Age-Related Retinal Layer Thickness Changes Measured by OCT in APP NL-F/NL-F Mice: Implications for Alzheimer's Disease.
Lidia Sánchez-PueblaRosa de de HozElena Salobrar-GarciaAlberto Arias-VázquezMaría González-JiménezAna I RamírezJose A Fernández-AlbarralJosé A MatamorosLorena Elvira-HurtadoTakaomi C SaidoTakashi SaitoCarmen Nieto VaqueroMaría I CuarteroMaría Angeles MoroJuan J SalazarInés López-CuencaJosé M RamírezPublished in: International journal of molecular sciences (2024)
In Alzheimer's disease (AD), transgenic mouse models have established links between abnormalities in the retina and those in the brain. APP NL-F/NL-F is a murine, humanized AD model that replicates several pathological features observed in patients with AD. Research has focused on obtaining quantitative parameters from optical coherence tomography (OCT) in AD. The aim of this study was to analyze, in a transversal case-control study using manual retinal segmentation via SD-OCT, the changes occurring in the retinal layers of the APP NL/F-NF/L AD model in comparison to C57BL/6J mice (WT) at 6, 9, 12, 15, 17, and 20 months of age. The analysis focused on retinal thickness in RNFL-GCL, IPL, INL, OPL, and ONL based on the Early Treatment Diabetic Retinopathy Study (ETDRS) sectors. Both APP NL-F/NL-F -model and WT animals exhibited thickness changes at the time points studied. While WT showed significant changes in INL, OPL, and ONL, the AD model showed changes in all retinal layers analyzed. The APP NL-F/NL-F displayed significant thickness variations in the analyzed layers except for the IPL compared to related WT. These thickness changes closely resembled those found in humans during preclinical stages, as well as during mild and moderate AD stages, making this AD model behave more similarly to the disease in humans.
Keyphrases
- optical coherence tomography
- diabetic retinopathy
- optic nerve
- oxidative stress
- stem cells
- mass spectrometry
- multiple sclerosis
- type diabetes
- machine learning
- signaling pathway
- metabolic syndrome
- deep learning
- cognitive decline
- case control
- immune response
- functional connectivity
- high intensity
- subarachnoid hemorrhage
- combination therapy
- pi k akt
- brain injury
- insulin resistance