Studies related to the intersection of normal aging have the privilege of an immutable standard on which to compare - namely chronological age. With this comes a complex of derivable and quantifiable measures that can be explored through cross-sectional, translational, and longitudinal methods in a variety of conditions. The development and validation of these measures are even more important in the context of senescence and aging given potential disease trajectories and therapeutics across the life span. This collection seeks to feature research that elucidates mechanisms, demonstrates new or refined technologies, or creates models that permit for specific quantifiable measures that can be ultimately applied to human and animal models in the context of normal and abnormal aging as well as dementia.
Age is related to many biomarkers itself, however, not all humans and animals reflect “age” in a similar manner. Chronological age, as a criterion and covariate, is ubiquitous in studies and analyses. This collection should serve as an update of works that reflect innovative and new techniques to explore the patterns or features that may explain variations not otherwise explainable due to chronological age. Such measures may explain the contribution of diseases that ‘mimic’ aging, or the vulnerability, or imperviousness, of individuals defined by their chronological age. As populations are living longer the need to explain and understand the difference between estimated or quantifiable age and canonical age is more important as older populations have more poorly defined age-based risk of disease. Measures that help define abnormal aging profiles of either tissue, cognition or physical abilities may help better explain relative risk not otherwise attributable to chronological age. Given the breadth of data available and the ability to share algorithms and code, this topic would hope to yield reproducible methods and works that could be integrated into existing platforms where differential effects of aging are not explicitly considered.
Methods related to the estimation, prediction of age in healthy and disease populations, with a focus on dementia using, but not limited to, the following modalities
• Neuroimaging
• Genetics
• Cognition
• Cardiovascular measures
• Psychiatric assessments
• Environmental Factors
• Social factors
Studies related to the intersection of normal aging have the privilege of an immutable standard on which to compare - namely chronological age. With this comes a complex of derivable and quantifiable measures that can be explored through cross-sectional, translational, and longitudinal methods in a variety of conditions. The development and validation of these measures are even more important in the context of senescence and aging given potential disease trajectories and therapeutics across the life span. This collection seeks to feature research that elucidates mechanisms, demonstrates new or refined technologies, or creates models that permit for specific quantifiable measures that can be ultimately applied to human and animal models in the context of normal and abnormal aging as well as dementia.
Age is related to many biomarkers itself, however, not all humans and animals reflect “age” in a similar manner. Chronological age, as a criterion and covariate, is ubiquitous in studies and analyses. This collection should serve as an update of works that reflect innovative and new techniques to explore the patterns or features that may explain variations not otherwise explainable due to chronological age. Such measures may explain the contribution of diseases that ‘mimic’ aging, or the vulnerability, or imperviousness, of individuals defined by their chronological age. As populations are living longer the need to explain and understand the difference between estimated or quantifiable age and canonical age is more important as older populations have more poorly defined age-based risk of disease. Measures that help define abnormal aging profiles of either tissue, cognition or physical abilities may help better explain relative risk not otherwise attributable to chronological age. Given the breadth of data available and the ability to share algorithms and code, this topic would hope to yield reproducible methods and works that could be integrated into existing platforms where differential effects of aging are not explicitly considered.
Methods related to the estimation, prediction of age in healthy and disease populations, with a focus on dementia using, but not limited to, the following modalities
• Neuroimaging
• Genetics
• Cognition
• Cardiovascular measures
• Psychiatric assessments
• Environmental Factors
• Social factors