ArticleJournal of Alzheimer's disease : JAD2021
Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer's Disease Mice.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- What Threshold of Amyloid Reduction Is Necessary to Meaningfully Improve Cognitive Function in Transgenic Alzheimer's Disease Mice?Journal of Alzheimer's disease reports · 2024Article
- Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics.Brain structure & function · 2024Article
- Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2023Article
- Apolipoprotein E knockout may affect cognitive function in D-galactose-induced aging mice through the gut microbiota-brain axis.Frontiers in neuroscience · 2022Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundApolipoprotein E (APOE) genotypes typically increase risk of amyloid-β deposition and onset of clinical Alzheimer's disease (AD). However, cognitive assessments in APOE transgenic AD mice have resulted in discord.
objectiveAnalysis of 31 peer-reviewed AD APOE mouse publications (n = 3,045 mice) uncovered aggregate trends between age, APOE genotype, gender, modulatory treatments, and cognition.
methodsT-tests with Bonferroni correction (significance = p < 0.002) compared age-normalized Morris water maze (MWM) escape latencies in wild type (WT), APOE2 knock-in (KI2), APOE3 knock-in (KI3), APOE4 knock-in (KI4), and APOE knock-out (KO) mice. Positive treatments (t+) to favorably modulate APOE to improve cognition, negative treatments (t-) to perturb etiology and diminish cognition, and untreated (t0) mice were compared. Machine learning with random forest modeling predicted MWM escape latency performance based on 12 features: mouse genotype (WT, KI2, KI3, KI4, KO), modulatory treatment (t+, t-, t0), mouse age, and mouse gender (male = g_m; female = g_f, mixed gender = g_mi).
resultsKI3 mice performed significantly better in MWM, but KI4 and KO performed significantly worse than WT. KI2 performed similarly to WT. KI4 performed significantly worse compared to every other genotype. Positive treatments significantly improved cognition in WT, KI4, and KO compared to untreated. Interestingly, negative treatments in KI4 also significantly improved mean MWM escape latency. Random forest modeling resulted in the following feature importance for predicting superior MWM performance: [KI3, age, g_m, KI4, t0, t+, KO, WT, g_mi, t-, g_f, KI2] = [0.270, 0.094, 0.092, 0.088, 0.077, 0.074, 0.069, 0.061, 0.058, 0.054, 0.038, 0.023].
conclusionAPOE3, age, and male gender was most important for predicting superior mouse cognitive performance.
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