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Year : 2020  |  Volume : 2  |  Issue : 2  |  Page : 85-91

Symptom clusters in men with prostate cancer: A pilot assessment using salivary measures

School of Nursing; Mays Cancer Center; Sam and Ann Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA

Date of Submission16-Sep-2020
Date of Decision18-Nov-2020
Date of Acceptance18-Nov-2020
Date of Web Publication04-Jan-2021

Correspondence Address:
Dr. Darpan I Patel
University of Texas Health Science Center San Antonio, 7703 Floyd Curl Dr, M/C 7975, San Antonio, TX 78229
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijptr.ijptr_23_20

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Context: Symptom clusters are an underutilized method for assessing cancer burden among men treated for prostate cancer (PCa).
Aim: The present study aimed to investigate associations between physiological and psychosocial symptoms that could better inform oncologists and family caregivers on how best to manage the care for men with PCa.
Settings and Design: Cross-sectional study design was implemented at a National Cancer Institute designated outpatient cancer center.
Subjects and Methods: Thirty men treated for PCa are included in this analysis (age: 70 ± 10; BMI: 29 ± 4.3). Participants completed the Brief Fatigue Inventory, the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), and the Short Form (SF)-36. The patient demographic data were extracted from medical records and salivary cortisol and C-reactive protein were quantified.
Statistical Analysis Used: Correlations and hierarchical cluster analysis were performed. Statistical significance was considered as P < 0.05.
Results: Fatigue had significant negative correlations with multiple subscales of the SF-36. Increased BMI was negatively associated with SF-36 subscales of physical function (−0.621; P = 0.001), energy/fatigue (−0.449; P = 0.02), social function (−0.409; P = 0.04), pain (−0.422; P = 0.04), and FACIT-F subscales of functional well-being (−0.546; P = 0.006), general health (−0.494; P = 0.01), and total score (−0.458; P = 0.02). Cluster analysis revealed 2 categories of clusters, both including fatigue as a central symptom.
Conclusion: The results of this study conclude that fatigue is associated with multiple QoL indicators in men with PCa. The management of this symptom cluster has the potential to improve QoL.

Keywords: Cluster analysis, Functional Assessment of Chronic Illness Therapy-Fatigue, Quality of life, Short Form-36, Stress, Symptom management

How to cite this article:
Patel DI. Symptom clusters in men with prostate cancer: A pilot assessment using salivary measures. Indian J Phys Ther Res 2020;2:85-91

How to cite this URL:
Patel DI. Symptom clusters in men with prostate cancer: A pilot assessment using salivary measures. Indian J Phys Ther Res [serial online] 2020 [cited 2021 Oct 18];2:85-91. Available from: https://www.ijptr.org/text.asp?2020/2/2/85/189941

  Introduction Top

Prostate cancer (PCa) is the most common cancer among men in the United States (US) with an estimated 174,650 new cases in the US[1] and rates in India ranging between 5.0 and 9.1/100,000/year.[2] Cancer-related fatigue (CRF) is the most commonly reported symptom in men with PCa with 50-90% of men reporting CRF as having a significant adverse event on quality of life (QoL).[3] PCa is the most prevalent cancer among men and a range of treatment modalities are available including active surveillance, androgen deprivation therapy (ADT), radiotherapy (external beam radiotherapy and brachytherapy), chemotherapy, and surgery (radical prostatectomy).[4] PCa treatments are associated with substantial risk of specific physical side effects including fatigue, depression, anxiety, and sleep disturbance.[4] When two or more concurrent symptoms (e.g., pain and energy/fatigue) are related to each other, they are defined as a symptom cluster.[5] The symptoms within a cluster are not required to have the same etiology, but are required to be related to one another and occurring concurrently.[5] It has been recently discovered that a cluster of symptoms (pain, energy/fatigue and general health [GH]) had a consistent negative effect on losses in function unrelated to patients' type of cancer treatment or stage of disease in a sample of 826 elderly patients with cancer.[5]

Individual symptoms related to PCa fatigue such as depression, cachexia, and obesity have been thoroughly investigated; yet the interaction between symptoms, or symptom clusters, remains underexplored. Although patients with PCa frequently report concurrent symptoms, symptom management has remained fragmented.[5] A recent literature search shows 1245 publications from 1985 to 2015 recognizing CRF in PCa as a distinct symptom. Yet, using the same search parameters, and keywords “symptom cluster” or “symptom combination” within the medical literature there have been only 12 articles published on symptom clusters related specifically to PCa fatigue.[6] This discrepancy in research provides clear evidence that we have been slow to recognize and study the impact of fatigue as the center of patient reported symptoms.

We proposed that CRF is the central symptom experienced by those with PCa and by managing patient fatigue we could mitigate or minimize patient experiences of fatigue-related symptoms. Currently, CRF and subsequent symptom clusters in men with PCa are often undiagnosed and poorly managed.[6] A study by Curt and Johnson found that physicians' responses in the US to CRF were to either: Do nothing or prescribe rest 77% of the time.[6] A comprehensive understanding of fatigue and its effect on symptom clusters will help close a major gap that is currently impeding progress in advancing the treatment of CRF.

The goal of this project was to identify symptom clusters in a small cohort of men treated for PCa in South Texas using salivary biomarkers in relation to patient reported outcomes. The present study hypothesize correlation between fatigue and multiple subscale related to quality of life (QoL) in men with PCa.

  Subjects and Methods Top

Men with PCa that met inclusion criteria were recruited from the surrounding community to participate in this cross-sectional study. Eligible patients were identified through the review of medical records and contacted with mailed recruitment letters. Subjects were eligible to participate in this study if they were 18 years of age or older and met one of the following: Clinically diagnosed and receiving treatment for PCa (as defined by the American Urological Association Clinical Practice Guidelines); clinically diagnosed and under active surveillance; or completed treatment for PCa and currently receiving follow up care. Patients were excluded if the eligibility criteria were not met. Medical history was collected on consenting participants and included age, race, weight, height, PCa diagnosis, and treatment plan. This study was approved by the University of Texas Health Science Center as San Antonio Institutional Review Board.

One hundred and fifty men were sent information regarding this study of which 30 men consented to participate (20%). Three men were unable to provide the saliva necessary to quantify salivary cortisol and C-reactive protein (CRP), thus were not included in this study. Thus, 27 men were included in the analysis. Demographic information about the 27 study participants is presented in [Table 1].
Table 1: Subject demographics (average±standard deviation or frequency [%])

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Consenting participants were asked to complete three questionnaires: The Brief Fatigue Inventory (BFI),[7] the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F)[8] and the Short Form (SF)-36.[9]

The BFI is an instrument used to assess the severity of fatigue experienced by cancer patients as well as the impact of fatigue on their ability to function over the previous 24 h.[9] The brevity of the BFI makes it a useful tool for clinical trials. It can be rapidly administered and easily understood and is thus well tolerated by patients suffering the most server degrees of fatigue.

The FACIT-F is a 13-item questionnaire that measures an individual's level of fatigue during their usual daily activities over the past week.[8] The FACIT-F has the subdomains of physical well-being, social/family well-being, emotional well-being (EWB), functional well-being (FWB), and fatigue subscale (FS).

The SF-36 is an abbreviated patient reported survey of participant health that provides a comprehensive measure of physical, emotional, and social wellbeing.[9] The measure provides scores within eight multi-item dimensions. The SF-36 has two subdomains: physical health and mental health. Within physical health, the scales include: Physical functioning (PF), role-physical), bodily pain, and GH. The mental health scales are: Vitality, social functioning, role-emotional, and mental health.

Measurements of stress and systemic inflammation were done using salivary concentrations of cortisol and CRP, respectively. Approximately 2 ml of saliva were collected from participants using the passive drool technique and placed immediately on frozen and stored at −80° until analysis. On day of assay, sample were allowed to thaw, vortexed, then centrifuged at 1500 × g (@3000 rpm) for 15 min to pellet the mucins. The clear supernatant was used for the assay. Concentrations were measured following manufacturer's suggested protocol using commercially available assay kits purchased from Salimetrics (College Station, PA, USA).

Two modes of analysis were used in this study using IBM SPSS 23 (Chicago, IL, USA). Descriptive statistics, including frequencies and percentages, were calculated for subject demographic variables. First, associations between symptoms were assessed using Spearman's Correlation. Second, relative distance among symptoms associated with PCa and PCa treatment was analyzed using hierarchical cluster analysis. This method considers each symptom as a cluster of size one; it then joins similar clusters together until a single cluster is obtained that contains all of the symptoms. Clusters were formed using the centroid method between symptoms items, standardized from −1 to 1 with the distances between symptom items calculated using squared Euclidian distances. The number of clusters was limited to 5 clusters. An alpha of P < 0.05 was considered statistically significant.

  Results Top

Descriptive statistics on symptom severity for the BFI, FACIT-F, and salivary biomarkers are shown in [Table 2]. [Figure 1] presents the results of the SF-36 survey with 95% confidence interval.
Figure 1: Short Form-36 subdomain results

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Table 2: Descriptive statistics for brief fatigue index, functional assessment of chronic illness therapy-fatigue and salivary biomarkers (mean±standard deviation)

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As shown in [Table 3], fatigue is significantly associated with social, emotional, and PF, including GH, pain, FWB, and role limitations associated with these domains. Secondarily, obesity, as measured by body mass index was negatively associated with many variables of the FACIT and SF-36 subscales. Finally, stress, as measured by salivary cortisol, was also negatively associated with similar variables. Not presented is a tendency for a positive association between fatigue and stress which has a weak correlation (0.386; P = 0.056).
Table 3: Correlation matrix of symptoms and outcomes associated with prostate cancer

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A hierarchical cluster analysis resulted in a total of 2 overarching clusters [Table 4]. Cluster 1 is identified as physiological fatigue including elevated BMI, stress, systemic inflammation and fatigue. Cluster 2 is identified as psychosocial manifestations including physical/FWB, social well-being, EWB, energy/fatigue and pain. In cluster 2, each of the subscales for the FACIT-F and SF-36 were grouped together, and thus, rather than reporting each individual subscale in this cluster, we grouped the overlapping symptoms and present the 5 major symptom categories in cluster 2. A central variable in each of these two clusters is fatigue, measured by the BFI and the FACIT subscale for fatigue. [Figure 2] presents the relative distance among symptoms associated with PCa and PCa treatment. Clusters were formed using the centroid method with distances between symptoms calculated using squared Euclidian distances. The visual representation shows the symptom items that are related and the distances between symptom items at each step in the analysis. The distance values 0–25 represent relative distances. The score of the BFI, FACIT-F FS are central to the two main clusters displayed.
Figure 2: Symptom cluster dendogram. Clusters were formed using the centroid method with distances between symptoms calculated using squared Euclidian distances. The visual representation shows the symptom items that are related and the distances between symptom items at each step in the analysis. The distance values 0–25 represent relative distances

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Table 4: Cluster organization

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  Discussion Top

If PCa is caught early enough, intervention with either Androgen deprivation therapy (ADT) chemotherapy, radiation therapy, or some combination of these interventions can increase survivorship in men with PCa. However, with all medical interventions, adverse effects exist, and in the case of PCa, can significantly impede QoL.

Hence, much of symptom management in men with PCa centers on physiological manifestations and tend to neglect the psychological and emotional adverse effects of PCa and its treatment. With so many men reporting fatigue as a debilitating adverse effect of PCa and treatment,[3] a better understanding of the clusters they form around fatigue to help improve outcomes in this patient population is warranted. The results of our study suggest that fatigue is a significant debilitating adverse effect that is associated with reduced functional, social, and EWB in men with PCa.

Although the sample size is relatively small in the present study, outcomes of this pilot study identify 2 major clusters, both including fatigue. In general, increase in fatigue is associated with decreased GH in our study population [Figure 1], thus, supporting current sentiments in the literature.[10] In the only manuscript on PCa symptom clusters, Maliski et al. found similar clusters in her analysis of data collected on 402 men with PCa.[11] The rational for fatigue playing a significant role in the QoL in men with PCa is the prevalence by which it is reported. In varying reports, 50%–90% of men with PCa report fatigue being present. While there are pharmaceutical interventions to attenuate fatigue in this population, the secondary effects of the disease, namely psychological and emotional distress are left untreated. Therefore, rather than simply focusing on the single symptom of fatigue, it would behoove the medical profession to review the symptom clusters that form around fatigue to treat the whole patient, rather than just the symptom.

In addition, the prevalence of fatigue in our population is as expected given the high number of men being treated with ADT.[12] Neoadjuvant and adjuvant treatment with ADT has wide ranging adverse effects associated with it, including fatigue, obesity, osteoporosis, and sarcopenia. In our study, 48% of the participants were being treated with ADT. Thus, the increased prevalence of fatigue in our sample is not unusual.

In the current study, fatigue was measured using the BFI and the FACIT-F. Because the FACIT-F includes multiple subscales, we decided to use the BFI as our fatigue measure based on its direct measurement of fatigue without additional subscales. In our study, BFI total Score was significantly correlated with every subscale of the FACIT and SF-36 surveys, pointing to the impact fatigue can have physical, social, emotion, and FWB. Case in point, the negative association between GH score and BFI score suggest that fatigue significantly impact QoL and health in our population.

Surprisingly, fatigue was not significantly correlated with BMI in the present study. Typically, with increased levels of fatigue, activity levels decrease, thus increasing weight gain, BMI, and obesity. Our sample presented with majority of patients (81.5%) with BMI in the overweight to obese category. While BMI was not correlated with fatigue, increases in BMI were significantly correlation with multiple emotional and physical subscales of our survey.

Increased stress has also been noted to be prevalent in men with PCa, due to uncertainty of cancer prognosis as well as having to deal with the adverse effects associated with treatment.[13] In the current study, stress was measured through salivary cortisol and found to be significantly correlating with many physical and psychological domains of the FACIT and SF-36 surveys. Many of the subscales significantly correlated between −0.427 and −0.511 showing weak to moderate correlation. However, for the subscale for role limitation due to emotional factors, stress was correlated at −0.627. Small sample size recruited for our study is an inherent limitation to our study. However, the data generated from this pilot study may provide valuable information in the development of future research studies. The aim of this study was to get a general understanding of symptom clusters in men with PCa. Future research may expand upon these findings by increasing sample size, broadening the symptoms studied, and adding a control group to determine if the associated symptom clusters are in fact due to PCa or due to the natural aging process.

The current study focused mostly on self-reported psychosocial symptoms and a few biological markers. Future research should expand to other symptoms such as other urological symptoms with better stratification on cancer progression and treatment. Furthermore, majority of the patients in this sample were being treated with either adjuvant or neo-adjuvant ADT, thus significantly limiting the interpretation to other treatment regimens. The clusters presented here might not be applicable to individuals that are solely under active surveillance (with little to no adverse effects on QoL) to those that have had complete prostatectomies. The procedures associated with more advanced cancer also have adverse effects which also need to be incorporated into future studies with a larger sample size. The use of continuous data for cluster analysis, rather than present, not present, may also effect the interpretation of our results. However, symptom severity is better indicated in a continuous fashion rather than a present/not-present reporting structure. Continued work needs to be done to differentiate the benefits and limitations to both types of reporting. Finally, the use of cross-sectional design fails to capture changes in symptom clusters over time. While the use of cross-sectional study design might be more feasible, future studies should make an effort to ensure equal stratification for cancer stage, treatment types, and age to better capture variations in symptoms clusters across the spectrum of PCa.

Future studies in symptoms research on men with PCa are encouraged to expand upon the symptoms that are reported here. For example, the area of allostatic load, which concerns the cumulative wear and tear on the body due to repeated cycles of stress has not been applied to men with PCa and warrants investigation. In previous studies, Maloney et al., (2009) to (2006). confirmed the association of chronic fatigue syndrome with high allostatic load levels, meaning, increased time and exposure undress stress increases symptoms associated with fatigue in this population.[14] Given that fatigue is a significant adverse effect associated with PCa and PCa treatment, studying allostatic load as a companion to symptom management may prove to be beneficial in managing QoL in this cohort of men.

  Conclusion Top

The results of this study suggest a complex interaction of symptoms that burden PCa patients, independent of their treatment trajectory. Based on the present findings, it is inherent that multidisciplinary teams be utilized to holistically treat these patients that present with a number of underlying effects to PCa and PCa treatment that may or may not be apparent. Furthermore, as evident by the clusters presented in this research, it may be appropriate for clinicians and care providers to presume that along with the symptoms of fatigue or stress that patients may present with during clinic visits, there are underlying conditions that accompany the observable symptoms that need to be assumed are present.

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Conflicts of interest

There are no conflicts of interest.

  References Top

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Mendoza TR, Wang XS, Cleeland CS, Morrissey M, Johnson BA, Wendt JK, et al. The rapid assessment of fatigue severity in cancer patients: use of the Brief Fatigue Inventory. Cancer 1999;85:1186-96.  Back to cited text no. 7
Butt Z, Lai JS, Rao D, Heinemann AW, Bill A, Cella D. Measurement of fatigue in cancer, stroke, and HIV using the Functional Assessment of Chronic Illness Therapy Fatigue (FACIT-F) scale. J Psychosom Res 2013;74:64-8.  Back to cited text no. 8
Brazier JE, Harper R, Jones NM, O'Cathain A, Thomas KJ, Usherwood T, et al. Validating the SF-36 health survey questionnaire: new outcome measure for primary care. BMJ 1992;305:160-4.  Back to cited text no. 9
Eton DT, Lepore SJ. Prostate cancer and health-related quality of life: A review of the literature. Psychooncology 2002;11:307-26.  Back to cited text no. 10
Maliski SL, Kwan L, Elashoff D, Litwin MS. Symptom clusters related to treatment for prostate cancer. Oncol Nurs Forum 2008;35:786-93.2.  Back to cited text no. 11
Sharifi N, Gulley JL, Dahut WL. Androgen deprivation therapy for prostate cancer. JAMA 2005;294:238-44.  Back to cited text no. 12
Carlson LE, Speca M, Patel KD, Goodey E. Mindfulness-based stress reduction in relation to quality of life, mood, symptoms of stress and levels of cortisol, dehydroepiandrosterone sulfate (DHEAS) and melatonin in breast and prostate cancer outpatients. Psychoneuroendocrinology 2004;29:448-74.  Back to cited text no. 13
Maloney EM, Gurbaxani BM, Jones JF, de Souza Coelho L, Pennachin C, Goertzel BN. Chronic fatigue syndrome and high allostatic load. Pharmacogenomics 2006;7:467-73.  Back to cited text no. 14


  [Figure 1], [Figure 2]

  [Table 1], [Table 2], [Table 3], [Table 4]


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