Associations between admission levels of multiple biomarkers and subsequent worse outcomes in acute ischemic stroke patients

Abstract

The modified Rankin Scale change score (ΔmRS) is useful for evaluating acute poststroke functional improvement or deterioration. We investigated the relationship between multiple biomarkers and ΔmRS by analyzing data on 6931 patients with acute ischemic stroke (average age 62.3 ± 11.3 years, 2174 (31.4%) female) enrolled from the Third China National Stroke Registry (CNSR-III) and 15 available biomarkers. Worse outcomes at 3 months were defined as ΔmRS_3m-discharge ≥1 (ΔmRS_3m-discharge = mRS_3m-mRS_discharge). Adjusted odds ratios (aORs) and their 95% confidence intervals (CIs) were calculated from logistic regression models. At 3-months poststroke, 1026 (14.8%) patients experienced worse outcomes. The highest quartiles of white blood cells (WBCs) (aOR [95%CI],1.37 [1.12–1.66]), high-sensitivity C-reactive protein (hs-CRP) (1.37 [1.12–1.67]), interleukin-6 (IL-6) (1.43 [1.16–1.76]), interleukin-1 receptor antagonist (IL-1Ra) (1.46 [1.20–1.78]) and YKL-40 (1.31 [1.06–1.63]) were associated with an increased risk of worse outcomes at 3 months. Results remained stable except for YKL-40 when simultaneously adding multiple biomarkers to the basic traditional-risk-factor model. Similar results were observed at 6 and 12 months after stroke. This study indicated that WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 were significantly associated with worse outcomes in acute ischemic stroke patients, and all inflammatory biomarkers except YKL-40 were independent predictors of worse outcomes at 3 months.

Keywords

Blood biomarkers modified Rankin Scale worse outcomes acute ischemic stroke inflammation

Introduction

Stroke outcomes are individual and dynamic; even in patients with similar initial stroke severities, the long-term stroke outcomes are different. The considerable social and economic burden resulting from long-term disability after acute stroke highlights the importance of investigating the mechanisms underlying neurological changes, including both improvements and deteriorations in disability, which represents a key area of interest in stroke research.¹ In contrast to static assessments of neurological function, dynamic measurements of neurological changes provide a richer set of information that can be used to investigate the mechanisms underlying stroke recovery. Genetic stroke studies can contribute significantly to this line of inquiry by leveraging well-defined and dynamic phenotypic measures, such as clinical neurological deficit scales, neuroimaging, and blood biomarkers, to capture the relevant mechanisms associated with stroke outcomes.^1,2

The modified Rankin Scale (mRS) is widely used to assess global neurological function in clinical trials.² The mRS change (ΔmRS) between two special time points, which serves as an indicator of changes in functional outcomes following stroke, represents a valuable phenotype for investigating stroke recovery.^2,3 Blood biomarkers can objectively reflect normal or pathological processes and provide more information on the relevant pathophysiological mechanisms of prognostic factors after stroke.⁴ Although many blood biomarkers, particularly those associated with inflammation, have been linked to poor functional outcomes in ischemic stroke,^5,6 the repertoire of blood biomarkers capable of predicting changes in functional outcomes following acute ischemic stroke remains limited.

The Third China National Stroke Registry (CNSR-III) enrolled patients with minor acute ischemic stroke and transient ischemic attack (TIA), aiming to identify biological markers for the prognosis of patients with ischemic stroke and further establish a predictive model of functional outcomes based on biological markers in ischemic cerebrovascular diseases.⁷ To establish a predictive model for functional outcomes based on biological markers in ischemic cerebrovascular diseases, we analyzed the association between a panel of 15 blood biomarkers and ΔmRS between different time points after acute ischemic stroke in the CNSR-III. The identification of biomarkers could provide insights into the mechanisms underlying worse outcomes following acute ischemic stroke.

Material and methods

Study design and participants

The study is an exploratory analysis of CNSR-III, a nationwide, prospective registry study. Between August 2015 and March 2018, patients with acute ischemic stroke or TIA within 7 days of symptom onset were enrolled consecutively from 201 centers in China. The detailed design of the CNSR-III has been described in a previous paper.⁷ Patients with TIA, without information on selected blood biomarkers, mRS at discharge, and 3 months of follow-up were excluded from this study. Informed consent was obtained from all participants or legally authorized representatives. The protocol of the CNSR-III was approved by the Ethics Committee of Beijing Tiantan Hospital (IRB approval number: KY2015-001-01) and all participating centers. This study was conducted according to the ethical standards of the Helsinki Declaration of 1975 (and as revised in 1983).

Baseline data collection

The baseline demography and characteristics of all participants, including age, sex, body mass index [weight (kg)/height² (m²)], systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking habits, and medical history, were collected by face-to-face interviews. The baseline mRS score and the National Institutes of Health Stroke (NIHSS) score at admission were assessed by trained neurologists. Information on secondary stroke prevention at discharge, laboratory tests of routine blood tests, biochemical tests, and lipid parameters were collected from medical records. All participants were examined using cranial magnetic resonance imaging (MRI)/computed tomography (CT) scans. The etiological subtypes of ischemic stroke were classified into large-artery atherosclerosis (LAA), cardioembolism (CE), small-vessel occlusion (SAO), other determined cause and unknown determined cause according to the criteria of the Trial of Org 10172 in Acute Stroke Treatment (TOAST).⁸

Selected biomarkers

A set of 15 blood biomarkers was selected based on previous research that identified markers of inflammation, estimated glomerular filtration rate (eGFR), serum lipid profiles, atherogenesis, fasting plasma glucose (FPG) and D-dimer (D-D) as being linked to functional outcomes following stroke.^4,9
–14 Specifically, the systemic inflammation biomarkers include white blood cells (WBCs), high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), interleukin-1 receptor antagonist (IL-1Ra), and monocyte chemoattractant protein-1 (MCP-1). The local vascular inflammation produced in atherosclerotic lesions includes lipoprotein-associated phospholipase A2 mass (Lp-PLA2) and activity (Lp-PLA2-A) and YKL-40. Lipid parameters include total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG).^4,15

Biomarker measurements

Blood samples were collected from a total of 171 study sites, whose staff were experienced in collecting biomarker samples. Blood samples were collected within 24 hours of admission and all participants were required to fast before sample collection. The extracted serum and plasma specimens were stored at −80 °C and transported to the core laboratory in Beijing Tiantan Hospital while maintaining the cold chain. The laboratory technicians who performed the measurements, were blinded to the clinical outcomes of the patients. All measurements were performed in accordance with the manufacturer’s recommendations. Enzyme-linked immunosorbent assay kits were used to determine the levels of IL-6, IL-1Ra, YKL-40, MCP-1, and Lp-PLA2 mass (catalog number: PHS600C for IL-6, PDRA00B for IL-1Ra, DC3L10 for YKL-40, eBioscience for MCP-1, and DPLG70 for Lp-PLA2 mass, R&D Systems, Inc, Minneapolis, MN, USA). The automatic enzyme assay system on a Hitachi 7600 analyzer was used to measure Lp-PLA2-A (PLAC test for Lp-PLA2-A, Diazyme Laboratories, Inc., Poway, CA), and hs-CRP was detected on Roche Cobas C501.¹⁴

Clinical outcomes and definition

Patients were assessed for mRS scores through face-to-face interviews at discharge (mRS_discharge) and at 3 months (mRS_3m). The mRS scores at 6 months (mRS_6 m) and 12 months (mRS_12 m) after stroke were collected over the telephone. The mRS scores range from 0 to 6, with 0 to 1 indicating no disability, 2 to 5 indicating increasing disability, and 6 indicating death. Trained research coordinators, who were blinded to the baseline clinical information of the participants, evaluated neurological disability. The primary outcome was worse outcomes at 3 months which was defined as ΔmRS_3m-discharge (mRS_3m-mRS_discharge =ΔmRS_3m-discharge) ≥1. The second outcome was worse outcomes at 6 months (ΔmRS_6m-3m ≥ 1) and 12 months (ΔmRS_12m-6m ≥ 1). Good outcomes at 3 months included neurological invariance (ΔmRS_3m-discharge = 0) and neurological improvement (ΔmRS_3m-discharge < 0).

Statistical analysis

Continuous variables are reported as the median (interquartile range), and categorical variables are presented as numbers (percentages). The distribution of all biomarkers was determined to be nonnormal or not using the Kolmogorov–Smirnov test. Independent t-tests or Wilcoxon rank sum tests were used for continuous variables, and chi-square tests were used for categorical variables.

The association between worse outcomes and quartiles of each biomarker level (quartiles treated as a categorical variable), and per standard deviation (SD) increase in each biomarker (each biomarker level was divided by its SD and then treated as a continuous variable) was examined using the multivariate logistic regression models with confounders (Model 1) and confounders plus all biomarkers except TC (Model 2) simultaneously in the same model to investigate whether blood biomarkers independently predicted the worse outcomes. TC was removed from Model 2 to avoid collinearity of multiple biomarkers, as we found that its variance inflation factors (VIF) > 10. Adjusted odds ratios (ORs) and their corresponding 95% confidence intervals (CIs) were reported for all the multiple logistic-regression models. The models were adjusted for the following potential confounders: demographic factors, traditional or clinical risk factors previously published in the literature, and medications used during the follow-up period. Sensitivity analysis was performed by excluding patients with stroke recurrence and death during 3 months of follow-up to assess the robustness of the results.

All p values were two-tailed, and a significance level of 0.05 was used. No further alpha adjustments were performed for multiple biomarkers or multiple follow-up visits, and all results were exploratory. All statistical analyses were carried out using SAS statistical software, version 9.4 (Cary, NC).

Results

Among 15166 patients enrolled in the CNSR-III, patients with TIA (n = 1009), without all 15 blood biomarkers (n = 7007), and with missing mRS data at discharge (n = 46) and at 3 months (n = 173) were excluded; ultimately, 6931 patients were included in this study (Figure 1).

Figure 1.

Flowchart of the study. CNSR-III: the Third China National Stroke Registry; TIA: transient ischemic attack; mRS: modified Rankin Scale.

The demographic characteristics, including age and sex, were mostly balanced between the included and excluded patients. However, patients included in this study had higher NIHSS scores and mRS scores at admission and were more likely to receive secondary stroke prevention medication during follow-up. Further details on the baseline characteristics of the included and excluded patients are presented in Table S1 in the supplemental data.

Baseline characteristics

The mean age of the included participants was 62.3 ± 11.3 years, and 2174 (31.4%) of them were female. The median (IQR) of mRS and NIHSS scores at admission were 2 (1–3) and 3 (2–6), respectively. A total of 1026 (14.8%) patients experienced worse outcomes at 3 months. Table 1 displays the baseline characteristics of the good and worse outcomes group. Patients with worse outcomes were more likely to be older and female, have higher NIHSS scores at admission, experience longer hospital stays and a greater number of hospital-acquired infections, have more LAA or CE subtype, and experience more stroke recurrence and death. In addition, patients with worse outcomes also had a greater prevalence of stroke/TIA history, hypertension, lipid metabolism disorders, coronary heart disease/myocardial infarction, atrial fibrillation/flutter, heart failure, and peripheral arterial disease. Conversely, those with good outcomes were more likely to be smokers, have high diastolic blood pressure, be prescribed antiplatelet drugs at discharge, and experience SAO stroke. No significant differences were observed in baseline mRS scores at admission, systolic blood pressure, medication with anticoagulant drugs, antihypertensive drugs, hypoglycemia drugs, lipid-lowering drugs at discharge, or history of diabetes mellitus or carotid stenosis.

Table 1.

Baseline demographic and clinical characteristics according to ΔmRS_3m-discharge.

Characteristics	Total	Good outcomes	Worse outcomes	p
N, %	6931	5905 (85.2)	1026 (14.8)
Age, years (mean, SD)	62.3 ± 11.3	61.8 ± 11.2	65.5 ± 11.6	<0.001
Female, N (%)	2174 (31.4)	1820 (30.8)	354 (34.5)	0.019
Smoking, N (%)	2251 (32.5)	1960 (33.2)	291 (28.4)	0.002
Baseline mRS, median (IQR)	2 (1–3)	2 (1–3)	1 (1–3)	0.251
Baseline NIHSS, median (IQR)	3 (2–6)	3 (2–6)	4 (1–6)	0.010
Blood pressure, mmHg, (mean, SD)
SBP	150.9 ± 22.3	150.9 ± 22.3	151.0 ± 22.4	0.606
DBP	87.7 ± 13.3	87.9 ± 13.3	86.6 ± 13.2	0.003
Medical history, N (%)
Stroke/TIA	1684 (24.3)	1400 (23.7)	284 (27.7)	0.006
Hypertension	4356 (62.8)	3668 (62.1)	688 (67.1)	0.003
Diabetes mellitus	1628 (23.5)	1368 (23.2)	260 (25.3)	0.129
Lipid metabolism disorders	563 (8.1)	461 (7.8)	102 (9.9)	0.021
CHD/MI	692 (10.0)	571 (9.7)	121 (11.8)	0.036
Atrial fibrillation/flutter	498 (7.2)	388 (6.6)	110 (10.7)	<0.001
Heart failure	50 (0.7)	35 (0.6)	15 (1.5)	0.002
Peripheral arterial disease	51 (0.7)	37 (0.6)	13 (1.3)	0.025
Carotid stenosis	69 (1.0)	55 (0.9)	14 (1.4)	0.197
Days in hospital, median (IQR)	13.0 (10.0–15.0)	13.0 (10.0–15.0)	13.0 (10.0–16.0)	0.001
Hospital-acquired infections, N (%)	451 (6.5)	348 (5.9)	103 (10.0)	<0.001
Go home at discharge, N (%)	6391 (92.2)	5445 (92.2)	946 (92.2)	0.99
Medication at discharge, N (%)
Antiplatelet drugs	6388 (92.2)	5471 (92.7)	917 (89.4)	<0.001
Anticoagulant drugs	220 (3.2)	180 (3.0)	40 (3.9)	0.152
Antihypertensive drugs	3482 (50.2)	2967 (50.2)	515 (50.2)	0.976
Hypoglycemic drugs	1687 (24.3)	1424 (24.1)	263 (25.6)	0.296
Lipid-lowering drugs	6471 (93.4)	5527 (93.6)	944 (92.0)	0.059
TOAST subtypes, N (%)				<0.001
LAA	1739 (25.1)	1467 (24.8)	272 (26.5)
CE	454 (6.6)	369 (6.2)	85 (8.3)
SAO	1570 (22.7)	1397 (23.7)	173 (16.9)
Other determined caused	83 (1.2)	69 (1.2)	14 (1.4)
Unknown determined caused	3085 (44.5)	2603 (44.1)	482 (47.0)
Stroke recurrence at 3 months, N (%)	420 (6.1)	296 (5.0)	124 (12.1)	<0.001
Death at 3 months, N (%)	85 (1.2)		85 (8.3)
Neurological improvement, N (%)	2369 (34.18)	2369 (40.12)

NIHSS: National Institute of Health stroke scale; mRS: Modified Rankin Scale; SBP: systolic blood pressure; DBP: diastolic blood pressure; CHD: coronary heart disease; MI: myocardial infarction; TOAST: the Trial of Org 10172 in Acute Stroke Treatment; LAA: large-artery atherosclerosis; CE: cardioembolism; SAO: small-vessel occlusion.

Distribution of blood biomarkers in the cohort

Compared with patients with good outcomes, patients with worse outcomes had significantly higher levels of inflammation biomarkers, including WBCs, hs-CRP, IL-6, IL-1Ra (all p < 0.05, Figure 2(a)), YKL-40 (p < 0.001, Figure 2(b)), and D-D (p < 0.001, Figure 2(d)). Conversely, patients with worse outcomes had significantly lower levels of TG (p = 0.019, Figure 2(c), Table S2) and eGFR (p < 0.001, Figure 2(d)). Notably, the levels of WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 were significantly higher in CE stroke than in other stroke subtypes (Table S3).

Figure 2.

Distribution of 15 biomarkers in the good outcomes group and worse outcomes group at 3 months. WBCs: white blood cells; hs-CRP: high-sensitivity c-reactive protein; IL-6: interleukin-6; IL-1Ra: interleukin-1 receptor antagonist; MCP-1: monocyte chemoattractant protein-1; Lp-PLA2 mass: lipoprotein-associated phospholipase A2 mass; Lp-PLA2-A: lipoprotein-associated phospholipase A2 activity; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; TG: triglyceride; eGFR: estimated glomerular filtration rate; FPG: fasting plasma glucose; D-D: D-dimer.

Among the 1739 patients with LAA stroke, 272 (15.6%) patients experienced worse outcomes at 3 months. Compared to the LAA stroke patients with good outcomes, those with worse outcomes had significantly higher median (interquartile) levels of inflammatory biomarkers, including hs-CRP (2.6 [1.1–7.8] mg/L versus 2.1 [1.0–5.5] mg/L, p = 0.003), IL-6 (3.7 [2.2–8.2] pg/ml versus 3.1 [1.9–5.7] pg/ml, p = 0.001), IL-1Ra (394.9 [278.1–586.9] ng/L versus 353.9 [262.6–495.3] ng/L, p = 0.003), and YKL-40 (78.9 [44.6–140.9] ug/ml versus 65.3 [38.5–113.0] ug/ml, p < 0.001). Comparable findings were also observed in patients with CE, SAO, and stroke of unknown cause, as presented in Table 2.

Table 2.

Levels of blood biomarkers between the worse outcomes group and good outcomes group according to TOAST subtypes.

Blood biomarkers	LAA		CE		SAO		Unknown
Blood biomarkers	Worse outcomes	Good outcomes	Worse outcomes	Good outcomes	Worse outcomes	Good outcomes	Worse outcomes	Good Outcomes
N, %	272 (15.6)	1467 (84.4)	85 (18.7)	369 (81.3)	173 (11.0)	1397 (89.0)	482 (15.6)	2603 (84.4)
WBCs, *10⁹/L	7.1 (5.8–8.8)	7.2 (5.9–8.7)	6.7 (5.8–8.4)	6.8 (5.6–8.4)	6.8 (5.6–8.3)	6.6 (5.5–7.8)	7.2 (6.0–8.6)	6.8 (5.6–8.3)
hs-CRP, mg/L	2.6 (1.1–7.8)	2.1 (1.0–5.5)	4.0 (1.4–12.5)	2.8 (1.0–8.2)	1.6 (0.9–3.7)	1.2 (0.7–3.0)	2.3 (0.9–6.8)	1.7 (0.8–4.2)
IL-6, pg/ml	3.7 (2.2–8.2)	3.1 (1.9–5.7)	6.0 (3.0–12.1)	4.3 (2.1–7.7)	2.5 (1.6–4.9)	2.2 (1.4–3.7)	2.9 (1.7–5.6)	2.3 (1.5–4.3)
IL-1Ra, ng/L	394.9 (278.1–586.9)	353.9 (262.6–495.3)	409.7 (331.3–596.2)	348.4 (255.2–529.4)	331.0 (251.4–508.3)	315.2 (239.7–448.3)	349.6 (263.8–514.5)	330.8 (246.2–472.8)
YKL-40, ug/ml	78.9 (44.6–140.9	65.3 (38.5–113.0)	107.7 (55.2–199.8)	92.2 (53.5–169.6)	64.6 (39.7–119.9)	57.3 (35.6–99.4)	77.6 (46.5–145.1)	64.3 (37.0–122.9)
MCP-1, pg/ml	258.3 (196.1–357.9)	255.7 (197.2–337.3)	260.9 (208.3–322.5)	259.9 (200.1–339.6)	270.4 (206.4–338.7)	250.8 (195.2–322.3)	272.3 (208.7–339.3)	263.5 (207.2–337.6)
eGFR, ml/min/17.3 m²	90.8 (73.9–100.7)	93.8 (83.0–102.0)	82.0 (72.9–92.1)	85.2 (69.1–94.4)	90.1 (79.4–99.8)	94.9 (85.2–102.8)	91.2 (79.0–100.5)	94.3 (83.3–102.9)
TC, mmol/L	4.1 (3.4–4.8)	4.0 (3.3–4.8)	3.7 (3.1–4.3)	3.9 (3.2–4.7)	4.0 (3.3–4.7)	4.0 (3.3–4.7)	3.9 (3.2–4.8	3.9 (3.3–4.7)
LDL-C mmol/L	2.4 (1.8–3.1)	2.4 (1.8–3.1)	2.1 (1.7–2.9)	2.4 (1.7–3.1)	2.3 (1.7–2.9)	2.3 (1.7–3.0)	2.2 (1.7–3.0)	2.3 (1.7–3.0)
HDL-C, mmol/L	0.9 (0.8–1.1)	0.9 (0.8–1.1)	1.0 (0.8–1.2)	1.0 (0.8–1.2)	0.9 (0.8–1.1)	0.9 (0.8–1.1)	1.0 (0.8–1.1)	1.0 (0.8–1.1)
TG, mmol/L	1.4 (1.0–1.8)	1.3 (1.0–1.8)	1.1 (0.9–1.5)	1.2 (0.9–1.7)	1.3 (1.0–1.8)	1.4 (1.1–1.9)	1.3 (1.0–1.9)	1.4 (1.0–1.9)
Lp-PLA2_MASS, nmol/min/ml	179.9 (134.6–222.5)	175.6 (125.6–231.7)	171.1 (136.1–210.3)	173.9 (125.2–227.6)	178.4 (122.2–224.3)	172.0 (124.2–219.0)	178.4 (131.6–230.6)	175.0 (127.6–225.0)
Lp-PLA2-A, ng/ml	163.3 (133.6–196.5)	165.7 (126.4–198.6)	160.9 (129.8–185.8)	160.2 (125.9–194.9)	161.1 (129.8–191.2)	160.6 (127.5–191.0)	159.5 (129.3–193.8)	162.6 (126.4–193.0)
FPG, mmol/L	5.7 (4.9–7.7)	5.7 (5.0–7.1)	5.3 (4.8–6.8)	5.4 (4.8–6.6)	5.3 (4.8–6.7)	5.5 (4.9–6.9)	5.6 (4.9–7.1)	5.5 (4.9–6.9)
D-D, ug/ml	1.4 (0.7–2.4)	1.1 (0.6–2.0)	1.9 (0.8–2.7)	1.4 (0.7–2.5)	1.1 (0.6–1.8)	1.0 (0.5–1.9)	1.2 (0.6–2.3)	1.1 (0.6–2.0)

TOAST: the Trial of Org 10172 in Acute Stroke Treatment; LAA: large-artery atherosclerosis; CE: cardioembolism; SAO: small-vessel occlusion; WBCs: white blood cells; hs-CRP: high-sensitivity c-reactive protein; IL-6: interleukin-6; IL-1Ra: interleukin-1 receptor antagonist; MCP-1: monocyte chemoattractant protein-1; eGFR: estimated glomerular filtration rate; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; TG: triglyceride; Lp-PLA2 mass: lipoprotein-associated phospholipase A2 mass; Lp-PLA2-A: lipoprotein-associated phospholipase A2 activity; FPG: fasting plasma glucose; D-D: D-dimer.

The relationship between 15 blood biomarkers and worse outcomes at 3 months

In Model 1 after adjusting for confounders, the top level (Q4) group of WBCs (adjusted OR [aOR], 1.37 [95% CI, 1.12–1.66]; p for trend < 0.001), hs-CRP (aOR, 1.37 [95% CI, 1.12–1.67]; p for trend = 0.001), IL-6 (aOR, 1.43 [95% CI, 1.16–1.76]; p for trend = 0.001), IL-1Ra (aOR, 1.46 [95% CI, 1.20–1.78]; p for trend <0.001) and YKL-40 (aOR, 1.31 [95% CI, 1.06–1.63]; p for trend = 0.035) were associated with a higher risk of worse outcomes (Table 3). When blood biomarkers were treated as continuous variables, per SD levels increase of hs-CRP (aOR, 1.12 [95% CI, 1.06–1.19]), IL-6 (aOR, 1.17 [95% CI, 1.10–1.25]), IL-1Ra (aOR, 1.07 [95% CI, 1.01–1.14]) and YKL-40 (aOR, 1.08 [95% CI, 1.01–1.16]) was associated with an increased risk of worse outcomes at 3 months (Table 3).

Table 3.

Relationship of blood biomarkers with worse outcomes at 3 months.

Blood biomarkers	Events, N (%)	Model 1^a			Model 2^b
Blood biomarkers	Events, N (%)	Adjusted OR (95% CI)	p	p for trend	Adjusted OR (95% CI)	p	p for trend
All	1026 (14.80)
WBCs				<0.001			0.021
Q1	230 (13.35)	Ref.	Ref.		Ref.	Ref.
Q2	242 (13.96)	1.10 (0.90–1.34)	0.344		1.08 (0.89–1.32)	0.437
Q3	262 (15.04)	1.23 (1.01–1.49)	0.040		1.18 (0.97–1.44)	0.106
Q4	292 (16.85)	1.37 (1.12–1.66)	0.002		1.26 (1.02–1.54)	0.029
Per SD increase		0.98 (0.89–1.08)	0.697		0.97 (0.86–1.10)	0.646
hs-CRP				0.001			0.254
Q1	205 (11.90)	Ref.	Ref.		Ref.	Ref.
Q2	226 (13.02)	1.07 (0.87–1.31)	0.518		1.02 (0.83–1.25)	0.870
Q3	268 (15.41)	1.23 (1.01–1.50)	0.041		1.12 (0.90–1.39)	0.302
Q4	327 (18.87)	1.37 (1.12–1.67)	0.002		1.12 (0.89–1.42)	0.333
Per SD increase		1.12 (1.06–1.19)	<0.001		1.08 (1.03–1.15)	0.005
IL-6				0.001			0.301
Q1	194 (11.20)	Ref.	Ref.		Ref.	Ref.
Q2	236 (13.63)	1.15 (0.93–1.41)	0.196		1.08 (0.88–1.34)	0.460
Q3	256 (14.76)	1.16 (0.94–1.43)	0.158		1.04 (0.83–1.29)	0.758
Q4	340 (19.62)	1.43 (1.16–1.76)	0.001		1.17 (0.91–1.49)	0.216
Per SD increase		1.17 (1.10–1.25)	<0.001		1.11 (1.03–1.20)	0.005
IL-1Ra				<0.001			0.022
Q1	210 (12.12)	Ref.	Ref.		Ref.	Ref.
Q2	241 (13.91)	1.13 (0.93–1.39)	0.227		1.10 (0.89–1.34)	0.386
Q3	253 (14.60)	1.16 (0.95–1.41)	0.160		1.07 (0.87–1.32)	0.509
Q4	322 (18.58)	1.46 (1.20–1.78)	<0.001		1.30 (1.05–1.61)	0.015
Per SD increase		1.07 (1.01–1.14)	0.023		1.04 (0.98–1.11)	0.215
YKL-40				0.035			0.226
Q1	185 (10.68)	Ref.	Ref.		Ref.	Ref.
Q2	251 (14.48)	1.25 (1.01–1.53)	0.037		1.23 (1.00–1.52)	0.050
Q3	266 (15.36)	1.18 (0.95–1.46)	0.130		1.14 (0.92–1.41)	0.250
Q4	324 (18.70)	1.31 (1.06–1.63)	0.013		1.21 (0.96–1.51)	0.101
Per SD increase		1.08 (1.01–1.16)	0.026		1.04 (0.97–1.13)	0.251
MCP-1				0.722			0.973
Q1	243 (14.03)	Ref.	Ref.		Ref.	Ref.
Q2	246 (14.20)	0.97 (0.80–1.18)	0.784		0.97 (0.80–1.18)	0.783
Q3	269 (15.53)	1.06 (0.87–1.28)	0.561		1.07 (0.88–1.30)	0.507
Q4	268 (15.46)	1.01 (0.83–1.22)	0.927		0.97 (0.79–1.18)	0.735
Per SD increase		1.01 (0.95–1.07)	0.777		1.00 (0.93–1.06)	0.893
eGFR				0.930			0.820
Q1	325 (18.81)	Ref.	Ref.		Ref.	Ref.
Q2	263 (15.13)	0.85 (0.71–1.02)	0.089		0.88 (0.73–1.06)	0.162
Q3	241 (13.92)	0.96 (0.78–1.17)	0.662		1.00 (0.81–1.22)	0.960
Q4	197 (11.36)	0.99 (0.78–1.26)	0.950		1.02 (0.80–1.31)	0.872
Per SD increase		0.96 (0.88–1.03)	0.242		0.97 (0.90–1.05)	0.495
TC				0.676			…
Q1	276 (16.10)	Ref.	Ref.		Ref.	Ref.
Q2	242 (13.91)	0.85 (0.71–1.03)	0.104		…	…
Q3	247 (14.32)	0.91 (0.75–1.10)	0.313		…	…
Q4	261 (14.90)	0.94 (0.78–1.14)	0.530		…	…
Per SD increase		0.98 (0.92–1.05)	0.581		…	…
LDL-C				0.500			0.561
Q1	258 (15.11)	Ref.	Ref.		Ref.	Ref.
Q2	263 (15.01)	0.99 (0.82–1.20)	0.923		0.98 (0.81–1.20)	0.851
Q3	253 (14.59)	0.96 (0.80–1.17)	0.707		0.96 (0.78–1.19)	0.719
Q4	252 (14.51)	0.94 (0.78–1.14)	0.536		0.94 (0.74–1.18)	0.573
Per SD increase		0.98 (0.92–1.05)	0.648		0.98 (0.91–1.07)	0.663
HDL-C				0.727			0.392
Q1	243 (14.79)	Ref.			Ref.	Ref.
Q2	236 (13.59)	0.91 (0.74–1.10)	0.321		0.93 (0.76–1.13)	0.459
Q3	263 (14.79)	0.98 (0.81–1.19)	0.836		1.02 (0.83–1.24)	0.880
Q4	284 (16.02)	1.01 (0.83–1.23)	0.946		1.07 (0.86–1.33)	0.533
Per SD increase		0.98 (0.92–1.06)	0.653		1.00 (0.93–1.08)	0.944
TG				0.463			0.597
Q1	271 (15.97)	Ref.	Ref.		Ref.	Ref.
Q2	282 (15.97)	1.03 (0.86–1.24)	0.746		1.05 (0.87–1.27)	0.633
Q3	230 (13.28)	0.98 (0.80–1.19)	0.177		0.91 (0.74–1.12)	0.349
Q4	243 (14.00)	0.87 (0.72–1.06)	0.834		0.99 (0.79–1.23)	0.916
Q1		0.97 (0.90–1.05)	0.432		0.96 (0.89–1.04)	0.332
Lp-PLA2-MASS				0.491			0.598
Q1	241 (13.92)	Ref.	Ref.		Ref.	Ref.
Q2	256 (14.76)	1.06 (0.87–1.29)	0.559		0.98 (0.79–1.21)	0.835
Q3	273 (15.76)	1.16 (0.96–1.41)	0.123		1.11 (0.88–1.41)	0.377
Q4	256 (14.76)	1.04 (0.86–1.27)	0.675		1.04 (0.79–1.36)	0.802
Per SD increase		1.04 (0.98–1.12)	0.205		1.06 (0.96–1.16)	0.244
Lp-PLA2-A				0.736			0.990
Q1	227 (13.14)	Ref.	Ref.		Ref.	Ref.
Q2	307 (17.67)	1.43 (1.18–1.72)	0.000		1.40 (1.12–1.72）	0.003
Q3	234 (13.50)	1.04 (0.85–1.27)	0.705		1.00 (0.78–1.29）	0.975
Q4	258 (14.89)	1.16 (0.95–1.41)	0.137		1.14 (0.86–1.50）	0.388
Per SD increase		1.04 (0.97–1.11)	0.255		1.06 (0.96–1.18）	0.261
FPG				0.306			0.112
Q1	274 (16.14)	Ref.	Ref.		Ref.	Ref.
Q2	234 (13.37)	0.77 (0.64–0.94)	0.008		0.78 (0.65–0.95)	0.014
Q3	239 (13.67)	0.75 (0.62–0.92)	0.004		0.73 (0.60–0.89)	0.002
Q4	279 (16.08)	0.95 (0.76–1.18)	0.623		0.89 (0.71–1.12)	0.320
Per SD increase		1.03 (0.95–1.12)	0.443		1.01 (0.93–1.09)	0.859
D-D				0.191			0.516
Q1	217 (12.96)	Ref.	Ref.		Ref.	Ref.
Q2	220 (13.70)	0.99 (0.81–1.22)	0.958		0.98 (0.80–1.20)	0.823
Q3	262 (15.14)	1.04 (0.86–1.27)	0.679		1.01 (0.82–1.23)	0.946
Q4	327 (17.02)	1.12 (0.93–1.36)	0.240		1.06 (0.87–1.29)	0.584
Per SD increase		1.01 (0.95–1.08)	0.763		0.99 (0.93–1.06)	0.866

Model 1: adjusted for age, sex, smoking, NIHSS scores on admission, days in hospital, medication at discharge (antiplatelet drugs, anticoagulant drugs, lipid-lowering drugs), DBP, history of stroke/TIA, hypertension, diabetes mellitus, lipid metabolism disorders, coronary heart disease/myocardial infarction, atrial fibrillation/flutter, heart failure, peripheral arterial disease, carotid stenosis, and TOAST subtypes.

Model 2: adjusted for the confounding factors in Model 1 and all biomarkers except TC.

In Model 2, after removal of TC, there was no multicollinearity for all 14 biomarkers (all VIFs < 3, Table S4). Compared with Q1 level group, the highest quartiles (Q4) group of WBCs (aOR, 1.26 [95% CI, 1.02–1.54]), and IL-1Ra (aOR, 1.30 [95% CI, 1.05–1.61]) were associated with a higher risk of worse outcomes (Table 3). When blood biomarkers were treated as continuous variables, per-SD-level increases of hs-CRP (aOR, 1.08 [95% CI, 1.03–1.15]), IL-6 (aOR, 1.11 [95% CI, 1.03–1.20]) were associated with an increased risk of worse outcomes at 3 months (Table 3).

In addition, increased multiple inflammatory biomarkers including WBCs (aOR, 1.26 [95% CI, 1.02–1.56], p for trend = 0.022), hs-CRP (aOR, 1.25 [95% CI, 1.01–1.55], p for trend = 0.024) and IL-1Ra (aOR, 1.41 [95% CI, 1.14–1.74], p for trend = 0.002), IL-6 (aOR, 1.09 [95% CI, 1.01–1.17]), per SD increase were associated with an increased risk of worse outcomes in the sensitivity analysis (Table S5).

The relationship between 15 blood biomarkers and neurological improvement at 3 months

Among 5905 patients with good outcomes, 2369 (40.12%) patients achieved neurological improvement at 3 months. In the multivariate regression analysis after adjusting for other confounders, the highest quartile levels of WBCs (aOR, 0.84 [95% CI, 0.72–0.98]), hs-CRP (aOR, 0.81 [95% CI, 0.69–0.94]), YLK-40 (aOR, 0.83 [95% CI, 0.72–0.97]), and MCP-1 (aOR, 0.78 [95% CI, 0.67–0.91]) were associated with a decreased probability of neurological improvement at 3 months. When analyzed in the continuous model, similar results were found for WBCs, hs-CRP, IL-6, YLK-40, and MCP-1 (Table 4).

Table 4.

Relationship of blood biomarkers with neurological improvement at 3 months.

Blood biomarkers	Events, N (%)	Neurological improvement at 3 months (ΔmRS_3m-discharge <0)
		Unadjusted		Adjusted
		Crude OR (95% CI)	p	Adjusted OR (95% CI)	p	p for trend
All	2369 (40.12)
WBCs						0.060
Q1	608 (41.39)	Ref.		Ref.
Q2	545 (38.06)	0.87 (0.75–1.01)	0.067	0.86 (0.74–1.00)	0.047
Q3	623 (40.80)	0.98 (0.84–1.13)	0.743	0.92 (0.79–1.06)	0.247
Q4	593 (40.15)	0.95 (0.82–1.10)	0.494	0.84 (0.72–0.98)	0.024
Per SD increase		0.81 (0.31–2.08)	0.656	0.33 (0.12–0.88)	0.027
hs-CRP						0.020
Q1	599 (40.92)	Ref.		Ref.
Q2	567 (38.10)	0.89 (0.77–1.03)	0.118	0.87 (0.75–1.01)	0.064
Q3	608 (41.19)	1.01 (0.87–1.17)	0.879	0.93 (0.80–1.08)	0.353
Q4	595 (40.28)	0.97 (0.84–1.13)	0.728	0.81 (0.69–0.94)	0.006
Per SD increase		1.03 (0.97–1.08)	0.357	0.99 (0.93–1.04)	0.617
IL-6						0.030
Q1	587 (39.80)	Ref.		Ref.
Q2	591 (40.01)	1.01 (0.87–1.17)	0.904	0.95 (0.82–1.11)	0.510
Q3	576 (39.02)	0.97 (0.84–1.12)	0.668	0.86 (0.74–1.00)	0.052
Q4	615 (41.64)	1.08 (0.93–1.25)	0.309	0.86 (0.74–1.01)	0.063
Per SD increase		1.02 (0.97–1.07)	0.493	0.95 (0.90–1.00)	0.046
IL-1Ra						0.418
Q1	597 (40.45)	Ref.		Ref.
Q2	594 (38.52)	0.92 (0.80–1.07)	0.279	0.91 (0.78–1.05)	0.192
Q3	570 (40.43)	1.00 (0.86–1.16)	0.991	0.96 (0.82–1.12)	0.585
Q4	608 (41.16)	1.03 (0.89–1.19)	0.692	0.92 (0.79–1.07)	0.270
Per SD increase		1.02 (0.97–1.07)	0.460	1.00 (0.95–1.05)	0.964
YKL-40						0.025
Q1	617 (41.77)	Ref.		Ref.
Q2	578 (39.19)	0.90 (0.78–1.04)	0.152	0.89 (0.76–1.03)	0.118
Q3	588 (39.84)	0.92 (0.80–1.07)	0.284	0.88 (0.76–1.03)	0.102
Q4	586 (39.68)	0.92 (0.79–1.06)	0.246	0.83 (0.72–0.97)	0.019
Per SD increase		0.98 (0.93–1.03)	0.383	0.94 (0.89–0.99)	0.025
MCP-1						0.002
Q1	659 (44.65)	Ref.		Ref.
Q2	576 (39.02)	0.79 (0.69–0.92)	0.002	0.81 (0.70–0.94)	0.006
Q3	574 (38.89)	0.79 (0.68–0.91)	0.002	0.82 (0.71–0.95)	0.010
Q4	560 (37.91)	0.76 (0.65–0.88)	<0.001	0.78 (0.67–0.91)	0.001
Per SD increase		0.92 (0.86–0.99)	0.019	0.91 (0.85–0.98)	0.017
eGFR						0.109
Q1	584 (39.54)	Ref.		Ref.
Q2	591 (40.07)	1.02 (0.88–1.19)	0.769	1.07 (0.92–1.24)	0.389
Q3	578 (39.19)	0.99 (0.85–1.14)	0.844	1.02 (0.88–1.19)	0.778
Q4	616 (41.68)	1.09 (0.94–1.27)	0.237	1.16 (0.99–1.34)	0.061
Per SD increase		1.05 (1.00–1.11)	0.072	1.07 (1.01–1.13)	0.019
TC						0.074
Q1	619 (42.34)	Ref.		Ref.
Q2	579 (39.28)	0.88 (0.76–1.02)	0.092	0.90 (0.77–1.04)	0.155
Q3	582 (39.38)	0.89 (0.76–1.03)	0.102	0.90 (0.77–1.05)	0.173
Q4	589 (39.50)	0.89 (0.77–1.03)	0.117	0.86 (0.74–1.00)	0.057
Per SD increase		0.96 (0.91–1.01)	0.153	0.95 (0.90–1.00)	0.049
LDL-C						0.237
Q1	609 (42.00)	Ref.		Ref.
Q2	569 (38.21)	0.85 (0.74–0.99)	0.036	0.86 (0.74–1.00)	0.049
Q3	589 (39.77)	0.91 (0.79–1.06)	0.220	0.90 (0.77–1.04)	0.158
Q4	602 (40.54)	0.94 (0.81–1.09)	0.421	0.90 (0.77–1.04)	0.150
Per SD increase		0.98 (0.93–1.03)	0.487	0.96 (0.91–1.01)	0.119
HDL-C						0.307
Q1	582 (41.57)	Ref.		Ref.
Q2	593 (39.51)	0.92 (0.79–1.07)	0.258	0.91 (0.78–1.06)	0.217
Q3	603 (39.80)	0.93 (0.80–1.08)	0.331	0.93 (0.80–1.08)	0.327
Q4	591 (39.69)	0.93 (0.80–1.07)	0.304	0.91 (0.78–1.06)	0.238
Per SD increase		0.96 (0.91–1.01)	0.142	0.95 (0.90–1.01)	0.076
TG						0.652
Q1	582 (40.81)	Ref.		Ref.
Q2	624 (40.92)	1.00 (0.87–1.16)	0.954	1.00 (0.86–1.16)	0.972
Q3	584 (39.97)	0.97 (0.83–1.12)	0.645	0.99 (0.85–1.15)	0.888
Q4	579 (38.78)	0.92 (0.79–1.07)	0.262	0.97 (0.83–1.13)	0.662
Per SD increase		0.98 (0.93–1.03)	0.456	1.00 (0.95–1.05)	0.906
Lp-PLA2-MASS						0.301
Q1	575 (38.96)	Ref.		Ref.
Q2	585 (39.63)	1.03 (0.89–1.19)	0.019	1.04 (0.89–1.21)	0.637
Q3	609 (41.26)	1.10 (0.95–1.28)	0.202	1.10 (0.94–1.28)	0.231
Q4	600 (40.62)	1.07 (0.93–1.24)	0.356	1.07 (0.92–1.24)	0.395
Per SD increase		1.06 (1.00–1.11)	0.034	1.06 (1.00–1.12)	0.039
Lp-PLA2-A						0.759
Q1	588 (39.86)	Ref.		Ref.
Q2	592 (40.14)	1.01 (0.87–1.17)	0.881	1.06 (0.91–1.23)	0.487
Q3	605 (40.99)	1.05 (0.91–1.21)	0.533	1.05 (0.90–1.22)	0.554
Q4	584 (39.49)	0.98 (0.85–1.14)	0.834	0.98 (0.84–1.14)	0.772
Per SD increase		1.01 (0.96–1.06)	0.792	1.01 (0.95–1.06)	0.850
FPG						0.399
Q1	597 (41.92)	Ref.		Ref.
Q2	576 (37.99)	0.85 (0.73–0.98)	0.030	0.85 (0.73–0.98)	0.030
Q3	583 (39.31)	0.90 (0.77–1.04)	0.152	0.86 (0.74–1.00)	0.055
Q4	613 (41.36)	0.98 (0.84–1.13)	0.759	0.93 (0.80–1.08)	0.315
Per SD increase		1.02 (0.97–1.07)	0.524	1.00 (0.95–1.05)	0.895
D-D						0.064
Q1	583 (40.01)	Ref.		Ref.
Q2	574 (41.41)	1.06 (0.91–1.23)	0.445	1.03 (0.88–1.20)	0.716
Q3	581 (39.58)	0.98 (0.85–1.14)	0.810	0.92 (0.79–1.08)	0.309
Q4	631 (39.59)	0.98 (0.85–1.13)	0.810	0.89 (0.77–1.04)	0.136
Per SD increase		1.03 (0.98–1.08)	0.277	1.00 (0.95–1.05)	0.903

Adjusted for age, sex, smoking, NIHSS scores on admission, days in hospital, days after stroke onset at discharge, medication at discharge (antiplatelet drugs, anticoagulant drugs, lipid-lowering drugs), DBP, history of stroke/TIA, hypertension, diabetes mellitus, lipid metabolism disorders, coronary heart disease/myocardial infarction, atrial fibrillation/flutter, heart failure, peripheral arterial disease, carotid stenosis, and TOAST subtypes.

The relationship between 15 blood biomarkers and worse outcomes at 6 months

In the multivariable logistic regression after adjusting for other confounders, compared with the bottom level, the risk of worse outcomes was significantly higher for patients with the highest level of WBCs (aOR, 1.34 [95% CI, 1.11–1.63]), hs-CRP (aOR,1.48 [95% CI, 1.21–1.81]), IL-6 (aOR, 1.29 [95% CI, 1.06–1.59]), IL-1Ra (aOR, 1.41 [95% CI, 1.15–1.72]), and YKL-40 (aOR, 1.33 [95% CI, 1.07–1.65]) (all p for trend <0.05). When blood biomarkers were used as continuous variables, hs-CRP, IL-6, and YKL-40 were strongly positively associated with the risk of worse outcomes at 6 months (Table S6 in the supplemental data).

The relationship between 15 blood biomarkers and worse outcomes at 12 months

Compared with the bottom level, the risk of worse outcomes was significantly higher for patients with the highest levels of WBCs (aOR, 1.29 [95% CI, 1.07–1.56]), hs-CRP (aOR, 1.59 [95% CI, 1.28–1.89]), IL-6 (aOR, 1.49 [95% CI, 1.22–1.83]), IL-1Ra (aOR, 1.42 [95% CI, 1.17–1.72]) and YKL-40 (aOR, 1.33 [95% CI, 1.09–1.63]) (all p for trend < 0.05). When blood biomarkers were used continuous variables, the levels of hs-CRP, IL-6 and YKL-40 were strongly positively associated with the risk of worse outcomes at 12 months (Table S7 in the supplement data).

Discussion

In this cohort study comprising 6931 patients with acute ischemic stroke, the aim was to investigate the potential predictive value of multiple biomarkers for disability changes in the subacute and chronic phases of stroke. Our findings reveal that increased levels of WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 were significantly linked to worse outcomes after stroke. WBCs, hs-CRP, IL-6 and IL-1Ra were also associated with worse outcomes at 3 months when simultaneously adding multiple biomarkers to the same model with traditional risk factors, and similar results were observed in sensitivity analysis.

The distribution of TOAST subtypes in this study was consistent with previous registry reports, revealing a significant proportion of ischemic stroke classified as undetermined cause.¹⁶ This may indicate that strict adherence to TOAST criteria could lead to an increased number of strokes categorized as undetermined cause.¹⁷ Neuroinflammation is known to exacerbate injury and cell death in all TOAST subtypes, with inflammatory processes appearing to be amplified in cardiogenic embolism.¹⁸ Our analysis indicates that the levels of WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 in cardiogenic embolism stroke were significantly elevated in comparison to other subtypes and were positively associated with worse outcomes. A better understanding of the inflammatory response of cerebral tissue to ischemic stroke is necessary to identify potential treatment strategies. Further research is needed to validate our findings and investigate potential therapeutic interventions.

Numerous prior studies have demonstrated that elevated levels of neuroinflammatory biomarkers are associated with adverse functional outcomes both in the long- and short-term after ischemic stroke.^19,20 These relationships have also been observed between infection risk, adverse outcomes, and plasma levels of hs-CRP and WBCs.^11,21,22 In addition, prior studies have indicated that the IL-6 concentration in patients with acute ischemic stroke is a predictor of poststroke infection and is related to brain infarct volume and poor neurological outcomes in the early or subacute phase of stroke.^23
–25 In line with previous research, we found that elevated levels of WBCs, hs-CRP, and IL-6 were independently associated with worse outcomes in this study.

This study contributes a unique finding regarding the relationship between plasma YKL-40 levels and worse outcomes as well as neurological improvement after ischemic stroke. YKL-40 is a protein produced by local inflammatory cells in inflamed tissues, such as apo lipophilic macrophages in the vascular wall, and reflects the atherosclerotic inflammation of the local vascular bed, which may contribute to the occurrence of vascular events.^26
–28 Prior research has found a close relationship between YKL-40 and the early and late phases of atherosclerosis development.²⁸ Additionally, elevated plasma YKL-40 levels have been associated with a higher risk of ischemic stroke in the general population and have been correlated with larger infarct volume, higher NIHSS scores, and poorer functional outcomes in ischemic stroke patients.^29
–31 These results suggest that YKL-40 is mainly released from astrocytes in the brain parenchyma in response to local neuroinflammation and reflects neuroinflammation or injury in acute ischemic stroke patients. The findings from this study provide additional support for this hypothesis.

IL-1 receptor antagonist (IL-1Ra) is an endogenous cytokine that has immunomodulatory effects by inhibiting the immune response.³² Previous studies have reported that the level of plasma IL-1Ra increases following acute stroke, and that elevated levels are independently associated with the risk of post-stroke infection.³³ In addition, therapeutic injection of IL-1Ra has been shown to be neuroprotective in ischemic stroke by modulating the inflammatory response in the tissue.³⁴ Although the role of IL-1Ra in atherosclerosis is not yet fully understood, it is possible that elevated IL-1Ra levels may be atheroprotective but insufficient to counteract the detrimental effects of IL-1 activity, or IL-1Ra itself may induce atherosclerosis. Our study found a positive correlation between elevated IL-1Ra levels and worse outcomes, and further studies are needed to explore the hypothesis that the binding of IL-1Ra and the IL-1 receptor does not trigger downstream signal transduction.

To our knowledge, this study presents the first prospective multicenter cohort study to evaluate the effect of multiple blood biomarkers on ΔmRS in the context of acute ischemic stroke. The mRS is a comprehensive measure of functional outcome that accounts for societal and medical factors and is a meaningful outcome measure in clinical trials.³⁵ At present, when trials employ ordinal analysis of the 3-month mRS to compare treatment and control groups, enrolling patients with pre-existing disability poses practical challenges in adjusting for varying levels of premorbid disability. To overcome this challenge, using a comparative measure such as ΔmRS can help mitigate this issue.³⁶ The measurement of mRS changes between discharge and three months, and even longer periods, is recommended for stroke recovery studies.² When discussing changes in NIHSS scores in the acute phase of stroke, it is often considered a “pure” metric of neurological impairments because it focuses specifically on the severity of neurological deficits rather than on overall functional disability or dependence. Nonetheless, an increase in mRS score may not indicate a linear increase in disability burden or prognosis. For instance, progression from mRS 2 to 3 does not carry the same weight as progression from 3 to 4 or from 4 to 5, and higher levels of disability such as mRS 3 or 4 can have a significant impact on the patient's ability to perform daily activities.^36,37 Therefore, this innovative method provides a more comprehensive assessment of stroke outcomes and should be considered for future studies.

Notwithstanding the strengths of our study, some limitations merit discussion. First, our study provided baseline measurements of multiple blood biomarkers but lacked analysis of their dynamic changes during follow-up. Second, the severity of ischemic stroke is a critical determinant of stroke outcomes,³⁸ and in our study, the median (IQR) NIHSS score at admission was 3 (2-6), which may have restricted the generalizability of our findings to patients with more severe ischemic stroke. Third, 30 of 201 centers in CNSR-III did not participate in the biomarker sub-study, potentially leading to selection bias. Fourth, brain volume is an important determinant of functional outcomes after acute stroke.³⁹ However, only routine clinical brain MRI sequences not for calculating brain volume including T1-weighted, T2-weighted, fluid-attenuated inversion recovery (FLAIR), and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) were collected in CNSR-III,⁷ and brain volume could not be measured accurately by these 2 D MRI sequences, but infarct volume could be measured by DWI.⁴⁰ Nevertheless, a previous study has demonstrated that infarction volume is an independent predictor of function outcomes at 3 months after acute ischemic stroke.⁴¹ We tried to use infraction volume as a surrogate of brain volume. We performed mediation analyses to assess whether the relationship between infarction volume and worse outcomes was mediated by these biomarkers. The results showed that there was no evidence of the mediation effect of these biomarkers on the association between infarction volume and worse outcomes (Table S7). Fifth, multiple biomarkers at baseline and functional outcomes at different periods were tested in the study, which may have resulted in false positives. However, our study was only an exploratory study. Last, our study only enrolled Chinese patients, and thus, the generalizability of our findings to patients of other races and ethnicities may be limited.

In summary, our study demonstrated significant associations between elevated levels of WBCs, hs-CRP, IL-6, IL-1Ra, and YKL-40 with increased risk of worse outcomes in acute ischemic stroke patients. These findings suggest that targeting the inflammatory response, possibly through novel anti-inflammatory therapies, may hold promise for improving neurological outcomes in stroke patients. Such interventions could be of great benefit as long-term strategies to manage the detrimental impact of these immunomodulators in acute ischemic stroke.

Supplemental Material

sj-pdf-1-jcb-10.1177_0271678X231214831 - Supplemental material for Associations between admission levels of multiple biomarkers and subsequent worse outcomes in acute ischemic stroke patients

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231214831 for Associations between admission levels of multiple biomarkers and subsequent worse outcomes in acute ischemic stroke patients by Wei-Li Jia, Ying-Yu Jiang, Yong Jiang, Xia Meng, Hao Li, Xing-Quan Zhao, Yi-Long Wang, Yong-Jun Wang, Hong-Qiu Gu and Zi-Xiao Li in Journal of Cerebral Blood Flow & Metabolism

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants from the Beijing Natural Science Foundation (Z200016), Beijing Hospitals Authority (QML20210501), the National Natural Science Foundation of China (92046016), the Ministry of Science and Technology of the People’s Republic of China (National Key R&D Programme of China, 2017YFC1310901).

Acknowledgements

We appreciate the staff and participants of the CNSR-III studies for their outstanding contributions, and the support of a public service platform for artificial intelligence screening and auxiliary diagnosis for the medical and health industry, Ministry of Industry and Information Technology of the People’s Republic of China (2020-0103-3-1).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Authors’ contributions

Wei-Li Jia, Hong-Qiu Gu and Zi-Xiao Li were responsible for the study design. Wei-Li Jia was responsible for drafting of the manuscript. Hong-Qiu Gu and Ying-Yu Jiang were responsible for the analysis. Hong-Qiu Gu and Zi-Xiao Li were responsible for critical revision and supervision. Yong Jiang, Xia Meng, Hao Li, Xing-Quan Zhao, Yi-Long Wang, Yong-Jun Wang, and Zi-Xiao Li were responsible for the acquisition of data and for revision of the manuscript.

Supplementary material

Supplemental material for this article is available online.

ORCID iDs

Wei-Li Jia

Hong-Qiu Gu

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