Cross-sectional correlates of myeloperoxidase and alpha-1-acid glycoprotein with adiposity,atherogenic and hematological indices in metabolic syndrome patients with or without diabetes

Abstract

Background:

Type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) are associated with obesity, which triggers the release of inflammatory substances. Myeloperoxidase (MPO), a peroxidase enzyme, and alpha-1-acid glycoprotein (AGP), an acute-phase protein, are known to be released in patients with inflammatory conditions and cardiovascular disease (CVD).

Methods:

In this study, we investigated the correlation between MPO and AGP levels in pre/diabetic and MetS patients by conducting a cross-sectional study at The University of Jordan Hospital (UoJH) at the diabetes and endocrinology outpatient clinics. A total of 237 patients were recruited and assessed for eligibility. Of these, 149 patients were excluded, and 88 patients were assigned as: 29 patients in a healthy lean normoglycemic control group; 29 patients in a nondiabetic MetS group; and 30 patients in a prediabetic/newly diagnosed T2DM MetS group.

Results:

MPO levels were only significantly higher in the nondiabetic MetS group compared with the control group (p = 0.026). AGP levels were significantly higher in both nondiabetic MetS and MetS-prediabetic/T2DM groups versus control (p = 0.007 and p = 0.015, respectively). Both biomarkers lacked inter-MetS-group discrepancy.

Conclusions:

Our results demonstrate an association between MPO and AGP with obesity and hyperglycemia, alongside their correlation with several adiposity, hematology and atherogenicity indices. Our findings reinforce the use of MPO and AGP as potentially putative and surrogate predictive/prognostic tools for MetS and its related disorders.

Keywords

adiposity indices hematology indices alpha 1-acid glycoprotein ELISA lipid ratios metabolic syndrome myeloperoxidase type 2 diabetes mellitus

Introduction

Diabetes mellitus (DM) is a metabolic disorder identified by hyperglycemia of either complete deficiency of insulin (type 1 DM) or loss of insulin function or secretion (type 2 DM). Type 2 DM (T2DM) could occur due to insulin resistance (IR) and obesity. Uncontrolled hyperglycemia is associated with dysfunction and damage to different organs.¹ Aside from T2DM, prediabetes (pre-DM) is a state whereby glucose levels are elevated but individuals are neither considered as DM patients nor normal, healthy individuals.² Metabolic syndrome (MetS) is a cluster of risk factors caused mainly by IR and characterized at most by abdominal obesity, atherogenic dyslipidemia, impaired glucose tolerance and high blood pressure; all together precipitate the risk of cardiovascular disease (CVD), stroke and diabetes.^2,3

Oxidative stress is present in all grades of obesity significantly.^4,5 The oxidative stress depends on not only the overproduction of reactive oxygen species (ROS) but also the deficiency of the antioxidant defense system. Both oxidative imbalance and vascular endothelial dysfunction leads to early development of atherosclerosis.⁶ Moreover, excessive generation of ROS and chronic activation of pro-inflammatory signals have relevant roles in T2DM.⁷ Myeloperoxidase (MPO) uses hydrogen peroxide to create hypochlorous acid in defending the host against invading microorganisms,^8,9 as well as causing injury and damage to the endothelium tissue.¹⁰

Alpha-1-acid glycoprotein (AGP; also called orosomucoid) is a member of the acute-phase reactant protein family. It has 183 amino acids with a molecular mass of 40 KDa. AGP is synthesized mainly in the liver¹¹ and released during inflammation, pregnancy, cancer¹² and diabetes.¹³ Evidently AGP is among the auspicious biomarkers determining metabolic health,¹⁴ as well as being an independent predictor in the prognosis of chronic heart failure.¹⁵

Hematological indices such as mean platelet volume (MPV), platelet count (PLT count) and white blood cell subtypes are among the inflammatory markers considered reliable prognostic indicators for diabetes complications.^16,17 Coronary artery disease risk is significantly associated with higher and lower triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C), respectively,¹⁸ alongside the atherogenic index of plasma (AIP), log TG/HDL-C), which is a surrogate estimate of the atherogenic dyslipidemia among T2DM patients.¹⁹ The risk of coronary heart diseases in diabetic patients could be greatly estimated using adiposity markers such as the conicity index (C-index), body adiposity index (BAI) and waist–height ratio (WHtR).²⁰

Participants, materials and methods

A cross-sectional study was conducted at The University of Jordan Hospital (UoJH) at the diabetes and endocrinology outpatient clinics. A total of 237 patients were recruited and assessed for eligibility according to our inclusion and exclusion criteria (Figure 1); 149 patients were excluded, while 88 patients were assigned as: 29 patients in a healthy lean normoglycemic control group; 29 patients in a non-DM MetS group; and 30 patients in a pre-DM/newly diagnosed T2DM MetS group.

Figure 1.

The study recruitment flowchart.

Ethical consideration

The study was approved by the scientific research committee at the School of Pharmacy at The University of Jordan (UoJH) and by UoJH Institutional Review Board. All potential candidates were approached and informed thoroughly about the study, and written informed consent was collected after obtaining their approval.

Data collection

Weight and height were measured using a balance mounted stadiometer. Waist circumference (WC) was measured using a nonstretchable tape at the midpoint between the latest rib and the upper of the iliac crest, and hip circumference (HC) was measured around the widest section of the buttocks. Body mass index (BMI) was calculated as body weight (kg) divided by the square of height (m²). Waist–hip ratio (WHR) and (WHtR) were calculated by dividing the WC (cm) by HC (cm) and height respectively. The C-index and BAI were calculated using the following formulas:

C-index formula:

C - index = \frac{Waist circumference (m)}{\sqrt[0.109]{\frac{Body weight (kg)}{Height (m)}}}

BAI formula:

BAI = \frac{Hipcircumference (cm)}{Height {(m)}^{1.5}} - 18

Systolic and diastolic blood pressures (SBP and DBP, respectively) were measured using a digital blood pressure meter.

Hematological and biochemical analysis

Blood samples were drawn from all participants using lithium heparin and ethylenediaminetetraacetic acid (EDTA)-coated test tubes, centrifuged and stored at −20°C for further analysis. Analysis of complete blood count (CBC), glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG) and lipid profile was done, and AIP was calculated as log TG/HDL-C. Metabolic biomarkers MPO and AGP serum levels were estimated using sandwich and competitive enzyme-linked immunosorbent assay (ELISA), respectively, based on the instructions of abcam human ELISA kits (Cambridge, MA, USA).

Statistical analysis

Data were expressed as mean ± standard error of the mean (SE). The Shapiro–Wilk test was used to assess the normality of distribution of the studied parameters. Comparisons between the study groups were performed by analysis of covariance (ANCOVA), considering age as a covariate. To assess possible correlations between metabolic biomarkers and investigated parameters, the Spearman correlation was used and p < 0.05 was set as statistically significant. Data were analyzed using SPSS version 22.

Results

General characteristics of study participants

Table 1 demonstrates the general and clinical characteristics of study participants. The mean age of the study participants is significantly higher in the MetS pre-DM/T2DM group compared with the nondiabetic MetS and control group. BMI, WC, HC, SBP, DBP and lipid profile were substantially greater in both MetS groups. Mean FBG and HbA1c were significantly higher in the pre-DM/T2DM MetS group compared with the nondiabetic and control groups.

Table 1.

General characteristics of study participants.

Variable	MetS pre- /T2DM	Nondiabetic MetS	Control	p value^*	p value^**	p value^***
Age^$	51.80	44.79	30.48
BMI (kg/m²)	34.44 ± .91	33.64 ± 0.82	21.48 ± 0.99	0.507	<0.001	<0.001
WC (cm)	105.64 ± 1.77	103.22 ± 1.60	78.67 ± 1.92	0.297	<0.001	<0.001
HC (cm)	119.28 ± 1.97	116.40 ± 1.78	93.96 ± 2.14	0.267	<0.001	<0.001
SBP (mmHg)	131. 21 ± 2.93	131. 69 ± 2.65	112.2 ± 3.2	0.900	<0.001	<0.001
DBP (mmHg)	81.22 ± 2	79.67 ± 1.81	69.90 ± 2.17	0.555	<0.001	<0.001
FBG (mg/dl)	122.48 ± 4.72	91.81 ± 4.26	85.80 ± 5.11	<0.001	<0.001	0.437
HbA1c (%)	6.37 ± 0.14	5.21 ± 0.13	5.13 ± 0.15	<0.001	<0.001	0.697
TG (mg/dl)	211.34 ± 18.34	187.67 ± 16.57	64.93 ± 19.86	0.326	<0.001	<0.001
LDL-C(mg/dl)	134.36 ± 6.92	122.10 ± 6.25	98.24 ± 7.49	0.178	0.002	0.020
HDL-C(mg/dl)	39.98 ± 2.08	40.90 ± 1.88	47.64 ± 2.26	0.737	0.029	0.030
TC (mg/dl)	216.38 ± 7.79	200.48 ± 7.04	159.77 ± 8.44	0.122	<0.001	<0.001

Results are expressed as mean ± SE (by ANCOVA); bolded numbers represent statistical significance.

Mean (by ANOVA).

MetS pre/T2DM versus nondiabetic MetS.

MetS pre/T2DM versus control.

***

Nondiabetic MetS versus control.

ANCOVA, analysis of covariance; ANOVA, analysis of variance; BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; HbA1c%, percent glycosylated hemoglobin; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; SBP, systolic blood pressure; SE, standard error of the mean; TC, total cholesterol; TG, triglycerides; T2DM, type 2 diabetes mellitus; WC, waist circumference.

Comparisons of indices between study groups

Table 2 shows the comparisons of several adiposity, hematological and atherogenic indices between the study groups. Both MetS groups had significantly higher means in WHR, WHtR, C-index, BAI, MPV, monocytes, low-density lipoprotein cholesterol (LDL-C)/HDL-C ratio, total cholesterol (TC)/HDL-C ratio and AIP compared with the control group. On the other hand, lymphocytes were significantly lower in MetS pre-DM/T2DM group compared with the nondiabetic MetS group, while monocytes, monocyte/lymphocyte ratio (MLR), platelet count/lymphocyte ratio (PLR) were significantly lower compared with the nondiabetic MetS group.

Table 2.

Comparisons of indices and metabolic biomarkers between study groups.

Variable	MetS pre- /T2DM	Nondiabetic MetS	Control	p value^*	p value^**	p value^***
Adiposity indices
WHR	0.89 ± .01	0.89 ± .01	0.84 ± 0.01	0.993	0.033	0.016
WHtR	0.63 ± .01	0.62 ± .01	0.48 ± 0.01	0.433	<0.001	<0.001
C-index	1.28 ± .01	1.26 ± .01	1.21 ± .01	0.460	0.002	0.005
BAI	36.68 ± 1.23	35.94 ± 1.12	26.47 ± 1.34	0.647	<0.001	<0.001
Hematological indices
RDW (%)	13.82 ± 0.21	13.52 ± 0.19	13.47 ± 0.22	0.217	0.318	0.88
MPV (fl)	8.66 ± 0.39	9.03 ± 0.35	6.92 ± 0.42	0.467	0.009	<0.001
PLT count (×10³/µl)	262.35 ± 12.15	275.98 ± 10.98	234.76 ± 13.16	0.393	0.175	0.023
Monocytes (×10³/µl)	0.43 ± 0.04	0.55 ± 0.04	0.24 ± 0.05	0.040	0.003	<0.001
Neutophils (×10³/µl)	4.66 ± 0.30	4.42 ± 0.273	4.91 ± 0.33	0.541	0.627	0.273
Lymphocytes (×10³/µl)	2.88 ± 0.16	2.35 ± 0.14	2.54 ± 0.17	0.012	0.206	0.396
MLR	0.16 ± 0.09	0.24 ± 0.02	0.10 ± .020	<0.001	0.052	<0.001
NLR	1.70 ± 0.14	1.94 ± 0.13	2.10 ± 0.16	0.202	0.096	0.449
PLR	96.40 ± 8.39	124.91 ± 7.58	105.92 ± 9.08	0.011	0.495	0.125
Atherogenecity indices
LDL-C/HDL-C	3.53 ± 0.20	3.08 ± 0.18	2.11 ± 0.22	0.098	<0.001	0.002
TC/HDL-C	5.69 ± 0.28	5.14 ± 0.26	3.37 ± 0.31	0.100	<0.001	<0.001
AIP	0.69 ± 0.05	0.64 ± 0.05	0.19 ± 0.05	0.436	<0.001	<0.001
Metabolic risk biomarkers
MPO (pg/ml)	91.56 ± 11.72	107.34 ± 10.71	67.92 ± 13.09	0.312	0.235	0.026
AGP (µg/ml)	2018.43 ± 107.83	2011.47 ± 98.54	1566.23 ± 120.39	0.961	0.015	0.007

Bolded numbers represent statistical significance.

MetS pre/T2DM versus nondiabetic MetS.

MetS pre/T2DM versus control.

***

Nondiabetic MetS versus control.

AGP, alpha-1-acid glycoprotein; AIP, atherogenic index of plasma; BAI, body adiposity index; C-index, conicity index; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; MLR, monocyte-to-lymphocyte ratio; MPO, myeloperoxidase; MPV, mean platelet volume; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; RDW, red cell width; TC, total cholesterol; T2DM, type 2 diabetes mellitus; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio.

Comparisons of metabolic biomarkers between the study groups

As shown in Table 2, in the nondiabetic MetS group, MPO was observed in significantly higher levels compared with the control group, and MPO levels in the pre-DM/T2DM MetS group were higher than the control group, yet lower than the nondiabetic group. Moreover, considerably high levels of AGP were notable in both MetS groups compared with normoglycemic individuals.

Correlations of metabolic biomarkers with clinical parameters and indices

Table 3 shows the correlations between MPO and AGP with clinical parameters and indices. In the nondiabetic MetS group, MPO correlated significantly with WHR (p = 0.010) and monocytes (p = 0.037), while AGP correlated significantly with only HbA1c (p < 0.001). On the other hand, in the pre-DM/T2DM MetS group, only MPO had a positive correlation with PLT count (p = 0.040), monocytes (p = 0.024), MLR (p = 0.010) and PLR (p = 0.035).

Table 3.

Correlations of metabolic biomarkers with clinical parameters and adiposity, hematological and atherogenic indices.

Parameter		MPO (pg/ml)			AGP (µg/ml)
Parameter		MetS pre- /T2DM	Nondiabetic MetS	Control	MetS pre- /T2DM	Nondiabetic MetS	Control
BMI (kg/m²)	r _s	0.092	0.148	0.183	0.099	0.352	0.443 ^*
	Significance	0.629	0.443	0.360	0.604	0.066	0.016
WC (cm)	r _s	0.006	0.165	0.644 ^**	0.111	0.061	0.264
	Significance	0.974	0.392	<0.001	0.560	0.756	0.167
HC (cm)	r _s	0.052	−0.282	0.328	0.252	−0.174	0.277
	Significance	0.784	0.139	0.095	0.180	0.377	0.146
SBP (mmHg)	r _s	−0.048	0.322	−0.114	0.009	0.263	−0.067
	Significance	0.802	0.088	0.573	0.963	0.176	0.729
DBP (mmHg)	r _s	−0.273	0.071	−0.001	−0.058	0.317	0.179
	Significance	0.145	0.715	0.998	0.759	0.100	0.354
FBG (mg/dl)	r _s	−0.257	−0.099	0.184	0.146	−0.099	0.027
	Significance	0.170	0.609	0.357	0.441	0.618	0.888
HbA1c (%)	r _s	0.206	0.048	0.104	−0.033	0.576 ^**	0.187
	Significance	0.275	0.805	0.607	0.864	0.001	0.330
TG (mg/dl)	r _s	−0.025	−0.032	−0.154	−0.169	0.099	−0.135
	Significance	0.895	0.869	0.442	0.372	0.618	0.486
LDL-C (mg/dl)	r _s	0.231	0.106	0.224	0.058	0.308	0.146
	Significance	0.220	0.585	0.262	0.762	0.111	0.449
HDL-C(mg/dl)	r _s	−0.091	0.056	−0.312	0.046	0.082	0.353
	Significance	0.633	0.772	0.113	0.809	0.680	0.060
TC (mg/dl)	r _s	0.082	0.071	0.096	−0.022	0.301	0.418 ^*
	Significance	0.668	0.715	0.633	0.906	0.120	0.024
WHR	r _s	0.097	0.472 ^**	0.300	−0.128	0.328	−0.099
	Significance	0.609	0.010	0.129	0.500	0.088	0.608
WHtR	r _s	0.149	0.066	0.439 ^*	0.018	0.198	0.526
	Significance	0.432	0.734	0.022	0.925	0.313	0.003
C-index	r _s	0.050	0.028	0.409 ^*	−0.012	−0.206	0.121
	Significance	0.792	0.884	0.034	0.949	0.293	0.533
BAI	r _s	0.198	−0.239	−0.032	0.067	0.065	0.434 ^*
	Significance	0.295	0.211	0.875	0.726	0.743	0.019
RDW (%)	r _s	−0.035	−0.363	−0.515^**	0.144	0.361	0.167
	Significance	0.853	0.053	0.006	0.447	0.059	0.388
MPV (fl)	r _s	0.059	0.089	−0.179	0.174	0.102	−0.135
	Significance	0.757	0.645	0.371	0.357	0.604	0.485
PLT count (×10³/µl)	r _s	0.377 ^*	0.294	−0.176	0.059	0.232	0.308
	Significance	0.040	0.121	0.379	0.755	0.235	0.104
Monocytes (×10³/µl)	r _s	0.412 ^*	0.390 ^*	−0.113	−0.253	−0.067	0.033
	Significance	0.024	0.037	0.573	0.178	0.733	0.865
Neutophils (×10³/µl)	r _s	0.072	−0.014	0.111	0.047	−0.339	−0.100
	Significance	0.706	0.942	0.582	0.805	0.078	0.605
Lymphocytes (×10³/µl)	r _s	−0.123	0.292	0.061	−0.289	−0.171	−0.187
	Significance	0.519	0.124	0.761	0.121	0.383	0.331
MLR	r _s	0.462 ^*	0.153	−0.103	−0.060	−0.026	0.073
	Significance	0.010	0.429	0.608	0.753	0.897	0.705
NLR	r _s	0.179	−0.350	0.111	0.235	−0.041	−0.098
	Significance	0.345	0.063	0.581	0.212	0.836	0.614
PLR	r _s	0.387 ^*	0.026	−0.136	0.284	0.264	0.352
	Significance	0.035	0.895	0.498	0.128	0.175	0.061
LDL-C/HDL-C	r _s	0.176	−0.061	0.379	0.261	−0.033	−0.129
	Significance	0.351	0.754	0.051	0.180	0.862	0.506
TC/HDL-C	r _s	0.142	−0.055	0.298	0.239	−0.144	−0.163
	Significance	0.456	0.776	0.131	0.220	0.446	0.398
AIP	r _s	0.131	−0.061	0.023	−0.154	0.050	−0.293
	Significance	0.491	0.754	0.910	0.416	0.802	0.123

Bolded numbers represent statistical significance.

Correlation is significant at the 0.05 level (two tailed).

Correlation is significant at the 0.01 level (two tailed).

AGP, alpha-1-acid glycoprotein; AIP, atherogenic index of plasma; BAI, body adiposity index; BMI, body mass index; C-index, conicity index; DBP, diastolic blood pressure; FBG, fasting blood glucose; HbA1c, glycosylated hemoglobin; HC, hip circumference; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MetS, metabolic syndrome; MLR, monocyte-to-lymphocyte ratio; MPO, myeloperoxidase; MPV, mean platelet volume; NLR, neutrophil-to-lymphocyte ratio; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; PLR, platelet-to-lymphocyte ratio; PLT, platelet; r_s, Spearman’s correlation coefficient; RDW, red cell width; SBP, systolic blood pressure; TC, total cholesterol; TG, triglyceride; T2DM, type 2 diabetes mellitus.

Discussion

Consistent with our study, several clinical parameters and indices were elevated in pre-DM/T2DM patients compared with normoglycemic nondiabetic individuals in other studies, implying an increased risk for cardiovascular events.^21–23 Our findings confirm a significant difference in MPO levels in nondiabetic MetS patients compared with normoglycemic individuals (p = 0.026), which is supported by the study conducted by Fonseca and colleagues.²⁴ Nevertheless, in our study this was not the case regarding the pre-DM/T2DM MetS group compared with the control group, as no significant variation was detected. On the contrary, in a recent study by Agarwal and colleagues, MPO levels in pre-DM patients were significantly higher than in healthy normoglycemic participants.²¹ Type 2 DM is associated with a subclinical chronic inflammation state due to its linkage with obesity and IR, which triggers the release of pro-inflammatory cytokines.²⁵ Alpha-1-acid glycoprotein is an acute-phase protein known to be released in inflammatory conditions,²⁶ which is linked to the development of diabetes.²⁷ In line with our findings, suggesting a substantial statistical variation in AGP levels in MetS pre-DM/T2DM group compared with the control group (p = 0.015), Pickup and colleagues¹³ found that AGP levels were significantly higher in diabetic patients with and without MetS compared with healthy nondiabetic individuals.

Interestingly, a pronounced discrepancy in AGP could be spotted in women with MetS when compared with overweight/obese women without MetS;¹⁴ which is in concordance with our findings. Significant distinction is obtained from comparing the normoglycemic lean group with the MetS group (p = 0.007). Alpha-1-acid glycoprotein also reported a significant correlation with WC, which is similar to the study by Duncan and colleagues.²⁸

Conclusion

Metabolic risk biomarkers MPO and AGP were increased in metabolic dysregularities and were associated with obesity and increased risk for developing diabetes. Adiposity and atherogenicity, as well as hematological indices, were elevated in both MetS and MetS-pre/T2DM groups versus the control group, which may predict the risk of developing CVD. MPO and AGP demonstrated correlations with several adiposity, hematological and atherogenic indices. Our data further signifiy both MPO and AGP in the MetS and diabetes pathophysiologic pathways and their clinical relevance as potential therapeutic targets. This is a cross-sectional study which limits the interpretation and speculation of the results in order to obtain a cause–effect relationship between the biomarkers and obesity, as well as diabetes. The inclusion of a diabetic group on medication could have provided us with further evidence of the effect of diabetes duration. Additional clues for whether a good glycemic control could have had any influence on the circulating biomarkers may be inferred.

Footnotes

Author(s) note

Prof. Yasser Bustanji is currently and simultaneously affiliated with both School of Pharmacy, The University of Jordan, Jordan and Hamdi Mango Center for Scientific Research, The University of Jordan, Jordan.

Funding

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

Conflict of interest statement

The authors declare that there is no conflict of interest.

ORCID iD

Violet Kasabri

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