life Article Blood Biomarkers to Predict Long-Term Mortality after Ischemic Stroke Laura Ramiro 1 , Laura Abraira 2,3 , Manuel Quintana 2,3 , Paula García-Rodríguez 1 , Estevo Santamarina 2,3 , Jose Álvarez-Sabín 3,4 , Josep Zaragoza 5 , María Hernández-Pérez 6 , Xavier Ustrell 7 , Blanca Lara 8 , Mikel Terceño 9 , Alejandro Bustamante 1,6, * and Joan Montaner 1,10 1 Neurovascular Research Laboratory, Vall d’Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, 08035 Barcelona, Spain; laura.ramiro@vhir.org (L.R.); paula.garcia.rodriguez@vhir.org (P.G.-R.); joan.montaner@vhir.org (J.M.) 2 Epilepsy Unit, Neurology Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain; laura.abraira@vhir.org (L.A.); maquintavh@gmail.com (M.Q.); esantama@vhebron.net (E.S.) 3 Department of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain; josalvarez@vhebron.net 4 Neurology Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain 5 Stroke Unit, Verge de la Cinta University Hospital, 43500 Tortosa, Spain; jzaragoza.ebre.ics@gencat.cat 6 Stroke Unit, Germans Trias i Pujol University Hospital, 08916 Badalona, Spain; mhernandez@igtp.cat 7 Stroke Unit, Joan XXIII University Hospital, 43005 Tarragona, Spain; xustrell.hj23.ics@gencat.cat 8 Stroke Unit, Bellvitge University Hospital, 08907 Barcelona, Spain; blara@bellvitgehospital.cat 9 Stroke Unit, Josep Trueta University Hospital, 17007 Girona, Spain; mterceno.girona.ics@gencat.cat   10 Stroke Research Program, Institute of Biomedicine of Seville, IBiS/Hospital Universitario Virgen del Rocío/CSIC/University of Seville & Department of Neurology, Hospital Universitario Virgen Macarena, Citation: Ramiro, L.; Abraira, L.; 41009 Seville, Spain Quintana, M.; García-Rodríguez, P.; * Correspondence: abustamanter.germanstrias@gencat.cat Santamarina, E.; Álvarez-Sabín, J.; Zaragoza, J.; Hernández-Pérez, M.; Abstract: Stroke is a major cause of disability and death globally, and prediction of mortality repre- Ustrell, X.; Lara, B.; et al. sents a crucial challenge. We aimed to identify blood biomarkers measured during acute ischemic Blood Biomarkers to Predict stroke that could predict long-term mortality. Nine hundred and forty-one ischemic stroke patients Long-Term Mortality after Ischemic Stroke. Life 2021, 11, 135. https:// were prospectively recruited in the Stroke-Chip study. Post-stroke mortality was evaluated during a doi.org/10.3390/life11020135 median 4.8-year follow-up. A 14-biomarker panel was analyzed by immunoassays in blood samples obtained at hospital admission. Biomarkers were normalized and standardized using Z-scores. Mul- Academic Editors: Pedro Castro tiple Cox regression models were used to identify clinical variables and biomarkers independently and Elsa Azevedo associated with long-term mortality and mortality due to stroke. In the multivariate analysis, the Received: 27 January 2021 independent predictors of long-term mortality were age, female sex, hypertension, glycemia, and Accepted: 7 February 2021 baseline National Institutes of Health Stroke Scale (NIHSS) score. Independent blood biomarkers Published: 10 February 2021 predictive of long-term mortality were endostatin > quartile 2, tumor necrosis factor receptor-1 (TNF-R1) > quartile 2, and interleukin (IL)-6 > quartile 2. The risk of mortality when these three Publisher’s Note: MDPI stays neutral biomarkers were combined increased up to 69%. The addition of the biomarkers to clinical predic- with regard to jurisdictional claims in tors improved the discrimination (integrative discriminative improvement (IDI) 0.022 (0.007–0.048), published maps and institutional affil- p < 0.001). Moreover, endostatin > quartile 3 was an independent predictor of mortality due to stroke. iations. Altogether, endostatin, TNF-R1, and IL-6 circulating levels may aid in long-term mortality prediction after stroke. Keywords: ischemic stroke; biomarker; mortality; endostatin; IL-6; TNF-R1 Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons Attribution (CC BY) license (https:// Stroke is a leading cause of death and long-term disability worldwide. In fact, in creativecommons.org/licenses/by/ 2017, someone died due to stroke every 3.5 min, a total of 2.7 million individuals in the 4.0/). whole year, so it is a major health and socioeconomic concern [1]. An early prediction Life 2021, 11, 135. https://doi.org/10.3390/life11020135 https://www.mdpi.com/journal/life Life 2021, 11, 135 2 of 11 of this fatal outcome after cerebral ischemia can facilitate decision-making processes, such as patients’ inclusion to clinical trials or early admission to specialized stroke units, ultimately optimizing patients’ management. However, predicting stroke outcome remains challenging. Various attempts to identify characteristics associated with poor outcomes have been made. Different predictive models for mortality in stroke patients have been developed, with an accuracy around 80% in most models [2–5], although none of them are routinely used in clinical practice yet. Many of these models include clinical variables, such as age and stroke severity, as predictors of poor outcome and mortality [6–8]. It has been suggested that the addition of blood biomarkers to a clinical model might improve its prediction capacity. However, the vast majority of studies have explored the role of blood biomarkers in predicting outcome and mortality early after stroke, being the outcome measured in most studies 3 months after the event [9–12]. Whether circulating proteins measured acutely after ischemic stroke can predict long-term mortality is still unclear. For this reason, in the present study, we aimed to analyze whether blood biomarkers measured during acute ischemic stroke could predict its long-term mortality. To that end, the association between 14 molecules analyzed in the blood in the acute phase of stroke and five-year mortality was assessed in 941 ischemic stroke patients. 2. Materials and Methods 2.1. Study Design A prospective longitudinal study was conducted to evaluate long-term mortality in 941 adult patients with ischemic stroke, previously recruited in the Stroke-Chip study (PR(AG)80/2012) [13]. In summary, inclusion criteria were suspected stroke at first medical assessment, with persistent symptoms when arriving at the emergency room; age > 18 years; <6 h from symptom onset to blood sample collection; blood collection preceding throm- bolytic treatment; and signed informed consent. The only exclusion criteria was impossi- bility of collecting blood samples. Moreover, patients with an unclear diagnosis 1 month after the index event were excluded from the analysis. Stroke diagnosis was performed by trained neurologists at each center, according to the World Health Organization [14] defini- tion, and confirmed by neuroimaging. Clinical and demographic data, as well as biomarker results obtained during the acute phase in the Stroke-Chip study, were analyzed with data obtained over a median follow-up of 4.8 years to explore predictors of long-term mortality. The study was approved by the Ethics Committee of each recruiting center, and all patients or relatives gave written informed consent. 2.2. Blood Sample Collection and Biomarker Measurement Blood samples were drawn at hospital admission, within 6 h after symptom onset, and before any treatment was given. Blood was collected into ethylenediaminetetraacetic acid (EDTA) tubes, centrifuged at 1500× g for 15 min at 4 ◦ C, and plasma aliquots were frozen at −80 ◦ C until biomarker analysis. A 14-biomarker panel was used in the orig- inal study to distinguish between strokes and stroke mimics, as well as ischemic stroke from intracranial hemorrhage, and these were analyzed in the present study. This panel included apolipoprotein CIII (ApoC-III), D-dimer, endostatin, Fas ligand (FasL), growth- related oncogene-α (GROA), heat shock 70 kDa protein-8 (Hsc70), insulin-like growth factor-binding protein-3 (IGFBP-3), interleukin-6 (IL-6), neuron cell adhesion molecule (NCAM), N-terminal pro-B-type natriuretic peptide (NT-proBNP), S100 calcium-binding protein (S100B), tumor necrosis factor receptor-1 (TNF-R1), vascular adhesion protein-1 (VAP-1), and Von Willebrand factor (vWF). Biomarker measurements were performed using immunoassays, according to the manufacturers’ instructions and blinded to clinical diagnoses, as described before [13]. All samples were tested in duplicate, and the mean coefficient of variation (CV) was <20%. Inter-assay variation was checked by testing in duplicate a commercial internal control (human serum type AB, male, from clotted; Sigma- Aldrich, St. Louis, MO, United States, cat #H6914) in every plate. Biomarker values were log-transformed with a base of 10 and divided by the internal control value of each plate. Life 2021, 11, 135 3 of 11 Due to the high intra-assay variability for some molecules, all values were standardized by plate by Z-scores (mean 2, standard deviation [SD] 1). 2.3. Clinical Assessment All of the patients included in the present study were contacted by telephone and interviewed, using a structured questionnaire designed precisely for this study. When a patient was not available, a family relative or caregiver was interviewed, and if these attempts failed, data were compiled by chart review (PR(AG)397/2016). Cause of death was recorded for each patient. Mortality due to stroke was considered when the patient died due to the index stroke or a recurrent cerebrovascular event during follow-up. Clinical and radiological variables related to stroke onset were collected for the original Stroke-Chip study [13]. Stroke-related variables comprise, among others, stroke severity, ac- cording to the National Institutes of Health Stroke Scale (NIHSS); symptomatic hemorrhagic transformation, based on the European Cooperative Acute Stroke Study III criteria (any hemorrhagic transformation with worsening of ≥4 NIHSS points); etiology (Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification) [15]; and affected cerebral artery territory, according to the Oxford Community Stroke Project (OCSP) classification [16]. 2.4. Statistical Analysis Statistical analyses were conducted with IBM SPSS Statistics, version 22.0 for Windows (SPSS Inc., Chicago, IL, United States) and R software (version 3.4.4). Mortality rates during follow-up were assessed with the Kaplan–Meier product limit survival method, using the log–rank test to check statistical significance between groups and conducting simple Cox proportional hazard models to determine differences in continuous variables. The “survivalROC” R package was utilized to execute time-dependent receiver-operating characteristic (ROC) curve analyses to calculate quartile cut-offs for biomarkers with p- values <0.1 in the simple Cox models with the best specificity and sensitivity to predict mortality; optimal quartile cut-offs were obtained using the Youden index maximum value (sensitivity + specificity − 1). All variables with a p-value <0.1 on univariate analysis were entered into multiple Cox regression models with the forward stepwise method, in order to detect factors independently associated with mortality during follow-up. Two models were performed, one with only clinical variables and another with both categorized biomarkers and clinical variables. The results are presented as hazard ratios (HRs) with 95% confidence interval (CIs). ROC curves were obtained to assess the performance of the models and determine their ability to predict mortality. In order to evaluate the additional value to the logistic regression models of the selected biomarker combinations with the clinical predictors, DeLong’s method was used to compare areas under the curve (AUCs) of ROC curves. The integrative discriminative improvement (IDI) and the net reclassification improvement (NRI) were used to evaluate the incremental effect of adding significant biomarkers to the model built only with clinical data. The “survIDINRI” R package was utilized to obtain IDI and NRI indices in prediction models with censored survival data. A p-value lower than 0.05 was considered statistically significant in all tests. 3. Results 3.1. Demographics Of the 941 ischemic stroke patients in the original Stroke-Chip study [13], four were lost to follow-up, leaving 937 patients that fulfilled the inclusion criteria for the present study. Baseline demographic and clinical characteristics of stroke patients are shown in Table 1. Mean ± SD age was 72.8 ± 13.0 years, and 53.9% of patients were men. Arterial hypertension was the most common vascular risk factor in the sample (n = 686, 73.2%), followed by dyslipidemia (n = 461, 49.2%). The median baseline NIHSS score was 7 (interquartile range (IQR) = 3–15), and median follow-up was 4.8 years (IQR = 1.6–5.2) (Table 1). Life 2021, 11, 135 4 of 11 Table 1. Demographic and clinical characteristics of patients. Death during Follow-Up Death Due to Stroke n = 937 p-Value p-Value No (n = 574) Yes (n = 363) No (n = 804) Yes (n = 133) Age (years, mean ± SD) 72.8 ± 13.0 68.4 ±12.9 79.7 ± 9.6 <0.001 71.5 ±13.1 80.4 ± 9.7 <0.001 Gender (male, n, %) 505 (53.9) 325 (56.6) 180 (49.6) 0.033 435 (54.1) 70 (52.6) 0.683 Smoking (n, %) 151 (16.1) 116 (20.2) 35 (9.6) <0.001 133 (16.5) 18 (13.5) 0.317 Alcoholism (n, %) 64 (6.8) 48 (8.4) 16 (4.4) 0.021 55 (6.8) 9 (6.8) 0.828 Arterial Hypertension (n, %) 686 (73.2) 391 (68.1) 295 (81.3) <0.001 573 (71.3) 113 (85.0) 0.001 Diabetes mellitus (n, %) 240 (25.6) 121 (21.1) 119 (32.8) <0.001 199 (24.8) 41 (30.8) 0.107 Dyslipidemia (n, %) 461 (49.2) 285 (49.7) 176 (48.5) 0.719 392 (48.8) 69 (51.9) 0.508 Atrial fibrillation (n, %) 329 (35.1) 155 (27.0) 174 (47.9) <0.001 263 (32.7) 66 (59.6) <0.001 Coronary artery disease (n, %) 152 (16.2) 74 (12.9) 78 (21.5) 0.001 121 (15.0) 31 (23.3) 0.017 Previous Stroke (n, %) 162 (17.3) 81 (14.1) 81 (22.3) 0.002 132 (16.4) 30 (22.6) 0.086 Previous mRS (median, IQR) 0 (0–1) 0 (0–0) 1 (0–3) <0.001 0 (0–0) 1 (0–3) <0.001 Functionality (mRS > 2) (n, %) 141 (15.5) 30 (5.4) 111 (31.4) <0.001 107 (13.7) 34 (26.6) <0.001 Systolic blood pressure 156.2 ± 28.9 157.3 ± 28.5 154.4 ± 29.5 0.151 156.2 ± 28.9 156.3 ± 28.8 0.993 (mmHg) Diastolic blood pressure 83.0 ± 16.9 84.3 ± 16.8 80.8 ± 16.8 0.006 82.8 ± 16.6 84.0 ± 18.4 0.504 (mmHg) Glycemia (mg/dL) 133.5 ± 46.6 127.5 ± 42.3 142.9 ± 51.3 <0.001 131.6 ± 45.7 144.8 ± 50.1 0.001 Baseline NIHSS score 7.0 (3–15) 5.0 (2–10) 12.5 (6–19) <0.001 6.0 (2–12) 17.5 (11–21) <0.001 (median [IQR]) Type of TIA (n, %) 102 (10.9) 80 (13.9) 22 (6.1) 100 (12.5) 2 (1.5) ischemic <0.001 <0.001 Ischemic stroke 835 (89.1) 494 (86.1) 341 (93.9) 704 (87.6) 131 (98.5) stroke (n, %) TACI (n, %) 306 (37.6) 123 (25.3) 183 (56.1) 214 (31.1) 92 (73.6) OCSP PACI (n, %) 310 (38.1) 210 (43.1) 100 (30.7) 288 (41.9) 22 (17.6) <0.001 <0.001 classification LACI (n, %) 127 (15.6) 103 (21.1) 24 (7.4) 123 (17.9) 4 (3.2) POCI (n, %) 70 (8.6) 51 (10.5) 19 (5.8) 63 (9.2) 7 (5.6) Cardioembolic 387 (41.6) 210 (36.8) 177 (49.2) 325 (40.7) 62 (47.0) (n, %) TOAST Atherothrombotic 131 (14.1) 87 (15.2) 44 (12.2) <0.001 114 (14.3) 17 (12.9) <0.001 classificaiton (n, %) Lacunar (n, 120 (12.9) 100 (17.5) 20 (5.6) 118 (14.8) 2 (1.5) %) Undetermined 275 (29.5) 158 (27.7) 117 (32.5) 224 (28.0) 51 (38.6) (n, %) Other causes 18 (1.9) 16 (2.8) 2 (0.6) 18 (2.3) 0 (0.0) (n, %) Reperfusion therapy (n, %) 389 (41.6) 232 (40.5) 157 (43.4) 0.119 322 (40.1) 67 (50.4) 0.017 tPA (n, %) 363 (38.8) 213 (37.2) 150 (41.4) 0.037 298 (37.2) 65 (48.9) 0.005 Thrombectomy (n, %) 72 (7.7) 46 (8.0) 26 (7.2) 0.770 60 (7.5) 12 (9.0) 0.559 Symptomatic hemorrhagic 17 (1.8) 1 (0.2) 16 (4.4) <0.001 4 (0.5) 13 (9.8) <0.001 transformation (n, %) mRS: modified Rankin scale; NIHSS: National Institutes of Health Stroke Scale; IQR: interquartile range; TIA: transient ischemic attack; OCSP: Oxfordshire Community Stroke Project; TACI: total anterior circulation infarct; PACI: partial anterior circulation infarct; LACI: lacunar infarct; POCI: posterior circulation infarct; TOAST: Trial of ORG 10172 in Acute Stroke Treatment; tPA: tissue plasminogen activator. 3.2. Clinical Characteristics and Mortality Three hundred and sixty-three patients (38.7%) died during the 4.8-year follow-up. Table 1 shows demographic and clinical differences between those patients who died and those who survived. Deceased patients were older, had higher prevalence of hypertension and atrial fibrillation, and a higher baseline NIHSS score. Patients’ cause of death during follow-up is summarized in Figure 1, which shows that the index stroke was the most common cause of death among patients (n = 107). In the multiple regression analysis, the independent predictors of mortality were age (hazard ratio (HR) = 1.063, 95% confidence interval (CI) = 1.049–1.078, p < 0.001), female sex (HR = 1.559, 95% CI = 1.243–1.956, p < 0.001), hypertension (HR = 1.359, 95% CI = 1.028– 1.797, p = 0.031), previous modified Rankin scale (mRS) (HR = 1.247, 95% CI = 1.152–1.351, p < 0.001), glycemia (HR = 1.002, 95% CI = 1.000–1.004, p = 0.043) and baseline NIHSS (HR = 1.089, 95% CI = 1.089–1.073, p < 0.001). Life2021, Life 11,x135 2021,11, FOR PEER REVIEW 55of of11 11 20.9% Index stroke 29.5% Cancer Recurrent stroke Respiratory disease 20.9% Cardiovascular disease 7.7% Other 7.2% 6.6% Unknown 7.2% Figure Figure1.1.Patients’ Patients’cause causeof ofdeath deathduring duringfollow-up. follow-up. 3.3. Blood In theBiomarkers and Mortality multiple regression analysis, the independent predictors of mortality were age (hazard ratio (HR) Patients = 1.063, who died 95% the during confidence follow-up interval (CI)higher time had = 1.049–1.078, baseline plevels < 0.001), female of D-dimer, sex (HR = 1.559, endostatin, IL-6,95% CI = 1.243–1.956, NT-proBNP, p < 0.001), VAP-1, vWF, hypertension and TNF-R1, (HR =levels and lower 1.359,of95% CI = 1.028– apolipoprotein CIII (APOC-III) 1.797, than patients p = 0.031), previous whoRankin modified survived. scaleFor APOC-III, (mRS) D-dimer, (HR = 1.247, 95% CIendostatin, IL-6, = 1.152–1.351, pNT-proBNP, vWF, and < 0.001), glycemia (HRTNF-R1, the optimal = 1.002, 95% quartile cut-off CI = 1.000–1.004, point p = 0.043) was and quartileNIHSS baseline 2, whereas (HR =for VAP-1, 1.089, 95%quartile 3 was optimal CI = 1.089–1.073, (Table 2). p < 0.001). Table 2. Blood biomarker meanBiomarkers 3.3. Blood values and and best Mortality quartile cut-off points to predict mortality during follow-up. Patients who died during the follow-up time Death during Follow-Up had during Death higherFollow-Up baseline levels of D-dimer, endostatin, IL-6, NT-proBNP, Cut-Off VAP-1, vWF, and TNF-R1, and lower levels of apolipopro- Biomarkers p-Value p-Value No Yes (Quartile) No Yes (n = 574) tein CIII (APOC-III) (n = 363) than patients who survived. For APOC-III, (n = 574) D-dimer, (n = 363) endostatin, IL- 6, NT-proBNP, vWF, and TNF-R1, the optimal quartile cut-off point was quartile 2, APOC-III 2.10 ± 0.92 1.88 ± 1.03 <0.001 >Q2 304 (53.4%) 162 (44.8%) 0.007 whereas for VAP-1, quartile 3 was optimal (Table 2). D-dimer 1.84 ± 0.93 2.51 ± 0.84 <0.001 >Q2 217 (38.1%) 247 (69.0%) <0.001 Endostatin 1.89 ± 0.84 In ± 2.49 the multiple<0.001 0.97 regression model, >Q2after adjusting for those 222 (38.0%) 245clinical (68.2%) variables associ- <0.001 GroA 1.97 ± 0.97 ated with 2.02 mortality in ± 0.97 univariate analysis, 0.286 — endostatin— > quartile 2 — (HR = 1.373, 95% CI = IL-6 1.77 ± 0.93 1.061–1.776, 2.35 ± 0.89p = 0.016), TNF-R1 > quartile <0.001 >Q2 2 (HR214 = 1.392, (39.4%)95% CI = 1.071–1.808, <0.001 232(67.1%) p = 0.013) NT-proBNP 1.86 ± 0.88 and2.59 ± >0.88 IL-6 quartile 2<0.001 (HR = 1.316, 95% >Q2CI = 1.025–1.690, 220 (38.6%) 247(68.2%) p = 0.032) <0.001pre- were independent VAP-1 1.97 ± 0.96 dictors 2.21 ±of1.05 long-term<0.001 >Q3 with age mortality, together 118(HR (20.7%) = 1.058,115 95%(31.9%) <0.001 p < CI = 1.042–1.073, vWF 1.87 ± 0.99 2.28 ± 0.89 <0.001 >Q2 246 (42.9%) 224 (61.7%) <0.001 0.001), female sex (HR = 1.674, 95% CI = 1.308–2.142, p < 0.001), hypertension (HR = 1.338, IGFBP-3 1.97 ± 0.91 2.05 ± 1.09 0.208 — — — FAS-L 1.99 ± 0.98 95% CI ± 1.96 = 0.992–1.806, 1.00 p = 0.057), previous 0.677 — mRS (HR =— 1.232, 95% CI =— 1.131–1.343, p < 0.001), TNF-R1 1.82 ± 0.85 glycemia (HR = 1.002, 2.45 ± 1.03 95% CI = 1.000–1.004, <0.001 >Q2 p =205 0.071) and baseline (38.3%) NIHSS (HR 226 (69.5%) = 1.087, <0.001 NCAM 1.97 ± 0.99 95% CI± 2.03 = 0.97 1.069–1.105,0.416 p < 0.001). — — — S100B 1.99 ± 0.99 2.07 ± 0.96 0.322 — — — Hsc70Table 2. 2.04 Blood± biomarker 0.94 2.06 ± 0.97 mean 0.757quartile cut-off values and best —points to predict — mortality during — follow-up. APOC-III: apolipoprotein CIII (µg/mL); GroA: growth-related oncogene α (pg/mL); IL-6: interleukin 6 (pg/mL); NT-proBNP: N-terminal Death pro-B-type natriuretic during peptide Follow-Up (pg/mL); Death during VAP-1: vascular adhesion protein-1 (pg/mL); vWF: von Willebrand Follow-Up factor (%); IGFBP-3: insulin-like growth Biomarkersfactor binding protein-3 No (pg/mL); FasL: Yes Fas ligand (pg/mL); TNF-R1: tumor p-Value Cut-Off (Quartile) necrosis factor receptor-1 No (pg/mL); NCAM: neural Yes p-Value cell adhesion molecule (pg/mL); S100B: S100 calcium-binding protein B (pg/mL); Hsc70: heat shock 70 kDa protein-8 (ng/mL). (n = 574) (n = 363) (n = 574) (n = 363) APOC-III 2.10 ± 0.92 1.88 ± 1.03 <0.001 >Q2 304 (53.4%) 162 (44.8%) 0.007 In the multiple regression model, after adjusting for those clinical variables associated D-dimer 1.84 ± 0.93with mortality 2.51 ± 0.84 <0.001 analysis, >Q2 in univariate 217 (38.1%) endostatin > quartile 2 (HR =247 (69.0%) 1.373, 95% CI <0.001 = 1.061– Endostatin 1.89 ± 0.841.776, 2.49 ± 0.97 <0.001 >Q2 222 (38.0%) 245 (68.2%) p = 0.016), TNF-R1 > quartile 2 (HR = 1.392, 95% CI = 1.071–1.808, p = 0.013) <0.001and GroA 1.97 ± 0.97IL-6 > 2.02 ± 0.97 quartile 2 (HR =0.286 1.316, 95% CI = --- --- were independent 1.025–1.690, p = 0.032) --- predictors IL-6 1.77 ± 0.93of long-term mortality, 2.35 ± 0.89 together with>Q2 <0.001 age (HR = 1.058, 214 (39.4%) 232(67.1%) p < 95% CI = 1.042–1.073, 0.001), <0.001 NT-proBNP 1.86 ± 0.88female sex (HR 2.59 ± 0.88 = 1.674, 95% <0.001 CI = 1.308–2.142, >Q2 p < 0.001), hypertension 220 (38.6%) (HR 247(68.2%) = 1.338, 95% <0.001 VAP-1 1.97 ± 0.96CI = 0.992–1.806, 2.21 ± 1.05 p = 0.057), <0.001 previous mRS >Q3 (HR = 1.232, 95% CI = 1.131–1.343, 118 (20.7%) 115 (31.9%) <0.001 p < 0.001), glycemia (HR = 1.002, 95% CI = 1.000–1.004, p = 0.071) and baseline NIHSS (HR = 1.087, vWF 1.87 ± 0.99 2.28 ± 0.89 <0.001 >Q2 246 (42.9%) 224 (61.7%) <0.001 95% CI = 1.069–1.105, p < 0.001). IGFBP-3 1.97 ± 0.91 2.05 ± 1.09 0.208 --- --- --- NCAM 1.97 ± 0.99 2.03 ± 0.97 0.416 --- --- --- S100B S100B 1.99 1.99 ± 0.99 ± 0.99 2.07 2.07 ± 0.96 ± 0.96 0.322 0.322 ------ ------ ------ Hsc70 Hsc70 2.04 2.04 ± 0.94 ± 0.94 2.06 2.06 ± 0.97 ± 0.97 0.757 0.757 ------ ------ ------ APOC-III:apolipoprotein APOC-III: apolipoproteinCIII CIII(µg/mL); (µg/mL);GroA: GroA:growth-related growth-relatedoncogene oncogeneα α(pg/mL); (pg/mL);IL-6: IL-6:interleukin interleukin6 6(pg/mL); (pg/mL);NT- NT- proBNP: N-terminal pro-B-type natriuretic peptide (pg/mL); VAP-1: vascular adhesion protein-1 (pg/mL); proBNP: N-terminal pro-B-type natriuretic peptide (pg/mL); VAP-1: vascular adhesion protein-1 (pg/mL); vWF: von Wil- vWF: von Wil- Life 2021, 11, 135factor (%); IGFBP-3: insulin-like growth factor binding protein-3 (pg/mL); FasL: Fas ligand (pg/mL); TNF-R1: tu-6 of 11 lebrand lebrand factor (%); IGFBP-3: insulin-like growth factor binding protein-3 (pg/mL); FasL: Fas ligand (pg/mL); TNF-R1: tu- mor mor necrosis necrosis factor factor receptor-1 receptor-1 (pg/mL); (pg/mL); NCAM: NCAM: neural neural cell cell adhesion adhesion molecule molecule (pg/mL); (pg/mL); S100B: S100B: S100 S100 calcium-binding calcium-binding pro- pro- tein tein B (pg/mL); B (pg/mL); Hsc70: Hsc70: heat heat shock shock 7070 kDakDa protein-8 protein-8 (ng/mL). (ng/mL). The risk The riskofof death ofdeath increased increasedupup deathincreased uptoto 69% to69% 69% when these whenthese three thesethree biomarkers threebiomarkers were biomarkerswere combined werecombined combined The risk when (Figure (Figure 2). 2). The The area area under under the the ROC ROC curve curve (AUC) (AUC) of of the the predictive predictive model model was was significantly significantly (Figure 2). The area under the ROC curve (AUC) of the predictive model was significantly higher when higher combining whencombining biomarkers combiningbiomarkers biomarkerswithwith clinical withclinical variables clinicalvariables (85.5%; variables(85.5%; 95% 95%CICI (85.5%;95% CI= ==81.7–87.5%) 81.7–87.5%) 81.7–87.5%) higher when than when clinical variables were used alone (84.4%; 95% CI = 82.9–88.1%) (De Long’s test than than when clinical variables were used alone (84.4%; 95%CICI = 82.9–88.1%) (De Long’s test p = when 0.017)clinical (Figurevariables 3). were used alone (84.4%; 95% = 82.9–88.1%) (De Long’s test p =p 0.017) = 0.017) (Figure (Figure 3).3). Figure Figure 2. Combination of predictive predictive blood bloodbiomarkers biomarkers(endostatin (endostatin>>quartile quartile 2,TNF-R1 TNF-R1> quartile > quartile 2, and IL-6 > quartile Figure 2. 2. Combination Combination ofof predictive blood biomarkers (endostatin > quartile 2, 2, TNF-R1 > quartile 2, 2, andand IL-6 IL-6 > quartile > quartile 2).2). 2). RiskRisk of death increased up to 36.3% when adding any of the biomarkers, up to 38.7% when combining two of three Risk ofof death death increased increased upup toto 36.3% 36.3% when when adding adding any any ofof the the biomarkers, biomarkers, upuptoto 38.7% 38.7% when when combining combining twotwo ofof three three bi-bi- biomarkers, omarkers, omarkers, and and and toup upup to 69%to when 69% 69% whenwhen combining combining combining allall allbiomarkers. three three three biomarkers. biomarkers. IL-6: IL-6: IL-6: interleukin-6; interleukin-6; interleukin-6; TNF-R: TNF-R: TNF-R: tumor tumor tumor necrosis necrosis necrosis factor factor factor receptor- receptor- 1. 1. receptor-1. Figure Figure Figure 3. 3. 3. Receiver-operating Receiver-operating Receiver-operating characteristic characteristic characteristic (ROC) (ROC)(ROC) curve curve ofof curve the the predictive ofpredictive the model predictive model forfor model long-term mor- for long-term long-term mor- tality. tality. The The mortality. predictive predictive The capacity capacity predictive ofof capacity the the model ofmodel the was was model slightly slightly was higher higher slightly when when higher combining combining when combining clinical clinical variables variables clinical variables and and blood blood biomarkers biomarkers (endostatin, (endostatin, IL-6, IL-6, andand TNF-R1) TNF-R1) than than when when using using clinical variables clinical alone. and blood biomarkers (endostatin, IL-6, and TNF-R1) than when using clinicalvariables variablesalone. alone. The IDI value was 0.022 (95% CI = 0.007–0.048, p < 0.001) (IDI events = 0.011 and IDI non-events = −0.011), and the NRI was 0.218 (95% CI = 0.157–0.349, p < 0.001) (NRI events = 0.600 and NRI non-events = 0.382), so the inclusion of the biomarkers significantly improved the model when compared to the model built only with clinical variables. 3.4. Mortality Due to Stroke One hundred and thirty-three patients (14.2%) died due to stroke during follow-up. Of those, 107 (29.5%) died due to the index event, and 26 (7.2%) due to a recurrent stroke. Table 1 shows demographic and clinical differences between those patients who died Life 2021, 11, 135 7 of 11 because of stroke and those who survived. Patients who died due to stroke were older, had higher prevalence of hypertension, atrial fibrillation, and coronary artery disease, and had a higher baseline NIHSS score. In the multivariate analysis, the independent predictors of mortality due to stroke were age (HR = 1.058, 95% CI = 1.037–1.079, p < 0.001), hypertension (HR = 1.852, 95% CI = 1.131–3.030, p = 0.014) and basal NIHSS (HR = 1.138, 95% CI = 1.111–1.167, p < 0.001). Patients who died because of stroke during the follow-up time had higher levels of D-dimer, endostatin, IL-6, NT-proBNP, VAP-1, vWF, and TNF-R1 than patients who did not die. Each biomarker was divided into quartiles and optimal quartile cut-off points to predict long-term mortality due to stroke. For D-dimer, IL-6, NT-proBNP, vWF, and TNF-R1, the optimal quartile cut-off point was quartile 2, whereas for endostatin and VAP-1 quartile 3 was the optimal (Table 3). Table 3. Blood biomarker mean values and best quartile cut-off points to predict mortality due to stroke during follow-up. Death Due to Stroke Death Due to Stroke Cut-Off Biomarkers p-Value p-Value No Yes (Quartile) No Yes (n = 804) (n = 133) (n = 804) (n = 133) APOC-III 2.03 ± 0.98 1.95 ± 0.93 0.329 — — — D-dimer 2.02 ± 0.95 2.58 ± 0.84 <0.001 >Q2 371 (46.4%) 93 (72.1%) <0.001 Endostatin 2.06 ± 0.91 2.58 ± 1.05 <0.001 >Q3 173 (21.7%) 61 (46.2%) <0.001 GroA 1.97 ± 0.97 2.11 ± 1.01 0.103 — — — IL-6 1.94 ± 0.95 2.40 ± 0.89 <0.001 >Q2 361 (47.3%) 85 (68.0%) <0.001 NT-proBNP 2.07 ± 0.94 2.59 ± 0.92 <0.001 >Q2 382 (47.8%) 85 (63.9%) <0.001 VAP-1 2.03 ± 0.99 2.24 ± 1.05 0.021 >Q3 190 (23.8%) 43 (32.6%) 0.027 vWF 1.98 ± 0.97 2.35 ± 0.89 <0.001 >Q2 386 (48.0%) 84 (63.2%) <0.001 IGFBP-3 2.01 ± 0.96 1.97 ± 1.14 0.768 — — — FAS-L 1.98 ± 0.99 1.98 ± 0.98 0.945 — — — TNF-R1 1.99 ± 0.94 2.51 ± 1.09 <0.001 >Q2 351 (47.1%) 80 (69.6%) <0.001 NCAM 1.97 ± 0.99 2.11 ± 0.93 0.177 — — — S100B 2.01 ± 0.99 2.09 ± 0.90 0.434 — — — Hsc70 2.04 ± 0.96 2.11 ± 0.88 0.421 — — — ApoCIII: apolipoprotein CIII (µg/mL); GroA: growth-related oncogene α (pg/mL); IL-6: interleukin 6 (pg/mL); NT-proBNP: N-terminal pro-B-type natriuretic peptide (pg/mL); VAP-1: vascular adhesion protein-1 (pg/mL); vWF: von Willebrand factor (%); IGFBP-3: insulin-like growth factor binding protein-3 (pg/mL); FasL: Fas ligand (pg/mL); TNF-R1: tumor necrosis factor receptor-1 (pg/mL); NCAM: neural cell adhesion molecule (pg/mL); S100B: S100 calcium-binding protein B (pg/mL); Hsc70: heat shock 70 kDa protein-8 (ng/mL). In the multiple regression model, after adjusting for significant clinical variables, endo- statin > quartile 3 was an independent predictor of mortality due to stroke (HR = 1.835, 95% CI = 1.196–2.815, p = 0.005), together with age (HR = 1.054, 95% CI = 1.029–1.079, p < 0.001), hypertension (HR = 1.626, 95% CI = 0.953–2.772, p = 0.074), and basal NIHSS (HR = 1.144, 95% CI = 1.110–1.178, p < 0.001). The AUC of the predictive model was moderately higher when combining biomarkers with clinical variables (83.6%; 95% CI = 80.2–86.9%) than when clinical variables were used alone (82.5%; 95% CI = 79.1–86.0%), although this dif- ference was not significant (De Long’s tests p = 0.066) (Figure 4). The inclusion of the biomarkers improved the model when compared to the model built only with clinical vari- ables, given that the IDI value was 0.020 (95% CI = 0.002–0.050, p = 0.02) (IDI events = 0.016 and IDI non-events = −0.004) and the NRI was 0.296 (95% CI = 0.043–0.390, p = 0.04) (NRI events = 0.472 and NRI non-events = 0.176). Hsc70 2.04 ± 0.96 2.11 ± 0.88 0.421 --- --- --- ApoCIII: apolipoprotein CIII (µg/mL); GroA: growth-related oncogene α (pg/mL); IL-6: interleukin 6 (pg/mL); NT- proBNP: N-terminal pro-B-type natriuretic peptide (pg/mL); VAP-1: vascular adhesion protein-1 (pg/mL); vWF: von Wil- Life 2021, 11, 135 lebrand factor (%); IGFBP-3: insulin-like growth factor binding protein-3 (pg/mL); FasL: Fas ligand (pg/mL); TNF-R1: tu- 8 of 11 mor necrosis factor receptor-1 (pg/mL); NCAM: neural cell adhesion molecule (pg/mL); S100B: S100 calcium-binding pro- tein B (pg/mL); Hsc70: heat shock 70 kDa protein-8 (ng/mL). Figure 4. Receiver-operating characteristic (ROC) curve of the predictive model for long-term Figure 4. Receiver-operating characteristic (ROC) curve of the predictive model for long-term mor- mortality tality due due to to stroke. stroke. The predictive The predictive capacity capacity of theof the model model was slightly was slightly higherhigher whenwhen combining combining clinical variables with a blood biomarker (endostatin) than when using clinical variables alone. clinical variables with a blood biomarker (endostatin) than when using clinical variables alone. 4. Discussion 4. Discussion The present study explored the association of 14 blood biomarkers measured in the The present study explored the association of 14 blood biomarkers measured in the acute phase with long-term mortality after ischemic stroke. We followed up with a cohort acute phase with long-term mortality after ischemic stroke. We followed up with a cohort of 941 ischemic stroke patients over a median time of almost 5 years after the event, in order of 941 ischemic stroke patients over a median time of almost 5 years after the event, in to determine whether some blood biomarkers can predict mortality in this population. order to determine whether some blood biomarkers can predict mortality in this popula- The proportion of patients that die after ischemic stroke is influenced by different variables, tion. suchThe proportion as subtype of patients of stroke thatresources or health die afterof ischemic stroke According each country. is influenced by literature, to the different variables, 40–60% of ischemic stroke patients die within 5 years after the event [17–19]. to such as subtype of stroke or health resources of each country. According In the the literature, 40–60% of ischemic stroke patients die within 5 years present study, we found that 38.7% of ischemic stroke patients died during the medianafter the event [17–19]. In the present study, we found that 38.7% of ischemic stroke patients 4.8-year follow-up period, and those who died were older, had higher prevalence of somedied during the median 4.8-year vascularfollow-up period, risk factors, such and those who died as hypertension and were atrialolder, had higher fibrillation, prevalence and had a higher of some baseline vascular risk NIHSS score. factors, such as hypertension and atrial fibrillation, and had a higher baseline NIHSS Thescore. independent predictors of mortality were age, sex, hypertension, previous mRS, and basalindependent The NIHSS, which predictors are among of mortality were age, the most frequent sex, hypertension, clinical and demographical previous mRS, variables and basal NIHSS, associated whichmortality, with stroke are amongasthe most frequent reported clinical and in the literature demographical [8,20]. In addition,varia- from bles the associated with stroke mortality, proposed biomarkers, we found as reported that in the literature after adjusting [8,20]. for clinical In addition, variables, from increased the proposed levels biomarkers, of endostatin, we TNF-R1 IL-6, and found that were after adjusting predictors independent for clinicalofvariables, long-termincreased mortality levels of endostatin, IL-6, and TNF-R1 were independent predictors following stroke. In fact, when these three biomarkers were combined, the risk of of long-term mortal- death ity following stroke. In fact, when these three biomarkers were increased up to 69%. The best biomarkers identified in the present study are known combined, the risk of death increased key players up in to pathophysiological 69%. The best biomarkers pathways identified in theinpresent implicated stroke,study such as areangiogenesis known key players in pathophysiological (endostatin) and inflammatorypathways implicated response (IL-6 in stroke, such as angiogenesis (en- and TNF-R1). dostatin) It is and inflammatory widely reported that response cerebral (IL-6 and TNF-R1). ischemia triggers an inflammatory response both It is in the widely brain andreported peripheral that cerebral ischemia circulation, which leads triggers an inflammatory to up-regulation response both of inflammatory cy- in the brain tokines suchand peripheral as IL-6 circulation, [21]. Increased which leads circulating levelsto ofup-regulation IL-6 have beenofregularly inflammatory cy- associated tokines such as IL-6 [21]. with poor-outcome after Increased stroke [22].circulating The valuelevelsof IL-6 ofto IL-6 havemortality predict been regularly associ- after cerebral ischemia ated has also been after with poor-outcome studied before. stroke [22]. Various The value studies of IL-6support that to predict increased mortality baseline after cere- levels of IL-6 are associated with risk of death 3 months after the event [23–26]. Regarding long-term mortality, it has been revealed that IL-6 could also predict mortality both 1 year [27] and 2 years [28] after stroke. In the present study, we propose that IL-6 could also predict mortality even at later time-points, being associated with increased risk of mortality 5 years after cerebral ischemia. TNF-R1 is a transmembrane death receptor that is able to detect the presence of extracellular death signals and ultimately trigger cell apoptosis [29]. TNF-R1 can be Life 2021, 11, 135 9 of 11 activated by both membrane-bound and soluble forms of TNF-α [30]. It is well known that within the first hours after cerebral ischemia, cells of the ischemic core and penumbra suffer irreversible damage that ultimately leads to cell death by apoptosis [21]. Increased levels of TNF-α have previously been associated with poor outcome after ischemic stroke [31,32]. However, the association of TNF-R1 in stroke outcome and long-term mortality has never been described before. Here we found that those patients with increased risk of long-term death after stroke showed higher baseline levels of TNF-R1, reinforcing the crucial role of the TNF signaling pathway in stroke prognosis. Endostatin is derived from type XVIII collagen. It inhibits both the proliferation and migration of endothelial cells, ultimately leading to inhibition of angiogenesis [33]. Regarding outcome, it has been previously reported that tissue plasminogen activator (tPA)-treated stroke patients showing higher endostatin level at hospital admission had an impaired functional outcome three months after the event [34]. In relation to mortality, one study including 3463 acute ischemic stroke patients revealed that increased baseline endostatin levels were associated with increased risk of mortality and severe disability at 3 months [35]. However, to date, the association of endostatin and long-term mortality after stroke has not been explored. Interestingly, in the present study, we found that those patients who did not survive during follow-up had higher baseline levels of endostatin at hospital admission. This is in line with what has previously been reported, and therefore endostatin may be a useful biomarker to predict both early and long-term mortality. In ad- dition to this, we have reported for the first time that baseline endostatin levels are also associated with long-term mortality due to stroke, making endostatin even more interesting as a promising stroke biomarker. Remarkably, the risk of long-term death when these three biomarkers were combined increased up to 69%, while the risk was below 10% when none of these biomarkers were pathological. The combination of blood biomarkers and clinical variables showed a slight but significant additional predictive value over clinical data in AUC results, although the clinical model included stroke severity. Moreover, these findings are supported by positive results in NRI and IDI procedures. In addition, endostatin was also able to predict long-term mortality due to stroke, given that the combination of endostatin and clinical variables moderately increased the predictive value when compared to clinical data. All in all, Il-6 TNF-R1 and endostatin seem to be interesting candidates for further exploration as long-term mortality biomarkers after stroke. In this regard, the modulation of these molecules and pathways through angiogenic or anti-inflammatory drugs might be an attractive approach to improve patients’ outcome and reduce mortality. In fact, some pre-clinical studies already point in that direction [21]. The main strengths of the present study are the large sample size and the standardized follow-up after a long time of almost five years. However, our study has some limitations. First, in some cases, data were collected by chart review, which might lead to some missing data. Second, the specific cause of death of 20% of patients is unknown, so some deaths due to stroke may not have been diagnosed. Third, the need to standardize the results could have led to an underestimation of the biomarker effect. Finally, these patients were recruited in 2012–2013, so the actual high rates of thrombectomies that are being performed in hospitals, which may contribute to the reduction in mortality, might not be fully represented by the present cohort. For that reason, our results should be replicated in an independent cohort before solid conclusions can be drawn. In conclusion, we have identified that the acute upregulation of endostatin, IL-6, and TNF-R1 after ischemic stroke can predict long-term mortality. Along this line, a panel comprising these three biomarkers could be used to help identify high-risk patients, so that more aggressive therapeutic strategies can be targeted to those most likely to benefit. Additional studies are needed to validate these findings, in order to elucidate the real impact on stroke patients’ management. Life 2021, 11, 135 10 of 11 Author Contributions: L.R., L.A., E.S., A.B., and J.M conceived and designed the study; J.Z., M.H.-P., X.U., B.L., M.T., and A.B. collected data and blood samples from ischemic stroke patients; L.R. and M.Q. analyzed the data; L.R. and P.G.-R. wrote the paper; J.Á.-S. and J.M. obtained funding for study development. All authors have read and agreed to the published version of the manuscript. Funding: This work has been funded by Instituto de Salud Carlos III (PI18/00804) and by La Fundació La Marató (Reg. 84/240 proj. 201702). Neurovascular Research Laboratory takes part in the Spanish stroke research network INVICTUS+ (RD16/0019/0021). L.R. is supported by a pre-doctoral fellowship from the Instituto de Salud Carlos III (IFI17/00012). 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