KSPEN
KSSMN
ASSMN
Articles
- Page Path
- HOME > Ann Clin Nutr Metab > Volume 15(3); 2023 > Article
- Original Article Multi-biomarker approach to metabolic syndrome and associated diseases in Turkey: a cross sectional study
-
Semra Can Mamur1
, Omer Colak2, Selma Metintas3, Inci Arikan4, Mehmet Kara5 -
Annals of Clinical Nutrition and Metabolism 2023;15(3):88-96.
DOI: https://doi.org/10.15747/ACNM.2023.15.3.88
Published online: December 1, 2023
1Department of Biochemistry, Yunus Emre State Hospital, Eskisehir, Turkey
2Simge Medical Laboratory, Eskisehir, Turkey
3Department of Public Health, Medical Faculty, Eskisehir Osmangazi University, Eskisehir, Turkey
4Department of Public Health, Medical Faculty, Kutahya Health Sciences University, Kutahya, Turkey
5Department of Biochemistry, Medical Faculty, Karabuk University, Karabuk, Turkey
- Corresponding author: Semra Can Mamur, email: drsemracan@gmail.com.tr
• Received: August 17, 2023 • Revised: September 25, 2023 • Accepted: October 26, 2023
© 2023 The Korean Society of Surgical Metabolism and Nutrition · The Korean Society for Parenteral and Enteral Nutrition
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- 170 Views
- 3 Download
Abstract
-
Purpose Biomarker for cardiovascular diseases (CVDs) are important in the clinical monitoring of individuals with metabolic syndrome (MetS). The use of these biomarkers in combination may be predictive of CVDs. This study aimed to demonstrate the ability of multiple biomarkers to predict MetS, diabetes mellitus (DM), and CVDs. The use of multiple biomarkers instead of a single biomarker may be more useful in early diagnosis. We investigated the use of a multi-biomarker approach in MetS and associated diseases.
-
Methods The study was performed by selecting control (n=30), MetS (n=30), MetS+DM (n=30), and MetS+CVD (n=30) groups from data of the Eskisehir Healthy Hearts Project conducted from January 2008 to October 2009 in Turkey. We recorded serum level of biomarkers, including lipid profile, liver enzyme, paraoxonase, arylesterase and arginase to find their difference among the groups.
-
Results Compared to the control group, gamma-glutamyl transferase (GGT) and arginase levels increased, while paraoxonase and arylesterase activity and high-density lipoprotein–cholesterol levels were low in the patient groups (P<0.001). A negative correlation was observed between paraoxonase and arylesterase activity and MetS.
-
Conclusion We believe that the combined use of biomarkers, including GGT, arginase, paraoxonase, and arylesterase, may be useful in predicting diseases such as MetS and CVDs.
Introduction
Metabolic syndrome (MetS) includes insulin resistance, atherosclerotic dyslipidemia, hypertension, obesity, and elevated fasting glucose, and it is increasingly more commonly observed with the changing dietary habits and increasing popularity of a sedentary lifestyle worldwide, especially in industrialized countries. It is notable that, in MetS, the factors causing an increase in cardiometabolic risk play a combined role rather than acting alone, and they give us the opportunity to identify individuals at high risk for developing cardiovascular events and diabetes mellitus (DM) [1].
High-density lipoprotein cholesterol (HDL-C) possesses antioxidant, anti-inflammatory, and anti-atherogenic properties [2]. These protective effects are linked to HDL-C–associated proteins. Paraoxonase (PON-1), one such protein, protects against atherogenesis. Research has indicated that reduced PON-1 activity contributes to human endothelial dysfunction and associated diseases [3].
Evidence from numerous studies shows a notable increase in arginase (ARG) activity in a number of clinical conditions, including hypertension [4], DM [5], hypercholesterolemia [6], and atherosclerosis [7]. These collective findings strongly support the idea that this enzyme plays a crucial role in the pathogenesis of cardiovascular diseases (CVDs) and emphasize its important contribution. In the endothelial layer, the interaction between ARG and endothelial nitric oxide (NO) synthase creates a competitive environment that ultimately reduces NO availability. Studies have reported that this decrease in NO bioavailability inhibits vasodilatation, which subsequently contributes to the development of endothelial dysfunction [7,8].
Recently, it has been suggested that gamma-glutamyl transferase (GGT) might be a biomarker for MetS and CVD. The relationship between MetS and GGT is attributed to the major role of GGT in the antioxidant defense system and its usability as an oxidative stress marker [9].
Biomarkers for CVDs are important in the risk assessment as well as in the clinical follow-up of individuals with MetS [10]. The combined use of these biomarkers could predict MetS and CVD.
We investigated a multi-biomarker approach for MetS and associated diseases like CVD and DM. We aimed to demonstrate the ability of the biomarkers to predict MetS, DM, and CVD.
Methods
Permission for this investigation was granted by the Medical Faculty Ethical Committee of Osmangazi University (2010/123), and informed consent was obtained from all participants of the study.
It is the case-control type cross-sectional study. The study was designed based on the first-phase data from the semi-experimental intervention study the Eskisehir Healthy Hearts Project (EHHP). EHHP has been designed as both a community-based intervention and a research study [11]. It was described according to the STROBE statement (https://www.strobe-statement.org/).
The samples were selected from the previous survey results, which was done to raise awareness of people in two semi-rural regions of Eskisehir, Anatolia Turkey in terms of CVDs risk factors and to promote related heart-healthy behaviors between January 2008 and October 2009.
The total number of enrolled people aged 20 to 69 years from both regions was 7,057. In both regions, most people are employed in factory work and farming. In these two regions, the livelihood and lifestyles of the people are similar.
The first phase was implemented with a total of 2,766 people, including 1,117 (40.4%) man and 1,649 (59.6%) woman. In the study group, the crude prevalence of MetS was 857 (31.0%), and there were 259 (9.4%) and 1,102 (39.8%) DM and CVD cases, respectively. Meanwhile, the number of patients with concomitant MetS and DM in this group was 34 (1.2%), whereas the number of patients with concomitant MetS and CVD was 223 (8.1%). Finally, the number of healthy subjects without systemic disease or medication or tobacco/alcohol use was 1,136 (41.1%).
Outcome variables were anthropomectric values, including participants’ gender, waist circumference, hip circumference, systolic and diastolic blood pressure (DBP); and serum level of biomarkers, including alanine aminotransferase (ALT), arylesterase (ARE), ARG, aspartate aminotransferase (AST), GGT, high-density lipoprotein, low-density lipoprotein, total cholesterol (TC), and triglyceride (TG).
Data were extracted from an intervention study, which is done in two phases between January 2008 and October 2009, of which study samples were from people in several semirural settlement areas in Eskisehir, which is located in the Central Anatolia Region of Turkey. The number of people aged 20 to 69 in the intervention group was 2.376 and the number in the control group was 4.681 [11].
Using the collected data and a random sampling method (the distinct and distinctive feature of random sampling is that the probability of universe units being included in the sample can be predicted and the error resulting from the sampling can be calculated) patient group was formed containing individuals with diagnoses of MetS, DM, and/or CVD. Separately, a control group was formed containing healthy individuals who had no systemic diseases or medication or tobacco/alcohol use.
Included patients were diagnosed with MetS based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) criteria. According to these criteria, the presence of at least three of the following findings is adequate for the diagnosis of MetS: hypertension, elevated fasting blood glucose (FBG) level, increased waist circumference, hypertriglyceridemia, and low HDL-C [12]. Individuals with diagnoses such as hypertension, coronary heart disease, vascular occlusion, or atherosclerotic heart disease (dyslipidemia) as well as those who received medical treatment and follow-up for these conditions were included in the cardiovascular patient group.
The control group of 30 people (19 man and 11 woman) and a patient group of 90 subjects (36 man and 54 woman) which as mentioned above, consists of individuals diagnosed with DM, MetS and CVD, with ages ranging from 25 to 69 years were formed. The patient group was also divided into three subgroups of MetS: patients were diagnosed with MetS based on the NCEP-ATP III criteria; MetS+DM: those who have diabetes and receive medical treatment in addition to the diagnosis of MetS and MetS+CVD: individuals who have CVD (hypertension, coronary heart disease, vascular occlusion etc.) in addition to the diagnosis of MetS and receive medical treatment for it, respectively. Thirty subjects were selected for each group from patient protocol numbers based on a randomized numbers table created using the database.
Body weight, height, waist circumference, and hip circumference data for all participants were recorded using standard methods. Body weight was categorized according to body mass index (BMI) as normal (19.0–24.9 kg/m2), overweight (25.0–29.9 kg/m2), obese (30.0–39.9 kg/m2), and morbidly obese (≥40.0 kg/m2). Abdominal obesity was defined as waist circumference of ≥102 cm in man or ≥88 cm in woman. Patients with a systolic blood pressure (SBP) of ≥140 mmHg and/or a DBP of ≥90 mmHg and/or who were on anti-hypertensive therapy were considered hypertensive based on the World Health Organization recommendations. The subjects with a physician diagnosis or with an FBG value of ≥126 mg/dL were considered to have DM.
Blood was drawn from overnight-fasted individuals for biochemical analysis. After allowing for clotting for 30 minutes at room temperature, the blood was centrifuged for 15 minutes to obtain serum, which was then promptly transported to a certified clinical biochemistry laboratory on the same day in cold boxes filled with ice. TC, TG, HDL-C, low-density lipoprotein cholesterol (LDL-C), and FBG levels were analyzed on the same day. AST, ALT, and GGT levels as well as PON-1, ARE, and ARG activity were later assessed in serum samples stored at −80°C.
TC, TG, HDL-C, LDL-C, FBG, AST, ALT, and GGT levels were measured using an enzymatic colorimetric method in a Roche/Hitachi Modular system (Roche Diagnostics).
PON-1 and ARE activity was measured photometrically with the Rel Assay Diagnostics kit (Rel Assay Diagnostics) in a Modular P (Roche Diagnostics) analyzer.
ARG activity was analyzed spectrophotometrically using the thiosemicarbazide-diacetylmonoxime-urea method [13].
There is no selection bias of control and case groups, because it was randomly selected.
Thirty in the control group and 90 case group were randomly extracted. Sample size estimation was not done. The posthoc analysis showed that the power (1-beta probability) is 0.48, when effect size wasset to 0.5; alpha error probability, 0.05; control group size, 30; and the case group size, 30 with two tailed testing, using G*Power (Heinrich-Heine-Universität Düsseldorf).
The data were statistically analyzed using SPSS 13.0 (SPSS Inc.). To assess differences in clinical and metabolic variables between groups, normal-distribution conformity was examined using the Shapiro–Wilk test and graphical analysis. Variables conforming to a normal distribution were analyzed using one-way ANOVA and the least squares method. For variables without a normal distribution, Kruskal–Wallis tests were employed, and the Wilcoxon signed-rank test with Bonferroni’s correction was used to determine group differences. Partial correlation, adjusted for age and gender, examined the relationship between biochemical parameters and MetS components. Logistic regression analysis was used to calculate age- and gender-adjusted odds ratio (OR) and 95% confidence interval (95% CI) for MetS development risk. Crude OR values were also determined. Receiver operating characteristic curve (ROC) analysis was used to assess the predictive values of the studied parameters. The significance level was set at P<0.05.
Results
The demographic and biochemical findings of the control and patient groups were statistically examined and are presented in Table 1. The mean age of the patient group was greater than that of the control group, and the proportion of woman subjects in the MetS+CVD subgroup was greater compared to that in any other group. Waist and hip circumference, BMI, SBP, and DBP values were significantly higher in the patient group than in the control group (P<0.001).
TC, TG, glucose, and GGT levels and ARG activity were increased in the patient subgroups compared to the control group (P<0.001). In addition, the patient subgroups had lower HDL-C levels compared to the control group, with the lowest mean value observed in the MetS subgroup. While GGT levels were elevated in the patient subgroups compared to the control group, the MetS+CVD subgroup in particular had the highest levels. Meanwhile, the control group had the highest PON-1 activity among the study groups, while the MetS+DM and MetS+CVD subgroups had significantly lower PON-1 activity compared to the control group and MetS subgroup (P<0.001). Though ARE activity was low in all patient subgroups, it was lower in the MetS+CVD subgroup. No significant differences were found between the study groups in terms of AST and ALT levels (P>0.05). Finally, the patient subgroups were found to have higher ARG activity compared to the control group (P<0.001).
Partial correlation level of the study group parameters per MetS and diagnostic criteria with PON-1, ARE, and ARG activity and GGT level were presented in Table 2. Based on this evaluation, PON-1 activity was found to be negatively correlated with waist circumference, increased TG level, low HDL-C level, increased systolic and DBP, the number of MetS components, and MetS diagnosis itself (P<0.05). ARG activity was found to be positively correlated with an increased TG level, low HDL-C level, increased blood pressure, and MetS. It was also observed that GGT level was positively correlated with an increased TG level, low HDL-C level, high FBG level, high blood pressure, the number of MetS components, and MetS diagnosis (P<0.05).
Logistic regression analysis was performed to determine the relationship between MetS, demographics, and biochemical parameters (Table 3). Based on the unadjusted and adjusted OR values, the risk for MetS increases with man gender; higher age, waist and hip circumference, SBP, TC, LDL-C, TG, glucose, and GGT values; and greater ARG activity. In contrast to the unadjusted analysis result, DBP was also found to increase the MetS risk in the adjusted analysis. However, the risk for the development of MetS was found to decrease with increasing HDL-C level and PON-1 and ARE activity.
ROC analysis was performed to determine the predictive values of GGT, PON-1, ARE, and ARG in the evaluation of MetS; the results are presented in Table 4 and Fig. 1-4. Among the parameters of interest, GGT (73% sensitivity, 87% specificity), ARG (74% sensitivity, 77% specificity), ARE (93% sensitivity, 43% specificity), and PON-1 (76% sensitivity, 83% specificity) were found to have high sensitivity and specificity values. When the area under the ROC curve values of the parameters were compared in the ROC analysis of the parameters, the only difference was found between the area under the ROC values of PON-1 and ARE (Z-test result=2.163, P=0.0305).
The changes in sensitivity, specificity, and predictive values found when multiple markers were used to determine MetS are shown in Table 5. Among the combinations of biomarkers, GGT with PON-1 marker positivity had sensitivity and specificity values of >80%.
Discussion
MetS is considered a determinant of type 2 DM and CVD. The highest glucose levels were observed in the MetS+DM patient subgroup, and the risk for the development of MetS was found to increase with increasing glucose levels. Moreover, the higher levels of LDL-C found in this group emphasize the importance of MetS in DM and CVD. For the diagnosis of MetS, at least three of the NCEP-ATP III criteria must be present,It was important to compare each parameter with MetS in order to reveal the close relationship between them.
In our study (by OR calculation), it was found that the risk for the development of MetS increases with the elevation of TC, TG, and LDL-C levels and decreases with increasing HDL-C levels (Table 3). The mean HDL-C levels were similar among the patient subgroups, while PON-1 activity was highest in the MetS patient subgroup. The low PON-1 levels observed in the other two patient subgroups may be explained by the presence of DM and CVD in addition to MetS (Table 1).
We observed a negative correlation between PON-1 level, ARE activity, and MetS and its components (Table 2) while, using ROC analysis, PON-1 was found to have a good predictive value for MetS (Fig. 1). These results support the idea that PON-1 and ARE may be used as biomarkers for MetS and CVD.
It was found that ARG activity was positively correlated with MetS and most of its components (Table 2). Moreover, it was found that the risk for the development of MetS increases with greater ARG activity (by OR analysis). Furthermore, ARG was found to have a good predictive value for MetS (Fig. 2).
We also observed a positive correlation between GGT and MetS and most of its components (Table 2), while there was no significant difference between the control and patient groups in terms of AST and ALT levels (Table 1).
When the results in Tables 4 and 5 are evaluated together, it is observed that the sensitivity and specificity values increase with multiple measurements of markers. In addition, while negative predictive values were superior in GGT-ARG and ARE-ARG dual measurements, positive predictive values were superior in GGT-PON-1, GGT-ARE, PON-1-ARE, and PON-1-ARG dual measurements (Table 5). These results support the view that a multi-biomarker approach will provide better guidance for diagnosis and treatment.
In a prior study, it was proposed that a significant decrease in HDL-C level occurs in patients with MetS and that this decrease is associated with the development of CVD [14]. The result we obtained in our study in relation to this situation is consistent with those of the cited article.
PON-1 is acknowledged to be a key player in the modulation of anti-atherosclerotic processes. The constituent proteins of PON-1 possess notable characteristics of antioxidation and anti-atherogenicity. Specifically, the individual isoforms actively engage in inhibiting the oxidation of lipoproteins and inactivating toxic peroxidation byproducts [15,16]. PON-1 is an enzyme associated with HDL-C known for its antioxidant and anti-inflammatory properties [3,17]. In a small study, patients with high HDL-C but low PON-1 levels were demonstrated to have a greater propensity for CVD when compared to patients with low HDL-C but high PON-1 levels. It was emphasized that the capacity of HDL-C to prevent LDL-C oxidation was more important than the HDL-C concentration itself [18].
Epidemiological studies focusing on PON-1 activity indicate that PON-1 activity might hold greater importance as a risk factor for coronary heart disease than the genetic polymorphisms of PON-1 that have been investigated thus far. Mackness et al. [19] demonstrated that the activity and concentration of PON-1 were better predictors for CVD compared to the PON-1 genotype.
Many studies showed that MetS increases the incidence and severity of coronary artery disease (CAD). Granér et al. [20] noted that the activity and concentration of PON-1 are lower in individuals with CAD and there is a significant correlation between these lower values and the severity of CAD. In addition to the reduced PON-1 levels, ARE activity was also found to be low in these patients [21]. In our study, PON-1 level and ARE activity were found to be lower in the MetS+CVD patient subgroup.
Hyperglycemia induces non-enzymatic glycation of HDL-C and LDL-C; specifically, it concomitantly increases the propensity to oxidation [22]. Furthermore, the decreased enzymatic activity of PON-1 associated with HDL-C is attributed to enzymatic inactivation caused by glycation [23]. This suggests that the glycation of these proteins in DM patients may contribute to altered HDL function, potentially impacting the protective effects of HDL against CVD. The low PON-1 and ARE activity found in the MetS+DM patient subgroup in our study may be explained by this mechanism.
Recent studies suggest that the increase in ARG activity represents a risk for CVD. In the context of atherosclerotic disease, oxidized LDL (oxLDL) plays an important role by promoting the up-regulation and activation of ARG. This effect occurs at both the transcriptional and post-translational levels. The production of nitrogen oxides is reciprocally reduced, leading to impaired NO signaling in the vascular system. Consequently, oxLDL may contribute to the pathogenesis of atherosclerosis by disrupting the delicate balance of ARG and nitrogen oxides, thus affecting vascular NO signaling. Therefore, ARG has been proposed as a therapeutic target in oxLDL-induced endothelial dysfunction [24].
According to a study, there is a substantial upregulation of ARG gene expression in overweight individuals. Additionally, the levels of ARG mRNA closely correlate with phenotype biomarkers related to obesity, such as disrupted lipid profile values and endothelial dysfunction [25]. In our study, ARG activity was higher in the patient subgroups compared to the control group. However, the highest level of ARG activity was observed in the MetS+DM group, supporting the relationship between hyperglycemia and ARG. Moreover, intermittent measurement of ARG in patients under risk will allow for early diagnosis of MetS as well as the prevention of DM and important CVDs.
In many studies, GGT and ALT were reported to be independent risk factors for DM and CVD, and they were also suggested to be components of MetS. The direct relationship of these two enzymes with MetS was investigated, and it was found that MetS prevalence was increased in high quartiles of GGT and ALT levels. GGT and ALT levels were found to be significantly correlated with most MetS components [26].
In a large prospective study, Ruttmann et al. [27] found that GGT is independently related to CVD mortality. Lee et al. [28] and Devers et al. [29] also reported that GGT predicts the onset of MetS and cardiovascular incidence and mortality. In our study, GGT levels were higher in the patient subgroups compared to the control group. However, the MetS+CVD patient subgroup had the highest levels among all the patient subgroups. Furthermore, the risk of developing MetS was found to increase with increasing GGT levels (based on OR analysis). GGT levels were found to have a good predictive value for MetS. These results support the findings of the studies mentioned above and show that GGT is an important parameter that should be measured in MetS and CVD.
The age of case and control groups are not adjusted. The age distribution of control group is lower than that of case groups. There may be a bias in interpreting data.
Above results may be able to adapt to the other regions in Iran for the prediction of the MetS, DM, and CVDs.
Given the importance of early diagnosis for the effective management and cost reduction of MetS and associated diseases such as DM and CVD, the predictive values of PON-1, ARE, ARG, and GGT could be considered. Furthermore, the inclusion of these parameters in routine measurements may also provide an important advance in the use of a multi-biomarker approach for the assessment of CVD risk. This approach may hold promise for improving risk stratification and clinical decision-making in the assessment of MetS and its associated comorbidities.
Many biomarkers that may be associated with MetS are not included in the diagnostic process of MetS. We advocate for the use of multiple biomarkers instead of a single biomarker, which may be more useful in the early diagnosis of diseases such as MetS, CVD, and DM. Results of this study provides a new avenue for future studies that will be conducted in this field.
Supplementary materials
None.
Acknowledgments
None.
Authors’ contribution
Conceptualization: SCM. Data curation: SCM, OC, SM. Formal analysis: SCM, SM. Funding acquisition: OC. Investigation: SCM, IA, MK. Methodology: SCM, SM. Supervision: OC, SM. Validation: OC, SM. Visualization: SCM. Writing – original draft: SCM. Writing – review and editing: all authors.
Conflict of interest
The authors of this manuscript have no conflicts of interest to disclose.
Funding
This research was produced from the medical specialty thesis of Semra Can Mamur on ‘Multi-biomarker approach to metabolic syndrome and associated diseases’.
Data availability
Contact the corresponding author for data availability.
Table 1
Demographic and biochemical characteristics of the study groups
| Control (n=30) | MetS (n=30) | MetS+DM (n=30) | MetS+CVD (n=30) | Statistical comparison | |
|---|---|---|---|---|---|
| Age (yr) | 45±7 | 52±9 | 55±6 | 53±8 | P<0.001 |
| Gender | X2=11.648; P=0.009 | ||||
| Man | 19 (63.3) | 17 (56.7) | 12 (40.0) | 7 (23.3) | |
| Woman | 11 (36.7) | 13 (43.3) | 18 (60.0) | 23 (76.7) | |
| Waist (cm) | 78±8 | 104±7 | 97±9 | 98±10 | P<0.001 |
| Hip (cm) | 95±6 | 110±20 | 114±12 | 115±9 | P<0.001 |
| SBP (mmHg) | 108±20 | 151±19 | 156±25 | 158±24 | P<0.001a |
| DBP (mmHg) | 70±8 | 93±19 | 93±14 | 97±12 | P<0.001 |
| TC (mg/dL) | 152±22 | 205±38 | 224±50 | 214±51 | P<0.001 |
| LDL (mg/dL) | 91±18 | 128±33 | 153±41 | 143±46 | P<0.001 |
| HDL (mg/dL) | 48±5 | 34±4 | 38±4 | 37±3 | P<0.001 |
| TG (mg/dL) | 94±23 | 225±75 | 211±76 | 257±125 | P<0.001 |
| Glucose (mg/dL) | 81±7 | 97±26 | 173±59 | 102±26 | P<0.001a |
| AST (U/L) | 21±5 | 23±9 | 27±15 | 27±17 | P=0.781a |
| ALT (U/L) | 20±18 | 24±16 | 24±17 | 33±40 | P=0.399a |
| GGT (U/L) | 14±7 | 32±36 | 40±32 | 60±83 | P<0.001a |
| PON-1 (U/L) | 251±65 | 224±221 | 122±58 | 113±51 | P<0.001a |
| ARE (U/L) | 333±64 | 305±50 | 285±52 | 277±54 | P<0.001a |
| ARG (U/L) | 2.03±1.39 | 4.72±2.74 | 5.16±2.96 | 4.66±2.74 | P<0.001a |
Values are presented as mean±standard deviation or number (%).
MetS = metabolic syndrome; DM = diabetes mellitus; CVD = cardiovascular disease; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase.
aKruskal–Wallis test.
Table 2
The partial correlation (r) levels of the study group parameters per metabolic syndrome and diagnostic criteria
| Waist circumference |
Increased TG level |
Low HDL level |
FBG |
Blood pressure |
No. of MetS components | MetS | |
|---|---|---|---|---|---|---|---|
| Log PON-1 | −0.27** | −0.38** | −0.25* | −0.17 | −0.29* | −0.39** | −0.40** |
| Log ARE | −0.05 | −0.13 | −0.11 | −0.17 | −0.10 | −0.05 | −0.26* |
| Log ARG | 0.16 | 0.27* | 0.19* | 0.12 | 0.23* | 0.17 | 0.30** |
| Log GGT | 0.26* | 0.43** | 0.22* | 0.19* | 0.38** | 0.37** | 0.38** |
Table 3
Unadjusted and adjusted odds ratios and 95% confidence interval values for metabolic syndrome and its associated variables
| Variable |
Unadjusted OR (95% CI) |
Adjusted OR (95% CI)a |
|---|---|---|
| Age | 1.19 (1.12–1.28) | |
| Gender | 2.59 (1.10–6.09) | |
| Waist | 1.26 (1.16–1.37) | 1.24 (1.13–1.35) |
| Hip | 1.16 (1.09–1.22) | 1.10 (1.03–1.18) |
| BMI | 2.00 (1.53–2.62) | 1.96 (1.42–2.71) |
| SBP | 1.51 (1.19–1.91) | 1.56 (1.34–2.14) |
| DBP | 1.16 (1.00–1.23) | 1.14 (1.07–1.23) |
| TC | 1.06 (1.04–1.09) | 1.05 (1.02–1.07) |
| LDL | 1.07 (1.04–1.11) | 1.06 (1.02–1.09) |
| HDL | 0.64 (0.54–0.75) | 0.56 (0.42–0.76) |
| TG | 1.06 (1.04–1.09) | 1.10 (1.03–1.17) |
| Glucose | 1.10 (1.05–1.15) | 1.09 (1.03–1.15) |
| AST | 1.04 (0.99–1.09) | 1.07 (0.99–1.14) |
| ALT | 1.02 (0.99–1.05) | 1.03 (0.99–1.06) |
| GGT | 1.13 (1.06–1.20) | 1.16 (1.07–1.26) |
| PON-1 | 0.99 (0.99–0.99) | 0.99 (0.98–0.99) |
| ARE | 0.99 (0.98–0.99) | 0.98 (0.97–0.99) |
| ARG | 1.85 (1.38–2.48) | 1.64 (1.17–2.29) |
OR = odds ratio; 95% CI = 95% confidence interval; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase.
aAge- and gender-adjusted OR and 95% CI values.
Table 4
Comparative values of biomarkers for metabolic syndrome calculated using receiver operating characteristic curve analysis
Table 5
Resulting sensitivity, specificity, and predictive values when multiple markers were used to determine metabolic syndrome
- 1. Daskalopoulou SS, Mikhailidis DP, Elisaf M. Prevention and treatment of the metabolic syndrome. Angiology 2004;55:589-612. ArticlePubMedPDF
- 2. Durrington PN, Mackness B, Mackness MI. Paraoxonase and atherosclerosis. Arterioscler Thromb Vasc Biol 2001;21:473-80. ArticlePubMed
- 3. Jayakumari N, Thejaseebai G. High prevalence of low serum paraoxonase-1 in subjects with coronary artery disease. J Clin Biochem Nutr 2009;45:278-84. ArticlePubMedPMC
- 4. Kaminskiĭ IG, Suslikov AV, Tikhonova LA, Galimova MK, Ermakov GL, Tsvetkov VD, et al. [Arginase, nitrates, and nitrites in the blood plasma and erythrocytes in hypertension and after therapy with lisinopril and simvastatin]. Izv Akad Nauk Ser Biol 2011;5:524-31; Russian.
- 5. Shemyakin A, Kövamees O, Rafnsson A, Böhm F, Svenarud P, Settergren M, et al. Arginase inhibition improves endothelial function in patients with coronary artery disease and type 2 diabetes mellitus. Circulation 2012;126:2943-50. ArticlePubMed
- 6. Leiva A, de Medina CD, Salsoso R, Sáez T, San Martín S, Abarzúa F, et al. Maternal hypercholesterolemia in pregnancy associates with umbilical vein endothelial dysfunction: role of endothelial nitric oxide synthase and arginase II. Arterioscler Thromb Vasc Biol 2013;33:2444-53. PubMed
- 7. Ryoo S, Gupta G, Benjo A, Lim HK, Camara A, Sikka G, et al. Endothelial arginase II: a novel target for the treatment of atherosclerosis. Circ Res 2008;102:923-32. PubMed
- 8. Berkowitz DE, White R, Li D, Minhas KM, Cernetich A, Kim S, et al. Arginase reciprocally regulates nitric oxide synthase activity and contributes to endothelial dysfunction in aging blood vessels. Circulation 2003;108:2000-6. ArticlePubMed
- 9. Lee MY, Koh SB, Koh JH, Nam SM, Shin JY, Shin YG, et al. Relationship between gamma-glutamyltransferase and metabolic syndrome in a Korean population. Diabet Med 2008;25:469-75. PubMed
- 10. Thande N, Rosenson RS. Vascular biomarkers in the metabolic syndrome. Expert Rev Mol Diagn 2009;9:209-15. ArticlePubMed
- 11. Arıkan I, Metintaş S, Kalyoncu C. Application of healthy heart program in the two semi-rural areas in Eskişehir. Anadolu Kardiyol Derg 2011;11:485-91. PubMed
- 12. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285:2486-97. ArticlePubMed
- 13. Geyer JW, Dabich D. Rapid method for determination of arginase activity in tissue homogenates. Anal Biochem 1971;39:412-7. ArticlePubMed
- 14. Garin MC, Kalix B, Morabia A, James RW. Small, dense lipoprotein particles and reduced paraoxonase-1 in patients with the metabolic syndrome. J Clin Endocrinol Metab 2005;90:2264-9. ArticlePubMedPDF
- 15. Andreeva-Gateva P, Popova D, Orbetsova V. [Antioxidant parameters in metabolic syndrome -- a dynamic evaluation during oral glucose tolerance test]. Vutr Boles 2001;33:48-53; Bulgarian. PubMed
- 16. Kowalska K, Socha E, Milnerowicz H. Review: The role of paraoxonase in cardiovascular diseases. Ann Clin Lab Sci 2015;45:226-33. PubMed
- 17. Ross R. Atherosclerosis--an inflammatory disease. N Engl J Med 1999;340:115-26. ArticlePubMed
- 18. Ansell BJ, Navab M, Hama S, Kamranpour N, Fonarow G, Hough G, et al. Inflammatory/antiinflammatory properties of high-density lipoprotein distinguish patients from control subjects better than high-density lipoprotein cholesterol levels and are favorably affected by simvastatin treatment. Circulation 2003;108:2751-6. ArticlePubMed
- 19. Mackness B, Davies GK, Turkie W, Lee E, Roberts DH, Hill E, et al. Paraoxonase status in coronary heart disease: are activity and concentration more important than genotype? Arterioscler Thromb Vasc Biol 2001;21:1451-7. PubMed
- 20. Granér M, James RW, Kahri J, Nieminen MS, Syvänne M, Taskinen MR. Association of paraoxonase-1 activity and concentration with angiographic severity and extent of coronary artery disease. J Am Coll Cardiol 2006;47:2429-35. ArticlePubMed
- 21. Gur M, Aslan M, Yildiz A, Demirbag R, Yilmaz R, Selek S, et al. Paraoxonase and arylesterase activities in coronary artery disease. Eur J Clin Invest 2006;36:779-87. ArticlePubMed
- 22. Ikeda Y, Suehiro T, Arii K, Kumon Y, Hashimoto K. High glucose induces transactivation of the human paraoxonase 1 gene in hepatocytes. Metabolism 2008;57:1725-32. ArticlePubMed
- 23. Lu C, Gao Y, Zhou H, Tian H. The relationships between PON1 activity as well as oxLDL levels and coronary artery lesions in CHD patients with diabetes mellitus or impaired fasting glucose. Coron Artery Dis 2008;19:565-73. ArticlePubMed
- 24. Ryoo S, Lemmon CA, Soucy KG, Gupta G, White AR, Nyhan D, et al. Oxidized low-density lipoprotein-dependent endothelial arginase II activation contributes to impaired nitric oxide signaling. Circ Res 2006;99:951-60. ArticlePubMed
- 25. Kim OY, Lee SM, Chung JH, Do HJ, Moon J, Shin MJ. Arginase I and the very low-density lipoprotein receptor are associated with phenotypic biomarkers for obesity. Nutrition 2012;28:635-9. ArticlePubMed
- 26. Oda E, Kawai R, Watanabe K, Sukumaran V. Prevalence of metabolic syndrome increases with the increase in blood levels of gamma glutamyltransferase and alanine aminotransferase in Japanese men and women. Intern Med 2009;48:1343-50. ArticlePubMed
- 27. Ruttmann E, Brant LJ, Concin H, Diem G, Rapp K, Ulmer H. Vorarlberg Health Monitoring and Promotion Program Study Group. Gamma-glutamyltransferase as a risk factor for cardiovascular disease mortality: an epidemiological investigation in a cohort of 163,944 Austrian adults. Circulation 2005;112:2130-7. ArticlePubMed
- 28. Lee DS, Evans JC, Robins SJ, Wilson PW, Albano I, Fox CS, et al. Gamma glutamyl transferase and metabolic syndrome, cardiovascular disease, and mortality risk: the Framingham heart study. Arterioscler Thromb Vasc Biol 2007;27:127-33. PubMed
- 29. Devers MC, Campbell S, Shaw J, Zimmet P, Simmons D. Should liver function tests be included in definitions of metabolic syndrome? Evidence from the association between liver function tests, components of metabolic syndrome and prevalent cardiovascular disease. Diabet Med 2008;25:523-9. ArticlePubMed
References
Figure & Data
REFERENCES
Citations
Citations to this article as recorded by 
ePub Link
Cite
XML DownloadMulti-biomarker approach to metabolic syndrome and associated diseases in Turkey: a cross sectional study
Fig. 1
Receiver operating characteristic curve for paraoxonase level.
Fig. 2
Receiver operating characteristic curve for arylesterase activity.
Fig. 3
Receiver operating characteristic curve for gamma-glutamyl transferase level.
Fig. 4
Receiver operating characteristic curve for arginase activity.
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Multi-biomarker approach to metabolic syndrome and associated diseases in Turkey: a cross sectional study
Demographic and biochemical characteristics of the study groups
| Control (n=30) | MetS (n=30) | MetS+DM (n=30) | MetS+CVD (n=30) | Statistical comparison | |
|---|---|---|---|---|---|
| Age (yr) | 45±7 | 52±9 | 55±6 | 53±8 | P<0.001 |
| Gender | X2=11.648; P=0.009 | ||||
| Man | 19 (63.3) | 17 (56.7) | 12 (40.0) | 7 (23.3) | |
| Woman | 11 (36.7) | 13 (43.3) | 18 (60.0) | 23 (76.7) | |
| Waist (cm) | 78±8 | 104±7 | 97±9 | 98±10 | P<0.001 |
| Hip (cm) | 95±6 | 110±20 | 114±12 | 115±9 | P<0.001 |
| SBP (mmHg) | 108±20 | 151±19 | 156±25 | 158±24 | P<0.001 |
| DBP (mmHg) | 70±8 | 93±19 | 93±14 | 97±12 | P<0.001 |
| TC (mg/dL) | 152±22 | 205±38 | 224±50 | 214±51 | P<0.001 |
| LDL (mg/dL) | 91±18 | 128±33 | 153±41 | 143±46 | P<0.001 |
| HDL (mg/dL) | 48±5 | 34±4 | 38±4 | 37±3 | P<0.001 |
| TG (mg/dL) | 94±23 | 225±75 | 211±76 | 257±125 | P<0.001 |
| Glucose (mg/dL) | 81±7 | 97±26 | 173±59 | 102±26 | P<0.001 |
| AST (U/L) | 21±5 | 23±9 | 27±15 | 27±17 | P=0.781 |
| ALT (U/L) | 20±18 | 24±16 | 24±17 | 33±40 | P=0.399 |
| GGT (U/L) | 14±7 | 32±36 | 40±32 | 60±83 | P<0.001 |
| PON-1 (U/L) | 251±65 | 224±221 | 122±58 | 113±51 | P<0.001 |
| ARE (U/L) | 333±64 | 305±50 | 285±52 | 277±54 | P<0.001 |
| ARG (U/L) | 2.03±1.39 | 4.72±2.74 | 5.16±2.96 | 4.66±2.74 | P<0.001 |
Values are presented as mean±standard deviation or number (%).
MetS = metabolic syndrome; DM = diabetes mellitus; CVD = cardiovascular disease; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase.
aKruskal–Wallis test.
The partial correlation (r) levels of the study group parameters per metabolic syndrome and diagnostic criteria
| Waist circumference | Increased TG level |
Low HDL level |
FBG | Blood pressure |
No. of MetS components | MetS | |
|---|---|---|---|---|---|---|---|
| Log PON-1 | −0.27 |
−0.38 |
−0.25 |
−0.17 | −0.29 |
−0.39 |
−0.40 |
| Log ARE | −0.05 | −0.13 | −0.11 | −0.17 | −0.10 | −0.05 | −0.26 |
| Log ARG | 0.16 | 0.27 |
0.19 |
0.12 | 0.23 |
0.17 | 0.30 |
| Log GGT | 0.26 |
0.43 |
0.22 |
0.19 |
0.38 |
0.37 |
0.38 |
TG = triglyceride; HDL = high-density lipoprotein; FBG = fasting blood glucose; MetS = metabolic syndrome; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase; GGT = gamma-glutamyl transferase.
*P<0.05, **P<0.001.
Unadjusted and adjusted odds ratios and 95% confidence interval values for metabolic syndrome and its associated variables
| Variable | Unadjusted OR (95% CI) |
Adjusted OR (95% CI) |
|---|---|---|
| Age | 1.19 (1.12–1.28) | |
| Gender | 2.59 (1.10–6.09) | |
| Waist | 1.26 (1.16–1.37) | 1.24 (1.13–1.35) |
| Hip | 1.16 (1.09–1.22) | 1.10 (1.03–1.18) |
| BMI | 2.00 (1.53–2.62) | 1.96 (1.42–2.71) |
| SBP | 1.51 (1.19–1.91) | 1.56 (1.34–2.14) |
| DBP | 1.16 (1.00–1.23) | 1.14 (1.07–1.23) |
| TC | 1.06 (1.04–1.09) | 1.05 (1.02–1.07) |
| LDL | 1.07 (1.04–1.11) | 1.06 (1.02–1.09) |
| HDL | 0.64 (0.54–0.75) | 0.56 (0.42–0.76) |
| TG | 1.06 (1.04–1.09) | 1.10 (1.03–1.17) |
| Glucose | 1.10 (1.05–1.15) | 1.09 (1.03–1.15) |
| AST | 1.04 (0.99–1.09) | 1.07 (0.99–1.14) |
| ALT | 1.02 (0.99–1.05) | 1.03 (0.99–1.06) |
| GGT | 1.13 (1.06–1.20) | 1.16 (1.07–1.26) |
| PON-1 | 0.99 (0.99–0.99) | 0.99 (0.98–0.99) |
| ARE | 0.99 (0.98–0.99) | 0.98 (0.97–0.99) |
| ARG | 1.85 (1.38–2.48) | 1.64 (1.17–2.29) |
OR = odds ratio; 95% CI = 95% confidence interval; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase.
aAge- and gender-adjusted OR and 95% CI values.
Comparative values of biomarkers for metabolic syndrome calculated using receiver operating characteristic curve analysis
| AUC | SE | 95% CI | Cutoffvalue | Sensitivity (%) | Specificity (%) | |
|---|---|---|---|---|---|---|
| PON-1 | 0.85 | 0.0346 | 0.782–0.915 | <196 | 76 | 83 |
| ARE | 0.70 | 0.0601 | 0.610–0.781 | <346 | 93 | 43 |
| GGT | 0.83 | 0.0381 | 0.756–0.896 | >19 | 73 | 87 |
| ARG | 0.79 | 0.0436 | 0.716–0.867 | >2.8 | 74 | 77 |
AUC = area under the receiver operating characteristic curve; SE = standard error; 95% CI = 95% confidence interval; PON-1 = paraoxonase; ARE = arylesterase; GGT, gamma-glutamyl transferase; ARG = arginase.
Resulting sensitivity, specificity, and predictive values when multiple markers were used to determine metabolic syndrome
| Sensitivity | Specificity | PPV | NPV | |
|---|---|---|---|---|
| GGT-ARG | 84.4 | 73.3 | 61.1 | 90.5 |
| GGT-PON-1 | 80.0 | 83.3 | 93.5 | 58.1 |
| GGT-ARE | 94.4 | 43.3 | 83.3 | 72.2 |
| PON-1-ARE | 84.4 | 73.3 | 90.5 | 61.1 |
| PON-1-ARG | 92.2 | 66.7 | 89.2 | 74.1 |
| ARE-ARG | 100.0 | 30.0 | 81.1 | 100.0 |
PPV = positive predictive value; NPV = negative predictive value; GGT = gamma-glutamyl transferase; ARG = arginase; PON-1 = paraoxonase; ARE = arylesterase.
Table 1
Demographic and biochemical characteristics of the study groups
Values are presented as mean±standard deviation or number (%). MetS = metabolic syndrome; DM = diabetes mellitus; CVD = cardiovascular disease; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase. aKruskal–Wallis test.
Table 2
The partial correlation (r) levels of the study group parameters per metabolic syndrome and diagnostic criteria
TG = triglyceride; HDL = high-density lipoprotein; FBG = fasting blood glucose; MetS = metabolic syndrome; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase; GGT = gamma-glutamyl transferase. *P<0.05, **P<0.001.
Table 3
Unadjusted and adjusted odds ratios and 95% confidence interval values for metabolic syndrome and its associated variables
OR = odds ratio; 95% CI = 95% confidence interval; BMI = body mass index; SBP = systolic blood pressure; DBP = diastolic blood pressure; TC = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; TG = triglyceride; AST = aspartate aminotransferase; ALT = alanine aminotransferase; GGT = gamma-glutamyl transferase; PON-1 = paraoxonase; ARE = arylesterase; ARG = arginase. aAge- and gender-adjusted OR and 95% CI values.
Table 4
Comparative values of biomarkers for metabolic syndrome calculated using receiver operating characteristic curve analysis
AUC = area under the receiver operating characteristic curve; SE = standard error; 95% CI = 95% confidence interval; PON-1 = paraoxonase; ARE = arylesterase; GGT, gamma-glutamyl transferase; ARG = arginase.
Table 5
Resulting sensitivity, specificity, and predictive values when multiple markers were used to determine metabolic syndrome
PPV = positive predictive value; NPV = negative predictive value; GGT = gamma-glutamyl transferase; ARG = arginase; PON-1 = paraoxonase; ARE = arylesterase.
