A New Approach to Understanding Cancer-Related Fatigue: Leveraging the 3P Model to Facilitate Risk Prediction and Clinical Care

Simple Summary For the growing number of cancer survivors worldwide, fatigue presents a major hurdle to function and quality of life. Treatment options for cancer-related fatigue are still emerging, and our current understanding of its etiology is limited. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. We propose that the 3P model may be leveraged-particularly using metabolomics, the microbiome, and inflammation in conjunction with behavioral science-to better understand the pathophysiology of cancer-related fatigue. A major gap impeding development of new treatments for cancer-related fatigue is an inadequate understanding of the complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue. In this paper, we describe a new application of a comprehensive model for cancer-related fatigue: the predisposing, precipitating, and perpetuating (3P) factors model. This model framework outlined herein, which incorporates the emerging field of metabolomics, may help to frame a more in-depth analysis of the etiology of cancer-related fatigue as well as a broader and more personalized set of approaches to the clinical treatment of fatigue in oncology care. Included within this review paper is an in-depth description of the proposed biological mechanisms of cancer-related fatigue, as well as a presentation of the 3P model's application to this phenomenon. We conclude that a clinical focus on organization risk stratification and treatment around the 3P model may be warranted, and future research may benefit from expanding the 3P model to understand fatigue not only in oncology, but also across a variety of chronic conditions.

Automated summary

Fatigue presents a major challenge for cancer survivors, and our current understanding of its causes and treatment options is limited. This paper proposes a comprehensive model, the 3P factors model, which combines metabolomics, the microbiome, inflammation, and behavioral science to better understand the pathophysiology of cancer-related fatigue. The complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue hinder the development of new treatments. The application of the 3P model may provide a more in-depth analysis of the etiology of cancer-related fatigue and improve personalized approaches to its clinical treatment.