Evaluation of serum retinol-binding protein-4 levels as a biomarker of poor short-term prognosis in ischemic stroke

The aim was to investigate the relationship between retinol-binding protein 4 (RBP4) levels and short-term functional outcome, and to determine its possible role in acute ischemic stroke (AIS). In a prospective observational study, 299 first-ever AIS who were admitted to our hospital were included. Serum levels of RBP4 were assayed and severity of stroke was evaluated with the National Institutes of Health Stroke Scale (NIHSS) score on admission. The prognostic value of RBP4 to predict the poor outcome within 3 months was compared with the NIHSS and with other known outcome predictors. The median age of the included patients was 66 (interquartile range (IQR): 55–77) years and 155 (51.8%) were women. A poor functional outcome was found in 88 patients (29.4%), and significantly higher RBP4 values were found in poor outcomes rather than good outcomes patients (P<0.001). The poor outcomes distribution across the RBP4 quartiles ranged between 9.3% (first quartile) and 60.8% (fourth quartile). In multivariate models comparing the second(Q2), third, and fourth quartiles against the first quartile of the RBP4, RBP4 in Q3 and Q4 were associated with poor functional outcome, and increased risk of poor functional outcome by 144% (OR: 2.44; 95% confidence interval (CI): 1.22–5.03) and 602% (7.02; 3.11–12.24), respectively. Interestingly, RBP4 improved the NIHSS score (area under the curve (AUC) of the combined model, 0.79; 95% CI: 0.74–0.85; P<0.001). The data showed that elevated serum levels of RBP4 at admission were associated with severity and prognosis of AIS, suggesting that vitamin A metabolism or impaired insulin signaling could be involved.


Introduction
Plasma retinol-binding protein 4 (RBP4) is the 21-kDa transporter of all-trans retinol that circulates in plasma as a moderately tight 1:1 molar complex of the vitamin with the protein [1]. This protein belongs to the lipocalin family and is the specific carrier for retinol (vitamin A) in the blood [2].
Previous studies had suggested that the association of circulating levels of RBP4, a plasma retinol transporter and adipokine, with traditional (e.g. blood pressure) and non-traditional cardiovascular risk factors (e.g. cytokines) mainly through the impairment of glucose and lipid metabolism and adipose tissue dysfunction [3]. Circulating levels of RBP4 were found to correlate with insulin resistance [4], impaired glucose tolerance and type 2 diabetes [5,6], and metabolic syndrome [7]; subsequent studies also suggested that the associations of RBP4 with cardiovascular disease (CVD, [8]). It has recently been suggested that inflammation produced by RBP4 induces insulin resistance and CVD [9]. Although, the association of RBP4 with an increased risk of CVD has been proposed [10], its prognostic value in carotid [11] or coronary [12] atherosclerosis progression still lacks consensus.
Stroke is the second leading cause of death and the leading cause of serious, long-term disability worldwide [13]. Hypertension [14] and atherosclerosis [15] were common risk factors for stroke. Interestingly, previous study had showed that serum levels of RBP4 were associated with blood pressure [16] and carotid atherosclerosis [17]. Furthermore, Llombart et al. [18] suggested that RBP4 might be useful as diagnostic biomarkers to differentiate ischemic stroke and intracerebral hemorrhage. Therefore, we speculated that serum RBP4 might be associated with stroke risk and prognosis. In the present study, we sought to investigate the relationship between RBP4 levels and short-term functional outcome, and to determine its possible role in acute ischemic stroke (AIS).

Patients and study design
This was a prospective cohort study at Department of Emergency in our hospital. From January 2016 to December 2017, all patients with first-ever AIS were included. All patients were admitted within 24 h of experiencing a new focal or global neurological event. A baseline MRI scan was performed in all patients at admission to verify the diagnosis. The patients with the following criteria were excluded: (i) malignant tumor, (ii) and a history of recent surgery or trauma during the preceding 2 months; (iii) liver and kidney function insufficiency; (iv) acute and chronic inflammation; metabolic abnormalities (diabetes not included), (v) febrile disorders and autoimmune diseases; (vi) other neurological diseases (cerebral hemorrhage, Parkinson's disease, and Alzheimer's disease).
In the present study, 150 age and gender-matched healthy volunteers were assigned to the normal group. The median age of control cases was 65 (interquartile range (IQR): 55-77) years and 52% were women. The study was approved by the Ethics Committee of First Affiliated Hospital of Xinxiang Medical University The patients or their relatives gave written informed consent prior to entering the study.

Clinical variables and neuroimaging
At baseline, demographical and clinical data including: age, sex, body mass index (BMI), temperature, systolic blood pressure, conventional vascular risk factors (hypertension, diabetes mellitus, atrial fibrillation, coronary heart disease, smoking habit, and family history for stroke and/or CVD) were obtained. In addition, the information about pre-stroke therapy (oral anticoagulants or antiplatelet agents) and acute treatment (IV thrombolysis and/or mechanical thrombectomy) were also recorded. All patients received treatment according to current guidelines. Stroke severity was assessed on admission using the National Institutes of Health Stroke Scale (NIHSS, range from 0 to 42) by a neurologist [19]. Stroke subtype and the clinical stroke syndrome were classified according to TOAST (Trial of Org 10172 in Acute Stroke Treatment) criteria [20] and the Oxfordshire Community Stroke Project (total anterior circulation syndrome (TACS); partial anterior circulation syndrome (PACS); lacunar syndrome (LACS); and posterior circulation syndrome (POCS)) [21], respectively. Brain imaging (MRI) was performed routinely within 24 hours of admission. Diagnosis of stroke was based on the results of strict neurological examination (MRI) according to the International Classification of Diseases, ninth revision. MRI was performed using a stroke protocol (T1-, T2-, and diffusion-weighted imaging (DWI) sequences and a magnetic resonance angiography). DWI lesion volumes were calculated by an experienced neurologist (X.G.) unaware of the clinical and laboratory information according to the formula 0.5 × a × b × c (a = the maximal longitudinal diameter; b = the maximal transverse diameter perpendicular to a; c = the number of 10-mm slices containing infarct) [22].

End points and follow-up
The functional outcome was determined in month 3 according to the modified Rankin Scale (mRS, range from 0 to 6) [23] blinded to laboratory information. The primary end point was functional outcome, which was divided into poor functional outcome (defined as mRS score of 3-6 points) and good functional outcome (mRS: 0-2) according to mRS. Secondary end point was death or withdrawn from any cause within a 3-month follow-up. Outcome assessment was performed by a trained medical staff (Y.H.) with a structured follow-up telephone interview with the patient or, if not possible, with the relative.

Laboratory analyses
Fasting serum samples were drawn from the antecubital vein at the first morning after admission and within 48 h of symptom onset (within 0-6 (n=73), 6-12 (n=98), 12-24 (n=92), and 24-48 (n=36) h from symptom onset). After centrifugation, the serum samples were immediately stored at -80 • C before assay. The information about time from onset to blood collection was recorded. Concentrations of serum RBP4 were batch analyzed using a commercially available Quantikine R ELISA kit for human RBP4 (Cat#DRB400; R&D Systems, Minneapolis, MN). The lower detection limit for RBP4 was preset as 0.60 ng/ml and the detection range was 1.6-100 ng/ml. Inter-assay and intra-assay coefficients of variation were 5.0-9.0% and 4.0-7.5%, respectively. Due to the high levels of RBP4 (μg/ml) in the blood sample, 1:100 dilution was used. Other blood biomarkers, for instance triglyceride, cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), high-sensitivity C-reactive protein (Hs-CRP), fasting blood glucose (FBG) were tested using an enzyme cycling method by BS-2000M (Mindray, Shenzhen, China). All those tests were done in duplicates and samples with a CV exceeding 10% were reanalyzed.

Statistical analysis
Results are expressed as percentages for categorical variables and as medians (quartiles) for the continuous variables. Spearman's rank correlation was used for bivariate correlations. The difference between groups was assessed by χ 2 test (proportions) or Mann-Whitney U-test (asymmetrically distributed variables).
The influence of RBP4 on PSD was performed by univariate and multivariate binary logistic regression analysis. In the multivariate analysis, significant confounding factors which were tested in the univariate analysis were adjusted. Results were expressed as adjusted odds ratios (OR) with the corresponding 95% confidence interval (CI). For a more detailed exploration of the RBP4 and outcome, we also used multivariate analysis models to estimate adjusted OR and 95% CIs of PSD for RBP4 quartiles (with lowest quartile as reference).
Further, a receiver operating characteristic (ROC) curve analysis was used to identify the cut-off point on the serum levels of RBP4 on admission with the greatest sensitivity and specificity to predict poor outcomes at the 3-month follow-up. Area under the curve (AUC) was calculated as measurements of the accuracy of the test. The influence of elevated levels of RBP4 (≥cut-off) on poor outcomes was also performed. Furthermore, to test whether the RBP4 level improves score performance, we considered the two nested logistic regression models with RBP4 and the established risk factors as compared with the established risk factors only.
At last, we conducted analyses separately amongst cases who experienced with and without diabetes. All statistical analysis was performed with SPSS for Windows, version 22.0 (SPSS Inc., Chicago, IL, U.S.A.), the ROCR package (version 1.0-2), and GraphPad Prism 5.0. Statistical significance was defined as P<0.05.

Patient characteristics
In our study, 299 AIS patients completed 3-month follow-up and were included in the analysis. The median age was 66 (IQR: 55-77) years and 155 (51.8%) were women. In those patients, the median NIHSS score at admission was 9 points (IQR: 5-13). The median time from stroke onset to blood collected was 14.5 (IQR: 8.0-18.0) h. In addition, the number of tissue plasminogen activator-treated patients was 41 (13.7%). Basal characteristics of patients with AIS were provided in Table 1.
In univariate logistic regression analysis, we calculated the OR of RBP4 levels as compared with the NIHSS score and other risk factors as presented in Table 2. With an unadjusted OR of 1.18 (95% CI: 1.09-1.30), RBP4 had a strong association with poor functional outcome. After adjusting for all other significant outcome predictors in univariate logistic regression analysis, RBP4 remained an independent outcome predictor with an adjusted OR of 1.09 (95% CI: 1.06-1.13). For a more detailed exploration of the RBP4 and functional outcome relationship, we also used multivariate analysis models to estimate adjusted OR and 95% CIs of poor functional outcome for RBP4 quartiles (with first quartile as reference). In multivariate models comparing the second(Q2), third, and fourth quartiles against the first quartile of the RBP4 (Table 3), RBP4 in Q3 and Q4 were associated with poor functional outcome, and increased risk of poor functional outcome by 144% (OR: 2.44; 95% CI: 1.22-5.03) and 602% (7.02; 3.11-12.24), respectively. The independent association was confirmed using the likelihood ratio test (P<0.001).
Based on ROC curves, the optimal cut-off value of serum RBP4 level to diagnose the poor outcomes was 37.      Furthermore, in our study, we found that an increased risk of poor outcomes was associated with RBP4 levels ≥37.4 μg/ml, and increased risk of poor functional outcome by 733% (OR unadjusted: 8.33; 95% CI: 4.47-15.53) and 258% (OR adjusted: 3.58; 1.76-7.75), respectively (Table 3). Interestingly, patients with RBP4 levels ≥37.4 μg/ml was considered as an indicator to predict poor outcomes, the positive predictive value (PPV) and negative predictive value (NPV) were 67.2 and 80.3%, respectively. The diagnostic accordance rate was 77.6%.
Based on ROC curves, the optimal cut-off value of serum RBP4 level to diagnose the mortality was 37.5 μg/ml, which yielded the highest sensitivity and specificity (63. 8

Discussion
Adipose tissue secretes many types of adipokines (such as leptin, interleukin 6, and adiponectin) that play a role in the progression of stroke [24][25][26]. The present study offers intriguing and possibly important findings of the prognostic role of RBP4 in AIS. The findings of the present study were as following: (i) serum RBP4 levels in patients with AIS were higher compared with controls; (ii) elevated serum levels of RBP4 were powerful biological markers of risk of developing poor functional outcomes even after adjustment by variables. Thus, it may be used as a future therapeutic target in patients with ischemic stroke; (iii) the prognostic value of RBP4 was stronger amongst diabetic patients (OR = 1.12, 95% CI: 1.08-1.6; P<0.001) compared with non-diabetic patients (OR = 1.07, 95% CI: 0.04-1.11; P=0.001); (iv) RBP4 showed a significantly greater discriminatory ability to predict poor functional outcomes as compared with other biomarkers, such as Hs-CRP, FBG, and age.
Previous studies had suggested that different variables recorded on admission were associated with poor outcome in stroke patients, for example insulin resistance [27], vitamin D deficiency or 25-hydroxyvitamin D [28,29], fatty acid-binding protein 4 [30], C-reactive protein, and homocysteine [31]. However, most of these biomarkers were modest predictors of outcome and unable to improve the prognostic accuracy of the NIHSS. Interestingly, in the present study, we found that RBP4 improved the NIHSS score.
Consistent with our findings, Prentice et al. [32] reported that levels of RBP4 elevated amongst women who develop stroke compared with those who do not develop a stroke. Similarly, patients with CAD had showed elevated RBP4 serum level [3]. However, another study found that elevated levels of RBP4 were not associated with an increased risk of ischemic stroke [33]. In this study, significantly higher RBP4 values were found in poor outcomes rather than good outcomes patients, and elevated serum levels of RBP4 were powerful biological markers of risk of developing poor outcome. Similarly, Liu et al. [34] showed that plasma RBP4 levels were significantly associated with coronary lesion complexity in women with stable CAD and predict incident cardiovascular events, while other studies suggested that elevated serum RBP-4 levels were significantly associated with CAD [35] and CAD severity [3]. However, Liu et al. [36] found that higher RBP4 levels were non-significant associated with a decreased CVD mortality (P=0.09) amongst men with type 2 diabetes mellitus in a 22-year prospective study. Different populations, sample sizes, geographical regions, health status, RBP4 ELISA testing kits, and ethnicity may justify the conflicting results observed in those studies.
The possible mechanism of increased levels of RBP4 with poor outcome in acute stroke in patients has not yet established. Two important pathophysiological mechanisms involved in ischemic stroke were inflammation and oxidative stress [37]. Plasma RBP4 levels had been suggested to be associated with an adverse profile of oxidative stress and inflammatory markers in this Chinese population [38]. The following pathways could be considered for the role of RBP4 in the stroke. First, RBP4 has been known as a negative acute phase inflammatory reactant. RBP4 was suggested to induce in vitro inflammation in endothelial cells through stimulating expression of proinflammatory molecules [39], and another study showed that RBP4-induced inflammation was largely mediated by TLR4, and in part, through JNK and p38 MAPK signaling [40]. Second, most of the studies reported a positive relation between RBP4 and oxidative stress markers [9] and a negative relation between RBP4 and antioxidant glutathione [41]. Furthermore, cellular oxidative stress leads to the activation of vascular inflammation [42]. Because RBP4 concentrations were positively related to oxidative stress markers, RBP4 may have a role in the initiation of endothelial inflammation. Third, RBP4 levels independently predicted early endothelial dysfunction, linking adipose tissue inflammation and subclinical atherosclerosis in non-diabetic individuals [43]. The inverse relation between RBP4 and flow-mediated vasodilatation (flow-mediated dilation) endothelial dysfunction are evident in normotensive individuals and in diabetic patients [44]. A previous study suggested that epicardial and microvascular coronary endothelial dysfunction independently predict acute cardiovascular events in patients with and without CAD [45]. At last, elevated blood pressure was common during an acute stroke and was associated with unfavorable outcome [46]. Chiba et al. [47] found that increased levels of RBP4 in women were significantly associated with increased levels of systolic blood pressure. Interestingly, lowering RBP4 might reduce blood pressure through enhanced eNOS-mediated vasodilatation and may be a novel therapeutic approach for hypertension [48]. Thus, elevated RBP4 may have a role in stroke prognosis, and its mechanisms need to be elucidated.
Some limitations should be considered. First, the relatively small sample size (n=299) and single center might limit the generalization of the results of the present study. Second, without serial measurement of the circulating levels of RBP4, the present study yielded no data regarding when and how long biomarkers were elevated in these patients. In addition, the present study measured RBP4 in serum, not in cerebral spinal fluid (CSF). It was still uncertain whether peripheral RBP4 levels reflect similar changes in the central nervous system (CNS). Third, since the majority of patients with classical cardiovascular risk factors (e.g. diabetes, dyslipidemia, hypertension etc.) were already treated, we cannot rule out the plausible effects of pharmaceutical agents (e.g. statins) on RBP4, leading to underestimation of its predictive power. In addition, the binding of RBP4 to retinol and transthyretin (TTR) was affected by several factors (e.g. serum retinol, vitamin A intake, physical activity, protein-energy malnutrition, and liver and renal diseases) [9]. For studies to succeed, all of these covariates should be considered and adjustments made. However, it was difficult for us to adjust all. Fourth, the use of ELISA kits, with limited dynamic range, could result in conflicting results. Commercial ELISA kits do not differentiate between holo-and apo-RBP4 and evaluate the whole RBP4 concentration. Variability in findings may emerge due to a lack of high affinity and reliable methods for RBP4 measurement [49]. Furthermore, the absence of a standard and stable measurement method to assess blood RBP4 levels restrict the potential clinical use of this marker in the follow-up of ischemic stroke. At last, a major limitation of the present investigation was the cross-sectional design, which prevented us from inferring cause-effect relationship of RBP4 with stroke outcomes. Well-designed longitudinal studies that use larger sample sizes and that consider potential confounders were needed to confirm our observations.

Conclusion
The data showed that elevated serum levels of RBP4 at admission were associated with severity and prognosis of AIS, suggesting that vitamin A metabolism or impaired insulin signaling could be involved. Further studies should be carried out with respect to what was the cause of the increased serum levels of RBP4 and the role in the pathology of the stroke outcomes.