MétaCan
Menu
Retour à la cohorte
Enregistrement W4400863890 · doi:10.1097/cm9.0000000000003224

Retinal sub-layer thicknesses, presence of lacunes, and their interaction with cognitive performance in recent single subcortical infarction

2024· article· en· W4400863890 sur OpenAlexaboutno aff
Jingyu Cui, William Robert Kwapong, Yuying Yan, Le Cao, Chen Ye, Hang Wang, Shuai Jiang, Bo Wu

Notice bibliographique

RevueChinese Medical Journal · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueRetinal Imaging and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesWest China Hospital, Sichuan UniversitySichuan UniversityChina Postdoctoral Science FoundationNational Natural Science Foundation of China
Mots-clésDementiaMedicineRetinalHyperintensityStroke (engine)RetinaWhite matterMagnetic resonance imagingCognitionCardiologyInternal medicineDiseaseNeuroscienceOphthalmologyRadiologyPsychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

To the Editor: Recent single subcortical infarction (RSSI), formerly known as lacunar stroke, is a neurological syndrome that occurs when a small perforating artery in the brain becomes blocked, resulting in ischemia and neurological impairment.[1] Radiological indicators of cerebral small vessel disease (SVD), such as lacunes and white matter hyperintensity (WMH), have gained increasing attention because of their role in cognitive impairment and dementia.[1] The correlation among radiological indicators enables the investigation of their impact on both structural and functional damage to the brain. The brain and the retina share many characteristics such as embryologic origin, precise neuronal cell layers, and microvasculature.[2,3] The presence of ocular manifestations in RSSI and other forms of ischemic stroke emphasizes the strong relationship between the retina and the brain.[2,3] Quantitative changes in the retinal structure such as thinning of the retinal layers have been associated with RSSI and cerebral pathologies.[2,3] However, data on the association between both retinal structural changes and radiological markers in RSSI and how these factors jointly influence cognition are lacking. This study aimed to investigate the association between optical coherence tomography (OCT) metrics and the presence of lacunes on cognitive performance in RSSI patients. The study was approved by The West China Hospital of Sichuan University Ethics Committee (No. 2020[922]). All participants provided written informed consent before enrolling in the study. A total of 132 RSSI patients between January 2021 and October 2023 underwent magnetic resonance imaging, and SVD markers were assessed [Figure 1A]. SVD MRI markers such as lacunes, WMH, and enlarged perivascular spaces (PVS) were evaluated according to the Standards for ReportIng Vascular changes on Neuroimaging (STRIVE) consensus criteria.[1] The swept-source (SS)-OCT (VG200S; SVision Imaging, Henan, China; version 2.0.106) was used for retinal imaging in all participants. The specifications of the tool have been well described in our previous report.[4] For retinal structural thicknesses, automatic segmentation of the retinal thickness was done by the OCT tool. Here, we analyzed the retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) in a 3 mm × 3 mm[2] area around the fovea in the macula as shown in Figure 1A. The RNFL was defined as the thickness between the base of the inner limiting membrane (ILM) to the top border of the ganglion cell layer (GCL). GCIPL was defined as the thickness from the base of the RNFL to the top border of the inner nuclear layer (INL). The Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) was performed on all participants by a well-trained physician. In our study, cutoff MoCA-BJ score was 22 for the detection of cognitive impairment. Participants with MoCA scores ≥22 were characterized as cognitively normal (non-cognitively impaired [NCI]), while those with MoCA scores ≤21 were characterized as cognitively impaired (CI). Of the 132 RSSI patients included in our study, 81 were NCI while 51 were CI. Demographic and clinical characteristics and data analysis are shown in the Supplementary Tables 1−3, https://links.lww.com/CM9/C82. CI patients had thinner RNFL (Z = −2.290, P = 0.022) thickness compared to NCI as shown in the Supplementary Table 1, https://links.lww.com/CM9/C82; no significant difference was seen in the GCIPL thickness when both groups were compared (Z = −1.011, P = 0.313). RNFL (β = 0.366, P = 0.042) and GCIPL (β = 0.093, P = 0.034) thicknesses showed significant correlations with MoCA scores as shown in Figure 1B and Supplementary Table 2, https://links.lww.com/CM9/C82. Similarly, the presence of lacunes (β = −1.478, P = 0.029) significantly correlated with MoCA scores. In this RSSI cohort, there was a significant interaction between RNFL (β = 0.712, P = 0.036) and GCIPL thicknesses (β = 0.209, P = 0.013) and the presence of lacunes on MoCA scores, respectively [Supplementary Table 3, https://links.lww.com/CM9/C82].Figure 1: (A) Representative image of MRI markers and OCT metrics. RSSI in the basal ganglia on DWI (a). Moderate to severe PVS in the basal ganglia on T2-weighted imaging (b). High-grade periventricular WMH (Fazekas score 3) (c) and deep WMH (Fazekas score 2) (d) on FLAIR. Two chronic lacunes in the right centrum semiovale (arrowheads) on FLAIR (d) and T1-weighted imaging (e). The RNFL was defined as the thickness between the base of the ILM to the top border of the GCL; RNFL is shown as the thickness between the two red lines. GCIPL was defined as the thickness from the base of the RNFL to the top border of the INL. GCIPL is represented as the thickness between the lower red line and the blue line (f). (B) Correlation between MoCA scores and OCT metrics and presence of lacunes in RSSI patients. A positive correlation was seen between MoCA scores and RNFL (a) and GCIPL (b) thicknesses respectively. A negative correlation was seen between MoCA scores and the presence of lacunes (c). The X-axis and Y-axis of added variable plots were the residuals of the dependent variable and the independent variable when both of these variables were regressed on risk factors. DWI: Diffusion-weighted imaging; FLAIR: Fluid-attenuated inversion recovery; GCIPL: Ganglion cell-inner plexiform layer; GCL: Ganglion cell layer; ILM: Inner limiting membrane; INL: Inner nuclear layer; MoCA: Montreal Cognitive Assessment; MRI: Magnetic resonance imaging; OCT; Optical coherence tomography; PVS: Perivascular space; RNFL: Retinal nerve fiber layer; RSSI: Recent single subcortical infarction; WMH: White matter hyperintensity.Changes in the retinal structural thicknesses are suggested to reflect related neurodegeneration occurring in the brain.[3,4] Retinal imaging studies have shown that cerebral infarction patients have thinner RNFL thickness due to neurodegeneration.[3] This study showed that patients with CI have thinner RNFL thickness compared to NCI. The retinal sub-layer thicknesses are measures of retinal ganglion cell integrity; RNFL includes the retinal ganglion cell axons whilst the GCIPL contains the cell bodies and dendrites of the retinal ganglion cells.[4] Thinning of these retinal structural layers reflects neurodegeneration and is suggested to be associated with cognitive dysfunction. There is increasing evidence that cognitive impairment occurs after RSSI through microvascular dysfunction.[5] Given that microvascular dysfunction is associated with neurodegeneration and the retinal microvasculature is responsible for the metabolism of retinal neuronal integrity, RNFL thinning in CI compared to NCI may suggest that retinal neurodegeneration may be more severe in RSSI patients with CI. This study found a substantial correlation between the MoCA scores of RSSI patients and the GCIPL and RNFL, suggesting that OCT measurement may be a useful tool for identifying cognitive deterioration in these individuals. This study also showed that the presence of lacunes was significantly associated with cognitive function in RSSI patients. The cognitive function in RSSI associated with the presence of lacunes may well be due to damage to cortical-subcortical pathways, disrupting the complex and distributed networks that underpin cognition. The study showed an interaction between RNFL and GCIPL thicknesses and the presence of lacunes on MoCA scores in RSSI patients, respectively. In the retina, thinning of the RNFL and GCIPL (neurodegeneration) is suggested to be associated with reduced blood flow as a result of ischemia.[2,5] A similar mechanism involving hypoxia and/or cerebral ischemia has been proposed to contribute to the formation of lacunes. These similarities suggest that comparable mechanisms may occur concurrently in both the retina and brain of RSSI patients. Thus, the interaction between RNFL and GCIPL thicknesses and the presence of lacunes on cognitive performance in RSSI patients suggests that changes in OCT metrics (retinal structural thicknesses) and the presence of lacunes may jointly influence cognitive performance. Our findings have important clinical implications. Cognitive impairment after stroke is one of the major determinants of functional dependence in stroke survivors. As the pathophysiology and trajectory of cognitive decline after stroke are complex, with numerous determinants, thus, there is a need for biomarkers that may be sensitive to cognitive changes. An important finding in our study was the association between OCT metrics and the presence of lacunes with cognitive performance in RSSI patients. These findings suggest that changes in the retina and brain may occur concurrently leading to cognitive changes in RSSI patients. Therefore, retinal changes after RSSI in clinics should not be neglected. The retina and brain should be considered as means to observe pathophysiological changes associated with cognitive changes after RSSI. These findings may enable the assessment of treatment at an earlier stage. In conclusion, our study with quantitative measures of the retinal structural thicknesses and presence of lacunes in RSSI patients provides insight into temporal dynamics of neurodegeneration in the retina and brain on cognitive function. The clinical utility of OCT metrics and the presence of lacunes in predicting cognitive impairment in RSSI patients may require further study. Funding This work was supported by grants from the National Natural Science Foundation of China (Nos. 82271328, 82071320, 82371322, and 82301661), China Postdoctoral Science Foundation (Nos. 2022M712249 and 2023T160447), Post-Doctor Research Project, West China Hospital, Sichuan University (No. 2023HXBH007), National Key R&D Program of China (Nos. 2023YFC2506600, 2023YFC2506603), and the 1·3·5 Project for Disciplines of Excellence–Clinical Research Incubation Project of West China Hospital at Sichuan University (No. 2020HXFH012). Conflicts of interest None.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,286
Score d'incertitude au seuil0,436

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,300
Écart entre enseignants0,285 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueChinese Medical JournalMême sujetRetinal Imaging and AnalysisTravaux en français237 207