Cross-sectional correlates of increased IL-18 but reduced fetuin-A and oxytocin with adiposity and blood indices in metabolic syndrome patients with and without prediabetes

Abstract

Background:

Oxytocin (OXT), fetuin-A and interleukin-18 (IL-18) are involved in the development and progression of metabolic syndrome (MetS) and prediabetes (pre/T2DM).

Aims, participants and methods:

This study aimed to compare and correlate the plasma levels of OXT, fetuin-A and IL-18 with clinical parameters, haematological indices and adiposity indices in Jordanian MetS subjects. In a cross-sectional study, 30 normoglycaemic lean study participants (control), 30 MetS study participants, and 29 MetS pre/T2DM study participants were recruited.

Results:

Median circulating levels of both OXT and fetuin-A were lower in MetS and MetS pre/T2DM versus control group. OXT (pg/ml; median interquartile range): MetS 1975.4 and MetS pre/T2DM 1403 versus control 4176.6 (p = 0.009 and p = 0.001, respectively). For fetuin-A (ng/ml), MetS (5784) and MetS pre/T2DM (2154) were lower versus control (6756.3) (p = 0.040 and p = 0.007, respectively). Neither biomarker was described as substantially different in MetS versus MetS pre/T2DM (p = 0.071 and p = 0.155, respectively). Conversely, a non-significant increase in IL-18 was observed in the MetS and MetS pre/T2DM groups compared to normoglycaemic lean controls (232 and 287.5, p > 0.05 versus 108 for both). In addition, conicity index (C-index), atherogenicity index (TG-HDL-C), waist to hip ratio, mean platelet volume (MPV; fl) and red cell distribution width (RDW-CV%) in both MetS and MetS pre/T2DM were significantly higher (p < 0.001) versus controls. However all above MetS-related indices were not ascribed any statistically marked variation in the MetS group when compared to the MetS pre/T2DM group. Both total study pool of recruits’ fetuin-A (Spearman r = –2.66, p = 0.049) as well as MetS pre/T2DM group IL-18 (Spearman r = 0.380, p = 0.046) were inversely correlated with RDW-CV%. OXT in MetS inversely correlated with waist circumference/hip circumference ratio (Spearman r = −0.387, p = 0.038). No other pronounced associations between biomarkers could be detected in any study arm.

Conclusion:

These findings substantiate the clinical relevance and significance of OXT, fetuin-A and IL-18 as surrogate screening/prognostic tools and therapeutic targets to predict/prevent metabolic dysregularities and anomalies.

Keywords

adiposity indices atherogenicity indices fetuin-A haematological indices interleukin-18 metabolic syndrome oxytocin type 2 diabetes mellitus

Introduction

Diabetes mellitus is a chronic, progressive endocrine disorder, which is caused by inherited and/or acquired insulin deficiency and sensitivity.¹

Metabolic syndrome (MetS) is a cluster of risk factors that include hypertension (HTN), obesity, and hyperglycaemia, and which increases the risk of cardiovascular disease (CVD) and other health problems such as diabetes and stroke.²

Cytokines are small, non-structural proteins that all nucleated cells can synthesize, and are primarily involved with homeostatic mechanisms or host response to infection.³ There are 18 cytokines, among which are the interleukins (ILs) and tumor necrosis factor alpha (TNFα). Cytokines can be strongly associated with MetS.³ IL-18 has been associated with obesity,⁴ insulin resistance (IR),⁵ HTN⁶ and dyslipidaemia.⁷ Furthermore, IL-18 has been shown to be elevated in patients with MetS.⁸ Also, it plays a significant role in the development of type 2 diabetes (T2D) related microvascular complications such as atherosclerosis assessed with intima media thickness of the carotid artery and nephropathy.¹⁰

Similarly fetuin-A (a blood protein synthesized by the liver) plays an important role in free fatty acid-induced IR in the liver. Furthermore, in prediabetes, fetuin-A is a predictor of adverse glycaemic outcomes¹¹ and increased fetuin-A pro-inflammatory effects are linked to increased incidence of CVD, non-alcoholic fatty liver disease and atherogenesis progression.¹²

Oxytocin (OXT) is a neuropeptide produced by the hypothalamus which has a central role especially in energy balance regulation, besides its peripheral role in uterine contraction during labour and milk ejection during lactation.¹³ Impressively, OXT and its analogues have multiple therapeutic actions beyond the role of weight control and metabolism regulation.^14,15 They furthermore have lipid-lowering, insulin-sensitizing and insulin-secretory effects.¹⁶ Peripheral OXT treatment can control hyperphagia and food intake, reducing visceral fat mass, obesity, fatty liver, glucose intolerance and diabetes.¹⁶ Thus, this represents a new therapeutic avenue against diabetes, obesity and related lipid abnormalities, when administrated peripherally.¹⁶

This study aimed to evaluate and compare the correlations of IL-18 and fetuin-A as inflammatory biomarkers with OXT metabolic hormone plasma levels in non-diabetic MetS and newly diagnosed drug naïve pre-diabetic/diabetic MetS patients. We aimed to evaluate potential associations between the mentioned markers with haematological indices, adiposity markers and atherogenicity indices in the same study population. This study may substantiate a molecular crosstalk of OXT, fetuin-A and IL-18 in the MetS-related dysregularities, thus further promoting these biomarkers as surrogate predictive/prognostic tools and potential therapeutic targets to prevent and/or delay the progression of pre/T2DM and MetS.

Participants, materials and methods

Study design

This was a cross-sectional study with three different cohorts: 29 healthy, lean [body mass index (BMI) < 25 kg/m²] and non-diabetic participants (control arm); 30 non-diabetic patients with MetS, defined according to the International Diabetes Federation (IDF) definition of MetS¹⁷ (MetS non-diabetic arm); and 30 pre-diabetic or newly diagnosed T2DM patients who are drug naïve and defined based on American Diabetic Association and IDF definitions of T2DM and MetS respectively (MetS pre-diabetic/diabetic arm).^17,18 Exclusion criteria were as follows:

pregnant or breast-feeding females;

any prior treatment with anti-diabetic agents or naloxone or weight-reduction regimens;

obesity secondary to endocrine derangement other than T2DM;

inflammatory diseases such as inflammatory bowel disease;

autoimmune or life-threatening disease, alcohol/drug abuse, recently diagnosed and untreated endocrine disorder.

Sample size calculation

Sample size determination was based on the literature as follows:¹⁴

N = 2 \times {SD}^{2} {(Z_{α} + Z_{β})}^{2} / Δ^{2}

where: N = sample size; Z_α = type one error = 1.96 when α = 5%; Z_β = type two error = 1.28 when β = 10%; SD = standard deviation OXT baseline from study¹⁴ (1.38 ng/L); and Δ = the difference yielded between OXT levels of diabetic/pre-diabetic group versus the control group after a literature study¹⁴ (2.07 ng/L). From this equation, the number for each part = 9 patients per study arm; multiple participants were used to power the study and increase the pool of participants. Figure 1 demonstrates the study recruitment flowchart.

Figure 1.

Study recruitment flowchart.

Clinical setting and duration

The study was conducted in the outpatient clinics of National Center for Diabetes Endocrinology and Genetics (NCDEG) after approval from the Scientific Research Committee at the School of Pharmacy, Deanship of Academic Research, The University of Jordan, and approval from the NCDEG Institutional Review Board committee [Approval for the study was obtained from Institutional Review Board (IRB) affiliated with the National Center for Diabetes, Endocrinology, and Genetics (1151, 1152, 1153/9/SM).]. Participants who agreed to take part in the study were asked to sign an informed consent form. Data collection of patients’ medical histories started in February 2016 and finished in April 2016. Blood samples were drawn into lithium heparin after a 10 h overnight fast and subsequently centrifuged to be frozen at −80°C storage until thawed immediately before plasma biomarker and hormonal analyses. Commercially available human ELISA assays of plasma IL-18 (Affymetrix, USB and eBioscience, ThermoFisher Scientific, Waltham, MA, USA), OXT and fetuin-A (Abcam®, Cambridge, UK) were performed according to the manufacturers’ instructions.

Statistical analysis

All data were coded, entered and analysed using SPSS^© 22 (SPSS, Inc., Chicago, IL, USA). Chi-square tests were used for categorical variables. For continuous variables, comparison between study groups was conducted using the Mann–Whitney U test. The levels of markers were compared using multivariate one-way analysis of covariance (MANCOVA) with age as a covariate. Categorical variables were expressed as frequencies and percentages, and continuous variables were presented as medians ± interquartile range (IQR). Spearman correlation was used to assess the relationship between continuous variables and a p value < 0.05 was considered statistically significant.

Results

Demographic and clinical characteristics

The demographic characteristics of the study sample are summarized in Table 1. All participants were Caucasian; the majority (61.8%) were female. The median age was 41.5 (29–52) years and the median BMI was 27.4 (23.77–32.755) kg/m². Intergroup statistically significant variations in the demographic characteristics such as age and BMI between the control and both MetS groups (non-diabetic and pre/diabetic; p < 0.05) were observed. This demonstrates heterogeneity between study arms. The following parameters were significantly higher (p < 0.001) in both non-diabetic MetS and MetS pre/T2DM groups when compared to the control group: systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), low density lipoprotein- cholesterol(LDL-C), C-index, waist circumference to hip circumference (WC/HC) ratio, triglyceride–high-density lipoprotein cholesterol (TG/HDL-C) ratio and RDW-CV%. Meanwhile, HDL-C was significantly lower (p < 0.001) in both non-diabetic MetS and MetS pre/T2DM groups versus controls. Mean platelet volume (MPV) had higher levels in the MetS pre/T2DM group when compared to the control group (p < 0.05) (Table 1). As expected, MetS-related parameters (by definition) were not significantly different in the MetS pre/T2DM group when compared to the MetS group.

Table 1.

Clinical parameters of all participants and comparison between three study groups.

Parameter	Total sample N = 89	Control group N = 30	MetS group N = 30	MetS pre/T2DM N = 29	P1^a	P2^a	P3^a
DBP (mmHg)	80 (75–85)	75 (70–80)	84 (76.5–85.5)	85 (77.5–90)	<0.001	<0.001	0.634
FBG (mg/dl)	88 (81.5–100.5)	87 (79.5–90)	86 (79.8–93.3)	112 (103–118.3)	0.706	<0.001	<0.001
HbA1c (%)	5.5 (5.2–5.7)	5.25 (5.1–5.4)	5.4 (5.2–5.5)	5.9 (5.7–6.8)	0.072	<0.001	<0.001
TG (mg/dl)	148 (79.2–210.9)	72 (49.3–94.3)	199 (156.3–262)	179.5 (127.7–242.5)	<0.001	<0.001	0.308
TC (mg/dl)	185.5 (150.3–218.1)	169 (131.3–209.8)	207 (163.5–230.5)	187.9 (158.9–254)	0.025	0.058	0.755
LDL-C (mg/dl)	112.45 (83.3–147.3)	88 (74.3–119)	111.5 (84.5–155.0)	130.5 (101.7–174.3)	0.026	<0.001	0.228
HDL-C (mg/dl)	40 (36–48)	50 (44–70.5)	38 (34.5–40)	38.4 (34.6–42)	<0.001	<0.001	0.571
Age (years)	41.5 (29–52)	28 (22.5–30.5)	46 (37.8–54.3)	52 (43.5–55)	<0.001	<0.001	0.081
Gender N (%) *					0.136
Male	34 (38.2)	8 (26.7)	11 (36.7)	15 (51.7)
Female	55 (61.8)	22 (73.3)	19 (63.3)	14 (48.3)
BMI (kg/m²)	27.4 (23.8–32.8)	22.2 (19.9–24.5)	31.2 (27.2–35.8)	31.8 (27.5–35.2)	<0.001	<0.001	0.705
C-index	1.096 (1.0–1.2)	0.97 (0.9–1.1)	1.2 (1.1–1.2)	1.2 (1.1–1.2)	<0.001	<0.001	0.458
WC/HC ratio	0.89 (0.83–0.9)	0.8 (0.8–0.9)	0.9 (0.9–0.9)	0.9 (0.87–0.92)	<0.001	0.006	0.144
TG/HDL-C ratio	2.96 (1.5–5.6)	1.3 (0.9–2.0)	5.1 (3.8–6.8)	4.7 (2.2–6.6)	<0.001	<0.001	0.255
RDW-CV%	13.3 (12.5–14.1)	13.1 (12.2–13.7)	13.3 (12.5–14)	13.6 (12.9–14.6)	<0.001	0.011	0.248
MPV (fl)	9.8 (9.0–10.5)	9.3 (8.6–9.9)	9.9 (9.2–10.6)	10.4 (9.1–11.0)	0.053	0.015	0.277
Fetuin (ng/ml) *	6009.5 (4728.6–7055.9)	6756.3 (5557–8081)	5784 (4345–6779.1)	2154 (3843–6854)	0.040	0.007	0.155
IL-18 (ng/ml)*	190 (87.5–324)	108 (41–179.5)	232 (126–360)	287.5 (158–420)	0.155	0.303	0.795
OXT (pg/ml) *	2314 (1272.2–4041.6)	4176.6 (2407.1–5243.3)	1975.4 (1522.3–3191.2)	1403 (1034.0–2567.3)	0.009	0.001	0.071

Data are expressed as median (IQR).

= p value by Mann–Whitney U Test.

= p value by multivariate test.

P1 = Control group compared to MetS group. P2 = Control group compared to MetS pre/T2DM group. P3 = MetS group compared to MetS pre/T2DM group

C-index: conicity index; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; MPV: mean platelet volume; RDW: red cell distribution width; TG, triglyceride; WC/HC ratio: waist circumference to hip circumference ratio.

Fetuin-A, IL-18 and OXT levels and correlations

As demonstrated in Table 1, median circulating levels of both fetuin-A and OXT were significantly lower in patients with MetS or MetS pre/T2DM as compared to respective controls (p = 0.009 and p = 0.001, respectively). Fetuin-A plasma levels in the total pool of recruits and IL-18 plasma levels in the MetS pre/T2DM participants correlated significantly and inversely with RDW-CV% (r = −0.266, p < 0.05 and r = −0.380, p < 0.001, respectively) (Table 2). Furthermore, OXT correlated significantly and inversely with WC/HC ratio (r = −0.387, p = 0.038) (Table 2).

Table 2.

Correlations for plasma levels of IL-18, fetuin-A, OXT and haematological and adiposity indices in the total study MetS population, and the three study groups.

Correlation		Total sample			Control group			MetS group			MetS pre/T2DM group
Clinical biomarkers		IL-18(ng/ml)	Fetuin-A(ng/ml)	OXT(pg/ml)	IL-18(ng/ml)	Fetuin-A(ng/ml)	OXT(pg/ml)	IL-18(ng/ml)	Fetuin-A(ng/ml)	OXT(pg/ml)	IL-18(ng/ml)	Fetuin-A(ng/ml)	OXT(pg/ml)
TG/HDL-C ratio	R	0.149	−0.033	0.071	0.147	−0.240	0.052	0.084	0.106	−0.117	0.248	−0.181	0.137
	p value	0.265	0.802	0.602	0.454	0.209	0.792	0.664	0.578	0.545	0.194	0.355	0.487
RDW-CV %	R	−0.240	−0.266^*	0.040	−0.217	0.168	0.147	−0.075	0.132	0.296	−0.380^*	−0.050	−0.024
	p value	0.077	0.049	0.773	0.277	0.394	0.463	0.709	0.510	0.135	0.046	0.803	0.907
MPV (fl)	R	0.050	0.100	−0.019	−0.109	−0.033	−0.049	0.094	−0.138	0.177	0.046	0.174	−0.173
	p value	0.724	0.475	0.893	0.587	0.866	0.807	0.639	0.494	0.376	0.825	0.405	0.408
C- index	R	0.256	0.112	0.040	0.188	−0.188	0.185	0.303	0.278	−0.059	0.240	0.227	0.075
	p value	0.053	0.398	0.769	0.339	0.329	0.346	0.110	0.137	0.760	0.209	0.246	0.703
WC/HC ratio	R	0.085	−0.035	−0.111	−0.138	−0.265	0.122	0.060	0.140	−0.387^*	0.168	−0.001	0.013
	p value	0.524	0.795	0.409	0.483	0.165	0.537	0.759	0.460	0.038	0.382	0.998	0.946

= correlation is significant at the 0.05 level; ** at the 0.01 level (two-tailed).

C-index: conicity index; HDL-c, high-density lipoprotein cholesterol; MPV: mean platelet volume; R = correlation coefficient; RDW: red cell distribution width; TG, triglyceride; WC/HC ratio, waist circumference to hip circumference ratio.

Discussion

IL-18 findings

Hung et al showed that IL-18 concentration was inversely associated with BMI, waist circumference, TG, blood pressure and insulin levels in a sample of 1111 participants 9558 men and 553 women). Noticeably, IL-18 correlated more strongly with waist circumference (r = 0.39) and HDL-C (r = 0.31), unlike other metabolic traits, and was associated marginally with age (r = 0.10) (Table 3).⁷ In our study there was no significant difference in IL-18 between the groups – namely control versus MetS 108 (41–179.5) (p = 0.155), control versus MetS pre/T2DM 232 (126–360) (p = 0.303) and MetS versus MetS pre/T2DM 287.5 (158–420) (p = 0.795) (Table 1). Furthermore, in the MetS pre/T2DM group, IL-18 was significantly and inversely correlated with RDW-CV% (r = −0.380, p = 0.046) (Table 2).

Table 3.

Comparison of IL-18 levels in our study to the literature.⁷

Parameters	Hung and colleagues’ findings⁷				Our findings
Parameters	Group1	Group 2	Group 3	Group 4	Control	MetS	MetS pre/T2DM
TG (mg/dl)	16.21(14.4–16.2)	18.0(16.2–19.8)	25.2(23.4–27.0)	37.8(36.0–39.6)	72(49.3–94.3)	199(156.3–262)	179.5(127.7–242.5)
HDL-C (mg/dl)	28.2(27.4–28.8)	2576(25.2– 26.5)	22.34(21.4–23.1)	18.6(17.7–19.5)	50(44–70.5)	38(34.5–40)	38.4(34.6–42)
LDL-C (mg/dl)	61.3(59.5–63.1)	64.9(63.1–66.7)	68.5(64.9–70.3)	68.5(66.7–70.3)	88(74.3–119)	111.5(84.5–155.0)	130.5(101.7–174.3)
SBP (mmHg)	111(110–114)	130(126–132)	134(132–136)	139(136–141)	120(115–126)	135(127.5–141.3)	140(135–148)
DBP (mmHg)	72(71–73)	81(80–82)	83(81–84)	87(85–88)	75(70–80)	84(76.5–85.5)	85(77.5–90)
IL-18 (ng/ml)	255(243–268)	279(267–292)	315(300–331)	356(340–381)	108(41–179.5)	232(126–360)	287.5(158–420)

Data are mean or geometric mean and 95% confidence intervals for Hung and colleagues’ study⁷ and median (25–75) interquartile range (for our study). Group 1 = number of metabolic risk factors equal to 0. Group 2 = number of metabolic risk factors equal to 1. Group 3 = number of metabolic risk factors equal to 2. Group 4 = number of metabolic risk factors equals to 3 or higher.

DBP, diastolic blood pressure; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglyceride.

In a study¹⁹ by Yamaoka-Jojo et al consisting of 42 patients with MetS or pre-MetS and 14 control participants with an average BMI of 23.3, 28 patients were diagnosed with MetS (BMI = 30.9), and the remaining 14 patients were diagnosed with pre-MetS (BMI = 29.6). Serum levels of IL-18 were reduced with weight loss (p < 0.01), and the levels substantially correlated with the change in weight (p = 0.046). This conclusively showed that IL-18 concentrations are increased in patients with T2DM, obesity and polycystic ovary syndrome. Interestingly, serum levels of IL-18 correlated appreciably and significantly with the waist circumference in patients with MetS and pre-MetS.¹⁹

OXT findings

In table 4, we compare our findings to those of a recently published study²⁰ by Yuan et al, which consisted of 170 participants (75 participants with age- and gendermatched newly diagnosed MetS20 and 95 non-MetS).

Table 4.

Comparison of OXT and clinical parameters in our study to the literature.

Clinical parameter	Yuan and colleagues’ findings²⁰			Our study findings
Clinical parameter	MetS group N = 75	non-MetS N = 95	p value	Control group N = 30	MetS group N = 30	MetS pre/T2DM group N = 29	P1^a	P2^a	P3^a
Age (years)	47.8 ± 11.3	46.7 ± 9.4	0.517	28 (22.5–30.5)	46 (37.8–54.3)	52 (43.5–55)	<0.001	<0.001	0.081
Gender, male (%)	60.0	71.5	0.112	8 (26.7)	11 (36.7)	15 (51.7)	0.136
BMI (kg/m²)	26.8 ± 3.2	23.6 ± 2.7	<0.001	22.21 (19.9–24.5)	31.2 (27.2–35.8)	31.8 (27.5–35.2)	<0.001	<0.001	0.705
WC (cm)	93.2 ± 8.3	82.9 ± 8.8	<0.001	0.82 (0.8–0.9)	0.91 (0.88–0.93)	0.9 (0.87–0.92)	<0.001	0.006	0.144
SBP (mmHg)	130.8 ± 15.5	123.0 ± 12.0	<0.001	125 (121.3–128.8)	135 (130–140)	145 (140–155)	<0.001	<0.001	0.065
DBP (mmHg)	81.4 ± 9.4	77.7 ± 9.5	0.013	75 (70–80)	84 (76.5–85.5)	85 (77.5–90)	<0.001	<0.001	0.634
HbA1C (%)	8.1 ± 2.4	6.7 ± 2.5	<0.001	5.25 (5.1–5.4)	5.4 (5.2–5.5)	5.9 (5.7–6.8)	0.072	<0.001	<0.001
FPG (mg/dl)	151.9 ± 57.6	124.5 ± 58.7	0.003	87 (79.5–90)	86 (79.8–93.3)	112 (103–118.3)	0.706	<0.001	<0.001
TC (mg/dl)	90.3 ± 20	82.7 ± 17.4	0.010	169 (131.3–209.8)	207 (163.5–230.5)	187.9 (158.9–254)	0.025	0.058	0.755
TG (mg/dl)	50.6 ± 30.3	25.4 ± 14.4	<0.001	72 (49.3–94.3)	199 (156.3–262)	179.5 (127.7–242.5)	<0.001	<0.001	0.308
LDL-C (mg/dl)	55.5 ± 17.8	52.1 ± 15.5	0.206	88 (74.3–119)	111.5 (84.5–155.0)	130.5 (101.7–174.3)	0.026	<0.001	0.228
HDL-C (mg/dl)	19.1 ± 4.1	24.1 ± 7.2	<0.001	50 (44–70.5)	38 (34.5–40)	38.4 (34.6–42)	<0.001	<0.001	0.571
OXT (pg/ml)	8000 (6670–9490)	9070 (8080–10370)	<0.001	4176.6 (2407.1–5243.3)	1975.4 (1522.3–3191.2)	1403 (1034.0–2567.3)	0.009	0.001	0.071

P1 = Control group compared to MetS group. P2 = Control group compared to MetS pre/T2DM group. P3 = MetS group compared to MetS pre/T2DM group.

BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; OXT, oxytocin; SBP, systolic blood pressure; TC: total cholesterol; TG, triglyceride; WC, waist circumference.

In comparison to a recent study published by Qian et al, the T2DM group in our study had lower OXT.¹⁴ The T2DM group had lower OXT concentration (7.16 pg/ml) than normoglycaemic patients (9.23 pg/ml) (p < 0.001).¹⁴ Similarly in our study, OXT level was significantly lower in both MetS (non-diabetic and pre/diabetic) participants versus lean and normoglycaemic controls (Table 1). Consistent with other findings in the literature, OXT level did not vary significantly in non-diabetic MetS versus MetS pre/T2DM groups.

Importantly, OXT levels correlated inversely with WC/HC ratio in the non-diabetic MetS group. In findings from the literature,^14,20 OXT correlated negatively with WC.

Fetuin-A findings

In a study²¹ of 103 patients with T2DM, no significant difference in plasma fetuin-A levels was found between the normoglycaemic group and the diabetic group (r = 0.03, p = 0.76). In another study,²² fetuin-A levels were positively associated with: total cholesterol (TC), LDL-C, TG, WC, SBP, DBP and fasting blood glucose (FBG), but negatively associated with HDL-C level. In a clinical report,²³ circulating fetuin-A levels were investigated in a total of 205 patients with peripheral artery disease (PAD) and T2DM. The study identified that the highest fetuin-A concentration was found in T2DM PAD patients versus patients with normoglycaemic PAD (NGM-PAD) and versus T2DM non-PAD patients (p < 0.001). As well as PAD alone, patients’ (NGM-PAD) fetuin-A concentrations were higher in comparison with patients with T2DM non-PAD (p < 0.001).²³

In our study, fetuin-A level median (interquartile range) (ng/ml) was 6009.5 (4728.6–7055.9) in the total sample, 6756.3 (5557–8081) in the control group, 5784 (4345–6779.1) in the MetS group and 2154 (3843–6854) in the pre-diabetic/T2DM group. Thus, intergroup comparisons demonstrate that fetuin-A plasma levels were significantly different in control versus MetS (p = 0.040) and versus MetS pre-diabetic/T2DM groups (p = 0.007) (Table 1). Finally, fetuin-A was inversely correlated with RDW (r = −2.66, p = 0.049) in the total study pool of recruits (Table 2).

Interestingly, a study²⁴ consisting of 62 patients with MetS aged 35–83 years concluded that serum fetuin-A is correlated with some components of MetS, such as fasting plasma glucose (FPG) (p = 0.030), LDL-C (p = 0.039) and TG (p = 0.048).²⁴ Patients with greater FPG had lower fetuin-A levels. Of note, fetuin-A concentration positively correlated with LDL-C and TG levels. Based on statistical analysis, no associations between fetuin-A and WC, blood pressure, HDL-C, HbA1C and BMI have been found in patients with MetS.²⁴

Conclusion and limitations

To our knowledge, this is the first time fetuin-A and IL-18 have been investigated in Jordanian MetS and T2DM populations. Both fetuin-A and OXT circulating plasma levels were significantly different in the control versus MetS and versus MetS pre/T2DM groups (p < 0.05) in the total study population. Fetuin-A correlated significantly and inversely with RDW while OXT levels correlated significantly and inversely with WC/HC ratio. Finally, IL-18 correlated significantly and inversely with RDW in the MetS pre/T2DM group. This is a cross-sectional study with a single time point determination; thus, no causality relationship could be concluded. It cannot be precisely speculated whether the biomarker itself caused the metabolic disturbances of diabetes and/or MetS or the diabetes and/or MetS caused the changes in biomarker levels. Increasing the sample size and having a homogeneous gender sample of participants could help to clarify this. Finding age-matched participants in each arm was a challenge; it was a struggle to find either young MetS and T2DM patients or older healthy, lean controls. A larger sample size is required to establish correlations. However, we were unable to recruit a larger sample due to financial constraints. It is recommended that prospective cohort studies are performed to establish a cause -effect relationship. Further studies with a larger sample size and a homogeneous gender sample can help to verify our results.

Footnotes

Acknowledgements

The Deanship of Academic Research and Quality Assurance/The University of Jordan is graciously thanked for supporting this research.

Authors’s Note

Authors confirm that all patients provided their consent forms.

Funding

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Conflict of interest statement

The authors declare that there is no conflict of interest.

Author contributions

All authors contributed equally to the design of the study, data collection, analyses and drafting of the manuscript. All authors read and approved the final version of the manuscript.

ORCID iD

Violet Kasabri

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