ReviewCureus2024
Biomarkers for Early Detection of Stroke: A Systematic Review.
Review in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Multi-Omics Integration in Stroke: Neuroinflammatory Endotypes, Immune Cell Crosstalk, and Precision Biomarker Discovery.International journal of molecular sciences · 2026Review
- Diagnostic performance of circulating microRNAs for the early diagnosis of different types of acute cerebrovascular disease.PloS one · 2026Article
- Ischemic stroke and intervention strategies based on the timeline of stroke progression: Review and prospects.Acta pharmaceutica Sinica. B · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke remains a leading cause of mortality and disability worldwide. Identifying reliable biomarkers for stroke diagnosis and risk prediction could significantly improve patient outcomes through earlier intervention and better risk management. The objective of this systematic review is to systematically review recent studies investigating biomarkers for stroke diagnosis and risk prediction and to synthesize the most promising findings. We conducted a systematic review of 10 studies published between 2008 and 2023 that examined various biomarkers in relation to stroke. Studies were evaluated for quality using a simplified version of the Mixed Methods Appraisal Tool. The reviewed studies investigated a diverse array of biomarkers, including lipids, inflammatory markers, hemodynamic markers, microRNAs, metabolites, and neurodegenerative markers. Key findings include the following: (1) non-traditional lipid markers such as triglycerides and non-high-density lipoprotein cholesterol may be more predictive of stroke risk than low-density lipoprotein; (2) inflammatory markers, particularly IL-6, showed strong associations with stroke risk; (3) hemodynamic markers like midregional proatrial natriuretic peptide (MRproANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) demonstrated potential in distinguishing stroke subtypes; (4) specific microRNAs (miR-125a-5p, miR-125b-5p, miR-143-3p) were upregulated in acute ischemic stroke; (5) metabolomic studies identified novel markers such as tetradecanedioate and hexadecanedioate associated with cardioembolic stroke; (6) neurodegenerative markers (total-tau, neurofilament light chain) were linked to increased stroke risk. This review highlights the potential of various biomarkers in improving stroke diagnosis and risk prediction. While individual markers show promise, a multi-marker approach combined with clinical variables appears most likely to yield clinically useful tools. Further large-scale validation studies are needed before these biomarkers can be implemented in routine clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.