Circulating Zbed3 Levels in Subjects With and Without Metabolic Syndrome

Abstract

Background:

Zinc-finger BED domain-containing 3 (Zbed3) is a member of the zinc-finger domain protein superfamily. Recent studies have shown that Zbed3 is associated with insulin resistance and type 2 diabetes mellitus. However, no report has demonstrated the association of Zbed3 with metabolic syndrome (MetS) in humans. The purpose of this study is to examine the association between Zbed3 and MetS in a cross-sectional study.

Methods:

We conducted a cross-sectional study of a Chinese population, including 167 non-MetS subjects and 144 newly diagnosed MetS (nMetS) patients. Circulating Zbed3 levels were examined by enzyme-linked immunosorbent assay. The relationship between circulating Zbed3 levels and the components of MetS was assessed.

Results:

Circulating Zbed3 levels were significantly higher in nMetS patients than in non-MetS subjects (134.6 ± 32.1 vs. 106.5 ± 26.1 ng/L, P < 0.01). Circulating Zbed3 correlated positively with markers of adiposity (waist circumference, P < 0.01). It also correlated with glucose and lipid parameters (increasing fasting blood glucose and triglycerides and decreasing high-density lipoprotein cholesterol, all P < 0.01) and blood pressure (elevating systolic blood pressure and diastolic blood pressure, both P < 0.01) and inflammatory marker (elevating tumor necrosis factor alpha, P < 0.01). The relative risks for MetS showed significant elevation with an increase in Zbed3 quartiles. Circulating levels of Zbed3 were progressively elevated with an increased number of components of MetS.

Conclusions:

These data suggest that Zbed3 may correlate with the pathogenesis of MetS in humans. Clinical Trial Registration Number: ChiCTR-OCC-11001422.

Introduction

Metabolic syndrome (MetS), with its increasing prevalence, is one of the most important health and medical problems in developed and developing countries. MetS is a cluster of cardiovascular risk factors, including visceral obesity, dyslipidemia, type 2 diabetes mellitus (T2DM), and hypertension. Central obesity, dyslipidemia, hypertension, elevated fasting blood glucose (FBG), and insulin resistance (IR) are the most pivotal components of MetS.^1

–4 Cardiovascular risk is proportional to the number of MetS components in patients.⁵ IR is an important pathogenesis in the development of MetS.⁶ However, the pathomechanism in the development of MetS is not completely clear at present. Therefore, a reproducible accurate marker that predicts outcomes and therapeutic responses would assist clinical management of MetS.

Recently, genome-wide association studies have shown that zinc-finger BED domain-containing 3 (Zbed3) is associated with T2DM and IR.⁷ Zbed3 is a member of the zinc-finger domain protein superfamily. A recent study has found that the Zbed3 mRNA is highly expressed in mouse oocytes and fertilized eggs, where axin2 is also extremely enriched.⁸ Axin is a core protein in the Wnt/β-catenin signaling pathway.⁹ In another study, Zbed3 has been further identified as a novel axin-binding protein that can regulate Wnt/β-catenin signaling through its PPPPSPT motif by adopting a mechanism similar to LRP5/6.¹⁰ It has been reported that Wnt/β-catenin signaling regulates adipogenesis and relates to obesity, dysmetabolism, and IR.¹¹

Recently, we have identified that Zbed3 is a secreted protein and found that circulating Zbed3 levels in impaired glucose tolerance (IGT) and newly diagnosed T2DM (nT2DM) patients are higher than those in healthy subjects.¹² However, to date, no prior studies have demonstrated circulating Zbed3 levels in a large population with MetS. Therefore, in the current study, our aims were to compare circulating Zbed3 levels in newly diagnosed MetS (nMetS) and non-MetS subjects and to investigate the association of Zbed3 with key components of the metabolic syndrome (MetS).

Methods

Study subjects

Three hundred eleven subjects (144 nMetS and 167 non-MetS) were recruited for this study from outpatients attending the Internal Medicine Department at the Second Affiliated Hospital, Chongqing Medical University, the community, and schools through advertisement or routine medical checkups during the period 2014–2015. All subjects were screened for MetS according to the diagnostic guideline of the US National Cholesterol Education Program (NCEP) Expert Panel Adult Treatment Panel (ATP) III criteria.¹³

nMetS patients are diagnosed if they have more than three of following characteristics: (1) central obesity [waist circumference (WC) exceeds 90 cm for males and 80 cm for females, respectively]; (2) hypertension (systolic pressure ≥130 mmHg or diastolic pressure ≥85 mmHg); (3) elevated blood glucose (fasting glucose ≥5.5 mM) or T2DM; (4) elevated triglycerides [TG, ≥1.69 mM (150 mg/dL)]; and (5) low level of high-density lipoprotein cholesterol [HDL-C level ≤1.04 mM (40 mg/dL) for males and 1.29 mM (50 mg/dL) for females]. The exclusion criteria included tumors; renal or liver disease; a history of myocardial infarction, stroke, or transient ischemic attacks; and use of medications for diabetes mellitus, hypertension, and/or lipid-lowering drugs.

MetS patients were newly diagnosed and had not been treated with oral agents. Non-MetS individuals were not using any medication known to affect glucose tolerance, blood pressure, or body fat. All subjects gave their written informed consent before entering the study. This study was conducted in accordance with the Declaration of Helsinki and approved by the human research ethics committee of Chongqing Medical University.

Anthropometric and biochemical parameter measurements

All individuals were assessed and plasma was collected after overnight fasting for at least 10 hr. WC and body mass index (BMI) were measured by the same observer. BMI was calculated as weight divided by height squared. The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the following equation¹⁴: HOMA-IR = fasting insulin (FIns, mU/L) × FBG (mM/L)/22.5. Plasma glucose and HbA1c were measured by the glucose-oxidase method and anion-exchange high-performance liquid chromatography, respectively. Plasma insulin levels were measured by radioimmunoassay using human insulin as standard (Institute of atomic energy, China). Free fatty acids (FFAs) were measured with a commercial kit (Randox Laboratories Ltd., Antrim, UK). Total cholesterol, low-density lipoprotein cholesterol (LDL-C), TG, and HDL-C were determined enzymatically using an autoanalyzer (Hitachi 747; Hitachi, Tokyo, Japan).

Measurements of plasma Zbed3 and tumor necrosis factor alpha level

Circulating Zbed3 concentration was measured with an enzyme-linked immunosorbent assay (ELISA) from Elisa Biotech Co., Ltd. (Shanghai, China) following the manufacturer's protocol. The limit of detection was 5 ng/L, and intra- and interassay variations were 13% and 14%, respectively. The linear range of the assay was 31.2–1000 ng/L. The rabbit anti-human Zbed3 antibodies in the ELISA had no detectable cross-reactivity to Zbed1, Zbed2, or other cytokines in human blood.¹² In addition, plasma tumor necrosis factor alpha (TNF-α) levels were examined using a commercially available ELISA Kit (4A Biotech Co. Ltd, Beijing, China). The linear range of the assay was 1.56–100 pg/mL. The intra- and interassay CV were <10% and <12%, respectively.

Statistical analyses

All analyses were performed with SPSS, version 15.0 (SPSS, Chicago, IL). Data are expressed as mean ± standard deviation or median (interquartile range). Normal distribution of the data was tested using the Kolmogorov–Smirnov test. Several variables were skewed and logarithmically transformed to obtain a normal distribution.

Comparisons between groups were performed with nonparametric tests or a Student t test. Correlations between variables were assessed using Pearson correlation analyses by controlling for covariates. Multiple linear regression was performed to determine variables that had independent associations with circulating Zbed3 and included were all variables with significant associations or correlations with circulating Zbed3 and those with possible biological relevance. The trend of Zbed3 concentration associated with MetS was analyzed using the row mean score and Cochran–Armitage trend test. The odds ratios for Zbed3 and MetS were calculated by binary logistic regression. Receiver operating characteristic (ROC) curves of Zbed3 levels were constructed to determine the optimal cutoff point for the diagnosis of Mets.

Sample size was calculated using the following equation: N = [Z_α/2 σ/ɛμ]² (σ, standard; μ, mean; Z_α/2 = 1.96, α = 0.05, ɛ = 4%). P < 0.05 was considered significant.

Results

The demographic, anthropometric, and metabolic parameters of the 311 individuals enrolled in this study are summarized in Table 1. The non-MetS and nMetS groups were similar in age, TC, LDL-C, and FFA. However, nMetS patients had significantly higher BMI, WC, TG, FBG, 2-hr postglucose load blood glucose (2h-OGTT), systolic blood pressure (SBP), diastolic blood pressure (DBP), FIns, 2-hr plasma insulin after glucose overload (2h-Ins), HbA1c and HOMA-IR, and TNF-α levels, but lower HDL-C, compared with non-MetS subjects (Table 1). Circulating Zbed3 levels (ranged from 59.8 to 227 ng/L) were significantly higher in women than in men (122.0 ± 34.6 ng/L vs. 114.7 ± 26.6 ng/L; P < 0.05). Individuals with central obesity, defined by the MetS (NCEP ATPIII) Asian criteria, had significantly higher circulating Zbed3 levels than those without (128.1 ± 32.3 vs. 104.4 ± 25.8 ng/L, P < 0.01).

Table 1.

Main Clinical Features and Circulating Zbed3 Levels in Metabolic Syndrome and Nonmetabolic Syndrome Subjects

	Metabolic syndrome
Characteristics	No	Yes	P
N	167	144	—
Male sex	59	47	0.618
Age (year)	52.0 ± 10.1	54.0 ± 10.7	0.091
BMI (kg/m²)	22.8 ± 3.4	25.4 ± 3.1	<0.001
WC	81.3 ± 9.2	90.2 ± 7.3	<0.001
SBP (mmHg)	116.5 ± 14.3	126.2 ± 17.8	<0.001
DBP (mmHg)	75.1 ± 9.1	82.0 ± 13.9	<0.001
TG (mM)	1.07 ± 0.50	2.05 ± 1.02	<0.001
TC (mM)	4.95 ± 0.98	4.9 ± 1.07	0.670
HDL-C (mM)	1.5 ± 0.3	1.2 ± 0.3	<0.001
LDL-C (mM)	2.9 ± 0.8	2.8 ± 0.8	0.930
FFA (μM)	0.50 (0.33–0.70)	0.47 (0.31–0.74)	0.932
FBG (mM)	6.0 ± 2.4	7.7 ± 2.9	<0.001
2h-OGTT (mM)	7.62 (6.14–9.59)	11.58 (8.53–17.31)	<0.001
FIns (mU/L)	6.85 (5.06–8.81)	9.57 (7.03–14.87)	<0.001
2h-Ins (mU/L)	44.11 (25.34–67.04)	63.60 (41.18–95.15)	<0.001
HbA1c (%)	6.1 ± 1.4	7.1 ± 1.8	<0.001
HOMA-IR	1.62 (1.19–2.44)	3.08 (2.07–4.85)	<0.001
TNF-α	7.11 (5.36–11.39)	8.16 (5.41–17.89)	<0.05
Zbed3 (ng/L)	106.5 ± 26.1	134.6 ± 32.1	<0.001

Values are given as mean ± SD or median (interquartile Range).

2h-BG, 2-hr postglucose load blood glucose; 2h-Ins, 2-hr plasma insulin after glucose overload; BMI, body mass index; DBP, diastolic blood pressure; FBG, fasting blood glucose; FFA, free fatty acid; FIns, fasting plasma insulin; HbA1c, glycosylated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostasis model assessment of insulin resistance; LDL-C, low-density lipoprotein cholesterol; OGTT, oral glucose tolerance test; SBP, systolic blood pressure; SD, standard deviation; TC, total cholesterol; TG, triglyceride; TNF-α, tumor necrosis factor alpha; WC, waist circumference; Zbed3, zinc-finger BED domain-containing 3.

Importantly, circulating Zbed3 concentrations were significantly higher in nMetS patients compared with non-MetS subjects (P < 0.01, Table 1 and Fig. 1A). We further investigated the association of circulating Zbed3 with various anthropometric and metabolic parameters and cardiometabolic risk factors (Table 2) by using partial correlations. Circulating Zbed3 correlated positively with markers of adiposity (WC, P < 0.001). It also correlated with glucose and lipid parameters (increasing FBG and TG and decreasing HDL-C, both P < 0.001), blood pressure (elevating SBP and DBP, P < 0.01and P < 0.05), and inflammatory marker (elevating TNF-α, P < 0.01) (Table 2). All these correlations remained statistically significant after further adjustment for sex.

FIG. 1.

(A) Circulating Zbed3 levels in MetS patients and non-MetS subjects. (B) Prevalence of elevated MetS in different quartiles of Zbed3: quartile 1, <94.3 ng/L; quartile 2, 94.3–117.9 ng/L; quartile 3, 117.9–141.9 ng/L; and quartile 4, >141.9 ng/L (**P < 0.01 vs. quartile 1). (C) Circulating concentrations of Zbed3 elevated progressively with increasing number of components of MetS (*P < 0.05 or **P < 0.01 vs. 0 component). (D) ROC curve analysis was performed for the prediction of MetS. Values are given as means ± SD. *P < 0.05, **P < 0.01. MetS, metabolic syndrome; ROC, receiver operating characteristic; SD, standard deviation.

Table 2.

Partial Correlation Analysis of Variables Associated with Circulating Zbed3 Levels in Study Population

	Simple (sex-adjusted)		Multiple
	R	P	b	P
WC	0.378	<0.001	—	—
SBP	0.167	<0.01	—	—
DBP	0.143	<0.05	—	—
FBG	0.335	<0.001	1.612	0.039
TG	0.363	<0.001	7.426	0.001
TNF-α^a	0.195	<0.01	0.270	<0.05
HDL-C	−0.226	<0.001	—	—

Log transformed before analysis. In multiple linear stepwise regression analysis, values included for analysis were WC, SBP, DBP, FBG, TG, and HDL-C.

However, there was no significant correlation between circulating Zbed3 and TC or LDL-C. In multiple stepwise regression analysis, FBG, TG, and TNF-α were independent related factors with plasma Zbed3 levels (Table 2). The multiple regression equation was Y_Zbed3 = 97.419 + 1.612X_FBG + 7.426X_TG + 0.270X_TNF-α.

Binary logistic regression analysis showed that circulating Zbed3 levels were significantly associated with MetS even after controlling for anthropometric variables, lipid profile, FBG, and blood pressure (Table 3). In addition, decreasing levels of Zbed3 showed a significant linear trend and were independently associated with MetS when concentrations were analyzed both by a row mean score test and a Cochran–Armitage trend test (Table 4). The relative risks for MetS were significantly elevated along with increasing Zbed3 quartiles (Fig. 1B). To further assess the association of Zbed3 with MetS, we stratified the mean levels of circulating Zbed3 by the number of components of MetS. The results showed that circulating levels of Zbed3 elevated progressively with an increased number of components of MetS (P for trend <0.01) (Fig. 1C). Patients with 0, 1, 2, 3, 4, or 5 components of MetS had circulating Zbed3 levels of 90.3 ± 18.7, 106.2 ± 25.3, 113.0 ± 26.7, 130.4 ± 32.5, 133.3 ± 29.9, and 153.8 ± 29.8 ng/L, respectively.

Table 3.

Association of Circulating Zbed3 with Metabolic Syndrome in Fully Adjusted Models

	MetS
Model adjust	OR	95% CI	P
Age	2.418	1.900–3.076	<0.001
Age, gender	2.432	1.908–3.100	<0.001
Age, gender, WC	1.993	1.528–2.599	<0.001
Age, gender, WC, FBG	1.782	1.352–2.348	<0.001
Age, gender, WC, FBG, SBP	1.740	1.312–2.308	<0.001
Age, gender, WC, FBG, SBP, DBP	1.669	1.250–2.230	<0.001
Age, gender, WC, FBG, SBP, DBP, TG	1.340	1.153–1.883	0.033
Age, gender, WC, FBG, SBP, DBP, TG, HDL-C	1.303	1.012–1.862	0.046

Results of multivariate logistic regression analysis are presented as the odds ratio of being in MetS status increase in plasma Zbed3.

CI, confidence interval; OR, odds ratio.

Table 4.

Row Mean Scores Differ and Cochran–Armitage Trend Analysis of the Impact of Circulating Zbed3 Level on Metabolic Syndrome

	MetS
Model adjusted	χ²	P
Row mean score test	66.5202	0.0001
Cochran–Armitage trend test	−7.7340	0.0001

The circulating Zbed3 levels of all subjects were cut off, and adjusted for age, BMI, WC, SBP, DBP, and lipid profile.

Last, to investigate the predictive value of Zbed3 for MetS, we analyzed the ROC curves of circulating Zbed3 and revealed that the best cutoff value for circulating Zbed3 to predict MetS was 118.5 ng/L (sensitivity 74%, specificity 73.7%, and AUC 0.75; Fig. 1D).

Discussion

In the current study, we show that circulating Zbed3 concentrations are significantly higher in MetS patients. Circulating Zbed3 also correlates significantly with parameters of adiposity (WC), dyslipidemia (increasing triglycerides and decreasing HDL cholesterol), blood pressure and glucose metabolism (increasing FBG), and inflammatory marker (elevating TNF-α). Furthermore, circulating levels of Zbed3 progressively increase with an increasing number of components of MetS. The association between circulating Zbed3 and WC and TG in our study population suggests that Zbed3 might be relative to abdominal obesity and play a regulatory role in lipid metabolism in humans. Multiple linear regression analysis identified FBG, TG, and TNF-α as independent contributors to circulating Zbed3 levels. Binary logistic regression analysis showed that circulating Zbed3 levels were significantly associated with MetS even after controlling for anthropometric variables, lipid profile, FBG, and blood pressure.

To the best of our knowledge, this study is the first study showing that circulating Zbed3 is associated with MetS in humans. These novel findings suggest that circulating Zbed3 may act as a circulating biomarker of adiposity and obesity-related metabolic diseases, such as MetS.

Chronic subclinical inflammation has emerged as a characteristic feature of IR. TNF-α plays a key role in the pathogenesis of chronic inflammation and is also known to affect insulin signaling, lipid metabolism, and adipocyte function. In the current study, we found that plasma TNF-α levels in MetS patients were higher when compared with the controls and circulating Zbed3 correlated significantly with plasma TNF-α level. Therefore, one can speculate that increasing Zbed3 levels in MetS patients may be associated with a chronic low-grade inflammatory state.

In a previous study, we demonstrated that there were significantly higher levels of circulating Zbed3 in IGT and nT2DM patients relative to healthy subjects. Increasing levels of Zbed3 were independently associated with IGT and T2DM (12), suggesting that the Zbed3 protein may be a cytokine associated with IR in humans. The results of the current study have wider implications for the relationship between Zbed3 and IR. We speculate that the increase in circulating Zbed3 levels in MetS patients might be attributable to a defensive response, which may represent an ability to adapt to dysmetabolism. In addition, the increased Zbed3 levels might also suggest an increase in consumption for antagonizing metabolic stress, such as dyslipidemia, and low-grade inflammatory state. However, the mechanisms underlying increased Zbed3 levels in MetS patients remain elusive and need to be further explored in future studies.

To further analyze the relationship between Zbed3 and MetS, we stratified the mean levels of circulating Zbed3 by the number of components of MetS. A significant increase in Zbed3 levels was observed with a higher number of MetS components, suggesting an overlapping effect of MetS components on circulating Zbed3. In addition, we also analyzed the prevalence of MetS in different quartiles of circulating Zbed3 levels. The results showed that the relative risks for MetS were significantly elevated along with increasing Zbed3 quartiles. The subjects with the highest quartile of Zbed3 concentrations had an ∼11-fold higher risk of MetS than did the subjects with the lowest quartile. Last, we analyzed the association of circulating Zbed3 with MetS by ROC curves. Data from the ROC curve analyses indicated that Zbed3 might be a useful marker for the prediction of MetS.

The strength of our study is that this is the first study that has evaluated the relationship of Zbed3 and MetS in humans and provided a detailed analysis, including MetS components and regression models adjusted for anthropometric measurements and biochemical parameters. Another strength of the study is that its prospective design with inclusion of nMetS prevents pharmacotherapy and other confounding variables.

Limitations of this study include the following: (1) a cross-sectional design limits any firm conclusion about the possible causative role of Zbed3 in MetS, this would require longitudinal intervention studies and warrants future investigation; (2) the sample in the study constituted entirely of Chinese participants, therefore extrapolation of these results to other ethnic groups should be undertaken with caution; and (3) the relatively small sample size, although the number of subjects included would provide >90% power to demonstrate associations at the conventional α <0.05 level. Nonetheless, this study is sufficient to demonstrate novel associations of circulating Zbed3 with anthropometric and metabolic parameters in MetS patients.

In summary, the results of this study demonstrate that circulating Zbed3 concentrations were significantly higher in nMetS patients compared with healthy subjects. Circulating Zbed3 concentrations are associated with glucose and lipid parameters, markers of adiposity, and blood pressure. Circulating levels of Zbed3 elevated progressively with an increased number of components of MetS. We also determined that the optimal cutoff value of circulating Zbed3 for detection of MetS was 118.5 ng/L. The study suggests that circulating Zbed3 is the best marker for identifying MetS and adds more evidence for the hypothesis that the risks of MetS begin to increase at a relatively high Zbed3 level, which may be helpful for early detection of MetS.

Footnotes

Acknowledgments

This work was supported by research grants from the Natural Science Foundation Project of Chongqing CSTC (cstc2015jcyjA10084), the Science and Technology Key Program of Health Bureau of Chongqing (2015ZDXM038, 2016MSXM083), and the National Natural Science Foundation of China (81601214). W.H., L.L., B.T., and X.L. contributed to data collection and analysis. L.L. and G.Y drafted the manuscript. Y.L. and X.F. designed the analytic strategy. H.L. and L.Z. revised and edited the manuscript. Y.L. and X.F. are the guarantors of this work and, as such, had full access to all the data in this study and take responsibility for the integrity of the data and accuracy of data analysis.

Author Disclosure Statement

No conflicting financial interests exist.

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