The literature cluster reveals a maturing Alzheimer's disease research ecosystem organized around three converging trajectories: mechanistic discovery, computational diagnostics, and precision prevention. At the mechanistic core sits a proteostasis failure narrative—impaired autophagy and mitophagy, driven partly by reduced ULK1 expression, prevent clearance of amyloid-beta and tau aggregates, while disrupted glycolysis and pyruvate metabolism signal broader bioenergetic collapse. This has catalyzed a therapeutic pivot toward mTOR-independent autophagy enhancers (identified via DeepDrugDiscovery platforms), which cross the blood-brain barrier, clear tau aggregates in mouse models, and restore memory function, positioning autophagy restoration and mitochondria-targeted therapy as leading disease-modifying strategies that bypass the toxicity liabilities of classical mTOR inhibition.
A parallel and increasingly sophisticated diagnostic trajectory leverages machine learning across heterogeneous data modalities—volatile organic compounds from exhaled breath, handwriting-based convolutional neural networks, and mid-old stage senescence transcriptomic signatures—to distinguish Alzheimer's from non-Alzheimer's dementia and healthy controls with accuracies exceeding 90% internally, though external validation drops to more modest (75%) levels, underscoring generalizability challenges typical of AI-driven diagnostics moving toward clinical translation. These models increasingly incorporate stratification factors like APOE genotype and sex differences, reflecting a shift toward precision-phenotyping rather than one-size-fits-all diagnostic criteria.
The third trajectory centers on modifiable risk and prevention, anchored by the glymphatic hypothesis: aquaporin-4 genetic variants influence amyloid clearance both directly and through sleep-quality-mediated pathways, motivating proposed sleep intervention trials as a prevention strategy. Complementing this, large cohort studies (PREDIMED, RUSH Memory Aging Project, LonGenity) implicate cardiometabolic hormones, adipokines, and satiety hormones as predictors of incident dementia and AD in aging populations, framing a metabolic-aging interface where dietary intervention may delay clinical conversion in preclinical, biomarker-positive individuals.
Collectively, these threads depict a field triangulating molecular clearance mechanisms, AI-enabled early detection, and lifestyle/metabolic modulation—suggesting future clinical paradigms combining autophagy-targeted pharmacotherapy with sleep- and diet-based prevention, guided by multimodal biomarker and genetic stratification.