Alzheimer’s disease is an incurable chronic neurodegenerative disorder and the leading cause of dementia, imposing a growing economic burden upon society. The disease progression is associated with gradual deposition of amyloid plaques and the formation of neurofibrillary tangles within the brain parenchyma, yet severe dementia is the culminating phase of the enduring pathology. Converging evidence suggests that Alzheimer’s disease-related cognitive decline is the outcome of an extremely complex and persistent pathophysiological process. The disease is characterized by distinctive abnormalities apparent at systemic, histological, macromolecular, and biochemical levels. Moreover, besides the well-defined and self-evident characteristic profuse neurofibrillary tangles, dystrophic neurites, and amyloid-beta deposits, the Alzheimer’s disease-associated pathology includes neuroinflammation, substantial neuronal loss, apoptosis, extensive DNA damage, considerable mitochondrial malfunction, compromised energy metabolism, and chronic oxidative stress. Likewise, distinctive metabolic dysfunction has been named a leading cause and a hallmark of Alzheimer’s disease that is apparent decades prior to disease manifestation. State-of-the-art metabolomics studies demonstrate that altered branched-chain amino acids (BCAAs) metabolism accompanies Alzheimer’s disease development. Lower plasma valine levels are correlated with accelerated cognitive decline, and, conversely, an increase in valine concentration is associated with reduced risk of Alzheimer’s disease. Additionally, a clear BCAAs-related metabolic signature has been identified in subjects with obesity, diabetes, and atherosclerosis. Also, arginine metabolism is dramatically altered in Alzheimer’s disease human brains and animal models. Accordingly, a potential role of the urea cycle in the Alzheimer’s disease development has been hypothesized, and preclinical studies utilizing intervention in the urea cycle and/or BCAAs metabolism have demonstrated clinical potential. Continual failures to offer a competent treatment strategy directed against amyloid-beta or Tau proteins-related lesions, which could face all challenges of the multifaceted Alzheimer’s disease pathology, led to the hypothesis that hyperphosphorylated Tau and deposited amyloid-beta proteins are just hallmarks or epiphenomena, but not the ultimate causes of Alzheimer’s disease. Therefore, approaches targeting amyloid-beta or Tau are not adequate to cure the disease. Accordingly, the modern scientific vision of Alzheimer’s disease etiology and pathogenesis must reach beyond the hallmarks, and look for alternative strategies and areas of research.

The urea cycle is strongly implicated in the pathogenesis of Alzheimer’s disease (AD). Arginase-I (ARGI) accumulation at sites of amyloid-beta (Aβ) deposition is associated with L-arginine deprivation and neurodegeneration. An interaction between the arginase II (ARGII) and mTOR-ribosomal protein S6 kinase β-1 (S6K1) pathways promotes inflammation and oxidative stress. In this study, we treated triple-transgenic (3×Tg) mice exhibiting increased S6K1 activity and wild-type (WT) mice with L-norvaline, which inhibits both arginase and S6K1. The acquisition of spatial memory was significantly improved in the treated 3×Tg mice, and the improvement was associated with a substantial reduction in microgliosis. In these mice, increases in the density of dendritic spines and expression levels of neuroplasticity-related proteins were followed by a decline in the levels of Aβ toxic oligomeric and fibrillar species in the hippocampus. The findings point to an association of local Aβ-driven and immune-mediated responses with altered L-arginine metabolism, and they suggest that arginase and S6K1 inhibition by L-norvaline may delay the progression of AD.

Polyamines are nitrogen-rich polycationic ubiquitous bioactive molecules with diverse evolutionary-conserved functions. Their activity interferes with numerous genes' expression resulting in cell proliferation and signaling modulation. The intracellular levels of polyamines are precisely controlled by an evolutionary-conserved machinery. Their transient synthesis is induced by heat stress, radiation, and other traumatic stimuli in a process termed the polyamine stress response (PSR). Notably, polyamine levels decline gradually with age; and external supplementation improves lifespan in model organisms. This corresponds to cytoprotective and reactive oxygen species scavenging properties of polyamines. Paradoxically, age-associated neurodegenerative disorders are characterized by upsurge in polyamines levels, indicating polyamine pleiotropic, adaptive, and pathogenic roles. Specifically, arginase overactivation and arginine brain deprivation have been shown to play an important role in Alzheimer's disease (AD) pathogenesis. Here, we assert that a universal short-term PSR associated with acute stimuli is beneficial for survival. However, it becomes detrimental and maladaptive following chronic noxious stimuli, especially in an aging organism. Furthermore, we regard cellular senescence as an adaptive response to stress and suggest that PSR plays a central role in age-related neurodegenerative diseases' pathogenesis. Our perspective on AD proposes an inclusive reassessment of the causal relationships between the classical hallmarks and clinical manifestation. Consequently, we offer a novel treatment strategy predicated upon this view and suggest fine-tuning of arginase activity with natural inhibitors to preclude or halt the development of AD-related dementia.

Alzheimer’s disease (AD) is often comorbid with other pathologies. First, we review shortly the diseases most associated with AD in the clinic. Then we query PubMed citations for the co-occurrence of AD with other diseases, using a list of 400 common pathologies. Significantly, AD is found to be associated with schizophrenia and psychosis, sleep insomnia and apnea, type 2 diabetes, atherosclerosis, hypertension, cardiovascular diseases, obesity, fibrillation, osteoporosis, arthritis, glaucoma, metabolic syndrome, pain, herpes, HIV, alcoholism, heart failure, migraine, pneumonia, dyslipidemia, COPD and asthma, hearing loss, and tobacco smoking. Trivially, AD is also found to be associated with several neurodegenerative diseases, which are disregarded. Notably, our predicted results are consistent with the previously published clinical data and correlate nicely with individual publications. Our results emphasize risk factors and promulgate diseases often associated with AD. Interestingly, the comorbid diseases are often degenerative diseases exacerbated by reactive oxygen species, thus underlining the potential role of antioxidants in the treatment of AD and comorbid diseases.

Animal models, particularly transgenic mice, are extensively used in Alzheimer’s disease (AD) research to emulate key disease hallmarks, such as amyloid plaques and neurofibrillary tangles formation. Although these models have contributed to our understanding of AD pathogenesis and can be helpful in testing potential therapeutic interventions, their reliability is dubious. While preclinical studies have shown promise, clinical trials often yield disappointing results, highlighting a notable gap and disparity between animal models and human AD pathology. Existing models frequently overlook early-stage human pathologies and other key AD characteristics, thereby limiting their application in identifying optimal therapeutic interventions. Enhancing model reliability necessitates rigorous study design, comprehensive behavioral evaluations, and biomarker utilization. Overall, a nuanced understanding of each model’s neuropathology, its fidelity to human AD, and its limitations is essential for accurate interpretation and successful translation of findings. This article analyzes the discrepancies between animal models and human AD pathology that complicate the translation of findings from preclinical studies to clinical applications. We also delve into AD pathogenesis and attributes to propose a new perspective on this pathology and deliberate over the primary limitations of key experimental models. Additionally, we discuss several fundamental problems that may explain the translational failures and suggest some possible directions for more effective preclinical studies.

Autoimmune diseases (AIDs) are often co-associated, and about 25% of patients with one AID tend to develop other comorbid AIDs. Here, we employ the power of datamining to predict the comorbidity of AIDs based on their normalized co-citation in PubMed. First, we validate our technique in a test dataset using earlier-reported comorbidities of seven knowns AIDs. Notably, the prediction correlates well with comorbidity (R = 0.91) and validates our methodology. Then, we predict the association of 100 AIDs and classify them using principal component analysis. Our results are helpful in classifying AIDs into one of the following systems: (1) gastrointestinal, (2) neuronal, (3) eye, (4) cutaneous, (5) musculoskeletal, (6) kidneys and lungs, (7) cardiovascular, (8) hematopoietic, (9) endocrine, and (10) multiple. Our classification agrees with experimentally based taxonomy and ranks AID according to affected systems and gender. Some AIDs are unclassified and do not associate well with other AIDs. Interestingly, Alzheimer’s disease correlates well with other AIDs such as multiple sclerosis. Finally, our results generate a network classification of autoimmune diseases based on PubMed text mining and help map this medical universe. Our results are expected to assist healthcare workers in diagnosing comorbidity in patients with an autoimmune disease, and to help researchers in identifying common genetic, environmental, and autoimmune mechanisms.