key: cord-0792873-kzj3tpcy
authors: Sleight, Alix G.; Crowder, Sylvia L.; Skarbinski, Jacek; Coen, Paul; Parker, Nathan H.; Hoogland, Aasha I.; Gonzalez, Brian D.; Playdon, Mary C.; Cole, Steven; Ose, Jennifer; Murayama, Yuichi; Siegel, Erin M.; Figueiredo, Jane C.; Jim, Heather S. L.
title: A New Approach to Understanding Cancer-Related Fatigue: Leveraging the 3P Model to Facilitate Risk Prediction and Clinical Care
date: 2022-04-14
journal: Cancers (Basel)
DOI: 10.3390/cancers14081982
sha: 0781b91e21b0d31f76998beaccdc899b1fafe034
doc_id: 792873
cord_uid: kzj3tpcy

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. ABSTRACT: 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.

Cancer-related fatigue is defined by the National Comprehensive Cancer Network (NCCN) as a distressing, persistent sense of physical, emotional, and/or cognitive exhaustion related to cancer that is not proportional to activity and interferes with functioning [1] . Moderate to severe fatigue affects up to 90% of patients during chemotherapy and approximately 30-40% years after treatment completion [1, 2] . Unlike typical fatigue, cancer-related fatigue tends to be more severe, distressing, and unlikely to be relieved by rest [3] . Patients describe it as "devastating", "never-ending" and "totally consuming" [4, 5] . Fatigue is associated with worse quality of life and lower likelihood of returning to normal daily activities, including work [2, [6] [7] [8] [9] .

Treatment options for cancer patients with fatigue are limited. Behavioral and psychosocial interventions demonstrate benefit [10] [11] [12] [13] [14] but tend to be time intensive, limiting uptake, compliance, and maintenance. Medications to treat sleep problems such as paroxetine, sertraline, modafinil, and armodafinil have shown limited benefit for cancer-related fatigue in randomized trials [15] [16] [17] [18] [19] [20] . Evidence is mixed for methylphenidate, which may be poorly tolerated [21] [22] [23] [24] . Because of the limited benefit of pharmacotherapy and since patients often prefer to avoid additional medications, behavioral treatment options for cancer-related fatigue are urgently needed [25, 26] . A major gap impeding development of new treatments is an inadequate understanding of the complex biological, clinical, demographic, and lifestyle mechanisms underlying fatigue [6] . In this paper, we describe a new application of a comprehensive model for a better understanding of 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.

The pathophysiology of cancer-related fatigue is thought to be multifactorial [27, 28] . Despite this complexity, the large majority of studies on the biological mechanisms of cancer-related fatigue have focused on immune and inflammatory variables, which are hypothesized to induce fatigue via the effect of inflammatory mediators on brain systems involved in "sickness behaviors." Specific variants include genes regulating inflammation (e.g., IL6, TNFA, and IL1) [29] , inflammatory gene expression profiles (e.g., increased NF-kB) [30] [31] [32] , and circulating markers of inflammation (e.g., IL-1, TNFA, CRP, and IL-6) . Inflammation is linked with the dysregulation of biochemical and physiological systems including peripheral (muscles and tissues) and central mechanisms (central nervous system) [54] and may cause fatigue through cytokine dysregulation, hypothalamicpituitary-adrenal (HPA) axis dysfunction, 5-hydroxy-tryptophan (5-HT) neurotransmitter dysregulation, circadian rhythm disruption, alterations in adenosine triphosphate (ATP), muscle metabolism, and vagal afferent activation [28, 54, 55] . Recent research has suggested that agents with anti-inflammatory properties (i.e., non-steroidal anti-inflammatory drugs or NSAIDs, bupropion) could offer a safe, inexpensive, and widely-available means to revolutionize the treatment of cancer-related fatigue [56] [57] [58] [59] . These findings suggest that pathophysiologic pathways and genetic mechanisms hold promise for the identification of new causal mechanisms and potential treatment targets; however, these mechanisms are poorly understood. In addition, current models of pathophysiology do not take into account the complex psychosocial and behavioral factors that may also play a major role in cancer-related fatigue.

The most notable framework previously proposed to describe complex disease processes is the biopsychosocial model, an inter-disciplinary model that looks at the interconnection between biology, psychology, and socioenvironmental factors [60] . While the biopsychosocial model has played a crucial role in counteracting biological reductionism and progressing towards a more holistic philosophy of human health, it lacks the granularity necessary to understand how various factors contribute to disease [61] . In contrast, as shown in Table 1 , the 3P model can be utilized to describe the complex biological and psychological processes underlying cancer-related fatigue. The 3P model postulates that predisposing factors place patients at risk of developing baseline fatigue (e.g., 1. biobehavioral: age, biological sex, genetic variants, metabolomics, inflammation, body composition, nutritional quality, circadian disruption, and co-morbidities; 2. psychosocial: depressed mood, anxiety, insomnia, and perceived stress); precipitating factors spur the onset of fatigue (e.g., changes in metabolism and inflammation due to cancer and/or chemotherapy and treatment-related factors: systemic therapy and radiotherapy); and perpetuating factors worsen fatigue or cause it to become chronic (e.g., poor sleep, physical inactivity, and poor diet). The 3P model ( Figure 1 ) has been suggested for better understanding fatigue [62] and successfully applied to other chronic conditions including sleep and pain [24, 25] . available means to revolutionize the treatment of cancer-related fatigue [56] [57] [58] [59] . These findings suggest that pathophysiologic pathways and genetic mechanisms hold promise for the identification of new causal mechanisms and potential treatment targets; however, these mechanisms are poorly understood. In addition, current models of pathophysiology do not take into account the complex psychosocial and behavioral factors that may also play a major role in cancer-related fatigue.

The most notable framework previously proposed to describe complex disease processes is the biopsychosocial model, an inter-disciplinary model that looks at the interconnection between biology, psychology, and socioenvironmental factors [60] . While the biopsychosocial model has played a crucial role in counteracting biological reductionism and progressing towards a more holistic philosophy of human health, it lacks the granularity necessary to understand how various factors contribute to disease [61] . In contrast, as shown in Table 1 , the 3P model can be utilized to describe the complex biological and psychological processes underlying cancer-related fatigue. The 3P model postulates that predisposing factors place patients at risk of developing baseline fatigue (e.g., 1. biobehavioral: age, biological sex, genetic variants, metabolomics, inflammation, body composition, nutritional quality, circadian disruption, and co-morbidities; 2. psychosocial: depressed mood, anxiety, insomnia, and perceived stress); precipitating factors spur the onset of fatigue (e.g., changes in metabolism and inflammation due to cancer and/or chemotherapy and treatment-related factors: systemic therapy and radiotherapy); and perpetuating factors worsen fatigue or cause it to become chronic (e.g., poor sleep, physical inactivity, and poor diet). The 3P model ( Figure 1 ) has been suggested for better understanding fatigue [62] and successfully applied to other chronic conditions including sleep and pain [24, 25] . available means to revolutionize the treatment of cancer-related fatigue [56] [57] [58] [59] . These findings suggest that pathophysiologic pathways and genetic mechanisms hold promise for the identification of new causal mechanisms and potential treatment targets; however, these mechanisms are poorly understood. In addition, current models of pathophysiology do not take into account the complex psychosocial and behavioral factors that may also play a major role in cancer-related fatigue.

The most notable framework previously proposed to describe complex disease processes is the biopsychosocial model, an inter-disciplinary model that looks at the interconnection between biology, psychology, and socioenvironmental factors [60] . While the biopsychosocial model has played a crucial role in counteracting biological reductionism and progressing towards a more holistic philosophy of human health, it lacks the granularity necessary to understand how various factors contribute to disease [61] . In contrast, as shown in Table 1 , the 3P model can be utilized to describe the complex biological and psychological processes underlying cancer-related fatigue. The 3P model postulates that predisposing factors place patients at risk of developing baseline fatigue (e.g., 1. biobehavioral: age, biological sex, genetic variants, metabolomics, inflammation, body composition, nutritional quality, circadian disruption, and co-morbidities; 2. psychosocial: depressed mood, anxiety, insomnia, and perceived stress); precipitating factors spur the onset of fatigue (e.g., changes in metabolism and inflammation due to cancer and/or chemotherapy and treatment-related factors: systemic therapy and radiotherapy); and perpetuating factors worsen fatigue or cause it to become chronic (e.g., poor sleep, physical inactivity, and poor diet). The 3P model ( Figure 1 ) has been suggested for better understanding fatigue [62] and successfully applied to other chronic conditions including sleep and pain [24, 25] . 

Patient characteristics conceptualized as predisposing factors in cancer-related fatigue include biological sex [63] , genetics [34] , body composition (e.g., body fat and low muscle mass) [64] [65] [66] , and viral exposures [67, 68] . Additionally, circadian rhythms could play a significant role in the etiology of fatigue through the modulation of arousal and sleep [54] .

Predisposing risk factors for cancer-related fatigue include poor performance status, chemoradiotherapy, female sex, insomnia, neuroticism, pain, and depression [69] . In cancer-related fatigue, the role of genetic variation remains unclear. Twin studies have shown the heritability of fatigue to be between 6% and 50%, with a higher concordance in monozygotic twins than dizygotic twins. Some preliminary studies have identified sets of inflammation-related genetic polymorphisms that are associated with increased fatigue in cancer patients [34] , but the generality of these effects remains to be determined. Genome-wide association studies (GWAS) in fatigue-related diseases have identified variants in genes involved in cognition and circadian rhythms [70] [71] [72] . We propose that genetic variants altering metabolic including inflammatory traits may also be associated with cancer-related fatigue through inflammation pathways. Publicly available lists of high-scoring genetic-metabolomic associations known as "genetically influenced metabotypes" include several variants located in or near genes encoding enzymes central to human lipid metabolism, including polyunsaturated fatty acid biosynthesis (e.g., FADS1, ELOVL2) and biosynthesis of phospholipids (e.g., SPT16A) [73] , which have not been explored in cancer-related fatigue. Similarly, "genetically influenced inflammotypes" [74] can be identified by inflammatory-based genome-wide association studies (iWAS), but also have not yet been explored among cancer patients with fatigue.

Previous viral exposure-for example, to Epstein-Barr virus, human herpesvirus, Lyme disease, or COVID-19-may predispose individuals to fatigue through cell alterations, hyperinflammation, mitochondrial modulation, and autoimmunity, although research in this area is lacking [75, 76] . Additionally, anthropometry measurements (e.g., obesity) have been associated with links in apnea, sleep quality, and inflammatory biomarkers.

Factors that may initially precipitate the development of cancer-related fatigue remain unclear, though likely include metabolic dysregulation (alterations in metabolic genes and regulatory pathways), as well as inflammation (overproduction of pro-inflammatory cytokines) and accelerated cellular aging (e.g., the premature shortening of 

Patient characteristics conceptualized as predisposing factors in cancer-related fatigue include biological sex [63] , genetics [34] , body composition (e.g., body fat and low muscle mass) [64] [65] [66] , and viral exposures [67, 68] . Additionally, circadian rhythms could play a significant role in the etiology of fatigue through the modulation of arousal and sleep [54] .

Predisposing risk factors for cancer-related fatigue include poor performance status, chemoradiotherapy, female sex, insomnia, neuroticism, pain, and depression [69] . In cancer-related fatigue, the role of genetic variation remains unclear. Twin studies have shown the heritability of fatigue to be between 6% and 50%, with a higher concordance in monozygotic twins than dizygotic twins. Some preliminary studies have identified sets of inflammation-related genetic polymorphisms that are associated with increased fatigue in cancer patients [34] , but the generality of these effects remains to be determined. Genomewide association studies (GWAS) in fatigue-related diseases have identified variants in genes involved in cognition and circadian rhythms [70] [71] [72] . We propose that genetic variants altering metabolic including inflammatory traits may also be associated with cancer-related fatigue through inflammation pathways. Publicly available lists of highscoring genetic-metabolomic associations known as "genetically influenced metabotypes" include several variants located in or near genes encoding enzymes central to human lipid metabolism, including polyunsaturated fatty acid biosynthesis (e.g., FADS1, ELOVL2) and biosynthesis of phospholipids (e.g., SPT16A) [73] , which have not been explored in cancerrelated fatigue. Similarly, "genetically influenced inflammotypes" [74] can be identified by inflammatory-based genome-wide association studies (iWAS), but also have not yet been explored among cancer patients with fatigue.

Previous viral exposure-for example, to Epstein-Barr virus, human herpesvirus, Lyme disease, or COVID-19-may predispose individuals to fatigue through cell alterations, hyperinflammation, mitochondrial modulation, and autoimmunity, although research in this area is lacking [75, 76] . Additionally, anthropometry measurements (e.g., obesity) have been associated with links in apnea, sleep quality, and inflammatory biomarkers.

Factors that may initially precipitate the development of cancer-related fatigue remain unclear, though likely include metabolic dysregulation (alterations in metabolic genes and regulatory pathways), as well as inflammation (overproduction of pro-inflammatory cytokines) and accelerated cellular aging (e.g., the premature shortening of telomeres and altered DNA methylation) due to cancer treatment. For example, chemotherapy is known to accelerate aging [77, 78] . Chemotherapy may also damage mitochondria in muscle and deconditioning of muscle that may contribute to perceptions of fatigue [79] [80] [81] [82] .

Multiplicative interactions between precipitating factors may also exist. Studies investigating muscle fatigue in cancer patients show metabolic dysregulation, including energy, lipid, and amino acid metabolism [83] [84] [85] [86] . Furthermore, evidence supports that chemotherapy may damage mitochondria in muscle that in turn increases fatigue, and the deconditioning of muscle further contributes to perceptions of fatigue. In particular, studies have focused on tryptophan catabolism [87] [88] [89] . An essential amino acid, tryptophan drives de novo synthesis of serotonin and niacin. Serotonin modulates behavioral and neuropsychological processes and niacin produces NAD, a co-factor crucial for energy homeostasis that is linked with aging and circadian regulation (SIRT1). Trials modifying tryptophan have demonstrated reductions in physical and mental fatigue following endurance exercise [90] . Furthermore, metabolic disturbances related to chronic fatigue syndrome have included alterations in 20 metabolic pathways including sphingolipids, phospholipids, purine, cholesterol, microbial metabolites, pyroline-5-carboxylate, riboflavin, amino acids, peroxisomal and mitochondrial metabolism [91] . All are directly regulated by redox or the availability of NADPH, highlighting the importance of the mitochondria, cellular organelles that produce energy [91] . Sphingolipids and phospholipids accounted for almost 70% of the variation in metabolic phenotype in a study of 84 patients with chronic fatigue syndrome, and differences among males and females were observed. Area under the receiver operator characteristic curve analysis showed accuracies in predicting fatigue of 94% (95% CI = 84-100%) for males and 96% (95% CI = 86-100%) for females. Three other metabolomics studies of fatigue-associated diseases support the key role of sphingolipids and phospholipids in addition to irregularities in energy, amino acid, and nucleotide metabolism [92] [93] [94] . The alterations in sphingolipids may be related to impaired lipid metabolism and mitochondria energetics, with evidence suggesting that PPAR suppression in the muscle of cancer patients could mediate this [81, 95, 96] . Furthermore, in vitro studies have demonstrated that ceramides induce oxidant production in the mitochondria, have specific effects in certain tissues (e.g., adipocyte ceramides and inflammation) and increase oxidant activity [97] , depressing muscle fiber force and exacerbating muscle fatigue [98] . While a number of pathological pathways have been identified as playing a role in cancer-related fatigue, it is possible that different mechanisms are responsible for different dimensions of fatigue (e.g., mental fatigue vs. physical fatigue). Further delineation of unique dimensions of fatigue associated with each pathway will assist in the identification of new intervention targets for the specific type of fatigue experienced.

Perpetuating factors are conceptualized as characteristics and behaviors that may worsen or prolong fatigue including poor dietary pattern, irregular meal timing [99, 100] , physical inactivity [101] , and poor sleep [102] [103] [104] [105] [106] [107] . Previous research suggests that antiinflammatory dietary patterns, such as prudent and Mediterranean diets, offer a plausible mechanism to mitigate cancer-related fatigue through reducing inflammation and improving body composition [108] [109] [110] . The key components of the Mediterranean dietary pattern include high intake of vegetables, fruits, whole grains, legumes, and nuts; moderate intake of seafood and red wine; and olive oil as the main fat source [111, 112] . Anti-inflammatory dietary patterns are associated with improvements in the gastrointestinal (GI) microbiota and lessening of metabolic endotoxemia, defined as a 2-to 3-fold increase in circulating levels of bacterial endotoxin [113] . In comparison, pro-inflammatory dietary patterns, such as the Western dietary pattern, widely consumed in the United States, is characterized by high consumption of red and processed meats; high consumption of sugar-sweetened beverages and refined grains; and low consumption of fresh fruits, vegetables, and legumes [114] [115] [116] . Western diets contribute to metabolic endotoxemia through changes in the GI microbiome and bacterial fermentation end products, intestinal physiology and barrier function, and enterohepatic circulation of bile acids [113] . Additionally, the Western dietary pattern has been correlated with pro-inflammatory markers associated with cancer-related fatigue, including tumor necrosis factor (TNF)-α, C-reactive protein, interleukin (IL)-6, and IL-8 [117] .

Dietary patterns promoting hyperinsulinemia and chronic inflammation, including the empirical dietary index for hyperinsulinemia (EDIH) and empirical dietary inflammatory pattern (EDIP), strongly influence risk of weight gain, type 2 diabetes, cardiovascular disease, and cancer [118] . The EDIH and EDIP have predicted concentrations of known insulinemic and inflammatory biomarkers, and the EDIH further predicted risk of future cancer [119] .

In addition to evaluating dietary patterns based on self-reported questionnaires, the role of diet in cancer-related fatigue can be investigated through nutritional metabolomics, the study of food-related metabolites in a biofluid that can provide an objective measure of recent or habitual dietary intake [120] . Moreover, untargeted metabolomics offers a discovery tool to identify small molecules both influenced by dietary behavior and associated with disease, thus characterizing endogenous response to diet, and metabolic targets for dietary intervention for disease prevention. To our knowledge, nutritional metabolomics studies of cancer-related fatigue are yet to be implemented. The microbiota has been recognized to play a role in human disease [121] , and the mechanisms by which these microorganisms contribute to host health have been extensively investigated over the past decade. The microbiome, specifically bacterial metabolites, has been linked with inflammation and oxidation. Two studies in mice have suggested that the gut microbiota produces metabolites from dietary tryptophan that regulate inflammation in the gut and central nervous system [122] .

In terms of general lifestyle, prior research in fatigue-associated diseases highlights the role of lipid mediators including sphingolipids, phospholipids, and oxygenated polyunsaturated fatty acids (PUFAs) (oxylipins). Sphingolipid metabolites play key roles in the regulation of both trafficking and function of immune cells, and there are indications that sphingolipid metabolism might be altered by inflammation [123] . Ceramides, key sphingolipids, promote numerous inflammatory processes, including induction of macrophages and B cells [124] . Prior studies indicate alteration of ceramide metabolism among patients with chronic fatigue syndrome [92] . Intervention trials show that diet can lower ceramide levels [125] . In the PREDIMED study, a Mediterranean dietary intervention mitigated potential deleterious effects of elevated plasma ceramide concentrations on cardiovascular disease [126] . Similarly, omega-3 polyunsaturated fatty acid (n3-PUFA) is a common phospholipid, which plays an important role in immunomodulatory activities. Ceramides and its metabolites have been proposed as an intermediate link between over-nutrition and certain underlying abnormalities driving disease risk, insulin resistance and low-grade inflammation [127] [128] [129] . Data suggest beneficial effects of n3-PUFA in reducing fatigue in cancer patients [33, [130] [131] [132] [133] . N-3 PUFA therapy upregulates the muscle transcriptome, including several pathways that control mitochondrial function in both human [134] , and animal studies [135] [136] [137] , emphasizing the role of energy metabolism. Other metabolic pathways related to diet that might also contribute to fatigue include dysregulated tryptophan catabolism. Tryptophan is an amino acid metabolized into several molecules involved in energy production [87] [88] [89] . Potential interventions might target modulation of tryptophan-related molecules via administration of branched chain amino acids. In addition to endogenous metabolites, untargeted approaches may identify unexpected or novel exposures that might play an important role in cancer-related fatigue by implicating exogenously derived chemicals. Adherence to specific dietary patterns (e.g., time-restricted eating) may offer a novel, cost-effective strategy to reduce cancer-related fatigue while quantification of targeted metabolites may allow for a robust evaluation of metabolite changes in people with cancer-related fatigue over time [138] .

There is an interaction of diet, physical activity, and sleep on many levels (e.g., behavioral, circadian, obesity, metabolic). Particularly, intermittent fasting regimens have been hypothesized to influence metabolic regulation via effects on (a) circadian biology, (b) the gut microbiome, and (c) modifiable lifestyle behaviors, such as sleep [100] . Evidence suggests that irregular meal timing may impact metabolic health. Specifically, eating more frequently, reducing evening energy intake, and fasting for longer nightly intervals may lower systemic inflammation and subsequently reduce breast cancer risk [99] . In another study examining associations between fasting duration, timing of first and last meals, and cardiometabolic endpoints using data from the National Health and Nutrition Examination Survey (NHANES), evidence suggested that there were beneficial effects on cardiometabolic health of starting energy consumption earlier in the day [139] .

In addition to diet, physical inactivity and sleep disturbance represent key modifiable perpetuating factors associated with cancer-related fatigue [101] . In a systematic review and meta-analysis of randomized controlled trials, physical activity has been identified as effective for mitigating cancer-related fatigue in colorectal cancer [140] . Moderate-intensity aerobic exercise training and a combination of moderate-intensity aerobic and resistance training have reduced fatigue in patients with breast and prostate cancer, both during and following cancer therapy [141, 142] . Reductions in fatigue from exercise training appear to result from both independent and supervised interventions [143] , highlighting its potential applicability to a wide range of cancer patients and survivors. Similarly, sleep disturbance confers risk of cancer-related fatigue across various cancer diagnoses. A recent meta-analysis studying risk factors for cancer-related fatigue in 84 studies with 144,813 participants found that patients with insomnia had significantly higher odds of cancer-related fatigue [69] . Notably, the odds ratio for insomnia was higher than the odds ratio for treatment of chemoradiotherapy, although the magnitude of these effects was not formally compared. In patients with chronic myeloid leukemia receiving cognitive behavioral therapy for targeted-therapy-related fatigue, improvements in sleep and physical activity were associated with declines in fatigue [101] . Sleep disturbance and physical inactivity both contribute to known pathways for cancer-related fatigue (e.g., inflammation, circadian disruption) [144, 145] . Emerging evidence suggests that sleep disturbance and physical inactivity may also implicate additional pathways, such as accelerated aging and gene expression through DNA methylation. For example, one study of 2078 women found that those with insomnia showed advanced biological age relative to chronological age [146] . In another study, individuals with insufficient sleep showed hypomethylation of DNA in regions associated with neuroplasticity and neurodegeneration [147] . Further research is necessary to elucidate the nuanced role of sleep and physical activity in cancer-related fatigue. Please see Table 1 .

Use of the 3P model can benefit clinical oncology practice by offering pathways to prevention and mitigation strategies for cancer-related fatigue. First, screening for predisposing factors of cancer-related fatigue may facilitate risk stratification of cancer patients at the time of diagnosis. Risk stratification has been endorsed by experts in oncology healthcare delivery science as a personalized approach to care in which survivors are triaged to distinct care pathways based on their individual needs [148] . For example, patients who display a genetic predisposition to cancer-related fatigue and fit a predetermined risk profile of sociodemographic factors can be appropriately routed to prehabilitation and behavioral counseling to prevent fatigue such as by intervening upon the modifiable predisposing (e.g., obesity) or precipitating factors.

Additionally, if routed to appropriate care early in the onset of fatigue, these patients may learn mitigating physical and cognitive-behavioral strategies [149] [150] [151] . Prehabilitation, a multidisciplinary clinical process on the continuum of care that occurs between diagnosis and treatment, involves targeted interventions to reduce the incidence and severity of future impairments [152] . For patients deemed at high risk for cancer-related fatigue, prehabilitation may beneficially include prescribed physical activity regimens, energy conservation techniques, and dietary consultations to prevent fatigue onset during and after treatment. Targeting pre-treatment windows for multimodal prehabilitation can help patients increase their physiologic reserve or functional capacity as early as possible, mitigating the effects of fatigue-inducing circumstances such as physical inactivity, inflammation, anorexia, and skeletal muscle loss. A recent, single-group study involving Cancers 2022, 14, 1982 8 of 17 individualized, home-based aerobic and resistance training prior to breast cancer surgery demonstrated reduced fatigue over the intervention period [153] . More evidence, including randomized controlled trials of prehabilitation, is needed to confirm longer-term, posttreatment impact on fatigue. Multimodal prehabilitation interventions can also be delivered concurrently with cancer therapies, such as exercise during neoadjuvant chemotherapy or radiation therapy [154] . Prehabilitation may also profitably include cognitive behavioral therapy to reduce dysfunctional fatigue-related beliefs. If routed to appropriate care early in the onset of fatigue, patients can learn cognitive-behavioral strategies for optimizing sleep, emotion, and daily activity patterns in order to mitigate fatigue [149] [150] [151] 155] . In fact, a recent study found that reduced fatigue in cancer patients was attributable to changes in cognition (specifically self-efficacy) stemming from cognitive behavioral therapy rather than to changes in physical activity [156] . Interventions targeting self-efficacy may therefore be particularly beneficial for patients with higher risk of fatigue.

After stratifying patients by risk based on predisposing (e.g., genetic) and perpetuating (e.g., diet, physical activity) factors, healthcare practitioners can leverage the 3P model by conducting ongoing screening for precipitating factors of cancer-related fatigue. These factors-such as metabolic dysregulation and inflammation-may arise at any point during cancer treatment, and can be watched closely using biomarker testing. Through monitoring for precipitating factors, providers can identify at-risk individuals who are not initially flagged for prehabilitation due to predisposing factors. These individuals would then receive prehabilitation appropriate for their specific needs, including specific exercise interventions optimally timed for each patient's unique constellation of ability, challenges, and needs [157] [158] [159] .

While being monitored for the emergence of new precipitating factors, cancer patients can concurrently receive education and training in health self-management techniques to mitigate symptoms of cancer-related fatigue. Health self-management is defined as "the individual's ability to manage the symptoms, treatment, physical and psychosocial consequences and lifestyle changes inherent in living with a chronic condition" [160] . Crucially, self-management training can teach cancer survivors the tools they need to sustainably maintain physical activity, adhere to dietary recommendations, and exercise energy conservation and sleep hygiene techniques. These health behaviors reduce the risk of long-lasting fatigue after the conclusion of the treatment phase. Furthermore, research has demonstrated that symptoms may appear in a cascade pattern, and treating symptoms higher in the cascade may prevent downstream symptoms. For example, sleep disturbance contributes to fatigue, which in turn contributes to depressed mood. Interventions for cancer-related fatigue therefore may be specifically targeted to mitigate symptoms early in the cascade, such as sleep disturbance [161] .

Indeed, while clinicians may benefit from education about the 3P model in order to leverage its content for clinical practice, patients may also benefit from comprehensive education about the predisposing, precipitating, and perpetuating factors involved in cancer-related fatigue to promote sustainable, independent management of symptoms. Future health self-management training endeavors for cancer survivors may be enhanced by support from web-based health self-management training tools. This emerging area of practice provides patients with hyper-tailored health self-management plans, sometimes enhanced by machine-learning to be granularly responsive to the unique strengths and challenges of each person [162, 163] . Please see Figure 2 for an overview of recommended clinical actions organized using the 3P model. Further research is needed to establish web-based tools powered by artificial intelligence specifically for the self-management of cancer-related fatigue. based tools powered by artificial intelligence specifically for the self-management of can cer-related fatigue. 

While clinical care can leverage the 3P model to prevent or mitigate symptoms o cancer-related fatigue, future research is also warranted to further investigate its patho physiology and efficacy/effectiveness of adopting a 3P model in cancer care. One poten tially fruitful target area for study may be the metabolome. The metabolome directly re flects the underlying biochemical activity and state of cells and tissues, including energ production. Because the metabolome is downstream of genomics, transcriptomics, an proteomics, it may be the closest molecular phenotype to the patient-reported phenotyp of cancer-related fatigue. The metabolic state of an individual at the time of illness is pro duced by current environmental and host biological conditions, host susceptibility, an the aggregate history, time, and magnitude of exposures recorded as metabolic memor [164] . Moreover, preclinical research has recently identified inflammation-independen "metabolic reprogramming" as a mediator of cancer-induced fatigue in animal model [165, 166] . Thus, the metabolome could provide a comprehensive snapshot of cellular pro cesses at a single point in time that is also representative of cumulative exposures, idea for discovery of new mechanisms of cancer-related fatigue.

The 3P framework proposed here to conceptualize cancer-related fatigue may als be applicable to fatigue related to other conditions such as chronic fatigue syndrome, pos COVID syndrome, rheumatoid arthritis, and multiple sclerosis. Through applying the 3 framework, researchers and clinicians studying fatigue across a wide variety of illnesse and chronic diseases may be able to more readily understand the pathophysiology of fa tigue as it presents across various diagnoses, as well as systematically identify and mit gate predisposing, precipitating, and perpetuating factors relevant to unique population experiencing fatigue.

For the growing number of cancer survivors worldwide, fatigue presents a majo hurdle to a return to function and quality of life after cancer treatment. Treatment option for cancer-related fatigue are still emerging, and our current understanding of its etiolog is limited. The presented 3P model may be leveraged-particularly using metabolomic the microbiome, and inflammation in conjunction with behavioral science-to better un derstand the pathophysiology of cancer-related fatigue. While application of the 3P mode alone will not be enough to solve the complex problem of cancer-related fatigue, it ma represent a key step towards ameliorating this pervasive issue for patients. A clinical focu 

While clinical care can leverage the 3P model to prevent or mitigate symptoms of cancer-related fatigue, future research is also warranted to further investigate its pathophysiology and efficacy/effectiveness of adopting a 3P model in cancer care. One potentially fruitful target area for study may be the metabolome. The metabolome directly reflects the underlying biochemical activity and state of cells and tissues, including energy production. Because the metabolome is downstream of genomics, transcriptomics, and proteomics, it may be the closest molecular phenotype to the patient-reported phenotype of cancer-related fatigue. The metabolic state of an individual at the time of illness is produced by current environmental and host biological conditions, host susceptibility, and the aggregate history, time, and magnitude of exposures recorded as metabolic memory [164] . Moreover, preclinical research has recently identified inflammation-independent "metabolic reprogramming" as a mediator of cancer-induced fatigue in animal models [165, 166] . Thus, the metabolome could provide a comprehensive snapshot of cellular processes at a single point in time that is also representative of cumulative exposures, ideal for discovery of new mechanisms of cancer-related fatigue.

The 3P framework proposed here to conceptualize cancer-related fatigue may also be applicable to fatigue related to other conditions such as chronic fatigue syndrome, post-COVID syndrome, rheumatoid arthritis, and multiple sclerosis. Through applying the 3P framework, researchers and clinicians studying fatigue across a wide variety of illnesses and chronic diseases may be able to more readily understand the pathophysiology of fatigue as it presents across various diagnoses, as well as systematically identify and mitigate predisposing, precipitating, and perpetuating factors relevant to unique populations experiencing fatigue.

For the growing number of cancer survivors worldwide, fatigue presents a major hurdle to a return to function and quality of life after cancer treatment. Treatment options for cancer-related fatigue are still emerging, and our current understanding of its etiology is limited. The presented 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. While application of the 3P model alone will not be enough to solve the complex problem of cancer-related fatigue, it may represent a key step towards ameliorating this pervasive issue for patients. A clinical focus on organizing risk stratification and treatment around the 3P model would be warranted.

Future research may benefit from expanding the 3P model to understand fatigue not only in oncology, but also in the wider context of a variety of chronic conditions.

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Early retirement and non-employment after breast cancer

Effectiveness of behavioral techniques and physical exercise on psychosocial functioning and health-related quality of life in breast cancer patients and survivors-A meta-analysis

Psychosocial interventions for reducing fatigue during cancer treatment in adults

Systematic review and meta-analysis of psychological and activity-based interventions for cancer-related fatigue

Cancer-related fatigue: A systematic and meta-analytic review of nonpharmacological therapies for cancer patients

Comparison of Pharmaceutical, Psychological, and Exercise Treatments for Cancer-Related Fatigue: A Metaanalysis

A phase 3 trial of armodafinil for the treatment of cancer-related fatigue for patients with multiple myeloma

A phase 3 randomized, placebo-controlled, double-blind, clinical trial of the effect of modafinil on cancer-related fatigue among 631 patients receiving chemotherapy: A University of Rochester Cancer Center Community Clinical Oncology Program Research base study

Differential effects of paroxetine on fatigue and depression: A randomized, double-blind trial from the University of Rochester Cancer Center Community Clinical Oncology Program

Effect of paroxetine hydrochloride (Paxil) on fatigue and depression in breast cancer patients receiving chemotherapy

Modafinil for the treatment of fatigue in lung cancer: Results of a placebo-controlled, double-blind, randomized trial

Effect of sertraline on symptoms and survival in patients with advanced cancer, but without major depression: A placebo-controlled double-blind randomised trial

Psychotropic drugs for the management of cancer-related fatigue: A systematic review and meta-analysis

Effect of methylphenidate in patients with cancer-related fatigue: A systematic review and meta-analysis

Methylphenidate and/or a nursing telephone intervention for fatigue in patients with advanced cancer: A randomized, placebocontrolled, phase II trial

Phase III, randomized, double-blind, placebo-controlled study of long-acting methylphenidate for cancer-related fatigue: North Central Cancer Treatment Group NCCTG-N05C7 trial

Cancer-related fatigue in breast cancer patients: Factor mixture models with continuous non-normal distributions

A comparison of cognitive function, sleep and activity levels in disease-free breast cancer patients with or without cancer-related fatigue syndrome

The biology of cancer-related fatigue: A review of the literature

Mechanisms of cancer-related fatigue

Genetic predictors of fatigue in prostate cancer patients treated with androgen deprivation therapy: Preliminary findings

Genomic mechanisms of fatigue in survivors of colorectal cancer

Fatigue and gene expression in human leukocytes: Increased NF-kappaB and decreased glucocorticoid signaling in breast cancer survivors with persistent fatigue

Molecular signatures of peripheral blood mononuclear cells during chronic interferon-alpha treatment: Relationship with depression and fatigue

Fatigue, inflammation, and omega-3 and omega-6 fatty acid intake among breast cancer survivors

Cytokine genetic variations and fatigue among patients with breast cancer

Inflammation and behavioral symptoms after breast cancer treatment: Do fatigue, depression, and sleep disturbance share a common underlying mechanism?

Inflammatory biomarkers and fatigue during radiation therapy for breast and prostate cancer

Sleep disturbance, cytokines, and fatigue in women with ovarian cancer

Inflammatory biomarkers for persistent fatigue in breast cancer survivors

Cytokine gene polymorphisms and fatigue in breast cancer survivors: Early findings

Diurnal cortisol rhythms, fatigue and psychosocial factors in five-year survivors of ovarian cancer

Inflammation and fatigue dimensions in advanced cancer patients and cancer survivors: An explorative study

Factors related to persistent fatigue following completion of breast cancer treatment

Factors predicting clinically significant fatigue in women following treatment for primary breast cancer

Serum cytokines and chronic fatigue in adults surviving after childhood leukemia and lymphoma

The systemic inflammatory response and its relationship to pain and other symptoms in advanced cancer

Ancoli-Israel, S. Fatigue and sleep quality are associated with changes in inflammatory markers in breast cancer patients undergoing chemotherapy

Cognitive impairment, fatigue, and cytokine levels in patients with acute myelogenous leukemia or myelodysplastic syndrome

Levels of circulating interleukin-1 receptor antagonist and C-reactive protein in long-term survivors of testicular cancer with chronic cancer-related fatigue

Higher levels of fatigue are associated with higher CRP levels in disease-free breast cancer survivors

C-reactive protein predicts fatigue independently of depression in breast cancer patients prior to chemotherapy

Changes in plasma levels of inflammatory cytokines in response to paclitaxel chemotherapy

Cortisol and inflammatory processes in ovarian cancer patients following primary treatment: Relationships with depression, fatigue, and disability

Inflammatory cytokines are associated with the development of symptom burden in patients with NSCLC undergoing concurrent chemoradiation therapy

I'm so tired: Biological and genetic mechanisms of cancer-related fatigue

Fatigue associated with cancer and its treatment

A randomized placebo-controlled trial of bupropion for Cancer-related fatigue: Study design and procedures

Potential Role of Bupropion Sustained Release for Cancer-Related Fatigue: A Double-Blind, Placebo-Controlled Study. Asian Pac

Efficacy and safety of bupropion in cancer-related fatigue, a randomized double blind placebo controlled clinical trial

New agents for the treatment of cancer-related fatigue

The biopsychosocial model of illness: A model whose time has come

A Framework for Understanding the Role of Psychological Processes in Disease Development, Maintenance, and Treatment: The 3P-Disease Model

The role of neuro-immune interactions in cancer-related fatigue: Biobehavioral risk factors and mechanisms

Cancer-related fatigue: The impact of skeletal muscle mass and strength in patients with advanced cancer

Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis

Muscle mass as a target to reduce fatigue in patients with advanced cancer

Muscle mass and association to quality of life in non-small cell lung cancer patients

Twin analyses of fatigue

Twin analyses of chronic fatigue in a Swedish national sample

Prevalence and risk factors of cancer-related fatigue: A systematic review and meta-analysis

Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome

Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome

Genetic contributions to self-reported tiredness

A genome-wide perspective of genetic variation in human metabolism

A systematic review of the association between fatigue and genetic polymorphisms

Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Concern coronavirus may trigger post-viral fatigue syndromes

Effect of cytotoxic chemotherapy on markers of molecular age in patients with breast cancer

Accelerated aging among childhood, adolescent, and young adult cancer survivors is evidenced by increased expression of p16(INK4a) and frailty

Skeletal muscle atrophy and dysfunction in breast cancer patients: Role for chemotherapy-derived oxidant stress

Exercise Intolerance in Anthracycline-Treated Breast Cancer Survivors: The Role of Skeletal Muscle Bioenergetics, Oxygenation, and Composition

Skeletal Muscle Deconditioning in Breast Cancer Patients Undergoing Chemotherapy: Current Knowledge and Insights from Other Cancers. Front

Skeletal muscle mitochondrial dysfunction and muscle and whole body functional deficits in cancer patients with weight loss

Therapeutic effects of oral NADH on the symptoms of patients with chronic fatigue syndrome

Heterogeneity in chronic fatigue syndrome: Evidence from magnetic resonance spectroscopy of muscle

Reduced oxidative muscle metabolism in chronic fatigue syndrome

Altered muscle energy metabolism in post-absorptive patients with chronic renal failure. Scand

Fatigue in patients with lung cancer is related with accelerated tryptophan breakdown

5-hydroxytryptamine and a possible explanation for central fatigue

Tryptophan circuit in fatigue: From blood to brain and cognition

Branched-chain amino acids and central fatigue

Metabolic features of chronic fatigue syndrome

Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics

Metabolomic markers of fatigue: Association between circulating metabolome and fatigue in women with chronic widespread pain

Metabolomics study of fatigue in patients with rheumatoid arthritis naive to biological treatment

Human Breast Cancer Xenograft Model Implicates Peroxisome Proliferator-activated Receptor Signaling as Driver of Cancer-induced Muscle Fatigue

Breast cancer-associated skeletal muscle mitochondrial dysfunction and lipid accumulation is reversed by PPARG

Adipocyte Ceramides-The Nexus of Inflammation and Metabolic Disease

Sphingomyelinase stimulates oxidant signaling to weaken skeletal muscle and promote fatigue

Frequency and Circadian Timing of Eating May Influence Biomarkers of Inflammation and Insulin Resistance Associated with Breast Cancer Risk

Metabolic Effects of Intermittent Fasting

Fatigue Perpetuating Factors as Mediators of Change in a Cognitive Behavioral Intervention for Targeted Therapy-Related Fatigue in Chronic Myeloid Leukemia: A Pilot Study

The Impact of Exercise on Cancer Mortality, Recurrence, and Treatment-Related Adverse Effects

Influence of physical inactivity in psychophysiological state of breast cancer survivors

Lifestyle after Colorectal Cancer Diagnosis in Relation to Survival and Recurrence: A Review of the Literature

The role of gut microbiome in modulating response to immune checkpoint inhibitor therapy in cancer

Sleep disturbance and neurocognitive outcomes in older patients with breast cancer: Interaction with genotype

Ancoli-Israel, S. Decreased health-related quality of life in women with breast cancer is associated with poor sleep

Exercise and dietary change after diagnosis and cancer-related symptoms in long-term survivors of breast cancer: CALGB 79804

Examination of the association of diet and persistent cancer-related fatigue: A pilot study

Mediterranean dietary pattern, inflammation and endothelial function: A systematic review and meta-analysis of intervention trials

Accruing evidence on benefits of adherence to the Mediterranean diet on health: An updated systematic review and meta-analysis

Microbiome-Mediated Effects of the Mediterranean Diet on Inflammation

Western Diet and the Immune System: An Inflammatory Connection

Pretreatment Dietary Patterns Are Associated with the Presence of Nutrition Impact Symptoms 1 Year after Diagnosis in Patients with Head and Neck Cancer

Pretreatment Dietary Patterns, Serum Carotenoids and Tocopherols Influence Tumor Immune Response in Head and Neck Squamous Cell Carcinoma

Mediterranean-style dietary pattern improves cancer-related fatigue and quality of life in men with prostate cancer treated with androgen deprivation therapy: A pilot randomised control trial

Proinflammatory and Hyperinsulinemic Dietary Patterns Are Associated with Specific Profiles of Biomarkers Predictive of Chronic Inflammation, Glucose-Insulin Dysregulation, and Dyslipidemia in Postmenopausal Women

Inflammatory and Insulinemic Dietary Patterns: Influence on Circulating Biomarkers and Prostate Cancer Risk

The path toward using microbial metabolites as therapies

Regulating inflammation with microbial metabolites

Sphingolipid metabolites in inflammatory disease

Bioactive Lipids and Chronic Inflammation: Managing the Fire Within

Efficacy of nutritional interventions to lower circulating ceramides in young adults: FRUVEDomic pilot study

Plasma Ceramides, Mediterranean Diet, and Incident Cardiovascular Disease in the PREDIMED Trial

Ceramides in insulin resistance and lipotoxicity

Sphingolipids, insulin resistance, and metabolic disease: New insights from in vivo manipulation of sphingolipid metabolism

The ART of Lowering Ceramides

Better postdiagnosis diet quality is associated with less cancer-related fatigue in breast cancer survivors

Postdiagnosis diet quality is inversely related to a biomarker of inflammation among breast cancer survivors

Factors associated with cancer-related fatigue in breast cancer patients undergoing endocrine therapy in an urban setting: A cross-sectional study

Fatigue reduction diet in breast cancer survivors: A pilot randomized clinical trial

Effect of dietary n-3 PUFA supplementation on the muscle transcriptome in older adults

Eicosapentaenoic acid but not docosahexaenoic acid restores skeletal muscle mitochondrial oxidative capacity in old mice

Dietary fat influences the expression of contractile and metabolic genes in rat skeletal muscle

Dietary enrichment with fish oil prevents high fat-induced metabolic dysfunction in skeletal muscle in mice

Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement from the American Heart Association

Associations between Fasting Duration, Timing of First and Last Meal, and Cardiometabolic Endpoints in the National Health and Nutrition Examination Survey

Effects of Moderate-To-Vigorous Physical Activity on Cancer-Related Fatigue in Patients with Colorectal Cancer: A Systematic Review and Meta-Analysis

Exercise for the management of cancer-related fatigue in adults

The effect of exercise on fatigue and physical functioning in breast cancer patients during and after treatment and at 6 months follow-up: A meta-analysis

Exercise Guidelines for Cancer Survivors: Consensus Statement from International Multidisciplinary Roundtable

Developing preliminary blood metabolomics-based biomarkers of insufficient sleep in humans

Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study

Epigenetic Aging and Immune Senescence in Women with Insomnia Symptoms: Findings From the Women's Health Initiative Study

A distinctive DNA methylation pattern in insufficient sleep

Personalized Risk-Stratified Cancer Follow-Up Care: Its Potential for Healthier Survivors, Happier Clinicians, and Lower Costs

Internet-assisted cognitive behavioral intervention for targeted therapy-related fatigue in chronic myeloid leukemia: Results from a pilot randomized trial

The efficacy of Internet-based cognitive behavioral therapy for severely fatigued survivors of breast cancer compared with care as usual: A randomized controlled trial

Are the effects of cognitive behavior therapy for severe fatigue in cancer survivors sustained up to 14 years after therapy?

Cancer prehabilitation and its role in improving health outcomes and reducing health care costs

Feasibility of Prehabilitation Prior to Breast Cancer Surgery: A Mixed-Methods Study

Exercise intervention in people with cancer undergoing neoadjuvant cancer treatment and surgery: A systematic review

Moderators of the effect of psychosocial interventions on fatigue in women with breast cancer and men with prostate cancer: Individual patient data meta-analyses

Condition or cognition? Mechanism of change in fatigue in a randomized controlled trial of graded exercise therapy or cognitive behavior therapy for severe fatigue in patients with advanced cancer

Moderators of Exercise Effects on Cancer-related Fatigue: A Meta-analysis of Individual Patient Data

Examining the role of physical activity in reducing postcancer fatigue

Targeting Exercise Interventions to Patients with Cancer in Need: An Individual Patient Data Meta-Analysis

Management of Cancer and Health After the Clinic Visit: A Call to Action for Self-Management in Cancer Care

Lagged relationships among sleep disturbance, fatigue, and depressed mood during chemotherapy

Reasons for not reaching or using web-based self-management applications, and the use and evaluation of Oncokompas among cancer survivors

Patient and clinician perspectives of desired features for a web-based self-management program (icanmanage.ca): Exposing patients "hard work" of managing acute cancer

Metabolic features of the cell danger response. Mitochondrion

Tumor-Associated Fatigue in Cancer Patients Develops Independently of IL1 Signaling

Interleukin 6-independent metabolic reprogramming as a driver of cancer-related fatigue