An Alternative Model of Cancer-Related Fatigue: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Alix Sleight
,
Sylvia Crowder
,
,
Brian Gonzalez
,
Mary Playdon
,
Yuichi Murayama
,
Jane Figueiredo
,
Heather S. L. Jim

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. 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. In contrast, 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 has been suggested for better understanding fatigue and successfully applied to other chronic conditions including sleep and pain. 

  • fatigue
  • metabolomics
  • survivorship

1. Predisposing Factors

Patient characteristics conceptualized as predisposing factors in cancer-related fatigue include biological sex [1], genetics [2], body composition (e.g., body fat and low muscle mass) [3][4][5], and viral exposures [6][7]. Additionally, circadian rhythms could play a significant role in the etiology of fatigue through the modulation of arousal and sleep [8].
Predisposing risk factors for cancer-related fatigue include poor performance status, chemoradiotherapy, female sex, insomnia, neuroticism, pain, and depression [9]. 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 [2], 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 [10][11][12]. 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) [13], which have not been explored in cancer-related fatigue. Similarly, “genetically influenced inflammotypes” [14] 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 [15][16]. Additionally, anthropometry measurements (e.g., obesity) have been associated with links in apnea, sleep quality, and inflammatory biomarkers.

2. Precipitating Factors

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 [17][18]. Chemotherapy may also damage mitochondria in muscle and deconditioning of muscle that may contribute to perceptions of fatigue [19][20][21][22].
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 [23][24][25][26]. 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 [27][28][29]. 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 [30]. 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 [31]. All are directly regulated by redox or the availability of NADPH, highlighting the importance of the mitochondria, cellular organelles that produce energy [31]. 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 [32][33][34]. 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 [21][35][36]. 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 [37], depressing muscle fiber force and exacerbating muscle fatigue [38]. 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.

3. Perpetuating Factors

Perpetuating factors are conceptualized as characteristics and behaviors that may worsen or prolong fatigue including poor dietary pattern, irregular meal timing [39][40], physical inactivity [41], and poor sleep [42][43][44][45][46][47]. Previous research suggests that anti-inflammatory dietary patterns, such as prudent and Mediterranean diets, offer a plausible mechanism to mitigate cancer-related fatigue through reducing inflammation and improving body composition [48][49][50]. 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 [51][52]. 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 [53]. 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 [54][55][56]. 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 [53]. 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 [57]. 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 [58]. The EDIH and EDIP have predicted concentrations of known insulinemic and inflammatory biomarkers, and the EDIH further predicted risk of future cancer [59].
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 [60]. 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 [61], 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 [62].
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 [63]. Ceramides, key sphingolipids, promote numerous inflammatory processes, including induction of macrophages and B cells [64]. Prior studies indicate alteration of ceramide metabolism among patients with chronic fatigue syndrome [32]. Intervention trials show that diet can lower ceramide levels [65]. In the PREDIMED study, a Mediterranean dietary intervention mitigated potential deleterious effects of elevated plasma ceramide concentrations on cardiovascular disease [66]. 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 [67][68][69]. Data suggest beneficial effects of n3-PUFA in reducing fatigue in cancer patients [70][71][72][73][74]. N-3 PUFA therapy upregulates the muscle transcriptome, including several pathways that control mitochondrial function in both human [75], and animal studies [76][77][78], 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 [27][28][29]. 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 [79].
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 [40]. 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 [39]. 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 [80].
In addition to diet, physical inactivity and sleep disturbance represent key modifiable perpetuating factors associated with cancer-related fatigue [41]. 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 [81]. 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 [82][83]. Reductions in fatigue from exercise training appear to result from both independent and supervised interventions [84], 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 [9]. 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 [41]. Sleep disturbance and physical inactivity both contribute to known pathways for cancer-related fatigue (e.g., inflammation, circadian disruption) [85][86]. 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 [87]. In another study, individuals with insufficient sleep showed hypomethylation of DNA in regions associated with neuroplasticity and neurodegeneration [88].

This entry is adapted from the peer-reviewed paper 10.3390/cancers14081982

References

  1. Kilgour, R.D.; Vigano, A.; Trutschnigg, B.; Hornby, L.; Lucar, E.; Bacon, S.L.; Morais, J.A. Cancer-related fatigue: The impact of skeletal muscle mass and strength in patients with advanced cancer. J. Cachexia Sarcopenia Muscle 2010, 1, 177–185.
  2. Bower, J.E.; Ganz, P.A.; Irwin, M.R.; Castellon, S.; Arevalo, J.; Cole, S.W. Cytokine genetic variations and fatigue among patients with breast cancer. J. Clin. Oncol. 2013, 31, 1656–1661.
  3. van Baar, H.; Bours, M.J.; Beijer, S.; van Zutphen, M.; van Duijnhoven, F.J.; Kok, D.E.; Wesselink, E.; de Wilt, J.H.; Kampman, E.; Winkels, R.M. Body composition and its association with fatigue in the first 2 years after colorectal cancer diagnosis. J. Cancer Surviv. 2021, 15, 597–606.
  4. Neefjes, E.C.; Van Den Hurk, R.M.; Blauwhoff-Buskermolen, S.; van der Vorst, M.J.; Becker-Commissaris, A.; de van der Schueren, M.A.; Buffart, L.M.; Verheul, H.M. Muscle mass as a target to reduce fatigue in patients with advanced cancer. J. Cachexia Sarcopenia Muscle 2017, 8, 623–629.
  5. Bye, A.; Sjøblom, B.; Wentzel-Larsen, T.; Grønberg, B.H.; Baracos, V.E.; Hjermstad, M.J.; Aass, N.; Bremnes, R.M.; Fløtten, Ø.; Jordhøy, M. Muscle mass and association to quality of life in non-small cell lung cancer patients. J. Cachexia Sarcopenia Muscle 2017, 8, 759–767.
  6. Schur, E.; Afari, N.; Goldberg, J.; Buchwald, D.; Sullivan, P.F. Twin analyses of fatigue. Twin Res. Hum. Genet. 2007, 10, 729–733.
  7. Sullivan, P.F.; Evengard, B.; Jacks, A.; Pedersen, N.L. Twin analyses of chronic fatigue in a Swedish national sample. Psychol. Med. 2005, 35, 1327–1336.
  8. Barsevick, A.; Frost, M.; Zwinderman, A.; Hall, P.; Halyard, M.; Consortium, G. I’m so tired: Biological and genetic mechanisms of cancer-related fatigue. Qual. Life Res. 2010, 19, 1419–1427.
  9. Ma, Y.; He, B.; Jiang, M.; Yang, Y.; Wang, C.; Huang, C.; Han, L. Prevalence and risk factors of cancer-related fatigue: A systematic review and meta-analysis. Int. J. Nurs. Stud. 2020, 111, 103707.
  10. Schlauch, K.A.; Khaiboullina, S.F.; De Meirleir, K.L.; Rawat, S.; Petereit, J.; Rizvanov, A.A.; Blatt, N.; Mijatovic, T.; Kulick, D.; Palotas, A.; et al. Genome-wide association analysis identifies genetic variations in subjects with myalgic encephalomyelitis/chronic fatigue syndrome. Transl. Psychiatry 2016, 6, e730.
  11. Smith, A.K.; Fang, H.; Whistler, T.; Unger, E.R.; Rajeevan, M.S. Convergent genomic studies identify association of GRIK2 and NPAS2 with chronic fatigue syndrome. Neuropsychobiology 2011, 64, 183–194.
  12. Deary, V.; Hagenaars, S.P.; Harris, S.E.; Hill, W.D.; Davies, G.; Liewald, D.C.; McIntosh, A.M.; Gale, C.R.; Deary, I.J. Genetic contributions to self-reported tiredness. Mol. Psychiatry 2018, 23, 609–620.
  13. Illig, T.; Gieger, C.; Zhai, G.; Römisch-Margl, W.; Wang-Sattler, R.; Prehn, C.; Altmaier, E.; Kastenmüller, G.; Kato, B.S.; Mewes, H.W.; et al. A genome-wide perspective of genetic variation in human metabolism. Nat. Genet. 2010, 42, 137–141.
  14. Wang, T.; Yin, J.; Miller, A.H.; Xiao, C. A systematic review of the association between fatigue and genetic polymorphisms. Brain Behav. Immun. 2017, 62, 230–244.
  15. Rasa, S.; Nora-Krukle, Z.; Henning, N.; Eliassen, E.; Shikova, E.; Harrer, T.; Scheibenbogen, C.; Murovska, M.; Prusty, B.K. Chronic viral infections in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). J. Transl. Med. 2018, 16, 268.
  16. Wilson, C. Concern coronavirus may trigger post-viral fatigue syndromes. New Sci. 2020, 246, 10–11.
  17. Sanoff, H.K.; Deal, A.M.; Krishnamurthy, J.; Torrice, C.; Dillon, P.; Sorrentino, J.; Ibrahim, J.G.; Jolly, T.A.; Williams, G.; Carey, L.A.; et al. Effect of cytotoxic chemotherapy on markers of molecular age in patients with breast cancer. J. Natl. Cancer Inst. 2014, 106, dju057.
  18. Smitherman, A.B.; Wood, W.A.; Mitin, N.; Ayer Miller, V.L.; Deal, A.M.; Davis, I.J.; Blatt, J.; Gold, S.H.; Muss, H.B. Accelerated aging among childhood, adolescent, and young adult cancer survivors is evidenced by increased expression of p16(INK4a) and frailty. Cancer 2020, 126, 4975–4983.
  19. Guigni, B.A.; Callahan, D.M.; Tourville, T.W.; Miller, M.S.; Fiske, B.; Voigt, T.; Korwin-Mihavics, B.; Anathy, V.; Dittus, K.; Toth, M.J. Skeletal muscle atrophy and dysfunction in breast cancer patients: Role for chemotherapy-derived oxidant stress. Am. J. Physiol. Cell Physiol. 2018, 315, C744–C756.
  20. Beaudry, R.I.; Kirkham, A.A.; Thompson, R.B.; Grenier, J.G.; Mackey, J.R.; Haykowsky, M.J. Exercise Intolerance in Anthracycline-Treated Breast Cancer Survivors: The Role of Skeletal Muscle Bioenergetics, Oxygenation, and Composition. Oncologist 2020, 25, e852–e860.
  21. Mallard, J.; Hucteau, E.; Hureau, T.J.; Pagano, A.F. Skeletal Muscle Deconditioning in Breast Cancer Patients Undergoing Chemotherapy: Current Knowledge and Insights from Other Cancers. Front. Cell Dev. Biol. 2021, 9, 719643.
  22. Kunz, H.E.; Port, J.D.; Kaufman, K.R.; Jatoi, A.; Hart, C.R.; Gries, K.J.; Lanza, I.R.; Kumar, R. Skeletal muscle mitochondrial dysfunction and muscle and whole body functional deficits in cancer patients with weight loss. J. Appl. Physiol. 2022, 132, 388–401.
  23. Forsyth, L.M.; Preuss, H.G.; MacDowell, A.L.; Chiazze, L.; Jr Birkmayer, G.D.; Bellanti, J.A. Therapeutic effects of oral NADH on the symptoms of patients with chronic fatigue syndrome. Ann. Allergy Asthma Immunol. 1999, 82, 185–191.
  24. Lane, R.J.; Barrett, M.C.; Taylor, D.J.; Kemp, G.J.; Lodi, R. Heterogeneity in chronic fatigue syndrome: Evidence from magnetic resonance spectroscopy of muscle. Neuromuscul. Disord. 1998, 8, 204–209.
  25. McCully, K.K.; Natelson, B.H.; Iotti, S.; Sisto, S.; Leigh, J.S., Jr. Reduced oxidative muscle metabolism in chronic fatigue syndrome. Muscle Nerve 1996, 19, 621–625.
  26. Pastoris, O.; Aquilani, R.; Foppa, P.; Bovio, G.; Segagni, S.; Baiardi, P.; Catapano, M.; Maccario, M.; Salvadeo, A.; Dossena, M. Altered muscle energy metabolism in post-absorptive patients with chronic renal failure. Scand. J. Urol. Nephrol. 1997, 31, 281–287.
  27. Kurz, K.; Fiegl, M.; Holzner, B.; Giesinger, J.; Pircher, M.; Weiss, G.; Denz, H.A.; Fuchs, D. Fatigue in patients with lung cancer is related with accelerated tryptophan breakdown. PLoS ONE 2012, 7, e36956.
  28. Newsholme, E.A.; Blomstrand, E. Tryptophan, 5-hydroxytryptamine and a possible explanation for central fatigue. Adv. Exp. Med. Biol. 1995, 384, 315–320.
  29. Yamashita, M.; Yamamoto, T. Tryptophan circuit in fatigue: From blood to brain and cognition. Brain Res. 2017, 1675, 116–126.
  30. Newsholme, E.A.; Blomstrand, E. Branched-chain amino acids and central fatigue. J. Nutr. 2006, 136, 274S–276S.
  31. Naviaux, R.K.; Naviaux, J.C.; Li, K.; Bright, A.T.; Alaynick, W.A.; Wang, L.; Baxter, A.; Nathan, N.; Anderson, W.; Gordon, E. Metabolic features of chronic fatigue syndrome. Proc. Natl. Acad. Sci. USA 2016, 113, E5472–E5480.
  32. Nagy-Szakal, D.; Barupal, D.K.; Lee, B.; Che, X.; Williams, B.L.; Kahn, E.J.; Ukaigwe, J.E.; Bateman, L.; Klimas, N.G.; Komaroff, A.L.; et al. Insights into myalgic encephalomyelitis/chronic fatigue syndrome phenotypes through comprehensive metabolomics. Sci. Rep. 2018, 8, 10056.
  33. Freidin, M.B.; Wells, H.R.R.; Potter, T.; Livshits, G.; Menni, C.; Williams, F.M.K. Metabolomic markers of fatigue: Association between circulating metabolome and fatigue in women with chronic widespread pain. Biochim. Biophys. Acta Mol. Basis Dis. 2018, 1864, 601–606.
  34. Surowiec, I.; Gjesdal, C.G.; Jonsson, G.; Norheim, K.B.; Lundstedt, T.; Trygg, J.; Omdal, R. Metabolomics study of fatigue in patients with rheumatoid arthritis naive to biological treatment. Rheumatol. Int. 2016, 36, 703–711.
  35. Wilson, H.E.; Rhodes, K.K.; Rodriguez, D.; Chahal, I.; Stanton, D.A.; Bohlen, J.; Davis, M.; Infante, A.M.; Hazard-Jenkins, H.; Klinke, D.J.; et al. Human Breast Cancer Xenograft Model Implicates Peroxisome Proliferator-activated Receptor Signaling as Driver of Cancer-induced Muscle Fatigue. Clin. Cancer Res. 2019, 25, 2336–2347.
  36. Wilson, H.E.; Stanton, D.A.; Rellick, S.; Geldenhuys, W.; Pistilli, E.E. Breast cancer-associated skeletal muscle mitochondrial dysfunction and lipid accumulation is reversed by PPARG. Am. J. Physiol. Cell Physiol. 2021, 320, C577–C590.
  37. Chaurasia, B.; Talbot, C.L.; Summers, S.A. Adipocyte Ceramides—The Nexus of Inflammation and Metabolic Disease. Front. Immunol. 2020, 11, 576347.
  38. Ferreira, L.F.; Moylan, J.S.; Gilliam, L.A.; Smith, J.D.; Nikolova-Karakashian, M.; Reid, M.B. Sphingomyelinase stimulates oxidant signaling to weaken skeletal muscle and promote fatigue. Am. J. Physiol. Cell Physiol. 2010, 299, C552–C560.
  39. Marinac, C.R.; Sears, D.D.; Natarajan, L.; Gallo, L.C.; Breen, C.I.; Patterson, R.E. Frequency and Circadian Timing of Eating May Influence Biomarkers of Inflammation and Insulin Resistance Associated with Breast Cancer Risk. PLoS ONE 2015, 10, e0136240.
  40. Patterson, R.E.; Sears, D.D. Metabolic Effects of Intermittent Fasting. Annu. Rev. Nutr. 2017, 37, 371–393.
  41. Hyland, K.A.; Nelson, A.M.; Eisel, S.L.; Hoogland, A.I.; Ibarz-Pinilla, J.; Sweet, K.; Jacobsen, P.B.; Knoop, H.; Jim, H.S. Fatigue Perpetuating Factors as Mediators of Change in a Cognitive Behavioral Intervention for Targeted Therapy-Related Fatigue in Chronic Myeloid Leukemia: A Pilot Study. Ann. Behav. Med. 2022, 56, 137–145.
  42. Cormie, P.; Zopf, E.M.; Zhang, X.; Schmitz, K.H. The Impact of Exercise on Cancer Mortality, Recurrence, and Treatment-Related Adverse Effects. Epidemiol. Rev. 2017, 39, 71–92.
  43. Ariza-Garcia, A.; Galiano-Castillo, N.; Cantarero-Villanueva, I.; Fernandez-Lao, C.; Diaz-Rodriguez, L.; Arroyo-Morales, M. Influence of physical inactivity in psychophysiological state of breast cancer survivors. Eur. J. Cancer Care 2013, 22, 738–745.
  44. van Zutphen, M.; Kampman, E.; Giovannucci, E.L.; van Duijnhoven, F.J.B. Lifestyle after Colorectal Cancer Diagnosis in Relation to Survival and Recurrence: A Review of the Literature. Curr. Colorectal. Cancer Rep. 2017, 13, 370–401.
  45. Naqash, A.R.; Kihn-Alarcón, A.J.; Stavraka, C.; Kerrigan, K.; Vareki, S.M.; Pinato, D.J.; Puri, S. The role of gut microbiome in modulating response to immune checkpoint inhibitor therapy in cancer. Ann. Transl. Med. 2021, 9, 1034.
  46. Carroll, J.E.; Small, B.J.; Tometich, D.B.; Zhai, W.; Zhou, X.; Luta, G.; Ahles, T.A.; Saykin, A.J.; Nudelman, K.N.; Clapp, J.D.; et al. Sleep disturbance and neurocognitive outcomes in older patients with breast cancer: Interaction with genotype. Cancer 2019, 125, 4516–4524.
  47. Liu, L.; Fiorentino, L.; Rissling, M.; Natarajan, L.; Parker, B.A.; Dimsdale, J.E.; Mills, P.J.; Sadler, G.R.; Ancoli-Israel, S. Decreased health-related quality of life in women with breast cancer is associated with poor sleep. Behav. Sleep Med. 2013, 11, 189–206.
  48. Alfano, C.M.; Day, J.M.; Katz, M.L.; Herndon, J.E.; Bittoni, M.A.; Oliveri, J.M.; Donohue, K.; Paskett, E.D. Exercise and dietary change after diagnosis and cancer-related symptoms in long-term survivors of breast cancer: CALGB 79804. Psychooncology 2009, 18, 128–133.
  49. Zick, S.M.; Sen, A.; Han-Markey, T.L.; Harris, R.E. Examination of the association of diet and persistent cancer-related fatigue: A pilot study. Oncol. Nurs. Forum. 2013, 40, E41–E49.
  50. Schwingshackl, L.; Hoffmann, G. Mediterranean dietary pattern, inflammation and endothelial function: A systematic review and meta-analysis of intervention trials. Nutr. Metab. Cardiovasc Dis. 2014, 24, 929–939.
  51. Sofi, F.; Abbate, R.; Gensini, G.F.; Casini, A. Accruing evidence on benefits of adherence to the Mediterranean diet on health: An updated systematic review and meta-analysis. Am. J. Clin. Nutr. 2010, 92, 1189–1196.
  52. Mentella, M.C.; Scaldaferri, F.; Ricci, C.; Gasbarrini, A.; Miggiano, G.A.D. Cancer and Mediterranean Diet: A Review. Nutrients 2019, 11, 2059.
  53. Bailey, M.A.; Holscher, H.D. Microbiome-Mediated Effects of the Mediterranean Diet on Inflammation. Adv. Nutr. 2018, 9, 193–206.
  54. Christ, A.; Lauterbach, M.; Latz, E. Western Diet and the Immune System: An Inflammatory Connection. Immunity 2019, 51, 794–811.
  55. Crowder, S.L.; Sarma, K.P.; Mondul, A.M.; Chen, Y.T.; Li, Z.; Pepino, M.Y.; Zarins, K.R.; Wolf, G.T.; Rozek, L.S.; Arthur, A.E. Pretreatment Dietary Patterns Are Associated with the Presence of Nutrition Impact Symptoms 1 Year after Diagnosis in Patients with Head and Neck Cancer. Cancer Epidemiol. Biomarkers Prev. 2019, 28, 1652–1659.
  56. Argirion, I.; Arthur, A.E.; Zarins, K.R.; Bellile, E.; Crowder, S.L.; Amlani, L.; Taylor, J.M.; Wolf, G.T.; McHugh, J.; Nguyen, A.; et al. Pretreatment Dietary Patterns, Serum Carotenoids and Tocopherols Influence Tumor Immune Response in Head and Neck Squamous Cell Carcinoma. Nutr. Cancer 2021, 73, 2614–2626.
  57. Baguley, B.J.; Skinner, T.L.; Jenkins, D.G.; Wright, O.R.L. 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. Clin. Nutr. 2021, 40, 245–254.
  58. Shi, N.; Aroke, D.; Jin, Q.; Lee, D.H.; Hussan, H.; Zhang, X.; Manson, J.E.; LeBlanc, E.S.; Barac, A.; Arcan, C.; et al. Proinflammatory and Hyperinsulinemic Dietary Patterns Are Associated with Specific Profiles of Biomarkers Predictive of Chronic Inflammation, Glucose-Insulin Dysregulation, and Dyslipidemia in Postmenopausal Women. Front. Nutr. 2021, 8, 690428.
  59. Aroke, D.; Folefac, E.; Shi, N.; Jin, Q.; Clinton, S.K.; Tabung, F.K. Inflammatory and Insulinemic Dietary Patterns: Influence on Circulating Biomarkers and Prostate Cancer Risk. Cancer Prev. Res. 2020, 13, 841–852.
  60. Maruvada, P.; Lampe, J.W.; Wishart, D.S.; Barupal, D.; Chester, D.N.; Dodd, D.; Djoumbou-Feunang, Y.; Dorrestein, P.C.; Dragsted, L.O.; Draper, J.; et al. Perspective: Dietary Biomarkers of Intake and Exposure-Exploration with Omics Approaches. Adv. Nutr. 2020, 11, 200–215.
  61. Descamps, H.C.; Herrmann, B.; Wiredu, D.; Thaiss, C.A. The path toward using microbial metabolites as therapies. EBioMedicine 2019, 44, 747–754.
  62. Marsland, B.J. Regulating inflammation with microbial metabolites. Nat. Med. 2016, 22, 581–583.
  63. Maceyka, M.; Spiegel, S. Sphingolipid metabolites in inflammatory disease. Nature 2014, 510, 58–67.
  64. Chiurchiu, V.; Leuti, A.; Maccarrone, M. Bioactive Lipids and Chronic Inflammation: Managing the Fire Within. Front. Immunol. 2018, 9, 38.
  65. Mathews, A.T.; Famodu, O.A.; Olfert, M.D.; Murray, P.J.; Cuff, C.F.; Downes, M.T.; Haughey, N.J.; Colby, S.E.; Chantler, P.D.; Olfert, I.M.; et al. Efficacy of nutritional interventions to lower circulating ceramides in young adults: FRUVEDomic pilot study. Physiol. Rep. 2017, 5, e13329.
  66. Wang, D.D.; Toledo, E.; Hruby, A.; Rosner, B.A.; Willett, W.C.; Sun, Q.; Razquin, C.; Zheng, Y.; Ruiz-Canela, M.; Guasch-Ferré, M.; et al. Plasma Ceramides, Mediterranean Diet, and Incident Cardiovascular Disease in the PREDIMED Trial (Prevencion con Dieta Mediterranea). Circulation 2017, 135, 2028–2040.
  67. Summers, S.A. Ceramides in insulin resistance and lipotoxicity. Prog. Lipid. Res. 2006, 45, 42–72.
  68. Holland, W.L.; Summers, S.A. Sphingolipids, insulin resistance, and metabolic disease: New insights from in vivo manipulation of sphingolipid metabolism. Endocr. Rev. 2008, 29, 381–402.
  69. Summers, S.A. The ART of Lowering Ceramides. Cell Metab. 2015, 22, 195–196.
  70. Alfano, C.M.; Imayama, I.; Neuhouser, M.L.; Kiecolt-Glaser, J.K.; Smith, A.W.; Meeske, K.; McTiernan, A.; Bernstein, L.; Baumgartner, K.B.; Ulrich, C.M.; et al. Fatigue, inflammation, and omega-3 and omega-6 fatty acid intake among breast cancer survivors. J. Clin. Oncol. 2012, 30, 1280–1287.
  71. George, S.M.; Alfano, C.M.; Neuhouser, M.L.; Smith, A.W.; Baumgartner, R.N.; Baumgartner, K.B.; Bernstein, L.; Ballard-Barbash, R. Better postdiagnosis diet quality is associated with less cancer-related fatigue in breast cancer survivors. J. Cancer Surviv. 2014, 8, 680–687.
  72. George, S.M.; Neuhouser, M.L.; Mayne, S.T.; Irwin, M.L.; Albanes, D.; Gail, M.H.; Alfano, C.M.; Bernstein, L.; McTiernan, A.; Reedy, J.; et al. Postdiagnosis diet quality is inversely related to a biomarker of inflammation among breast cancer survivors. Cancer Epidemiol. Biomarkers Prev. 2010, 19, 2220–2228.
  73. Huang, X.; Zhang, Q.; Kang, X.; Song, Y.; Zhao, W. Factors associated with cancer-related fatigue in breast cancer patients undergoing endocrine therapy in an urban setting: A cross-sectional study. BMC Cancer 2010, 10, 453.
  74. Zick, S.M.; Colacino, J.; Cornellier, M.; Khabir, T.; Surnow, K.; Djuric, Z. Fatigue reduction diet in breast cancer survivors: A pilot randomized clinical trial. Breast Cancer Res. Treat. 2017, 161, 299–310.
  75. Yoshino, J.; Smith, G.I.; Kelly, S.C.; Julliand, S.; Reeds, D.N.; Mittendorfer, B. Effect of dietary n-3 PUFA supplementation on the muscle transcriptome in older adults. Physiol. Rep. 2016, 4, e12785.
  76. Johnson, M.L.; Lalia, A.Z.; Dasari, S.; Pallauf, M.; Fitch, M.; Hellerstein, M.K.; Lanza, I.R. Eicosapentaenoic acid but not docosahexaenoic acid restores skeletal muscle mitochondrial oxidative capacity in old mice. Aging Cell 2015, 14, 734–743.
  77. Mizunoya, W.; Iwamoto, Y.; Shirouchi, B.; Sato, M.; Komiya, Y.; Razin, F.R.; Tatsumi, R.; Sato, Y.; Nakamura, M.; Ikeuchi, Y. Dietary fat influences the expression of contractile and metabolic genes in rat skeletal muscle. PLoS ONE 2013, 8, e80152.
  78. Philp, L.K.; Heilbronn, L.K.; Janovska, A.; Wittert, G.A. Dietary enrichment with fish oil prevents high fat-induced metabolic dysfunction in skeletal muscle in mice. PLoS ONE 2015, 10, e0117494.
  79. St-Onge, M.P.; Ard, J.; Baskin, M.L.; Chiuve, S.E.; Johnson, H.M.; Kris-Etherton, P.; Varady, K. Meal Timing and Frequency: Implications for Cardiovascular Disease Prevention: A Scientific Statement from the American Heart Association. Circulation 2017, 135, e96–e121.
  80. Wirth, M.D.; Zhao, L.; Turner-McGrievy, G.M.; Ortaglia, A. Associations between Fasting Duration, Timing of First and Last Meal, and Cardiometabolic Endpoints in the National Health and Nutrition Examination Survey. Nutrients 2021, 13, 2686.
  81. Dun, L.; Xian-Yi, W.; Xiao-Ying, J. Effects of Moderate-To-Vigorous Physical Activity on Cancer-Related Fatigue in Patients with Colorectal Cancer: A Systematic Review and Meta-Analysis. Arch. Med. Res. 2020, 51, 173–179.
  82. Cramp, F.; Byron-Daniel, J. Exercise for the management of cancer-related fatigue in adults. Cochrane Database Syst. Rev. 2012, 11, CD006145.
  83. Juvet, L.K.; Thune, I.; Elvsaas, I.Ø.; Fors, E.A.; Lundgren, S.; Bertheussen, G.; Leivseth, G.; Oldervoll, L.M. 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. Breast 2017, 33, 166–177.
  84. Campbell, K.L.; Winters-Stone, K.; Wiskemann, J.; May, A.M.; Schwartz, A.L.; Courneya, K.S.; Zucker, D.; Matthews, C.; Ligibel, J.; Gerber, L.; et al. Exercise Guidelines for Cancer Survivors: Consensus Statement from International Multidisciplinary Roundtable. Med. Sci. Sports Exerc. 2019, 51, 2375–2390.
  85. Depner, C.M.; Cogswell, D.T.; Bisesi, P.J.; Markwald, R.R.; Cruickshank-Quinn, C.; Quinn, K.; Melanson, E.L.; Reisdorph, N.; Wright, K.P., Jr. Developing preliminary blood metabolomics-based biomarkers of insufficient sleep in humans. Sleep 2020, 43, zsz321.
  86. Xiao, Q.; Moore, S.C.; Keadle, S.K.; Xiang, Y.B.; Zheng, W.; Peters, T.M.; Leitzmann, M.F.; Ji, B.T.; Sampson, J.N.; Shu, X.O.; et al. Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study. Int. J. Epidemiol. 2016, 45, 1433–1444.
  87. Carroll, J.E.; Irwin, M.R.; Levine, M.; Seeman, T.E.; Absher, D.; Assimes, T.; Horvath, S. Epigenetic Aging and Immune Senescence in Women with Insomnia Symptoms: Findings From the Women’s Health Initiative Study. Biol. Psychiatry 2017, 81, 136–144.
  88. Lahtinen, A.; Puttonen, S.; Vanttola, P.; Viitasalo, K.; Sulkava, S.; Pervjakova, N.; Joensuu, A.; Salo, P.; Toivola, A.; Härmä, M.; et al. A distinctive DNA methylation pattern in insufficient sleep. Sci. Rep. 2019, 9, 1193.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
ScholarVision Creations
Feedback