidneys, allowing the salt to be excreted more easily through the kidneys, decreasing blood pressure. Because of the small sample size and lack of nutritional data, conclusions about the association between these adipocytokines and preeclampsia may be difficult to draw, since nutritional status could not be controlled in the multivariate analysis.

5. Conclusions

PE was found to be significantly predicted by low adiponectin and high leptin, resistin, and visfatin, with resistin being the greatest predictor when stratified by BMI categories. After controlling for age, parity, BMI, and family history of diabetes and preeclampsia, adiponectin was the greatest predictor.

Adiponectin concentration in patients with PE starts decreasing as early as 11 weeks of pregnancy and continues to decrease until after 24th weeks of pregnancy when proteinuria becomes apparent and blood pressure rises to an abnormal level and consequently, preeclampsia develops. The decrease in adiponectin contributes to the pathogenesis of PE and can be used to predict this disease.

Leptin concentration starts increasing by 11 weeks of pregnancy in patients who subsequently develop PE. The increase in leptin correlates with proteinuria and elevated systolic and diastolic blood pressure irrespective of maternal age and BMI and hence could be involved in the pathogenesis of GDM.

Resistin in pregnant women who go on to develop PE starts increasing between 11 and 13 weeks of gestation culminating in an excessive increase in blood pressure accompanied by proteinuria by 24 weeks of gestation when a diagnosis of PE becomes apparent.

Visfatin in pregnancies complicated by PE starts increasing during the first trimester of pregnancy and continues to increase until the second trimester when blood pressure increases resulting in the diagnosis of PE in women with concomitant proteinuria. This suggests that hypervisfatinemia can be used to predict hypertensive disorders during pregnancy and hence involved in the pathogenesis of PE.

Our findings suggest that BMI may have an effect on adiponectin, leptin, resistin, and visfatin, as well as a possible negative feedback mechanism in the metabolism of these adipocytokines during pregnancy. More importantly, BMI does not appear to have an effect on the predictive ability of these PE signaling molecules. Advanced maternal age was shown to be an important factor in the development of PE.

These biomarkers can be used in combination with maternal characteristics for the early prediction of PE. This will help health care providers to institute measures such as diet control, medication, and exercises tailored for pregnant women with these risk factors so as to reduce the incidence of preeclampsia.

Acknowledgments

1. Prof. Francis Agyemang Yeboah, Department of Molecular Medicine, School Medical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Telephone: [phone]. Email: [e-mail].

2. Prof. Robert Amadu Ngala, Department of Molecular Medicine, School Medical Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. Telephone: [phone]. Email: [e-mail].

3. Mr. Salifu Nanga, School of Basic and Biomedical Sciences, University of Health and Allied Sciences, Ho, Ghana. Telephone: [phone]. Email: [e-mail].

Conflict of interest

The authors declare no conflict of interest.

Abbreviations

BMI	body mass index
PE	preeclampsia
ADP	adiponectin
LP	leptin
RTN	resistin
VF	visfatin
TG	triglycerides
TC	total cholesterol
HDL	high-density lipoprotein cholesterol
LDL	low-density lipoprotein cholesterol
VLDL	very low-density lipoprotein cholesterol