Year: 2023
Type: editorial
Abstract: IntroductionDementia impacts over 55 million individuals globally, creating a pressing public health concern characterized by cognitive and functional decline beyond typical aging. As highlighted by the World Alzheimer Report (Alzheimer's Disease International, 2022) and the World Health Organization in 2022 (WHO, 2022), dementia is a principal cause of disability, dependence, and death among the elderly. Its repercussions, both personally and socially, vary in magnitude, often reflecting the inequality gradients of the respective nations. Today's research in aging, dementia, and other brain diseases is moving away from one-size-fits-all models to embrace multifaceted, heterogeneity-aware, and diversity-driven paradigms (Alladi and Hachinski, 2018; Parra et al., 2018; Dehghani et al., 2021; Parra et al., 2021; Frisoni et al., 2022; Ibanez, 2022; Moguilner et al., 2022; Parra et al., 2022; Ibanez and Zimmer, 2023; Maito et al., 2023; Santamaria-Garcia et al., 2023). This dynamic evolution in dementia studies is pioneering new interdisciplinary connections, spanning various methods, scales, populations, and regions. It dives into genetic diversity, protein aggregation patterns, synaptic activities, disparities across multiple fronts, advanced biomarkers, and the interplay of environmental triggers with physiological stress mechanisms. Moreover, a trend towards non-generalized samples and unique research designs in neuroscience has become evident. As we journey into the third decade of the 21st Century, remarkable strides have been made in Alzheimer's disease and related dementia. Frontiers has curated a series of Research Topics to spotlight these advancements in Aging Neuroscience. This special edition offers a concise overview of selected studies to enlighten and guide the research community.There has been a growing interest in different factors impacting cognitive function, dementia, and Alzheimer's disease (AD). Costa-Laparra et al., delved into the relationship between specific genetic factors, such as the APOE ε4 allele and G206D-PSEN1 mutation, and mitochondrial abnormalities in AD. Their study demonstrated that these genetic variations could lead to mitochondrial fragmentation and an increased propensity for oxidative stress-induced cell death, potentially contributing to AD and neurodegeneration. Yang et al., explored the impact of hearing aids on cognitive function in middle-aged and older adults with hearing loss, including those with AD, dementia, and depressive symptoms. Their meta-analysis revealed that for those with AD or dementia, hearing aids did not significantly enhance cognitive function. Thus, the efficacy of these aids in non-demented subjects remains uncertain. In a diverse population, Cheng et al., devised a predictive model for cognitive impairment in elderly illiterate Chinese women (n= 1864), determining that age, daily living activities, and waist-to-height ratio were critical predictors. Meanwhile, Hwangbo et al., drawing longitudinal data from South Korea (n=794,448), identified physical inactivity, diabetes, and hypertension as the top modifiable risk factors for dementia, emphasizing the importance of lifestyle modifications for prevention. Lastly, Balzamino et al., used a Reeler murine model, deficient in the Reelin protein, to show parallels between pathological hallmarks of AD and ocular degeneration, suggesting potential shared pathways in neurodegenerative diseases and age-related macular degeneration. Collectively, these studies underscore the multifaceted nature of cognitive decline and dementia, hinting at diverse intervention points for prevention and treatment. This Research Topic showcases handpicked studies from Alzheimer's disease and related dementia research. Our aspiration is that these insights not only spotlight past advancements but also illuminate the upcoming challenges in the domain.Author contributionsAI prepared the initial draft of this editorial. ABR, FA, and NC carefully revised the draft. All authors contributed to the contents of this article and approved the final version.
Source: Frontiers in Aging Neuroscience
Institutions: Consejo Nacional de Investigaciones Científicas y Técnicas, University of California, San Francisco, Trinity College Dublin, Adolfo Ibáñez University, University of San Andrés, Global Brain Health Institute, NYU Grossman Long Island School of Medicine, New York University, Instituto Peruano de Neurociencias, Vita-Salute San Raffaele University
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,295
Funders: Alzheimer's Association, Agencia Nacional de Investigación y Desarrollo, National Institutes of Health, National Institute on Aging, Fogarty International Center, Fondo de Fomento al Desarrollo Científico y Tecnológico, Fondo de Financiamiento de Centros de Investigación en Áreas Prioritarias
Awards: G103627852, G1581799926, G1919018801, G2118524674, G2363475639, MACHINE LEARNING FOR MENTAL HEALTH NEW DEVELOPMENTS AND APPLICATIONS USING MULTIMODAL DATA, G2822108074, G314322576, G318596217, G3481889345, G3628792472, CENTRO DE GEROCIENCIA SALUD MENTAL Y METABOLISMO (GERO), G4113034044, G4258903815, G5163463781, G5232646308, G5250673234, G5566439427, MACHINE LEARNING PARA EL DIAGNOSTICO PRECOZ Y PERFIL MULTIMODAL DE LA ENFERMEDAD DE ALZHEIMER BASADO EN MIRNA DE EXOSOMAS CIRCULANTES., G5881588278, G5996283446, G61846566, SENSITIVE NEUROCOGNITIVE MARKERS OF NEURODEGENERATION A MULTI-FEATURE FRAMEWORK, G6619092739, G7616641907, NATURAL SPEECH MARKERS OF NEURODEGENERATION AN INTERDISCIPLINARY APPROACH FOR DISEASE DISCRIMINATION AND SYMPTOM TRACKING, MAPPING AND MODELING THE HEALTHY AND PATHOLOGICAL AGING TRAJECTORIES OF THE HUMAN BRAIN, US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative Research, Frontotemporal Dementia: Genes, Images, and Emotions, An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populations
OpenAlex ID: W4386957723