The role of the iron-chelating agent deferoxamine in spinal cord injury: a systematic review and meta-analysis of preclinical studies
Notice bibliographique
Résumé
BACKGROUND CONTEXT: Despite advances in the medical, surgical and rehabilitation treatment of spinal cord injury (SCI), there remains a need to develop therapeutic strategies to address mechanisms of secondary injury. Numerous preclinical studies have investigated the efficacy of deferoxamine, an iron chelating agent, in improving functional outcomes in SCI models. Anti-inflammatory, antiferroptosis, and lipoperoxidation inhibitory mechanisms have been proposed as the underlying mechanisms of action. PURPOSE: Considering preclinical studies are the gate to translational medicine, we present a quantitative synthesis of the existing literature to shed light on gaps and guide future research in neuroprotection for SCI. STUDY DESIGN: Meta-analysis of preclinical studies involving the use of deferoxamine in in-vivo models of traumatic SCI, regardless of level of injury and/or animal species. METHODS: We conducted a comprehensive search in 3 databases (PubMed, Web of Science, Scopus) in accordance with PRISMA guidelines. Our review protocol was preregistered (PROSPERO registration ID: CRD420251007113). Ten preclinical studies that investigated deferoxamine administration and functional outcomes in animal models of SCI were chosen. We performed a meta-analysis, using a random effects approach, comparing normalized mean differences in deferoxamine-treated and control groups. To minimize heterogeneity, subgroup analysis and meta regression were conducted. We utilized the CAMARADES checklist for quality assessment of the included studies. RESULTS: Out of 157 articles, 10 were included for final analysis. Our results revealed that deferoxamine can improve functional scores in animal models of thoracic traumatic SCI (NMD=35.74% [18.74, 52.75]; p<.001). The funnel plot was symmetric and no publication bias was noted. Heterogeneity was high among included studies (I²: 95.98). The median score for CAMARADES was 5 (IQR range: 4-6), indicating moderate overall quality of include studies. Sample size calculation and allocation concealment were lacking in included studies. Meta regression was significant for assessment time (Coefficient: -0.574 [-0.905, -0.242]) and route of administration. CONCLUSION: We demonstrated that deferoxamine improves hindlimb motor function in animal models of SCI. Potential knowledge gaps exist, necessitating future animal studies across a more diverse range of injury levels and functional assessment tools before moving on to conducting clinical trials. Assessment time may play a critical role in preclinical models and needs to be adjusted to clinically plausible time points in future studies. More inter-species preclinical research, especially in nonhuman primates, is encouraged to foster generalizability and clinical applicability of the models. CLINICAL SIGNIFICANCE: Approved medical interventions for neuroprotection in SCI are lacking. Given the promising results of the included preclinical studies as well as the established clinical safety profile of deferoxamine, our paper provides a basic science foundation for the design and implementation of future randomized controlled trials. Notwithstanding, these trials may be withheld or approached with caution until more extensive preclinical research is available.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,015 | 0,022 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».