Potential blood biomarkers to differentiate ischemic and hemorrhagic strokes

doi:10.21203/rs.3.rs-4939245/v1

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Potential blood biomarkers to differentiate ischemic and hemorrhagic strokes

https://doi.org/10.21203/rs.3.rs-4939245/v1

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Background

Currently, besides neuroimaging, there is a lack of alternative methods for rapid differentiation of ischemic stroke (IS) and intracerebral hemorrhage (ICH), which significantly impacts the timely treatment of patients. This study aims to elucidate the causal relationship between circulating metabolites and susceptibility to IS and ICH.

Methods

A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of metabolites and metabolite ratios on IS/ICH. For exposure data, we extracted genetic variants associated with 1, 091 plasma metabolites and 309 metabolite ratios traits from the Canadian Longitudinal Study on Aging (CLSA) cohort (n = 8, 299). For outcomes, we selected IS and its three subtypes including cardioembolic stroke (CES), small vessel stroke (SVS), and large artery (LAS) from the latest stroke genome-wide association studies (GWAS) database (73, 652 patients). In addition, we have included ICH as a primary outcome (n = 1, 545 cases).

Results

In this MR analysis, there were 115, 105, 89, 70, and 48 plasma metabolites or metabolite ratios suggestive associated with IS, LAS, CES, SVS, and ICH. After false discovery rate (FDR) correction and sensitive analysis, 20 robust causative associations between 16 metabolites (e.g., ribitol, campesterol, and thymol sulfate)/ 4 metabolite ratios and IS or ICH were finally identified. Among them, six metabolites may serve as potential indicators for distinguishing between IS and ICH.

Conclusion

The finding of our study suggested that identified metabolites and metabolite ratios can be considered useful circulating biomarkers for IS and ICH screening and differential diagnosis in clinical practice.

Ischemic stroke

intracerebral hemorrhage

Plasma metabolites

Mendelian randomisation

In 2019, stroke was the second largest cause of death worldwide and the third leading cause of premature mortality, with 12 million incident strokes and 100 million had a previous history of stroke globally¹. Currently, intravenous thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) within 4.5 hours and endovascular treatment (EVT) within 24 hours are considered first-line treatment in the acute phase to improve clinical outcomes in ischemic stroke (IS)^{2, 3}. However, the time from stroke onset to treatment remains crucial for both therapeutic approaches. In fact, each 15-minute decrease in IVT administration was associated with a significantly lower risk of mortality and improved outcome⁴. However, no acute intervention has been proved efficient for intracerebral hemorrhage (ICH).

Whether patients can access timely therapies depends on fast and accurate differentiation of IS from ICH. Currently, differentiation mainly depends on clinical assessment and neuroimaging including brain CT and MRI. However, portable CT and MRI are scarce resources due to financial and technical limitations. In particular, lack of available neuroimaging remains the main obstacle for acute-phase treatment in low- and middle-income countries. An alternative approach to bring forward prehospital administration of rt-PA might be represented by using blood biomarkers. To date, several biomarkers have been studied including glial fibrillary acid protein (GFAP), N-terminal proB-type natriuretic peptide (NT-proBNP), and endostatin^5–7, but none has been approved for clinical application. Since there is still a huge gap between the above biomarkers and an ideal biomarker, which provides confidential information to rule out IS from ICH, the clinical application of blood biomarkers still warrants further investigation. We still need a systemic analysis to understand global changes in the metabolic process in response to ischemic and hemorrhagic strokes to identify enough potential biomarkers.

Metabolomics is now considered an important tool for clinical research and diagnosis of human diseases, which provides novel insights into the molecular mechanisms and endogenous biochemicals involved in key metabolic processes^{8, 9}. In this study, we undertook a series of large GWASs, aimed at validating and developing a panel of blood biomarkers with enough accuracy to guide prehospital thrombolysis in selected patients with IS.

Study design

The present study systematically evaluated the causal relationship between circulating human metabolites and the risk of IS and ICH through the application of a two-sample Mendelian randomization (MR) analysis. The efficacy of a compelling MR study is contingent upon adherence to three foundational assumptions: (1) Genetic instruments must exhibit direct associations with the exposure under investigation (i.e., metabolites in this study); (2) Genetic instruments are required to be unrelated to the outcome (i.e., IS and ICH in this study) and independent of any discernible or latent confounding factors; (3) The influence of instrumental variables (IVs) on the outcomes is exclusively mediated by the focal exposures of interest. The overview of this MR study was presented in Fig. 1.

GWAS datasets for plasma metabolites

We curated genetic instruments for 1,091 plasma metabolites (241 were categorized as unknown or ‘partially’ characterized molecules) and 309 metabolite ratios through a genome-wide association study (GWAS) involving 8,299 participants of European descent within the Canadian Longitudinal Study on Aging (CLSA) cohort¹⁰. As far as we know, this constitutes the most exhaustive analysis of human metabolites to date. Genetic variants that meet the following criteria were selected as IVs. Firstly, single-nucleotide polymorphisms (SNPs) significantly associated with the plasma metabolites and metabolite ratios were chosen as IVs, since only a few plasma metabolites and metabolite ratios had three or more independent SNPs at genome-wide significance levels (P < 5 × 10^− 8), a higher cut-off (P < 1 × 10^− 6) was used for obtaining SNPs to obtain more IVs and comprehensive results. Secondly, we clustered SNPs utilizing the European 1000 Genomes Project reference panel, employing a linkage disequilibrium threshold (r² < 0.001) and setting a clumping distance of greater than 10,000 kb. This approach was adopted to discern independent SNPs within the dataset. Thirdly, in cases where the minor allele frequency falls below 0.30 for each palindromic SNPs, the determination is made that the SNP is inferred as palindromic. Subsequently, these identified palindromic SNPs are excluded from further consideration. Finally, we exclude the weak SNPs when an F statistic is < 10.

GWAS datasets for ischemic stroke and intracerebral hemorrhage

In this study, we designated IS and ICH as primary endpoints, accompanied by an examination of three distinct subtypes of IS: Large Vessel Stroke (LAS), Small Vessel Stroke (SVS), and Cardioembolic Stroke (CES). The GWAS summary-level dataset pertaining to IS was sourced from the GIGASTROKE consortium¹¹, encompassing 62,100 cases for AIS, 6,399 cases for LAS, 10,804 cases for CES, and 6,811 cases for SVS. Additionally, the summary-level GWAS dataset for Intracerebral Hemorrhage (ICH) was acquired from the International Stroke Genetics Consortium (ISGC), comprising 1,545 cases of European origin¹².

Statistical analysis

The primary analyses were executed employing the inverse-variance weighted (IVW) MR method. This method operates under the assumption that all genetic variants serve as valid instrumental variables, yielding the most precise estimates¹³. However, if some SNPs contradict the MR assumptions, the analysis may give incorrect results. We have therefore performed the weighted median and MR-Egger as sensitivity analyses. The weighted median approaches give more weight to the instrumental variables that are more precise, and the estimate is consistent even when up to 50% of the information comes from invalid or weak instruments¹⁴. The MR-Egger could detect and adjust for directional pleiotropy, albeit with low precision¹³. Cochran's Q statistic was employed within the IVW model to evaluate the heterogeneity among variant-specific estimates. The MR Pleiotropy Residual Sum and Outlier (PRESSO) methodologies were applied for the identification of potential outliers. Subsequently, a leave-one-SNP-out analysis was conducted, systematically removing SNPs to scrutinize the impact of individual variants on the outcomes. Additionally, we conducted a bi-directional MR analysis to see if there is any proof that the occurrence of IS or ICH affected plasma metabolites.

For a better interpretation of metabolic changes, we excluded 241 unknown metabolites. Following the methods of the original study, the residual cohort comprising 850 metabolites underwent categorization into eight superpathways ((that is, lipid, amino acid, xenobiotics, nucleotide, cofactor and vitamins, carbohydrate, peptide and energy)¹⁰. The significance of MR results was determined using a false discovery rate (FDR) < 0.05 for each single superpathway and metabolite ratios. All effect estimates were computed as adjusted odds ratios (OR) with 95% confidence intervals (CI).

All MR analyses were performed using the TwoSampleMR (version 0.5.6), Mendelian randomization (version 0.5.1), and MRPRESSO (version 1.0) packages in

R (version 4.2.3).

Identification of plasma metabolites associated with stroke and its subtypes risk

A total of 850 unique quantified metabolites and 309 metabolite ratios were included in our study. We only retained metabolites or metabolite ratios that had at least 2 eligible SNPS after harmonising with the outcome (Table S1-S5).

First, we examined the relationship between genetically determined metabolites levels/metabolite ratios and risk of IS and its subtypes and identified a total of 115 metabolites or metabolite ratios suggestive associated with IS, 105 suggestive associations with LAS, 89 suggestive associations with CES and 70 suggestive associations with SVS (P < 0.05) (Fig. 2A, 2C, 2D, 2E, Table S6-S9). As shown in Fig. 3, using the IVW method, 3 causal associations with multiple-testing corrected significance (FDR P < 0.05) could be observed in IS, all of which were metabolite ratios: phosphate to tryptophan ratio (OR [95% CI]: 0.79 [0.70, 0.89], P = 8.32 × 10^− 5); histidine to asparagine ratio (OR [95% CI]: 1.06 [1.03, 1.10], P = 2.64 × 10^− 4) and arachidonate (20:4n6) to paraxanthine ratio (OR [95% CI]: 1.12 [1.05, 1.20], P = 3.67 × 10^− 4). 11 causal associations with multiple-testing corrected significance (FDR P < 0.05) could be observed in LAS, all of which were metabolites from lipid metabolic pathways. They were as follows: campesterol (OR [95% CI]: 1.67 [1.32, 2.11], P = 1.87 × 10^− 5); 1-palmitoyl-2-dihomo-linolenoyl-GPC (16:0/20:3n3 or 6) (OR [95% CI]: 0.79 [0.70, 0.88], P = 3.98 × 10^− 5); 1,2-dilinoleoyl-GPC (18:2/18:2) (OR [95% CI]: 0.79 [0.70, 0.88], P = 7.24 × 10^− 5); 1-stearoyl-2-docosahexaenoyl-GPC (18:0/22:6) (OR [95% CI]: 1.33 [1.15, 1.54], P = 1.21 × 10^− 4); 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) (OR [95% CI]: 1.33 [1.15, 1.55], P = 1.49 × 10^− 4); 1,2-dilinoleoyl-GPE (18:2/18:2) (OR [95% CI]: 0.75 [0.64, 0.88], P = 3.01 × 10^− 4); arachidonate (20:4n6) (OR [95% CI]: 1.29 [1.12, 1.48], P = 3.42 × 10^− 4); 1-lignoceroyl-GPC (24:0) (OR [95% CI]: 1.68 [1.25, 2.24], P = 5.04 × 10^− 4); 1-myristoyl-2-arachidonoyl-GPC (14:0/20:4) (OR [95% CI]: 1.24 [1.10, 1.40], P = 5.67 × 10^− 4); 1-arachidonylglycerol (20:4) (OR [95% CI]: 1.31 [1.12, 1.54], P = 8.09 × 10^− 4) and 1-oleoyl-GPE (18:1) (OR [95% CI]: 0.75 [0.63, 0.90], P = 1.70 × 10^− 3). And then, we found 5 causal associations in SVS, involving three xenobiotics, one amino acid and one metabolite ratio: thymol sulfate (OR [95% CI]: 1.59 [1.29, 1.96], P = 1.43 × 10^− 5), n-succinyl-phenylalanine (OR [95% CI]: 0.80 [0.72, 0.89], P = 2.91 × 10^− 5); 1-methylxanthine (OR [95% CI]: 0.82 [0.73, 0.91, P = 2.93 × 10^− 4); ferulic acid 4-sulfate (OR [95% CI]: 1.24 [1.10, 1.40, P = 4.11 × 10^− 4) and spermidine to N-acetylputrescine ratio (OR [95% CI]: 0.80 [0.70, 0.90, P = 3.64 × 10^− 4).

We found no evidence of heterogeneity by using Cochran Q test, and MR-Egger intercepts and MR-PRESSO did not reveal any directional pleiotropic effects (Fig. 3). The results from the weighted median method also supported the principal analyses (IVW), which were not significant (Table S11). Leave-one-SNP-out analysis showed that results were robust with all SNPs and were not driven by any single SNP. Finally, the result of bi-directional MR analysis shows that there is no evidence that the occurrence of IS and its subtypes affect these plasma metabolites or metabolite ratios (Table S11).

Identification of plasma metabolites associated with intracerebral hemorrhage risk

Subsequently, we conducted an investigation to elucidate the correlation between genetically determined levels of metabolites and metabolite ratios with the risk of ICH. The results revealed 48 causative associations between plasma metabolites/ metabolite ratios and the risk of ICH (Fig. 2B, Table S10). After FDR correction, we only observed one metabolite with significant causative correlations to ICH: ribitol (OR [95% CI]: 0.54 [0.37, 0.79], P = 1.41 × 10^− 3), which is from the carbohydrate metabolism pathway.

Sensitivity analysis using Cochran’s Q statistic and MR-Egger method indicated no notable heterogeneity and directional pleiotropy across instrument SNP effects (Fig. 2). The result of the weighted median method also supported a protective effect of ribitol (Table S11). No distortion in the leave-one-out plot suggested that no single SNP was driving the observed effect in any analysis. We found no evidence that the occurrence of ICH significantly affects the change of plasma metabolite (Table S11).

Rapid differentiation of ischemic stroke and intracerebral hemorrhage using metabolites

In order to expedite the differential diagnosis between IS and ICH, we conducted an isolation of all metabolites and metabolite ratios that exhibited significant associations with either IS or ICH. Subsequently, wesummarize their effects across all IS and ICH phenotypes. As shown in Fig. 4, we found that elevated level of ribitol, 1-arachidonylglycerol (20:4), arachidonate (20:4n6) and 1-lignoceroyl-GPC (24:0) reduced the risk of ICH, but increased the risk of IS. In contrast, 1,2-dilinoleoyl-GPC (18:2/18:2) levels and 1-palmitoyl-2-dihomo-linolenoyl-GPC (16:0/20:3n3 or 6) levels were positively associated with the risk of ICH, but negatively associated with the risk of LAS. These metabolites may provide new and more convenient indicators for the differential diagnosis of IS and ICH in the future.

Identifying a panel of biomarkers that is capable of differentiating ischemic stroke from intracranial hemorrhage and providing prognostic prediction remains an extremely complex challenge. The screening of potential biomarkers might be performed with plasma metabolites. In this study, a two-sample Mendelian randomization (MR) analysis based on a GWAS datasets of 1,091 plasma metabolites and 309 metabolite ratios from 8,299 participants was performed. We identified 20 metabolites or metabolite ratios that are casually related with IS and its subtypes, and one metabolite that have causative association with the risk of ICH. Among the above biomarkers, 1,2-dilinoleoyl-GPC (18:2/18:2), 1-palmitoyl-2-dihomo-inolenoyl-GPC (16:0/20:3n3 or 6), ribitol, and histidine to asparagine ratio are potential ones that might provide information in the differentiation of hemorrhagic and ischemic stroke. On a more generalized view, sub pathways including pentose, histidine and phosphatidylcholine pathways should be investigated in further studies.

In the present study, rather than identifying the accuracy and efficiency of current biomarkers, we focused on screening novel biomarkers that have not been described in previous studies. For instance, ribitol is a natural pentose alcohol present in some plants and animals and considered as a metabolic intermediate or end product. Ribitol-phosphate glycosylation, an important part of carbohydrate metabolism and pentose metabolism, is a crucial post-translational modification that is involved in numerous biological events¹⁵. Despite its biological significance well recognized, its relationship with various diseases remained incompletely elucidated.

A biomarker panel that is able to safely identify a subgroup of patients with IS would allow pre-hospital thrombolysis in selected cases¹⁶. Optimal blood biomarkers may have the advantage of being minimally invasive, rapidly obtainable, quantitative and reproducible. Blood sampling can be easily repeated at distinct time-points, thus reflecting disease evolution in real-time.

In previous small to moderate sample size studies, several potential biomarkers have been proposed that may aid in early diagnosis, differentiation between ischemic and hemorrhagic stroke, and prediction of hemorrhagic transformation in ischemic stroke. The biomarkers included GFAP (glial fibrillary acidic protein), MMP-9 (matrix metalloproteinase-9), s100b, NT-proBNP (Brain natriuretic peptide), IMA (ischemia-modified albumin), adrenomedullin, miR 124-3p, miR 16 and several small metabolites of lactate, pyruvate, glycolate etc¹⁷.

GFAP is the main intermediate filament protein in mature astrocytes and is involved several processes including cell-cell communication and astrocyte-neuron interaction¹⁸. It is released into the bloodstream when rupture of blood-brain barrier and apoptosis of astrocytes occurred, and is the most commonly studied biomarker with the highest diagnostic accuracy to date. However, it still can not provide enough information in the selection of a subgroup of IS nor the differentiation from ICH⁶. Therefor, attempts with combination of various biomarkers are warranted. Our study provide an overall insight into the metabolic profiles between IS and ICH. If our findings could be confirmed in future cohorts, with pre-hospital or pre-thrombolysis blood samples obtained, the present study may be milestones in the field of acute stroke treatment.

Our study presents some limitations. First, this is a genetic study that identified potential metabolites and pathways which are causally related with IS and ICH, thereafter, it is unable to provide direct information in differentiation and diagnosis of IS and ICH. We need to collect more information with blood samples from patients in further clinical studies, specifically, metabolomics should be performed with the above samples. Second, unlike previous biomarkers like GFAP, our biomarker panel has not be reported in previous studies. Thereafter, more specific metabolites that are able to distinguish IS from ICH should be screened in our identified pathways including pentose, histidine and phosphatidylcholine pathways from blood samples.

In conclusion, this study provides a valuable resource describing the causal relationship of metabolites and pathways and delivers insights into their roles in stroke and its subtypes, thereby offering opportunities for diagnostic targets. These findings provide a foundation for future research in differentiation of IS and ICH and could have a significant impact on the acute treatment.

IS: ischemic stroke; OR: odds ratio; CI: confidence interval; DBP: diastolic blood pressure; GWAS: genome-wide association studies; ICH: intra- cranial hemorrhage; LD: linkage disequilibrium; IVW: Inverse variance weighted; MR: Mendelian randomization;RCT: randomized controlled trials; SNP: single nucleotide polymorphisms; IVT: intravenous thrombolysis; EVT: endovascular treatment; GFAP: glial fibrillary acid protein; CLSA: Canadian Longitudinal Study on Aging; LAS: Large Vessel Stroke; SVS: Small Vessel Stroke; CES: Cardioembolic Stroke;

Acknowledgements We thank all the consortium studies for making the summary association statistics data publicly available.

Author Contribution J.H.S and D.H.Y: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing – Original Draft. J.L.Z and W.W.C: Writing – Review & Editing,Methodology,Software. D.W.Q:Visualization,Data Curation. J.H.S:Writing – Review & Editing, Supervision. D.H.Y and J.L.Z:Writing – Review & Editing.

Funding This study was not supported by any funding.

Data Availability The data used to perform the analyses in the present study were obtained from public GWASs summary statistics (please see methods section).

Ethics Approval All relevant ethics approvals are from original GWASs.

Consent to Participate This study only used publicly available summary statistics from published GWASs. No individual-level data were involved, and no additional informed consent is needed in this study.

Consent for Publication No individual-level data were involved, and no consent for publication is needed for this study.

Conflicting interests The Authors declares that there is no conflict of interest.

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No competing interests reported.

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Potential blood biomarkers to differentiate ischemic and hemorrhagic strokes

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Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Material and methods

Study design

GWAS datasets for plasma metabolites

GWAS datasets for ischemic stroke and intracerebral hemorrhage

Statistical analysis

Results

Identification of plasma metabolites associated with stroke and its subtypes risk

Identification of plasma metabolites associated with intracerebral hemorrhage risk

Rapid differentiation of ischemic stroke and intracerebral hemorrhage using metabolites

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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