Abstract

AbstractBackgroundAging, chromosomal sex, and genetic risk factors interact to determine the trajectory of an individual’s age‐related biological changes and, in turn, may predispose individual risk or resilience to AD. To advance translational validity of the JAX humanized APOE (hAPOE) mouse model, we investigated whether a well‐documented preclinical AD symptom, inexplicable weight loss, was evident in our hAPOE mouse colony. Further we determined whether this hallmark symptom could be used to detect subpopulations of vulnerable vs resilient.MethodWe used an autoregressive hidden Markov model (AHMM) to identify trajectories of JAX humanized APOE mice based on their longitudinal weight data. We assumed 10 hidden states to estimate proper trajectories. We ran 10 AHMM models to ensure stability and each model was run with 1500 iterations to reach convergence. The AHMM with maximum trace value was selected as the main model. The key trajectories were then identified by clustering together mice ending in the same hidden state. Weight data were collected from November 2018 to September 2021 for 1196 hAPOE mice (652M, 544F) at different time points ranging from 5 to 28 months of age. Genotypes included APOEe3/3, APOEe3/4 and APOEe4/4.ResultAHMM results identified 5 main weight trajectories for mice ending in one of the 5 most probable ending states (formally named A,B,C,D,E). Of these, three states (A,B,D) captured weight loss trajectories for 426 (36%) mice, one state (C) was characterized by an increase in weight for 152 (36%) mice and 403 (13%) mice ending in a state stable (E) with no significant weight loss or increase. Weight loss was more apparent in female mice, while weight increase was more typical of male mice.ConclusionThis mouse cohort displays weight loss that may correspond to individuals exhibiting a hallmark inexplicable weight loss starting around 15/20 months (corresponding to human 50/55 years old) that can be symptomatic of the prodromal phase of AD.AcknowledgementsThis work was supported R01AG057931, RF1AG059093 and the Center for Innovation in Brain Science to RDB.

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