Circulating levels of neurofilament light chain as a biomarker of infarct and white matter hyperintensity volumes after ischemic stroke.

Serum neurofilament light chain protein (sNfL) shows promise as a biomarker for infarct size in acute ischemic stroke and for monitoring cerebral small vessel disease (cSVD). However, distinguishing the cSVD contribution after stroke may not be possible due to post-stroke sNfL increase. Additionally, it remains unclear if etiologic subtype differences exist. We measured infarct and white matter hyperintensity (WMH) volumes using MRI at the index stroke in ischemic stroke patients (n = 316, mean age 53 years, 65% males) and at 7-year follow-up (n = 187). Serum NfL concentration was measured in the acute phase (n = 235), at 3-months (n = 288), and 7-years (n = 190) post stroke. In multivariable regression, acute and 3-month sNfL concentrations were associated with infarct volume and time since stroke, but not with stroke etiology or infarct location. Seven years post-stroke, sNfL was associated with WMHs and age, but not with stroke etiology. Nonlinear regression estimated that sNfL peaks around 1 month, and declines by 50% at 3 months, and 99% at 9 months. We conclude that sNfL can indicate infarct volume and time since brain injury in the acute and subacute phases after stroke. Due to the significant post-stroke sNfL increase, several months are needed for reliable assessment of cSVD activity.

A Nonlinear Relation between Body Mass Index and Long-Term Poststroke Functional Outcome-The Importance of Insulin Resistance, Inflammation, and Insulin-like Growth Factor-Binding Protein-1.

Both high serum insulin-like growth factor-binding protein-1 (s-IGFBP-1) and insulin resistance (IR) are associated with poor functional outcome poststroke, whereas overweight body mass index (BMI; 25-30) is related to fewer deaths and favorable functional outcome in a phenomenon labeled "the obesity paradox". Furthermore, IGFBP-1 is inversely related to BMI, in contrast to the linear relation between IR and BMI. Here, we investigated s-IGFBP-1 and IR concerning BMI and 7-year poststroke functional outcome. We included 451 stroke patients from the Sahlgrenska Study on Ischemic Stroke (SAHLSIS) with baseline measurements of s-IGFBP1, homeostasis model assessment of IR (HOMA-IR), BMI (categories: normal-weight (8.5-25), overweight (25-30), and obesity (>30)), and high-sensitivity C-reactive protein (hs-CRP) as a measure of general inflammation. Associations with poor functional outcome (modified Rankin scale [mRS] score: 3-6) after 7 years were evaluated using multivariable binary logistic regression, with overweight as reference due to the nonlinear relationship. Both normal-weight (odds-ratio [OR] 2.32, 95% confidence interval [CI] 1.30-4.14) and obese (OR 2.25, 95% CI 1.08-4.71) patients had an increased risk of poor functional outcome, driven by deaths only in the normal-weight. In normal-weight, s-IGFBP-1 modestly attenuated (8.3%) this association. In the obese, the association was instead attenuated by HOMA-IR (22.4%) and hs-CRP (10.4%). Thus, a nonlinear relation between BMI and poor 7-year functional outcome was differently attenuated in the normal-weight and the obese.

Plasma fibrinogen and risk of vascular recurrence after ischaemic stroke: An individual participant and summary-level data meta-analysis of 11 prospective studies.

INTRODUCTION: Inflammation is an emerging target for secondary prevention after stroke and randomised trials of anti-inflammatory therapies are ongoing. Fibrinogen, a putative pro-inflammatory marker, is associated with first stroke, but its association with major adverse cardiovascular events (MACE) after stroke is unclear. MATERIALS AND METHODS: We did a systematic review investigating the association between fibrinogen and post-stroke vascular recurrence. Authors were invited to provide individual-participant data (IPD) and where available we did within-study multivariable analyses with adjustment for cardiovascular risk factors and medications. Adjusted summary-level data was extracted from published reports from studies that did not provide IPD. We pooled risk ratios (RR) by random-effects meta-analysis by comparing supra-median with sub-median fibrinogen levels and performed pre-specified subgroup analysis according to timing of phlebotomy after the index event. RESULTS: Eleven studies were included (14,002 patients, 42,800 follow-up years), of which seven provided IPD. Fibrinogen was associated with recurrent MACE on unadjusted (RR 1.35, 95% CI 1.17-1.57, supra-median vs sub-median) and adjusted models (RR 1.21, 95% CI 1.06-1.38). Fibrinogen was associated with recurrent stroke on univariate analysis (RR 1.19, 95% CI 1.03-1.39), but not after adjustment (RR 1.11, 95% CI 0.94-1.31). The association with recurrent MACE was consistently observed in patients with post-acute (⩾14 days) fibrinogen measures (RR 1.29, 95% CI 1.16-1.45), but not in those with early phlebotomy (<14 days) (RR 0.98, 95% CI 0.82-1.18) (Pinteraction = 0.01). Similar associations were observed for recurrent stroke. DISCUSSION AND CONCLUSION: Fibrinogen was independently associated with recurrence after stroke, but the association was modified by timing of phlebotomy. Fibrinogen measurements might be useful to identify patients who are more likely to derive benefit from anti-inflammatory therapies after stroke.

Association of Plasma Brain-Derived Tau With Functional Outcome After Ischemic Stroke.

OBJECTIVES: To investigate whether circulating acute-phase brain-derived tau (BD-tau) is associated with functional outcome after ischemic stroke. METHODS: Plasma tau was measured by a novel assay that selectively quantifies BD-tau in the Sahlgrenska Academy Study on Ischemic Stroke (SAHLSIS), which includes adult cases with ischemic stroke and controls younger than 70 years, and in an independent cohort of adult cases of all ages (SAHLSIS2). Associations with unfavorable 3-month functional outcome (modified Rankin scale score >2) were analyzed by logistic regression. Various stratified and sensitivity analyses were performed, for example, by age, stroke severity, recanalization therapy, and etiologic subtype. RESULTS: This study included 454 and 364 cases from the SAHLSIS and SAHLSIS2, with a median age of 58 and 68 years, respectively. Higher acute BD-tau concentrations were significantly associated with increased odds of unfavorable outcome after adjustment for age, sex, day of blood draw, and stroke severity (NIH stroke scale score) in both cohorts (OR per doubling of BD-tau: 2.9 [95% CI 2.2-3.7], P = 1 × 10-15 and 1.8 [1.5-2.2], P = 7 × 10-9, respectively). The association was consistent in the different stratified and sensitivity analyses. DISCUSSION: BD-tau is a promising blood-based biomarker of ischemic stroke outcomes, and future studies in larger cohorts are warranted.

C-Reactive Protein, Interleukin-6, and Vascular Recurrence According to Stroke Subtype: An Individual Participant Data Meta-Analysis

BACKGROUND AND OBJECTIVES: Anti-inflammatory therapies reduce major adverse cardiovascular events (MACE) in coronary artery disease but remain unproven after stroke. Establishing the subtype-specific association between inflammatory markers and recurrence risk is essential for optimal selection of patients in randomized trials (RCTs) of anti-inflammatory therapies for secondary stroke prevention. METHODS: Using individual participant data (IPD) identified from a systematic review, we analyzed the association between high-sensitivity C-reactive protein, interleukin-6 (IL-6), and vascular recurrence after ischemic stroke or transient ischemic attack. The prespecified coprimary end points were (1) any recurrent MACE (first major coronary event, recurrent stroke, or vascular death) and (2) any recurrent stroke (ischemic, hemorrhagic, or unspecified) after sample measurement. Analyses were performed stratified by stroke mechanism, per quarter and per biomarker unit increase after loge transformation. We then did study-level meta-analysis with comparable published studies not providing IPD. Preferred Reporting Items for Systematic Review and Meta-Analyses IPD guidelines were followed. RESULTS: IPD was obtained from 10 studies (8,420 patients). After adjustment for vascular risk factors and statins/antithrombotic therapy, IL-6 was associated with recurrent MACE in stroke caused by large artery atherosclerosis (LAA) (risk ratio [RR] 2.30, 95% CI 1.21-4.36, p = 0.01), stroke of undetermined cause (UND) (RR 1.78, 1.19-2.66, p = 0.005), and small vessel occlusion (SVO) (RR 1.71, 0.99-2.96, p = 0.053) (quarter 4 [Q4] vs quarter 1 [Q1]). No association was observed for stroke due to cardioembolism or other determined cause. Similar results were seen for recurrent stroke and when analyzed per loge unit increase for MACE (LAA, RR 1.26 [1.06-1.50], p = 0.009; SVO, RR 1.22 [1.01-1.47], p = 0.04; UND, RR 1.18 [1.04-1.34], p = 0.01). High-sensitivity CRP was associated with recurrent MACE in UND stroke only (Q4 vs Q1 RR 1.45 [1.04-2.03], p = 0.03). Findings were consistent on study-level meta-analysis of the IPD results with 2 other comparable studies (20,136 patients). DISCUSSION: Our data provide new evidence for the selection of patients in future RCTs of anti-inflammatory therapy in stroke due to large artery atherosclerosis, small vessel occlusion, and undetermined etiology according to inflammatory marker profile.

Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.

Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.