Smelling the Risk: Early Olfactory Deficits, Brain Networks, and Blood Markers of Alzheimers Disease Risk in Humanized APOE Mice
Moon, H. S.; Han, Z. Y.; Anderson, R. J.; Mahzarnia, A.; Stout, J. A.; Niculescu, A.; Tremblay, J.; Badea, A.
Show abstract
Olfactory impairment is a hallmark of early Alzheimers disease (AD), but the underlying mechanisms connecting sensory decline to genetic and environmental risk factors remain unclear. Our integrative analysis combines ethologically relevant olfactory behavior assays, high-resolution diffusion MRI connectomics, and blood transcriptomics in a large cohort of humanized APOE mice stratified by APOE genotype (APOE2, APOE3, APOE4), age, sex, high-fat diet, and immune background (HN). Behaviorally, APOE4 mice exhibited accelerated deficits in odor salience, novelty detection, and memory, especially when exposed to a high-fat diet, whereas APOE2 mice showed resilience (ANOVA: APOE x HN, F(2,1669)=77.25, p<0.001, eta squared effect size = 0.08). Notably, age and diet exerted compounding effects, with older and HFD-fed mice displaying reduced odor-guided exploration (diet x age: F(1,1669)=16.04, p<0.001, eta squared effect size = 0.01). Memory analyses revealed robust genotype- and age-dependent impairments: at 24- and 48-hour delays, recognition indices were significantly lower in APOE4 mice compared to APOE2 (long-term memory: APOE x HN, F(2,395)=5.6, p=0.004). Elastic Net-regularized multi-set canonical correlation analysis (MCCA) linked behavior to brain network substrates, revealing subnetworks whose connectivity explained up to 24 percent of behavioral variance (sum of canonical correlations: 1.27, 95% CI [1.18, 1.85], p<0.0001). High-weighted connections between the ventral orbital cortex, somatosensory cortex, and cerebellar-brainstem pathways were identified as critical nodes for risk or compensation. Integrative blood transcriptomics revealed eigengene modules strongly correlated with imaging changes in olfactory-memory circuits (for example, eigengene 2 vs. subiculum diffusivity: r = -0.5, p < 1e-30, explaining up to 24 percent of variance). Gene ontology analysis pinpointed shared pathways in synaptic signaling, translation, and metabolic regulation across brain and blood. Notably, glutamatergic and synaptic pathways were enriched among genes linking peripheral and central compartments. Collectively, these results demonstrate that olfactory behavior, quantitatively shaped by genotype, age, diet, and immune status, serves as a sensitive and translatable early biomarker of Alzheimers disease risk. Our systems-level approach identifies specific brain networks and peripheral molecular signatures underlying sensory-cognitive vulnerability, providing a robust framework for early detection and targeted intervention in AD.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Scanning ultrasound-mediated memory and functional improvements do not require amyloid-β reduction 94%
- Aging disrupts the coordination between mRNA and protein expression in mouse and human midbrain 94%
- Genome-wide consensus transcriptional signatures identify synaptic pruning linking Alzheimer's disease and epilepsy 94%
Similar papers in this journal
- Endothelial Cells are Heterogeneous in Different Brain Regions and are Dramatically Altered in Alzheimer's Disease 95%
- Developmental olfactory dysfunction and abnormal odor memory in immune-challenged Disc1+/- mice 95%
- Entorhinal-hippocampal circuit integrity is related to mnemonic discrimination and amyloid-β pathology in older adults 94%
Similar papers in this journal
- Western diet consumption impairs memory function via dysregulated hippocampus acetylcholine signaling 95%
- Microglial SIRT2 deficiency aggravates cognitive decline and amyloid pathology in Alzheimer's disease 94%
- Assessment of neurobehavioural traits under axenic conditions: an approach for multiple longitudinal analyses in the same mouse. 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.