Modeling the impact of socioeconomic disparity, biological markers and environmental exposures on phenotypic age using mediation analysis and structural equation models.

Daniele Pala, Yuezhi Xie, Jia Xu, Li Shen

Department of Computer, Electrical and Biomedical Engineering, University of Pavia, Pavia, Italy; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: [email protected]. · pubmed

Paper

Abstract

Average age is increasing worldwide, raising the public health burden of age-related diseases, as more resources will be required to manage treatments. Phenotypic Age is a score that can be useful to provide an estimate of the probability of developing aging-related conditions, and prevention of such conditions could be performed efficiently studying the mechanisms leading to an increased phenotypic age. The objective of this study is to characterize the mechanisms that lead to aging acceleration from the interactions among socio-demographic factors, health predispositions and biological phenotypes.

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 mechanisms leading to aging acceleration by examining the interactions among socio-demographic factors, health predispositions, and biological phenotypes. This focus on understanding the underlying factors contributing to phenotypic age is relevant to longevity research. However, the study appears to be more of a solid research effort rather than presenting groundbreaking findings, thus the impact score reflects a limited but meaningful contribution to the field.

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