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Neopterin and kynurenic acid as predictors of stroke recurrence and mortality: a multicentre prospective cohort study on biomarkers of inflammation measured three months after ischemic stroke

BMC Neurology volume 21, Article number: 476 (2021) Cite this article

Abstract

Background

Chronic low-grade inflammation is associated with both ischemic stroke and sedentary behaviour. The aim of this study was to investigate the predictive abilities of biomarkers of inflammation and immune modulation associated with sedentary behaviour for ischemic stroke recurrence and mortality in a stroke population.

Methods

Patients admitted to hospital for acute stroke were recruited to the prospective multicentre cohort study, the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study, from May 2015 until March 2017. Patients with ischemic stroke, blood samples available from the three-month follow-up, and no stroke recurrence before the three-month follow-up were included. Serum was analysed for C-reactive protein (CRP) with high-sensitive technique, and plasma for interleukin-6 (IL-6), neopterin, pyridoxic acid ratio index (PAr-index: 4-pyridoxic acid: [pyrioxal+pyridoxal-5′-phosphate]) and kynurenic acid (KA). Ischemic stroke recurrence and death were identified by the Norwegian Stroke Registry and the Cause of Death Registry until 31 December 2018.

Results

The study included 354 patients, 57% male, mean age 73 (SD 11) years, mean observation time 2.5 (SD 0.6) years, and median National Institute of Health Stroke Scale of 0 (IQR 1) at three months. CRP was associated with mortality (HR 1.40, CI 1.01, 1.96, p = 0.046), and neopterin was associated with the combined endpoint (recurrent ischemic stroke or death) (HR 1.52, CI 1.06, 2.20, p = 0.023), adjusted for age, sex, prior cerebrovascular disease, modified Rankin Scale, and creatinine. When adding neopterin and KA to the same model, KA was negatively associated (HR 0.57, CI 0.33, 0.97, p = 0.038), and neopterin was positively associated (HR 1.61, CI 1.02, 2.54, p = 0.040) with mortality. Patients with cardioembolic stroke at baseline had higher levels of inflammation at three months.

Conclusion

Neopterin might be a valuable prognostic biomarker in stroke patients. The use of KA as a measure of anti-inflammatory capacity should be investigated further.

Trial registration

The study was registered at Clinicaltrials.gov (NCT02650531). First posted on 08/01/2016.

Peer Review reports

Background

Ischemic stroke is associated with chronic low-grade inflammation, and there is a need for more knowledge about mechanisms and management [1,2,3,4]. Prognosis, outcomes, and management of stroke depends on stroke subtype, defined by the assumed aetiology of atherosclerosis, cardioembolism, or small vessel disease [5, 6]. In the context of inflammation, atherosclerosis is the most studied subtype, but inflammation has been found to contribute to all three categories [7,8,9,10]. Despite the evidence, clinical trials using drugs targeting inflammation have so far been inconclusive, and there are to date no established preventive treatment strategies that target inflammation in particular [4, 11], possibly related to the challenge of obtaining an optimal balance between potential benefits and an increased risk of (fatal) infections [4]. Obviously, treatment strategies that minimise severe side effects are desired.

Reducing chronic low-grade inflammation by targeting sedentary behaviour has shown promising results [12]. Still, there is a need for valid biomarkers associated with inflammation with explanatory and predictive properties for outcomes like vascular disease and mortality [3]. In a prior study, we investigated blood biomarkers of inflammation and immune modulation in a stroke population measured 3 months after the acute ischemic event and their association to objectively measured sedentary time. We identified the biomarkers C-reactive protein (CRP), interleukin-6 (IL-6), neopterin, and the pyridoxal acid ratio-index (PAr-index) to be positively associated, and kynurenic acid (KA) to be negatively associated with objectively measured sedentary behaviour [13]. CRP [14], IL-6 [15], neopterin [16, 17] and PAr-index [18] are inflammatory biomarkers associated with vascular disease. KA is part of a negative feedback loop inducing immune tolerance [19, 20]. Hence, even though it will rise in response to inflammation, this response is believed to be beneficial for disease progression during an inflammatory state [20]. The association to vascular disease progression is unclear.

In this study, we investigated the associations between biomarkers of inflammation and stroke recurrence and all-cause mortality. Finally, given the above-mentioned clinical importance of the stroke subtypes, we also investigated the association to the stroke subtype at baseline.

Material and methods

Subjects

The patients were included in the prospective multicentre cohort study, the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study, at admission for acute stroke between May 2015 and March 2017. The inclusion criteria were 1) hospital admission to one of the five participating hospitals, 2) acute stroke following the World Health Organisation definition or finding of acute stroke on imaging 3) being able to communicate in one of the Scandinavian languages, 4) above 18 years of age, 5) living in the catchment area. Patients were excluded if they had a life expectancy of less than 3 months. Details about the Nor-COAST population are described in prior studies [21, 22]. For this study, only patients with ischemic stroke at baseline who participated at the three-month follow-up and with data on at least one of the relevant biomarkers were included. To avoid the acute inflammatory response associated with the acute stroke event, we chose the three-month follow-up after the acute stroke as the baseline for this study, assuming the inflammatory biomarkers were not affected by the acute event at this time point (Fig. 1) [23].

Fig. 1
figure 1

Patient selection. 1 Other reasons: i.e.: delirious patient, hearing, uncertainty about the diagnosis, multi morbid, nursing home resident. 2 Failed to screen: infrastructure on the ward, vacation/weekends. 3 Not haemorragic transformation

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Clinical data

Demographic data and information about stroke properties, namely lesion type and stroke subtype, and stroke risk factors, were collected at baseline. Waist circumference, smoking status, stroke severity and functional outcomes were assessed at the three-month follow-up. Stroke severity was measured using the National Institute of Health Stroke Scale (NIHSS), global function by use of the modified Rankin Scale (mRS), and basic activities of daily living (ADL) by the Barthel Index (BI). The diagnosis of diabetes mellitus was defined at baseline by medical history and/or medication use (Anatomical therapeutical chemical Classification (ATC): A10) and/or HbA1c ≥6.5% at baseline. Hypercholesterolemia at baseline was defined by medication use (ATC: C10) and/or total cholesterol > 6.2 mmol/L and/or LDL ≥ 4.1 mmol/L at baseline. Hypertension at baseline was defined by medication use (ATC: C02, C03, C04, C07, C08, C09).

Laboratory analyses

At the three-month follow-up, non-fasting blood samples were collected for routine analysis and for storage in the biobank.

Routine clinical–chemical analyses: serum C-reactive protein (CRP) – measured using a high-sensitive technique (mg/L) – and serum creatinine (μmol/L) were analysed at the local laboratories of the inclusion centres.

Sample collection of research analyses: Aliquots of serum and plasma were immediately frozen at − 80 °C, and sent on dry ice to BioBank1, Central Norway Regional Health Authority for storage. The inflammatory biomarkers were analysed in 2019. Two aliquots of plasma were used, one for each of the two laboratories. The samples were thawed only once. The selection of biomarkers for this study was based on prior findings [13] and included CRP (mg/L), interleukin-6 (IL-6, pg/ml), neopterin (nmol/L), 4-pyridoxic acid (nmol/L), pyridoxal, pyridoxal 5′-phosphate (nmol/L), and kynurenic acid (KA, nmol/L). The pyridoxic acid ratio index – PAr-index: 4-pyridoxic acid (pyrioxal+pyridoxal 5′-phosphate) [24] was calculated. The cytokines were analysed at Research Laboratory Nordland Hospital using the Bio-Plex technology kits obtained from Bio-Rad Laboratories Inc., Hercules, CA.. The other biomarkers were analysed as part of analytic platform D at Bevital A/S (Bergen, Norway) by liquid chromatography/tandem mass spectrometry. Because of the sample size, IL-6 was analysed using multiple trays. The upper and lower detection limit varied among the trays according to the standard curve. There were no values above or below the detection limits for IL-6. For the biomarkers from BeVital AS (neopterin, kynurenic acid, and the B6 vitamers), the performance of the method has been published previously [25]. There were no values out of range.

Outcomes

Recurrent ischemic stroke was identified by the Norwegian Stroke Registry. The coverage was 84–87% in the period from 2015 to 2018, and stroke was defined according to the WHO’s definition [26]. Death was identified by the Norwegian Cause of Death Registry, which has a general coverage of 98% of deaths of Norwegian citizens within the country or abroad [27]. The data extraction was performed on 31 December 2018.

Patient outcomes were defined based on the first event: recurrent ischemic stroke, death or no event. Some of the patients died during the follow-up period after first having a stroke recurrence. In the regression analyses, these patients were included in both groups. A third outcome was defined as the composite of recurrent ischemic stroke and death. The patients with both outcomes were counted only once.

Statistics

For the baseline and three-month descriptions, patients were stratified by first event and analysed using the chi-squared test for categorical variables (numbers and percentages), one-way ANOVA for continuous variables, and Kruskal–Wallis H-test for the continuous variables that were not normally distributed. Data were presented as means with standard deviations (SD) and medians with interquartile range (IQR).

One-way ANOVA were used for the analyses of laboratory values according to stroke subtype at baseline. The subgroup “other determined” was excluded.

The associations between the individual biomarkers and the outcomes stroke, mortality and the composite endpoint were analysed using Cox regressions. The start of the follow-up time was defined as the date of the three-month visit to the hospital. For the outcome “recurrent ischemic stroke”, an additional analysis using a competing-risk Cox regression based on the method of Fine and Grey [28] as an alternative to the regular Cox regression was used [29], with death as a competing outcome for ischemic stroke. All the biomarkers were right-skewed. After log-transformation, regression residuals approached normal distribution. The variables were standardised, and the hazard ratios are given per standard deviation change of the variable value. To account for potential confounders, we added modified Rankin scale and prior cerebrovascular disease as independent variables, because they have been found to be associated with both stroke recurrence [30] and with inflammation (mRS [31], prior cerebrovascular disease (before the baseline stroke in this study) [32]). We also added age and sex. Creatinin was included because some of the biomarkers had renal clearance (Model 1). As the blood samples were taken at the three-month follow-up, we used the results of the mRS recorded at this time point. To account for the different pathophysiology of the stroke subtypes, we did a second analysis where we added the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria to the model (Model 2), defining five stroke subtypes (large artery atherosclerosis, cardioembolic, cerebral small vessel disease, other determined, undetermined). The stroke subtype “other determined” is used when the cause of stroke is determined (i.e. dissection, vasculitis). This group was excluded from this analysis because of low sample size. Because KA is part of a feedback loop in response to inflammation, a third regression model including both KA and neopterin in addition to the same covariates as in Model 1 was made.

The significance level was set to 0.05, but p-values > 0.01 should be evaluated with caution due to a high number of tests. The analyses were performed in STATA/SE 16.1 (Stata Corp LLC, College Station, TX, USA).

Results

Patient characteristics and laboratory values

Of the 700 patients attending the three-month follow-up, 354 were included in this sub-study. The remaining 346 patients were excluded due to at least one of the following reasons: stroke before 3 months (n = 25), haemorrhagic stroke at baseline (n = 64), or no relevant biomarker available (n = 277).

The mean follow-up time from baseline for the whole group was 2.5 (0.6) years, and the mean time-to-event was 1.5 (1.0) and 1.6 (0.7) years for stroke and death, respectively. From the three-month follow-up and throughout the follow-up time, 16 (4.5%) patients had ischemic stroke recurrence. In total, 28 (7.9%) patients died, and 25 (7.1%) of them died without being registered as having stroke recurrence.

The baseline and three-month data are presented in Table 1, stratified by the first event. The patients who died were older compared to those with no event. Patients with no events had a higher Barthels Index (p 0.007) and a lower mRS (p < 0.001). There were overall differences between the groups in the presence of prior ischemic stroke (p < 0.001), prior coronary artery disease (p = 0.007), and atrial fibrillation at baseline (p = 0.034), the patients experiencing no events showing the lowest prevalence. There were overall differences between the groups in the values for IL-6, neopterin, and the PAr-index, where the patients who died had the highest values.

Table 1 Baseline and three-month characteristics according to the first event after three-month follow-up
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Stroke subtype

The biomarker levels by stroke subtype at baseline are presented in Table 2. There was a significant overall difference between the groups in the level of CRP, IL-6, and neopterin. Patients with cardioembolic stroke at baseline showed the highest values.

Table 2 Biomarker values at three-month follow-up by ischemic stroke subtype at baseline1
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Regression analyses

The crude and adjusted hazard ratios (HR) for the outcome’s ischemic stroke recurrence, death, and the composite endpoint of stroke and death, investigated for each of the biomarkers individually, are presented in Table 3. We did not find any associations with stroke recurrence. In the crude analyses, all biomarkers except for KA were significantly associated with the risk of death or the composite endpoint. In the adjusted analyses, we found that CRP was associated with death (Model 1: HR 1.40, (CI 1.01, 1.96), p = 0.046) and neopterin with the combined endpoint (Model 1: HR 1.52, (CI 1.06, 2.20), p = 0.023. Model 2: HR 1.55, (CI 1.06, 2.27), p = 0.025).

Table 3 Crude and adjusted Cox regressions of the association between the individual biomarkers and ischemic stroke recurrence, death and the composite endpoint (ischemic stroke recurrence and death)
Full size table

In the additional competing risk regression analyses, where we used death as a competing event for stroke recurrence, the risk of ischemic stroke recurrence did not change significantly (see Supplementary Table 1).

The regression analyses, including both KA and neopterin in addition to the variables in Model 1, are shown in Table 4. KA was negatively associated with mortality (HR 0.57, (0.33, 0.97), 0.038), and neopterin was positively associated with mortality (HR 1.61, (1.02, 2.54), 0.040). Neopterin was also associated with the composite endpoint (HR 1.59, (1.11, 2.27), 0.011).

Table 4 Adjusted Cox regression (Model 1) of the combined contributions of kynurenic acid and neopterin in stroke, mortality and the composite endpoint (stroke and death)
Full size table

Discussion

In this study, we showed that higher levels of C-reactive protein (CRP) measured 3 months after acute ischemic stroke were associated with an increased risk of death. Neopterin was associated with the composite endpoint of stroke and death. In a model including both neopterin and kynurenic acid, neopterin and kynurenic acid were associated with an increased and a reduced risk of death, respectively. Furthermore, patients with cardioembolic stroke at baseline and those who died during follow-up had higher levels of pro-inflammatory biomarkers at 3 months.

Stroke recurrence

We did not find any association between inflammatory biomarkers and stroke recurrence. This could be due to low sample size, the relatively short time of follow-up, or not all recurrent strokes being clinically acknowledged leading to a statistical type II error. The role of inflammation in stroke recurrence, investigated by traditional biomarkers such as IL-6 and CRP, has been well established [14, 15, 33, 34]. The novel biomarkers; neopterin, PAr-index, and KA, are less studied. Neopterin has shown to predict future coronary events [16, 17] and stroke recurrence [35]. Neopterin has also been associated with complex carotid plaques in patients with stable angina pectoris [36], with the extent of cerebral small vessel disease (CSVD) in stroke patients [37] and with the presence, but not the future risk, of atrial fibrillation [38]. The PAr-index has been associated with future stroke in a population study [18] but has never been studied in a stroke population nor in the context of stroke recurrence. The role of KA in stroke recurrence is not clear, but KA has been found to be associated with coronary events [39, 40] and aortic stiffness, which in turn is associated with atrial fibrillation [41]. In a recent study, Baumgarten et al. found that the branch of the kynurenine pathway, leading to the formation of KA, was downregulated in atherosclerotic plaques, and this downregulation was associated with an increased risk of cerebrovascular events [19].

Mortality

We found that inflammation, in general, was significantly associated with mortality in the unadjusted analyses. In the adjusted analyses, only CRP and neopterin showed added value as predictors compared to traditional parameters such as prior vascular disease and mRS [30]. The lack of association to the remaining biomarkers could again be explained by statistical type II error due to the low sample size and short time of follow-up.

Several inflammatory biomarkers have been found to be independent predictors of all-cause and cardiovascular mortality. Both IL-6 and CRP have been found to be associated with increased mortality [34, 42], and neopterin has been identified as an independent predictor of all-cause and cardiovascular mortality [16]. The PAr-index has been associated with all-cause mortality in patients with coronary artery disease [43].

KA was negatively associated with mortality first after adjusting for inflammation. We did this analysis based on the increasing evidence of KA as a contributor to the body’s anti-inflammatory capacity, as part of a negative feedback loop [19, 20, 44]. KA increases in response to inflammation, and our hypothesis, that a higher level of KA in response to inflammation, measured by neopterin [45] would be associated with a beneficial outcome, was supported as we found a higher value of KA to be associated with reduced risk of mortality when adjusted for inflammation. In fact, both risk estimates were strengthened after adjustment, indicating that they mutually camouflaged some of their individual contributions.

Our current finding is in concordance with KA’s role in vascular disease, amongst others, by inhibiting leukocyte recruitment to atherosclerotic plaques [19]. The result is in contrast to findings of KA being associated with mortality after coronary artery disease when being analysed without adjusting for inflammation [39]. Although exploratory, our results indicate that downregulation of KA might be one of the molecular pathways mediating the hazards of sedentary behaviour in disease progression. This should be further investigated.

Stroke subtype

We found that patients with cardioembolic strokes at baseline had higher levels of CRP, IL-6, and neopterin at 3 months, indicating a difference in the inflammatory profile between these subgroups. The link between stroke and inflammation has primarily been investigated in all the subtypes combined [14, 46] or in stroke caused by the progression of atherosclerosis [47] or CSVD [48]. The role of inflammation in cardioembolic strokes, in particular, is less studied. However, increasing evidence exists of the importance of inflammation for the presence of atrial fibrillation and the associated cardioembolic risk [7, 8, 49]. It can be argued that concurrent vascular disease might mediate the increased risk of stroke associated with inflammation found in patients with atrial fibrillation. However, Packer argued in a review for the contribution of inflammation as a risk factor independent of vascular disease, as the risk exceeded that predicted by cardiovascular risk factors [50]. Inflammatory biomarkers might be important for identifying those in need of prolonged ECG monitoring to identify paroxysmal atrial fibrillation, and when evaluating stroke risk in patients with atrial fibrillation [7, 38, 50,51,52]. Altogether, these observations indicate that the stroke subtype is relevant when studying inflammation in stroke and underlines the importance that such studies are powered for stratified analyses.

Strength and weaknesses

The information about stroke recurrence was based on the Norwegian Stroke Registry, which identifies patients with stroke admitted to hospital. The registry reports coverage of 84–87% in this period [26] and has been shown to be reliable [53]. The registry includes only patients admitted to hospital and where the recurrent stroke has been clinically recognised. Not all patients are admitted to hospital, and not all strokes are clinically overt. Zeestraten et al. found a recurrence rate of 3% based on clinical judgement, while 27% had new lacunas on imaging at the five-year follow-up [54]. These strokes might be more clinically subtle but are still of clinical importance. The use of imaging, such as MRI, could increase the sensitivity of the stroke diagnosis, and this has been included in the updated stroke definition [55]. Hence, we might have underestimated the incidence of ischemic stroke recurrence.

The mean follow-up time of the patients was 2.5 years, and for the patients included at the end of the study, the follow-up was limited to 16 months after the three-month follow-up. Khanevski et al. studied patients with ischemic stroke and transient ischemic attack and found that 5.4% had stroke recurrence after 1 year, 10.7% after 5 years, and 14.2% at the end of the study (mean 5.6 years) [30]. In addition, we had to exclude half of the stroke recurrences because they occurred before the three-month follow-up. The short follow-up and the low number of stroke recurrences in our study increase the risk of a type II error, and in particular negative findings should be evaluated with caution.

To avoid the acute inflammatory response associated with the acute stroke event, we chose the three-month follow-up after the acute stroke as the baseline for this study, assuming the inflammatory biomarkers were not affected by the acute event at that point in time-point [16]. The correlation between the inflammatory biomarkers before and 3 months after is not known. Still, it characterises these subgroups of patients and could be valuable for prospective studies. Stroke subtype is based on clinical judgement of the most likely cause of the stroke in question and may be challenging to determine [6]. Also, several causes of stroke in a single patient are common, i.e. a combination of large artery atherosclerosis, CSVD, and atrial fibrillation. These patients are often included in the category “unknown”.

Conclusion

Neopterin seems to be a useful prognostic biomarker in stroke populations. The novel finding of a potential protective effect of KA in response to inflammation in disease progression should be investigated further. The results also underline the importance of stroke subtypes when investigating the impact of inflammation in ischemic stroke. The results highlights the importance of inflammation in cerebrovascular disease. Measures should be made to reduce inflammation as a secondary preventive strategy, i.e. by reducing sedentary behaviour. In addition, patients with undetected atrial fibrillation are receiving suboptimal drug treatment, and investigating the usefulness of inflammatory biomarkers to identify these patients could have significant therapeutic consequences for these individuals.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to Norwegian regulations and conditions for informed consent but are available from the corresponding author on reasonable request.

Abbreviations

ATC:

Anatomical therapeutic chemical classification

ADL:

Activities of daily living

BI:

Barthel Index

CI:

Confidence interval

CRP:

C-reactive protein

CSVD:

Cerebral small vessel disease

eGFR:

Estimated glomerular filtration rate

ECG:

Electro cardiogram

HR:

Hazard ratio

IL-6:

Interleukin-6

IL-10:

Interleukin-10

IQR:

Interquartile range

KA:

kynurenic acid

LDL:

Low density lipoprotein

mRS:

Modified Rankin Scale

MRI:

Magnetic resonance imaging

NIHSS:

National Institute of Stroke Scale

Nor-COAST:

Norwegian Cognitive Impairment After Stroke Study

PAr-index:

Pyridoxal acid ratio index

PLP:

Pyridoxal-5-phosphate

PL:

Pyridoxal

PA:

Pyridoxal acid

REK:

regional ethics committee

SD:

Standard deviation

TOAST:

Trial of Org 10,172 in Acute Stroke Treatment

WHO:

World Health Organization

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Acknowledgements

We want to thank the Nor-COAST research group and the dedicated staff at St Olavs hospital, Haukeland University hospital, Ålesund hospital, Vestre Viken Hospital Trust; Bærum Hospital, and Oslo University Hospital, Ullevål. We would also like to thank all participants and their relatives for volunteering for this project. The authors appreciate the valuable advice and practical assistance provided by expert personnel at Biobank1®, the research biobank of Central Norway, in collection, processing and storage of the biological material.

Authors´ contributions

KNA has planned this study. She has participated in the processing of the registry data, planned and performed the statistical analyses and interpreted the results. She has written the article. AU has contributed to the statistical analyses and the interpretation of the results. He has contributed with detailed knowledge of the biomarkers from Bevital and to the process of writing the manuscript. TA has contributed to the design of this study and was responsible for the physical activity data from the main study. She has contributed to the revision of the manuscript. JA has supervised the process of developing and describing the statistical method and contributed to the interpretation and presentation of the data. TEM has analysed the cytokines used in this study. He has contributed in the post-analytical work and defined the method of handling the data and interpreting the results. He has also contributed in the revision of the manuscript. MN has contributed to the development of the study and revised the manuscript. HN has contributed to the development of the study and revised the manuscript. He has also contributed to the development of the statistical method and the interpretation of the results. IS is the principal investigator of the main project and has contributed to the revision of the manuscript. PMU has been in charge of analysing the biomarkers from Bevital. He has also contributed in the preparation phase of designing the study and selecting the analytic platform, and in interpreting the results and the revision of the manuscript. ABK has contributed to the development of the study and to the selection of biomarkers and interpretation of the results. She has also contributed to the writing process. All authors have read and agreed to the published version of the manuscript and are accountable for the content.

Funding

The lead author was granted a PhD-scholarship from the Western Norway Regional Health Authority, including funding for the laboratory analyses. The Nor-COAST study was granted by the Norwegian Health Association and the Norwegian University of Science and Technology (NTNU). The funders do not have an active role in the further scientific process.

Author information

Authors and Affiliations

  1. Institute of Clinical Medicine (K1), University of Bergen, Bergen, Norway

    Katinka Nordheim Alme & Halvor Næss

  2. Department of Internal Medicine, Haraldsplass Deaconess Hospital, Bergen, Norway

    Katinka Nordheim Alme & Mala Naik

  3. Bevital AS, Bergen, Norway

    Arve Ulvik & Per-Magne Ueland

  4. Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Science, NTNU-Norwegian University of Science and Technology, Trondheim, Norway

    Torunn Askim & Ingvild Saltvedt

  5. Centre for Clinical Research, Haukeland University Hospital, Bergen, Norway

    Jörg Assmus

  6. Department of Immunology, Oslo University Hospital and University of Oslo, Oslo, Norway

    Tom Eirik Mollnes

  7. Research Laboratory, Nordland Hospital, Bodø, and K.G. Jebsen TREC, University of Tromsø, Tromsø, Norway

    Tom Eirik Mollnes

  8. Centre of Molecular Inflammation Research, NTNU-Norwegian University of Science and Technology, Trondheim, Norway

    Tom Eirik Mollnes

  9. Department of Clinical Science (K2), University of Bergen, Bergen, Norway

    Mala Naik

  10. Department of Neurology, Haukeland University Hospital, Bergen, Norway

    Halvor Næss

  11. Centre for age-related medicine, Stavanger University Hospital, Stavanger, Norway

    Halvor Næss

  12. Department of Geriatrics, Clinic of internal medicine, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway

    Ingvild Saltvedt

  13. Department of Geriatric Medicine, Oslo University Hospital. Ullevaal, Oslo, Norway

    Anne-Brita Knapskog

Authors
  1. Katinka Nordheim Alme
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  2. Arve Ulvik
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  3. Torunn Askim
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  6. Mala Naik
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  7. Halvor Næss
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  9. Per-Magne Ueland
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Corresponding author

Correspondence to Katinka Nordheim Alme.

Ethics declarations

Ethics approval and consent to participate

The study was conducted in accordance with the institutional guidelines and approved by the Regional Committee of Medical and Health Research Ethics (REK) (Nor-COAST study, application number: REK no: 2017/2060. This sub-study, application number: REK no: 2015/171, Application ID: 12253). The study was registered at Clinicaltrials.gov (NCT02650531). To participate, the patients had to be able to give written informed consent for themselves. Patients who were unable to express consent for themselves were also included if their next of kin did not decline. This is in line with the Norwegian consent procedures for patients not able to consent for themselves.

Consent for publication

Not applicable. The publication does not contain individual data.

Competing interests

ABK has been/is the principal site investigator in three clinical trials (Boehringer-Ingelheim 1346.0023, Roche BN29553, and Novo Nordisk NN6535–4730), IS has been an investigator in the clinical trial Boehringer-Ingelheim 1346.0023 and part of the advisory board for Biogen.

The rest of the authors do not have any competing interests to declare.

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Alme, K.N., Ulvik, A., Askim, T. et al. Neopterin and kynurenic acid as predictors of stroke recurrence and mortality: a multicentre prospective cohort study on biomarkers of inflammation measured three months after ischemic stroke. BMC Neurol 21, 476 (2021). https://doi.org/10.1186/s12883-021-02498-w

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Keywords

BMC Neurology

ISSN: 1471-2377

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