APOEϵ4 and exercise interact to influence systemic and cerebral risk factors for dementia
Foley, K. E.; Diemler, C. A.; Hewes, A. A.; Garceau, D. T.; Sasner, M.; Howell, G. R.
Show abstract
INTRODUCTIONAPOE{varepsilon}4 is the strongest genetic risk factor for Alzheimers disease and related dementias (ADRDs) affecting many different pathways that lead to cognitive decline. Exercise is one of the most widely proposed prevention, and intervention strategies to mitigate risk and symptomology of ADRDs. Importantly, exercise and APOE{varepsilon}4 affect similar processes on the body and brain. While both APOE{varepsilon}4, and exercise have been studied extensively, their interactive effects are not well understood. METHODSTo address this, male and female APOE{varepsilon}3/{varepsilon}3, APOE{varepsilon}3/{varepsilon}4 and APOE{varepsilon}4/{varepsilon}4 mice ran voluntarily from wean (1mo) to midlife (12mo). Longitudinal and cross-sectional phenotyping was performed on the periphery and the brain, on markers of risk for dementia such as weight, body composition, circulating cholesterol composition, activities of daily living, energy expenditure, and cortical and hippocampal transcriptional profiling. RESULTSData revealed chronic running decreased age-dependent weight gain, lean and fat mass, and serum LDL concentration dependent on APOE genotype. Additionally, murine activities of daily living and energy expenditure were significantly influenced by an interaction between APOE genotype and running in both sexes. Transcriptional profiling of the cortex and hippocampus predicted that APOE genotype and running interact to affect numerous biological processes including vascular integrity, synaptic/neuronal health, cell motility, and mitochondrial metabolism, in a sex-specific manner. DISCUSSIONThese data provide compelling evidence that APOE genotype should be considered for population-based strategies that incorporate exercise to prevent ADRDs.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Activation of the muscle-to-brain axis ameliorates neurocognitive deficits in an Alzheimer disease mouse model via enhancing neurotrophic and synaptic signaling 96%
- Sexual Dimorphic Metabolic and Cognitive Responses of C57BL/6 Mice to Fisetin or Dasatinib and Quercetin Cocktail Oral Treatment 95%
- Hypothalamic Sex-Specific Metabolic Shift by Canagliflozin during Aging 95%
Similar papers in this journal
- The effects of caloric restriction on adipose tissue and metabolic health are sex- and age-dependent 95%
- The effects of 17α-estradiol treatment on endocrine system revealed by single-nucleus transcriptomic sequencing of hypothalamus 94%
- Lipid hydroperoxides promote sarcopenia through carbonyl stress 94%
Similar papers in this journal
- Gut microbiota-dependent increase in phenylacetic acid induces endothelial cell senescence during aging 94%
- Nerve-associated macrophages control adipose homeostasis across lifespan and restrain age-related inflammation 94%
- GDF3 promotes adipose tissue macrophage-mediated inflammation via altered chromatin accessibility during aging 94%
Similar papers in this journal
- Aging disrupts the coordination between mRNA and protein expression in mouse and human midbrain 94%
- Deletion of Crtc1 leads to hippocampal neuroenergetic impairments associated with depressive-like behavior 93%
- Distinct roles for MNK1 and MNK2 in social and cognitive behavior through kinase-specific regulation of the synaptic proteome and phosphoproteome 93%
Similar papers in this journal
- Fasting is required for many of the benefits of calorie restriction in the 3xTg mouse model of Alzheimer&aposs disease 96%
- Distinct and additive effects of calorie restriction and rapamycin in aging skeletal muscle 95%
- MOTS-c is an Exercise-Induced Mitochondrial-Encoded Regulator of Age-Dependent Physical Decline and Muscle Homeostasis 95%
"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.