Multidimensional Modeling of Biological Aging: Integrating Gait, Eye Movement, Rest‐State Functional Connectivity, and Plasma Biomarkers in non Dementia Elderly Individuals.

Jingyi Lin, Ziyu Ouyang, Tianyan Xu, Lu Shen, Bin Jiao

Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, ChinaHunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China · pubmed

Paper

Abstract

Aging exhibits significant variation among individuals, with biological age as a more reliable predictor of current health status compared to chronological age. Predicting biological age is crucial for facilitating timely interventions aimed at improving the adaptation to the aging process. Given the intricate and multifactorial nature of aging, a scientific approach involves constructing a prediction model for biological age that incorporates multiple dimensions systematically.

The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.

Research summary

The paper addresses the prediction of biological age through a multidimensional approach, which is relevant to understanding the aging process and potentially improving health outcomes in the elderly. However, while it presents a systematic model integrating various factors, the contribution appears to be solid but limited in its immediate implications for longevity research. It does not propose novel interventions or solutions that directly target the root causes of aging, which diminishes its overall impact in the field.

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