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Open AccessArticle

Body Mass Index and Total Symptom Burden in Myeloproliferative Neoplasms Discovery of a U-shaped Association

Department of Hematology, Zealand University Hospital, 4000 Roskilde, Denmark
Department of Hematology/Oncology, UT Health San Antonio MD Anderson Cancer Center, San Antonio, TX 78229, USA
Hematologic Malignancies, Incyte Corporation, Wilmington, DE 19803, USA
Department of Population Health Sciences, UT Health San Antonio, San Antonio, TX 78229, USA
Department of Hematology, University Hospital of Copenhagen at Rigshospitalet, 2100 Copenhagen, Denmark
Department of Occupational and Environmental Medicine, Bispebjerg University Hospital, 2400 Copenhagen, Denmark
Authors to whom correspondence should be addressed.
Cancers 2020, 12(8), 2202;
Received: 25 June 2020 / Revised: 30 July 2020 / Accepted: 2 August 2020 / Published: 6 August 2020
(This article belongs to the Special Issue New Insights into Myeloproliferative Neoplasms)


Elevated body mass index (BMI) is a global health problem, leading to enhanced mortality and the increased risk of several cancers including essential thrombocythemia (ET), a subtype of the Philadelphia-chromosome negative myeloproliferative neoplasms (MPN). Furthermore, evidence states that BMI is associated with the severity of symptom burden among cancer patients. MPN patients often suffer from severe symptom burden. The purpose of this study was to examine whether deviations from a normal BMI in an MPN population are associated with higher symptom burden and reduced quality of life (QoL). A combined analysis of two large cross-sectional surveys, the Danish Population-based Study, MPNhealthSurvey (n = 2044), and the international Fatigue Study (n = 1070), was performed. Symptoms and QoL were assessed using the validated Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF). Analysis of covariance was used to estimate the effects of different BMI categories on symptom scores while adjusting for age, sex, and MPN subtype. A U-shaped association between BMI and Total Symptom Burden was observed in both datasets with significantly higher mean scores for underweight and obese patients relative to normal weight (mean difference: underweight 5.51 (25.8%), p = 0.006; obese 5.70 (26.6%) p < 0.001). This is an important finding, as BMI is a potentially modifiable factor in the care of MPN patients.
Keywords: Philadelphia-negative myeloproliferative neoplasms (MPN), body mass index; symptom burden; quality of life; chronic inflammation Philadelphia-negative myeloproliferative neoplasms (MPN), body mass index; symptom burden; quality of life; chronic inflammation

1. Introduction

Body mass index (BMI) is a key global health issue worldwide, as the prevalence of obesity has increased to a pandemic extent, and obesity has become one of the leading risk factors for premature death [1,2]. Furthermore, comprehensive research documents an association between obesity and the increased risk of multiple adverse health outcomes [3] including diabetes, cardiovascular diseases [2,3,4,5,6], and several common cancers (e.g., colon cancer, gastric cancer, breast cancer) [7,8,9,10]. Interestingly, recent studies indicate that obesity might increase the risk of essential thrombocythemia (ET), a subtype of the myeloproliferative neoplasms, as well [11,12,13,14,15]. Besides ET, Philadelphia-negative myeloproliferative neoplasms (MPN) encompass polycythemia vera (PV), myelofibrosis (MF), and MPN unclassified (MPN-U). They are acquired clonal hematologic cancers characterized by a state of chronic inflammation [16,17]. Through their lifelong course of disease, MPN patients are at risk of developing splenomegaly, vascular complications, bone marrow failure and progressing to acute myeloid leukemia [18], and in their daily lives, the patients often suffer from debilitating symptoms including fatigue, pruritus, abdominal discomfort, night sweats, and early satiety [19]. Inflammation may represent a key component in several of these symptoms [20]. Like MPN, obesity is known to trigger a state of chronic inflammation with elevated levels of pro-inflammatory cytokines [21,22,23,24,25,26,27], but how obesity affects symptom burden among MPN patients is largely unknown [28,29,30,31]. However, a broad body of literature evidences that an elevated BMI is associated with increased symptom burden and reduced health-related quality of life (HRQoL) [32,33,34,35,36,37,38,39,40]. Bearing in mind the rapidly increasing prevalence of obesity and the multiple adverse effects of MPN symptoms such as compromised social functioning, physical activity, self-reliance, productivity, and reduced quality of life, it is paramount to gain further knowledge in an MPN context [19,41]. Accordingly, the aim of this study is to examine the association between BMI and symptom burden and quality of life, respectively.

2. Results

2.1. Patient Demographics

In total, 3114 patients (1852 females, 1256 males) were included in the combined analysis, with 2044 patients from the Danish MPNhealthSurvey and 1070 patients from the International Fatigue Study (Table 1). Patients were of typical age (mean 65.6 years) for the disorder with a distribution of participants consisting of 1047 patients with ET (33.7%), 1301 patients with PV (41.8%), 330 patients with MF (10.6%), and 433 with MPN-U (13.9%). The majority of patients had a duration of disease of a least a few years (>3y [n = 2242, 71%], 1–3 y [n = 623, 20%], ½–1 y [n = 170, 5.8%], <½ y [n = 67, 2.2%]). Comparing patients from the Danish MPNhealthSurvey and the Fatigue Study, Danish patients were significantly older (69.2 y vs. 60.9 y, p < 0.0001), a smaller proportion was female (56.5%, vs. 67.3%, p < 0.0001) and fewer patients were diagnosed with MF (3.3% vs. 24.6%, p < 0.0001). Time since MPN diagnosis was similar between the studies except for a greater proportion of newly diagnosed patients in the Fatigue Study (5.1% vs. 0.6%, p < 0.0001%). In both the US and the Danish dataset, patients with Normal Weight were most frequent (DK/US = 52.4/46.3%), followed by Overweight patients (DK/US = 31.7/30.5%), Obese patients (DK/US = 13.2/21.7%), and least frequent Underweight patients (DK/US = 2.7%/1.5%). The mean BMI in the study population was 25.7, with no significant differences in mean BMI when stratifying by MPN subtype (Table S1). In contrast, we found an overall difference between the MPN subtypes with MF patients reporting a significantly higher TSS compared with ET patients (25.2 vs. 21.9, p < 0.05), especially underweight MF patients reported a high symptom burden (mean TSS = 41.0) (Table S2)

2.2. Body Mass Index and Symptom Burden

A U-shaped association between BMI and Total Symptom Score (TSS) was demonstrated, with a higher symptom burden among MPN patients who are underweight or obese compared with patients in the normal weight BMI category; a finding that was consistent in both the merged analysis and when looking at the two study populations separately (Figure 1). Likewise, the pattern remained when stratifying by gender and MPN subtype, respectively (Figure 2).
Furthermore, the U-shaped pattern (underweight > normal weight < obese) persisted for nearly all MPN-SAF items when evaluating the mean symptoms score for each symptom individually; fatigue (4.36, 4.08, 5.25; p < 0.001), early satiety (3.86, 2.58, 2.91; p < 0.001), abdominal pain (1.75, 1.27, 1.80; p < 0.001), abdominal discomfort (2.26, 1.78, 2.33; p < 0.001), inactivity (3.12, 2.38, 3.66; p < 0.001), headache (1.90, 1.76, 2.38; p < 0.001), concentration problems (3.09, 2.69, 3.59; p < 0.001), dizziness (2.71, 2.24, 2.82; p < 0.001), numbness (2.83, 2.25, 3.19; p < 0.001), insomnia (3.54, 3.04, 3.97; p < 0.001), sad mood (2.67, 2.40, 3.17; p < 0.001), cough (2.56, 1.61, 2.37; p < 0.001), bone pain (2.40, 1.84, 2.96; p < 0.001), fever (0.42, 0.31, 0.42; p = 0.057), and quality of life (3.61, 2.90, 3.50; p < 0.001) (Table 2).
In line with these findings, symptom prevalence (MPN-SAF score ≥ 1) by BMI also showed the characteristic U-shape for the vast majority of symptoms (underweight > normal weight < obese); early satiety (76.8%, 62.4%, 67.7%; p = 0.018), abdominal pain (44.9%, 38.2%, 50.1%; p < 0.001), abdominal discomfort (59.4%, 50.0%, 60.5%; p < 0.001), inactivity (69.2%, 61.5%, 76.7%; p < 0.001), concentration problems (68.6%, 63.5%, 72.6%; p = 0.003), dizziness (70.0%, 60.7%, 66.5%; p = 0.057), numbness (59.4%, 54.5%, 67.2%; p < 0.001), insomnia (71.0%, 68.2%, 76.8%; p = 0.004), sad mood (71.4%, 62.0%, 72.7%; p < 0.001), cough (64.3%, 47.0%, 58.2%; p < 0.001), itching (54.3%, 53.4%, 66.6%; p < 0.001), bone pain (50.0%, 45.9%, 60.9%; p < 0.001), and fever (20.3%, 12.4%, 15.8%; p = 0.047) (Table 3).
The U-shaped pattern did not exist for weight loss; obese patients had both the lowest average score and prevalence of this symptom while underweight patients had the highest score. Increasing means with increasing BMI categories were observed for night sweats (2.33, 2.40, 2.72, 3.28 p < 0.001) and sexuality problems (3.29, 3.36, 3.91, 4.37, p < 0.001) with obese patients reporting the greatest severity for these symptoms. Likewise, the same pattern was seen for symptom prevalence (night sweats: 52.9%, 59.4%, 61.5%, 69.5% p < 0.001; sexuality problems: 55.4%, 62.7%, 67.7%, 71.0%, p < 0.001) (Table 2 and Table 3).

2.3. Adjusted Differences in MPN-SAF Symptom Score

As shown in Table 4, adjusting for age, gender, and MPN subtype did not affect the observed association between BMI and symptoms markedly. For each of the single symptoms except weight loss, obese patients reported an average mean between 12.8–53.3% higher relative to patients with normal BMI. A notable difference in the mean of more than 25% existed for bone pain (53.3%, 0.98, p < 0.001), inactivity (51.3%, 1.22 p < 0.001), cough (47.8%, 0.77, p < 0.001), itching (41.1%, 0.86, p < 0.001), numbness (40.0%, 0.90, p < 0.001), abdominal pain (36.2%, 0.46, p < 0.001), night sweats (33.8%, 0.81, p < 0.001), sad mood (26.7%, 0.64, p < 0.001), total symptom score (26.8%, 5.73, p < 0.001), concentrations problems (26.4%, 0.71, p < 0.001), abdominal discomfort (25.8%, 0.46, p < 0.001), sexuality problems (25.7%, 0.89, p < 0.001), and insomnia (25.3%, 0.77, p > 0.001). Comparing obese and normal weight patients, fever was the only symptom that did not differ significantly. As mentioned, the patients in the underweight BMI category reported a worse symptoms score compared with patients with normal weight. Underweight patients had a more than 25 percent increased symptom score for cough (57.8%, 0.93, p = 0.002), early satiety (48.4%, 1.25, p < 0.001), inactivity (31.9%, 0.76, p = 0.026), total symptom score (25.1%, 5.37, p = 0.007), and, as anticipated, a much higher severity of weight loss (176.6%, 2.19, p < 0.001). Finally, in regard to overweight patients, the same pattern was observed, however, the differences in means compared with patients with normal weight were smaller with fewer significant findings.

3. Discussion

For many, being diagnosed with MPN means facing a life with substantial symptom burden throughout the lifelong disease course [19,41,42,43,44]. As MPN symptoms impact patient QoL, treatment and survival, alleviating symptom burden is fundamental in MPN therapy. During the last decade, our understanding of the MPN symptom profile (i.e., symptom prevalence and symptom severity) has increased markedly, due to the development of specific MPN symptom scoring tools [42,43]. However, our understanding of the multifactorial causes of the different disease-related symptoms are currently sparse, and further research uncovering contributing factors are crucial. Accordingly, this is the first study to investigate if total symptom burden (TSS), individual MPN symptoms, and quality of life are associated with BMI.
In this collaborative international study using the combined data from two comprehensive cross-sectional health-related quality of life studies, we found several interesting results. Most importantly, we discovered a U-shaped distribution of total symptom burden with the nadir centered around normal body weight. That is, when compared with MPN patients in the normal BMI category, underweight and obese patients had a significantly higher total symptom score (TSS). This novel finding was consistent both in the merged data analysis and by study as well as stratified by gender and MPN subtype, respectively. Furthermore, the U-shaped pattern was also found for several of the single symptoms and quality of life.
Supporting our findings are two recently published abstracts. A cross-sectional study focusing on nutrition among MPN patients showed a similar mean BMI (=25.9) that did also not differ significantly among MPN subtypes. Furthermore, the study found a BMI > 25 to be significantly associated with higher TTS compared with a BMI < 25 [29]. In the nutrition study, when stratifying by MPN subtype, higher symptom burden among overweight/obese patients persisted, though not reaching statistical significance in patients with ET. Likewise, a study of the impact of weight on symptom burden in MPN patients participating in an online yoga intervention indicated higher symptom burden (TSS) at the baseline among patients with BMI > 25 (p = 0.06) [31]. On the contrary, in a retrospective study of 380 myelofibrosis patients, BMI did not influence the achievement of symptom response during treatment with Ruxolitinib [30].
The association between BMI and the most important MPN-related symptoms have never been examined before, individually. We found that obese patient (BMI > 30) compared with patients in the normal weight category (BMI 18.5–25) had significantly higher mean scores for the entire spectrum of symptoms with fever and weight loss as the only exceptions. These findings were present both in the raw data and in the analysis adjusted for age, sex, and MPN subtype. Similarly, overweight MPN patients (BMI 25–30) had higher symptom mean scores for nearly all symptoms, as compared with normal weight MPN patients. However, the observed differences were of smaller magnitude, and fewer differences reached statistical significance. Our results align with previously published data in other cancer populations and the general population, demonstrating that obese patients suffer from increased symptom burden. For example, studies in patients with breast cancer report higher BMI to be associated with increased pain [45,46], sadness [33], numbness [33], fatigue [33,47], and other treatment-related symptoms [48]. Moreover, a comprehensive review of the effect of obesity on health outcomes suggests robust evidence of obesity being associated with reduced sexual functioning which is in accordance with our findings [49]. Likewise, epidemiologic research has shown obese individuals more often suffer from headache, as compared with those of normal weight [50].
One of the most striking findings in our study was the U-shaped pattern of body mass index and overall quality of life with normal weight MPN patients reporting the best quality of life. To our knowledge, comparable studies in MPN patients have not been published. However, consistent with our results, health-related quality of life studies in healthy people, cancer patients, and cancer survivors suggest an inferior quality of life among obese persons [32,35,40,51,52,53,54,55] of all ages [37,56,57,58,59]. Noteworthy, a health survey from England of >14.000 persons showed a similar U-shaped pattern of HRQoL with the best HRQoL close to a BMI of 25 with worse HRQOL for both higher and lower BMI values [36].
The pathogenesis behind the increased symptom burden in obese and underweight MPN patients, respectively, is probably distinct. However, inflammation is likely to play a key role in both pathways. Chronic low-grade inflammation is a hallmark feature of the MPN disease as the JAK2V617F mutation induces constitutive activation of the Janus kinase cascade, resulting in increased levels of pro-inflammatory cytokines [20,60,61,62,63], and recent studies indicate that specific MPN symptoms are associated with specific markers of inflammation [20,61,62,63]. Equally, it is well established that obesity leads to systemic chronic inflammation [21,22,23,24,25,26,27]. The elevated inflammatory status in obese persons originates from infiltrating immune cells, in particular, macrophages in the adipose tissue with a altered production of pro-inflammatory molecules, “adipokines” [64]. We speculate, that a contributing factor to higher symptom burden, observed in our study among obese patients as compared to normal weight patients, is the accumulate inflammatory effect of being obese and having an MPN disease. Our hypothesis is supported by research in humans and animals showing that several of the increased pro-inflammatory molecules in the two conditions are overlapping (e.g., TNF-α, IL1, IL-6, MCP-1, CRP) [24,26,27,60,61,64,65].
Consistent with previous research highlighting fatigue as one of the most prominent symptoms in MPN [19,42], we found MPN patients across all BMI categories often experience fatigue. However, in the obese category, the prevalence was even higher, with almost 90% of obese MPN patients reporting fatigue. The pro-inflammatory marker, TNFα, has been shown to induce fatigue in other malignancies [66] and interestingly, TNFα is elevated in both MPN patients [61] and obese persons in the general population [27,67]. Thus, a potential additive effect of TNFα might be a contributing cause of the pronounced fatigue among obese MPN patients in the current study, however this hypothesis remains to be explored. Obesity-related inflammation is thought to be a key feature of insulin resistance and development of type 2 diabetes [27,68], a comorbidity also known to be associated with fatigue [69,70] and therefore another possible contributing cause of the high prevalence of fatigue. In addition, research in cancer patients suggests fatigue is associated with higher levels of depressive symptoms [71,72] and in agreement with this, MPN patients in the obese BMI category also reported higher prevalence and severity of “sad mood”. The exact pathophysiological causes are yet to be fully determined, however, circulating levels of cytokines including IL6 have been linked to fatigue and depressed mood [73,74,75], and as IL6 is elevated in both obesity and MPN further illumination of the underlying mechanisms is crucial. Apart from being associated with depression, levels of IL6 along with IL1 and CRP have been shown to be increased in patients with cognitive impairment [76,77,78,79] and in obese persons [22,24,26], respectively. With those findings in mind, it is of great interest that obese MPN patients had a 26% higher mean score for concentration problems compared with normal weight MPN patients. Abdominal-related symptoms including abdominal discomfort, pain, and early satiety are commonly reported among MPN patients, but also those complaints were more frequent and severe among obese MPN patients. Splenomegaly is an important cause of abdominal-related symptoms, however, it is more difficult to diagnose by palpation in people with abdominal obesity. Furthermore, the enhanced inflammation in obese MPN patients may also be of importance in the increased severity of abdominal-related symptoms, as cytokines can induce the enlargement of the spleen and may worsen abdominal pain, and pain in the general, by cytokine-induced nerve hyperstimulation [20,61].
Interestingly, not only obese MPN patients but also underweight patients reported increased total symptom burden with higher severity of the majority of single symptoms. Whilst many have studied the impact of underweight on mortality, little is known about underweight MPN patients’ health-related quality of life, however, studies in the general population including patients with chronic diseases likewise demonstrate a deterioration of health-related quality of life among underweight individuals [39,80,81].
Underweight is described as a rather heterogeneous condition, which can be found in otherwise healthy persons, patients suffering from malnutrition or eating disorders as well as chronic diseases and cancer [80,82]. In an MPN context, undesired weight loss is a well-known complication, particularly in regards to MF [19]. However, the mean BMI of the MF patients was not significantly lower than the other MPN subtypes, which might be due to selection bias, i.e., underweight patients may be too sick to overcome participating in a questionnaire study. Furthermore, only few underweight MF patients participated in the present study. Of notable interest, underweight MF patients reported markedly higher symptom burden compared with all other patients across MPN subtypes and BMI categories, indicating that their low BMI might be due to cachexia. A syndrome characterized by the loss of skeletal muscle and fat mass [83,84].
Along with other constitutional symptoms, cachexia may be a sign of disease progression [30,85]. Accordingly, we found, that constitutional symptoms such as weight loss and fever were most prevalent among underweight patients. Cachexia is a multifactorial syndrome, with several triggering causes including low nutritional intake due to bothersome splenomegaly [84], which might be a plausible explanation of the high level of “early satiety” reported by the underweight MPN patients in the present study. Besides splenomegaly, it is currently assumed that both the metabolic disturbances and the constitutional symptoms in MF, at least partly, are a result of a cytokine-driven systemic inflammatory state induced by the dysregulation of the JAK-STAT pathway [84,86]. The JAK-STAT pathway is vital in the regulation of pro-inflammatory cytokines such as IL-6 and TNF-α of which both have been shown to be involved in the modulation of cachexia [84,87]. Furthermore, the inflammatory marker, C-reactive protein, is increased in patients suffering from cancer-cachexia [84].
Bearing in mind that individuals with very high or low body weights often have elevated inflammatory levels and that the present study demonstrate reduced health-related quality of life in underweight and obese patients, and finally that inflammation is suggested to contribute to disease progression by promotion of neoplastic stem cell growth and suppression of normal hematopoiesis, interventions trying to reduce inflammation are vital. Several studies indicate that an anti-inflammatory diet and exercise have the potential to attenuate inflammation [88,89,90,91,92,93] and improve HRQoL [40,47,53,55,94,95,96,97]. Within the MPN field, only recently studies have started to investigate these promising non-pharmacological interventions to dampen inflammation and alleviate symptom burden [98]. A pilot study of yoga as supplementary care in MPN found that yoga was beneficial, leading to a significant reduction in total symptom burden and, furthermore, 75% of the participants felt yoga was helpful in coping with their MPN symptoms [99]. On the contrary, a five-day interdisciplinary exercise-based rehabilitation intervention followed by a 12-week self-exercising program, including 45 MPN patients, did not find a significant impact on symptom burden, however, the median adherence to self-exercise was low, which might explain the lack of impact [100]. Interestingly, studies in other hematological cancers suggest a positive impact of physical activity on a variety of patient-reported outcomes [101]. Hence, more research is needed to make evidence-based recommendations for exercise as a symptom management strategy in MPN treatment. Additionally, only preliminary data exist in regard to the potential of anti-inflammatory diet as a tool in MPN care; a cross-sectional study of nutrition among 1329 MPN patients revealed that pro-inflammatory food as fast food and refined sugar was associated with an increased total symptom score. The survey further revealed that 96.2% of MPN patients were willing to restrict their diet if it helped reduce their symptoms [98,102].
Some limitations of the current study should be noted. Firstly, the self-reported outcomes and measures lack the validation of MPN diagnosis and BMI. Secondly, the cross-sectional design makes the study unable to establish causality between BMI and symptom burden; although BMI is likely to influence HRQL, the direction of causality may also be the opposite, e.g., quality of life or fatigue may impact eating behavior and physical activity. Furthermore, the Danish version of MPN-SAF has not been psychometrically validated, however, the questionnaire has been translated and linguistically validated by a professional translator according to international guidelines on the translation and cultural adaptation of patient-reported outcomes [103], and the MPN-SAF is an internationally recognized valid questionnaire designed specifically to MPN patients [42]. Finally, the collaborative study on BMI was not a part of the originally planned research questions, hence, the current study must be characterized as explorative, and the results must be confirmed in a future study. Despite these limitations, we consider the findings in this study to be of high clinical importance, as the study, given the large number of participating MPN patients, provides high significant results. Moreover, the results are very consistent across different types of symptoms, which support the hypnotized association between BMI and symptom burden. Another strength of the current study is the good coverage of all BMI categories, proving results that cover the majority of MPN patients.

4. Materials and Methods

4.1. Descriptions of Surveys

A comprehensive analysis of data from two large cross-sectional surveys of health-related quality of life in MPN patients was done by combining data from the Danish Population-based Study, MPNhealthSurvey (n = 2044) [104], with data from the international Fatigue Study (n = 1040) [28]. Both surveys are described in detail elsewhere, but in brief, the MPNhealthSurvey is a Danish nationwide population-based survey, where patients with an MPN diagnosis registered in the Danish National Patient Register (NPR) between 1977 (at the time of NPR initiation) and March 31, 2013, and who were alive on September 4, 2013, when the survey population was formed, were invited to participate [105]. The invitations were sent on September 11, 2013, and the survey period ended December 31, 2013. Patients were asked to complete a survey booklet received by mail or complete the same survey online. Information on addresses, age, and sex was retrieved from the Danish Civil Registration System [106]. The Fatigue Study is a comprehensive internet-based survey developed by MPN investigators and patients at the Mayo Clinic Survey Research Center. The survey was available online via multiple MPN-related web sites including MPN Voice, MPN Research Foundation, the MPN Forum, and their respective Facebook pages from late February to March of 2014. Participants were self-identified MPN patients who understood written English with the majority of participants residing in the US (65%).
The MPNhealthSurvey was approved by the The Danish Data Protection Agency (SJ-RO-02). In Denmark, approval from the National Committee on Health Research Ethics is not required for questionnaire studies (Committee Act section 14, 2) [107]. The completion of the survey was deemed as an agreement of consent from the respondents. The Fatigue Study was approved by The Mayo Clinic Institutional Review Board and the Ethics committee, as an online minimal risk study, patients completed a modified consent before accessing the survey. Both studies were conducted in accordance with the Declaration of Helsinki.

4.2. Symptoms, Lifestyle Factors, and BMI Categories

In both surveys, symptom burden and quality of life were assessed utilizing the validated Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF) in conjunction with the Brief Fatigue Inventory (BFI) [42]. Patients reported 18 MPN-related symptoms (worst fatigue (BFI score), early satiety, abdominal pain, abdominal discomfort, inactivity, concentration problems, dizziness, numbness, insomnia, sad mood, sexuality problems, cough, night sweats, itching, bone pain, fever, weight loss, and quality of life) on a scale of 0 (absent) to 10 (worst imaginable). For participants completing at least 6 of the 10 most representative MPN-SAF symptoms, a Total Symptom Score (TSS) was calculated by multiplying the average score across items by 10 to achieve a scaled score of 0 to 100 [43]. In addition, both surveys included questions regarding educational level and lifestyle factors such as tobacco use, alcohol intake, and physical activity. Furthermore, participants reported their current height and weight. BMI was calculated, and patients were split into underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9) and obese (≥30) in consensus with the prevailing WHO guidelines [108].

4.3. Statistical Analysis

We evaluated the associations of BMI both by study and combined. Kruskal–Wallis tests for continuous variables and chi-squared test and Fisher’s exact test for categorical variables. Analysis of covariance was used to estimate the effects of different BMI categories on symptom scores while adjusting for age, sex, and MPN subtype. We used locally weighted scatterplot smoothing (LOESS) to visualize the U-shaped BMI associations. All statistical testing was two-sided with a significance level of 0.05. The R (R Foundation for statistical computing, Vienna, Austria) statistical software was used within an accountable data analysis process [109].

5. Conclusions

We found a U-shaped relationship between BMI and total symptom burden, several single symptoms and not least QoL; a pattern that was consistent across countries and remained significant after adjusting for age, sex, and MPN subtype. Despite medicinal treatment, symptom burden remains a challenge, and due to the chronic nature of MPNs, patients may face several years of disabling symptoms and impairment in quality of life. Furthermore, underweight and obesity is often associated with elevated inflammatory levels and inferior survival. Recently, non-pharmacologic interventions targeting chronic inflammation have shown promising results. Bearing in mind that confounders may be present, the association between BMI and symptom burden is a critical finding, as BMI is a modifiable factor in the care of MPN patients with the potential of safe interventions to contribute to reduced symptom burden and improved QoL among MPN patients. Our findings need to be confirmed in future studies, and the impact of non-pharmacologic treatment modalities such as nutrition, weight alteration, and exercise as an adjunctive therapeutic strategy to diminish symptom burden in MPN need to be investigated in future trials.

Supplementary Materials

The following are available online at, Figure S1: The MPN-SAF questionnaire. Table S1: BMI by MPN Subtype. Table S2: TSS by MPN Subtype and BMI.

Author Contributions

Conceptualization, R.M.S., S.F.C.; methodology, R.M.S., N.B., C.L.A.; formal analysis, M.G., J.G.; investigation, R.S., N.B.; resources, R.M.S.; data curation, R.M.S., E.M.F.; writing—original draft preparation, S.F.C.; writing—review and editing, R.M.S., N.B., C.L.A., E.M.F., R.M.; visualization, M.G.; J.G.; supervision, R.M.S., R.M.; project administration, R.M., R.M.S.; funding acquisition, S.F.C., N.B., R.M. All authors have read and agreed to the published version of the manuscript.


This research was funded by Department of Hematology, Zealand University Hospital, Roskilde, Denmark; The Torben og Alice Frimodts Foundation, Denmark; Einar Willumsens Foundation, Denmark; Frimodt-Heinecke Foundation, Health Sciences Research Foundation. Departmental funds at UTHSCSA, US.


We wish to thank all the MPN patients who participated in the Fatigue Study and the MPNhealthSurvey.

Conflicts of Interest

R.M.: Consultant—Novartis, Sierra, Blueprint, La Jolla; Research Support—Incyte, CTI, Celgene, Abbvie. R.M.S.: Employment—Incyte. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


  1. Stevens, G.A.; Singh, G.M.; Lu, Y.; Danaei, G.; Lin, J.K.; Finucane, M.M.; Bahalim, A.N.; McIntire, R.K.; Gutierrez, H.R.; Cowan, M.; et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul. Health Metr. 2012, 10, 1. [Google Scholar] [CrossRef] [PubMed]
  2. Di Cesare, M.; Bentham, J.; Stevens, G.A.; Zhou, B.; Danaei, G.; Lu, Y.; Bixby, H.; Cowan, M.J.; Riley, L.M.; Hajifathalian, K.; et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet 2016, 387, 1377–1396. [Google Scholar] [CrossRef]
  3. Abarca-Gómez, L.; Abdeen, Z.A.; Hamid, Z.A.; Abu-Rmeileh, N.M.; Acosta-Cazares, B.; Acuin, C.; Adams, R.J.; Aekplakorn, W.; Afsana, K.; Aguilar-Salinas, C.A.; et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 2017, 390, 2627–2642. [Google Scholar] [CrossRef]
  4. Blüher, M. Obesity: Global epidemiology and pathogenesis. Nat. Rev. Endocrinol. 2019. [Google Scholar] [CrossRef] [PubMed]
  5. Mokdad, A.; Marks, J.; Stroup, D.; Gerberding, J. Actual causes of death in the United States, 2000. J. Am. Med. Assoc. 2004, 291, 1238–1246. [Google Scholar] [CrossRef] [PubMed]
  6. Fontaine, K.R.; Redden, D.T.; Wang, C.; Westfall, A.O.; Allison, D.B. Years of Life Lost Due to Obesity Kevin. JAMA 2003, 289, 187–193. [Google Scholar] [CrossRef] [PubMed]
  7. Renahan, A.; Tyson, M.; Egger, M.; Rf, H.; Zwahlen, M. Body-mass index and incidence of cancer: A systematic review and meta-analysis of prospective observational studies. Lancet 2008, 371, 569–578. [Google Scholar] [CrossRef]
  8. Wang, J.; Yang, D.L.; Chen, Z.Z.; Gou, B.F. Associations of body mass index with cancer incidence among populations, genders, and menopausal status: A systematic review and meta-analysis. Cancer Epidemiol. 2016, 42, 1–8. [Google Scholar] [CrossRef]
  9. Kyrgiou, M.; Kalliala, I.; Markozannes, G.; Gunter, M.J.; Paraskevaidis, E.; Gabra, H.; Martin-Hirsch, P.; Tsilidis, K.K. Adiposity and cancer at major anatomical sites: Umbrella review of the literature. BMJ 2017, 356, 1–10. [Google Scholar] [CrossRef]
  10. Lauby-Secretan, B.; Scoccianti, C.; Loomis, D. Body Fatness and Cancer—Viewpoint of the IARC Working Group. N. Engl. J. Med. 2016, 375, 794–798. [Google Scholar] [CrossRef]
  11. Leal, A.; Thompson, C.; Wang, A.; Vierkant, R.; Habermann, T.; Ross, J.; Mesa, R.; Virnig, B.; Cerhan, J. Anthropometric, medical history and lifestyle risk factors for myeloproliferative neoplasms in The Iowa Women’s Health Study (IWHS) cohort. Int. J. Cancer 2014, 134, 1741–1750. [Google Scholar] [CrossRef]
  12. Leiba, A.; Duek, A.; Afek, A.; Derazne, E.; Leiba, M. Obesity and related risk of myeloproliferative neoplasms among israeli adolescents. Obesity 2017, 25, 1187–1190. [Google Scholar] [CrossRef] [PubMed]
  13. Murphy, F.; Kroll, M.E.; Pirie, K.; Reeves, G.; Green, J.; Beral, V. Body size in relation to incidence of subtypes of haematological malignancy in the prospective Million Women Study. Br. J. Cancer 2013, 108, 2390–2398. [Google Scholar] [CrossRef] [PubMed]
  14. Engeland, A.; Tretli, S.; Hansen, S.; Bjørge, T. Height and body mass index and risk of lymphohematopoietic malignancies in two million Norwegian men and women. Am. J. Epidemiol. 2007, 165, 44–52. [Google Scholar] [CrossRef] [PubMed]
  15. Duncombe, A.S.; Anderson, L.A.; James, G.; de Vocht, F.; Fritschi, L.; Mesa, R.; Clarke, M.; McMullin, M.F. Modifiable Lifestyle and Medical Risk Factors Associated With Myeloproliferative Neoplasms. HemaSphere 2020, 4, 327. [Google Scholar] [CrossRef]
  16. Campbell, P.J.; Green, A.R. 5–15: The Myeloproliferative Disorders. N. Engl. J. Med. 2006, 355, 2452–2466. [Google Scholar] [CrossRef]
  17. Hasselbalch, H.C. Perspectives on chronic inflammation in essential thrombocythemia, polycythemia vera, and myelofibrosis: Is chronic inflammation a trigger atherosclerosis and second cancer? Blood 2012, 119, 3219–3225. [Google Scholar] [CrossRef]
  18. Tefferi, A.; Pardanani, A. Myeloproliferative neoplasms: A contemporary review. JAMA Oncol. 2015, 1, 97–105. [Google Scholar] [CrossRef]
  19. Mesa, R.A.; Niblack, J.; Wadleigh, M.; Verstovsek, S.; Camoriano, J.; Barnes, S.; Tan, A.D.; Atherton, P.J.; Sloan, J.A.; Tefferi, A. The burden of fatigue and quality of life in myeloproliferative disorders (MPDs): An international internet-based survey of 1179 MPD patients. Cancer 2007, 109, 68–76. [Google Scholar] [CrossRef]
  20. Geyer, H.L.; Dueck, A.C.; Scherber, R.M.; Mesa, R.A. Impact of Inflammation on Myeloproliferative Neoplasm Symptom Development. Mediat. Inflamm. 2015. [Google Scholar] [CrossRef]
  21. González-Muniesa, P.; Garcia-Gerique, L.; Quintero, P.; Arriaza, S.; Lopez-Pascual, A.; Martinez, J.A. Effects of hyperoxia on oxygen-related inflammation with a focus on obesity. Oxid. Med. Cell. Longev. 2016, 2016. [Google Scholar] [CrossRef] [PubMed]
  22. Bergens, O.; Nilsson, A.; Kadi, F. Cardiorespiratory Fitness Does Not Offset Adiposity-Related Systemic Inflammation in Physically Active Older Women. J. Clin. Endocrinol. Metab. 2019. [Google Scholar] [CrossRef] [PubMed]
  23. Stepanikova, I.; Oates, G.R.; Bateman, L.B. Does one size fit all? The role of body mass index and waist circumference in systemic inflammation in midlife by race and gender. Ethn. Health 2017, 22, 169–183. [Google Scholar] [CrossRef] [PubMed]
  24. Thorand, B.; Baumert, J.; Döring, A.; Herder, C.; Kolb, H.; Rathmann, W.; Giani, G.; Koenig, W.; Wichmann, H.E.; Löwel, H.; et al. Sex differences in the relation of body composition to markers of inflammation. Atherosclerosis 2006, 184, 216–224. [Google Scholar] [CrossRef]
  25. Lumeng, C.N.; Saltiel, A.R.; Lumeng, C.N.; Saltiel, A.R. Inflammatory links between obesity and metabolic disease Find the latest version: Review series Inflammatory links between obesity and metabolic disease. J. Clin. Investig. 2011, 121, 2111–2117. [Google Scholar] [CrossRef]
  26. Sun, S.; Ji, Y.; Kersten, S.; Qi, L. Mechanisms of Inflammatory Responses in Obese Adipose Tissue. Annu. Rev. Nutr. 2012, 32, 261–286. [Google Scholar] [CrossRef]
  27. Hotamislig, G.S. Inflammation and metabolic disorders. Nature 2006, 444, 861–867. [Google Scholar]
  28. Scherber, R.M.; Kosiorek, H.E.; Senyak, Z.; Dueck, A.C.; Clark, M.M.; Boxer, M.A.; Geyer, H.L.; McCallister, A.; Cotter, M.; Van Husen, B.; et al. Comprehensively understanding fatigue in patients with myeloproliferative neoplasms. Cancer 2016, 122, 477–485. [Google Scholar] [CrossRef]
  29. Scherber, R.; Langlais, B.; Geyer, H.; Dueck, A.; Huberty, J.; Padrnos, L.; Palmer, J.; Fleischman, A.; Ruben, M. The relationship of Body Mass Index to Symptom Burden in the Myeloproliferative Neoplams. Haematologia (Budap) 2018, 102, 215667. [Google Scholar]
  30. Breccia, M.; Bartoletti, D.; Bonifacio, M.; Palumbo, G.A.; Polverelli, N.; Palandri, F. Impact of comorbidities and body mass index in myelofibrosis patients treated with ruxolitinib: A retrospective analysis. Ann. Hematol. 2018, 98, 889–896. [Google Scholar] [CrossRef]
  31. Eckert, R.; Huberty, J.; Gowin, K.L.; Ginos, B.; Kosiorek, H.E.; Dueck, A.C.; Mesa, R.A. Impact of Weight on Symptom Burden Outcomes in Myeloproliferative Neoplasm Patients Participating in an Online Yoga Intervention. Blood 2016, 128, 5481. [Google Scholar] [CrossRef]
  32. Tsai, A.G.; Bessesen, D.H. Obesity. Ann. Intern. Med. 2019, 170, ITC33–ITC48. [Google Scholar] [CrossRef] [PubMed]
  33. Fang, P.; Tan, K.S.; Troxel, A.B.; Rengan, R.; Freedman, G.; Lin, L.L. High body mass index is associated with worse quality of life in breast cancer patients receiving radiotherapy. Breast Cancer Res. Treat. 2013, 141, 125–133. [Google Scholar] [CrossRef] [PubMed]
  34. Kroes, M.; Osei-Assibey, G.; Baker-Searle, R.; Huang, J. Impact of weight change on quality of life in adults with overweight/obesity in the United States: A systematic review. Curr. Med. Res. Opin. 2015, 32, 485–508. [Google Scholar] [CrossRef]
  35. Korhonen, P.E.; Seppälä, T.; Järvenpää, S.; Kautiainen, H. Body mass index and health-related quality of life in apparently healthy individuals. Qual. Life Res. 2014, 23, 67–74. [Google Scholar] [CrossRef]
  36. Søltoft, F.; Hammer, M.; Kragh, N. The association of body mass index and health-related quality of life in the general population: Data from the 2003 Health Survey of England. Qual. Life Res. 2009, 18, 1293–1299. [Google Scholar] [CrossRef]
  37. Ul-Haq, Z.; Mackay, D.F.; Fenwick, E.; Pell, J.P. Meta-analysis of the association between body mass index and health-related quality of life among adults, assessed by the SF-36. Obesity 2013, 21, 322–328. [Google Scholar] [CrossRef]
  38. Ul-Haq, Z.; MacKay, D.F.; Fenwick, E.; Pell, J.P. Meta-analysis of the association between body mass index and health-related quality of life among children and adolescents, assessed using the pediatric quality of life inventory index. J. Pediatr. 2013, 162, 280–286. [Google Scholar] [CrossRef]
  39. Huang, I.C.; Frangakis, C.; Wu, A.W. The relationship of excess body weight and health-related quality of life: Evidence from a population study in Taiwan. Int. J. Obes. 2006, 30, 1250–1259. [Google Scholar] [CrossRef]
  40. Hassan, M.K.; Joshi, A.V.; Madhavan, S.S.; Amonkar, M.M. Obesity and health-related quality of life: A cross-sectional analysis of the US population. Int. J. Obes. 2003, 27, 1227–1232. [Google Scholar] [CrossRef]
  41. Mesa, R.; Miller, C.B.; Thyne, M.; Mangan, J.; Goldberger, S.; Fazal, S.; Ma, X.; Wilson, W.; Paranagama, D.C.; Dubinski, D.G.; et al. Myeloproliferative neoplasms (MPNs) have a significant impact on patients’ overall health and productivity: The MPN Landmark survey. BMC Cancer 2016, 16, 1–10. [Google Scholar] [CrossRef] [PubMed]
  42. Scherber, R.; Dueck, A.C.; Johansson, P.; Barbui, T.; Barosi, G.; Vannucchi, A.M.; Passamonti, F.; Andreasson, B.; Ferarri, M.L.; Rambaldi, A.; et al. The Myeloproliferative Neoplasm Symptom Assessment Form (MPN-SAF): International prospective validation and reliability trial in 402 patients. Blood 2011, 118, 401–408. [Google Scholar] [CrossRef] [PubMed]
  43. Emanuel, R.M.; Dueck, A.C.; Geyer, H.L.; Kiladjian, J.J.; Slot, S.; Zweegman, S.; Te Boekhorst, P.A.W.; Commandeur, S.; Schouten, H.C.; Sackmann, F.; et al. Myeloproliferative neoplasm (MPN) symptom assessment form total symptom score: Prospective international assessment of an abbreviated symptom burden scoring system among patients with MPNs. J. Clin. Oncol. 2012. [Google Scholar] [CrossRef] [PubMed]
  44. Harrison, C.N.; Koschmieder, S.; Foltz, L.; Guglielmelli, P.; Flindt, T.; Koehler, M.; Mathias, J.; Komatsu, N.; Boothroyd, R.N.; Spierer, A.; et al. The impact of myeloproliferative neoplasms (MPNs) on patient quality of life and productivity: Results from the international MPN Landmark survey. Ann. Hematol. 2017, 96, 1653–1665. [Google Scholar] [CrossRef]
  45. Paxton, R.J.; Phillips, K.L.; Jones, L.A.; Chang, S.; Taylor, W.C.; Courneya, K.S.; Pierce, J.P.; Care, W.H. Associations among physical activity, bodt mass index, and health related quality of in breast cancer survivors. Cancer 2013, 118, 4024–4031. [Google Scholar] [CrossRef]
  46. Forsythe, L.; Alfano, C.; George, S.; McTiernan, A.; Baumgartner, K.; Bernstein, L.; Ballard-Barbash, R. Pain in long-term breast cancer survivors: The role of body mass index, physical activity, and sedentary behavior. Breast Cancer Res. Treat. 2013, 137, 617–630. [Google Scholar] [CrossRef] [PubMed]
  47. Gerber, L.H.; Stout, N.; McGarvey, C.; Soballe, P.; Shieh, C.Y.; Diao, G.; Springer, B.A.; Pfalzer, L.A. Factors predicting clinically significant fatigue in women following treatment for primary breast cancer. Support. Care Cancer 2011, 19, 1581–1591. [Google Scholar] [CrossRef]
  48. Morcos, B.; Ahmad, F.A.; Anabtawi, I.; Sba’, A.M.A.; Shabani, H.; Yaseen, R. Development of breast cancer-related lymphedema: Is it dependent on the patient, the tumor or the treating physicians? Surg. Today 2014, 44, 100–106. [Google Scholar] [CrossRef]
  49. Kolotkin, R.L.; Zunker, C.; Ostbye, T. Sexual functioning and obesity: A review. Obesity 2012, 20, 2325–2333. [Google Scholar] [CrossRef]
  50. Chai, N.C.; Scher, A.; Moghekar, A.; Bond, D.; Peterlin, B.L. The Epidemiology of Obesity and Headache: Epidemiology of Obesity. Headache 2014, 54, 219–234. [Google Scholar] [CrossRef]
  51. Fontaine, K.R.; Barofsky, I. Obesity and health-related quality of life. Obes. Rev. 2001, 2, 173–182. [Google Scholar] [CrossRef] [PubMed]
  52. Fine, J.T.; Colditz, G.A.; Coakley, E.H.; Moseley, G.; Manson, J.E.; Willett, W.C. A prospective study of weight change and health-related quality of life in women. J. Am. Med. Assoc. 1999, 282, 2136–2142. [Google Scholar] [CrossRef] [PubMed]
  53. Koutoukidis, D.A.; Knobf, M.T.; Lanceley, A. Obesity, diet, physical activity, and health-related quality of life in endometrial cancer survivors. Nutr. Rev. 2015, 73, 399–408. [Google Scholar] [CrossRef] [PubMed]
  54. Jansen, L.; Koch, L.; Brenner, H.; Arndt, V. Quality of life among long-term (≥5 years) colorectal cancer survivors—Systematic review. Eur. J. Cancer 2010, 46, 2879–2888. [Google Scholar] [CrossRef]
  55. Smits, A.; Smits, E.; Lopes, A.; Das, N.; Hughes, G.; Talaat, A.; Pollard, A.; Bouwman, F.; Massuger, L.; Bekkers, R.; et al. Body mass index, physical activity and quality of life of ovarian cancer survivors: Time to get moving? Gynecol. Oncol. 2015, 139, 148–154. [Google Scholar] [CrossRef]
  56. De Beer, M.; Hofsteenge, G.H.; Koot, H.M.; Hirasing, R.A.; Delemarre-Van De Waal, H.A.; Gemke, R.J.B.J. Health-related-quality-of-life in obese adolescents is decreased and inversely related to BMI. Acta Paediatr. Int. J. Paediatr. 2007, 96, 710–714. [Google Scholar] [CrossRef]
  57. Williams, J.; Wake, M.; Hesketh, K.; Maher, E.; Waters, E. Health-related quality of life of overweight and obese children. J. Am. Med. Assoc. 2005, 293, 70–76. [Google Scholar] [CrossRef]
  58. Hairi, N.N.; Bulgiba, A.; Cumming, R.G.; Naganathan, V.; Mudla, I. Relationship between obesity, hypertension and diabetes, and health-related quality of life among the elderly and Fernando Rodrı. BMC Public Health 2010, 10, 492. [Google Scholar]
  59. Kostka, T.; Bogus, K. Independent contribution of overweight/obesity and physical inactivity to lower health-related quality of life in community-dwelling older subjects. Z. Gerontol. Geriatr. 2007, 40, 43–51. [Google Scholar] [CrossRef]
  60. Hasselbalch, H.C.; Bjørn, M.E. MPNs as Inflammatory Diseases: The Evidence, Consequences, and Perspectives. Mediat. Inflamm. 2015. [Google Scholar] [CrossRef]
  61. Tefferi, A.; Vaidya, R.; Caramazza, D.; Finke, C.; Lasho, T.; Pardanani, A. Circulating interleukin (IL)-8, IL-2R, IL-12, and IL-15 levels are independently prognostic in primary myelofibrosis: A comprehensive cytokine profiling study. J. Clin. Oncol. 2011, 29, 1356–1363. [Google Scholar] [CrossRef] [PubMed]
  62. Vaidya, R.; Gangat, N.; Jimma, T.; Finke, C.M.; Lasho, T.L. Plasma cytokines in polycythemia vera: Phenotypic correlates, prognostic relevance, and comparison with myelofibrosis. Am. J. Hematol. 2012, 1003–1005. [Google Scholar] [CrossRef] [PubMed]
  63. Pourcelot, E.; Trocme, C.; Mondet, J. Cytokine profiles in polycythemia vera and essential thrombocythemia patients: Clinical implications. Exp. Hematol. 2019, 360–368. [Google Scholar] [CrossRef] [PubMed]
  64. Weisberg, S.P.; Leibel, R.L.; Ferrante, A.W., Jr.; Mccann, D.; Desai, M.; Rosenbaum, M.; Leibel, R.L.; Ferrante, A.W. Obesity is associated with macrophage accumulation in adipose tissue. J. Clin. Investig. 2003, 112, 1796–1808. [Google Scholar] [CrossRef] [PubMed]
  65. Xu, H.; Barnes, G.; Yang, Q.; Tan, G.; Louis, T.; Chen, H. Chronic inflammation in fat Plays a crucial role in development of obesity-related insulin resistance. Screen 2003, 112, 1821–1830. [Google Scholar] [CrossRef]
  66. Bower, J.E.; Ganz, P.A.; Irwin, M.R.; Kwan, L.; Breen, E.C.; Cole, S.W. Inflammation and Behavioral Symptoms After Breast Cancer Treatment: Do Fatigue, Depression, and Sleep Disturbance Share a Common Underlying Mechanism? J. Clin. Oncol. 2011, 29, 3517–3522. [Google Scholar] [CrossRef]
  67. Moon, Y.S.; Kim, D.H.; Song, D.K. Serum tumor necrosis factor-α levels and components of the metabolic syndrome in obese adolescents. Metabolism 2004, 53, 863–867. [Google Scholar] [CrossRef]
  68. De Luca, C.; Olefsky, J. Inflammation and Insulin Resistance. FEBS Lett. 2008, 582, 97–105. [Google Scholar] [CrossRef]
  69. Pickup, J.C. Inflammation and Activated Innate Immunity in the Pathogenesis of Type 2 Diabetes. Diabetes Care 2004, 27, 813–823. [Google Scholar] [CrossRef]
  70. Fritschi, C.; Quinn, L. Fatigue in patients with diabetes: A review. J. Psychosom. Res. 2010, 69, 33–41. [Google Scholar] [CrossRef]
  71. Bower, J.E.; Ganz, P.A.; Desmond, K.A.; Bernaards, C.; Rowland, J.H.; Meyerowitz, B.E.; Belin, T.R. Fatigue in long-term breast carcinoma survivors: A longitudinal investigation. Cancer 2006, 106, 751–758. [Google Scholar] [CrossRef]
  72. Fossa, S.D.; Dahl, A.A.; Loge, J.H. Fatigue, anxiety, and depression in long-term survivors of testicular cancer. J. Clin. Oncol. 2003, 21, 1249–1254. [Google Scholar] [CrossRef] [PubMed]
  73. Maes, M.; Bosmans, E.; De Jongh, R.; Kenis, G.; Vandoolaeghe, E.; Neels, H. Increased serum IL-6 and IL-1 receptor antagonist concentrations in major depression and treatment resistant depression. Cytokine 1997, 9, 853–858. [Google Scholar] [CrossRef] [PubMed]
  74. Wang, X.S.; Williams, L.A.; Krishnan, S.; Liao, Z.; Liu, P.; Mao, L.; Shi, Q.; Mobley, G.M.; Woodruff, J.F.; Cleeland, C.S. Serum sTNF-R1, IL-6, and the development of fatigue in patients with gastrointestinal cancer undergoing chemoradiation therapy. Brain Behav. Immun. 2012, 26, 699–705. [Google Scholar] [CrossRef] [PubMed]
  75. Musselman, D.L.; Miller, A.H.; Porter, M.R.; Manatunga, A.; Gao, F.; Penna, S.; Pearce, B.D.; Landry, J.; Glover, S.; McDaniel, J.S.; et al. Higher than normal plasma interleukin-6 concentrations in cancer patients with depression: Preliminary findings. Am. J. Psychiatry 2001, 158, 1252–1257. [Google Scholar] [CrossRef]
  76. Krabbe, K.S.; Reichenberg, A.; Yirmiya, R.; Smed, A.; Pedersen, B.K.; Bruunsgaard, H. Low-dose endotoxemia and human neuropsychological functions. Brain Behav. Immun. 2005, 19, 453–460. [Google Scholar] [CrossRef]
  77. Krabbe, K.S.; Pedersen, M.; Bruunsgaard, H. Inflammatory mediators in the elderly. Exp. Gerontol. 2004, 39, 687–699. [Google Scholar] [CrossRef]
  78. Zipp, F.; Aktas, O. The brain as a target of inflammation: Common pathways link inflammatory and neurodegenerative diseases. Trends Neurosci. 2006, 29, 518–527. [Google Scholar] [CrossRef]
  79. Meyers, C.A.; Albitar, M.; Estey, E. Cognitive impairment, fatigue, and cytokine levels in patients with acute myelogenous leukemia or myelodysplastic syndrome. Cancer 2005, 104, 788–793. [Google Scholar] [CrossRef]
  80. Lorem, G.F.; Schirmer, H.; Emaus, N. What is the impact of underweight on self-reported health trajectories and mortality rates: A cohort study. Health Qual. Life Outcomes 2017, 15, 1–14. [Google Scholar] [CrossRef]
  81. Katsura, H.; Yamada, K.; Kida, K. Both generic and disease specific health-related quality of life are deteriorated in patients with underweight COPD. Respir. Med. 2005, 99, 624–630. [Google Scholar] [CrossRef] [PubMed]
  82. Dobner, J.; Kaser, S. Body mass index and the risk of infection—From underweight to obesity. Clin. Microbiol. Infect. 2018, 24, 24–28. [Google Scholar] [CrossRef] [PubMed]
  83. Mattox, T.W. Cancer Cachexia: Cause, Diagnosis, and Treatment. Nutr. Clin. Pract. 2017, 32, 599–606. [Google Scholar] [CrossRef] [PubMed]
  84. Mesa, R.A.; Verstovsek, S.; Gupta, V.; Mascarenhas, J.O. Effects of Ruxolitinib Treatment on Metabolic and Nutritional Parameters in Patients With Myelofibrosis From COMFORT-I. Clin. Lymphoma Myeloma Leuk 2015, 15, 214–221. [Google Scholar] [CrossRef]
  85. Tefferi, A. Primary myelofibrosis: 2017 update on diagnosis, risk-stratification and management. Am. J. Hematol. 2016, 91, 1262–1271. [Google Scholar] [CrossRef]
  86. Tefferi, A.; Nicolosi, M.; Penna, D.; Mudireddy, M.; Szuber, N.; Lasho, T.L.; Hanson, C.A.; Ketterling, R.P.; Gangat, N.; Pardanani, A.D. Development of a prognostically relevant cachexia index in primary myelofibrosis using serum albumin and cholesterol levels. Blood Adv. 2018, 2, 1980–1984. [Google Scholar] [CrossRef]
  87. Fearon, K.C.H.; Glass, D.J.; Guttridge, D.C. Cancer cachexia: Mediators, signaling and metabolic pathways. Cell Metab. 2012, 16, 153–166. [Google Scholar] [CrossRef]
  88. Chrysohoou, C.; Panagiotakos, D.B.; Pitsavos, C.; Das, U.N.; Stefanadis, C. Adherence to the Mediterranean diet attenuates inflammation and coagulation process in healthy adults: The ATTICA study. J. Am. Coll. Cardiol. 2004, 44, 152–158. [Google Scholar] [CrossRef]
  89. Flynn, M.G.; McFarlin, B.K. Toll-like receptor 4: Link to the anti-inflammatory effects of exercise? Exerc. Sport Sci. Rev. 2006, 34, 176–181. [Google Scholar] [CrossRef]
  90. Shivappa, N.; Hebert, J.; Marcos, A.; Diaz, L.-E.; Gomez, S.; Nova, E.; Michels, N.; Arouca, A.; González-Gil, E.; Frederic, G.; et al. Association between dietary inflammatory index and inflammatory markers in the HELENA study. Mol. Nutr. Food Res. 2018, 61, 1–18. [Google Scholar] [CrossRef]
  91. Shivappa, N.; Hébert, J.R.; Rietzschel, E.R.; de Buyzere, M.L.; Debruyne, E.; Marcos, A.; Huybrechts, I. Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study. Br. J. Nutr. 2016, 113, 665–671. [Google Scholar] [CrossRef] [PubMed]
  92. Gleeson, M.; Bishop, N.C.; Stensel, D.J.; Lindley, M.R.; Mastana, S.S.; Nimmo, M.A. The anti-inflammatory effects of exercise: Mechanisms and implications for the prevention and treatment of disease. Nat. Rev. Immunol. 2011, 11, 607–610. [Google Scholar] [CrossRef] [PubMed]
  93. Mathur, N.; Pedersen, B.K. Exercise as a Mean to Control Low-Grade Systemic Inflammation. Mediat. Inflamm. 2008, 2008, 1–6. [Google Scholar] [CrossRef]
  94. Mishra, S.; Scherer, R.; Snyder, C.; Geigle, P.; Topaloglu, O. Exercise interventions on health-related quality of life for people with cancer during active treatment (Review). Cochrane Collab. 2012. [Google Scholar] [CrossRef]
  95. Kurzrock, R. The role of cytokines in cancer-related fatigue. Cancer 2001, 92, 1684–1688. [Google Scholar] [CrossRef]
  96. Ran, J.; Wang, J.; Bi, N.; Jiang, W.; Zhou, Z.; Hui, Z.; Liang, J.; Feng, Q.; Wang, L. Health-related quality of life in long-term survivors of unresectable locally advanced non-small cell lung cancer. Radiat. Oncol. 2017, 12, 1–8. [Google Scholar] [CrossRef]
  97. García-Morales, J.M.; Lozada-Mellado, M.; Hinojosa-Azaola, A.; Llorente, L.; Ogata-Medel, M.; Pineda-Juárez, J.A.; Alcocer-Varela, J.; Cervantes-Gaytán, R.; Castillo-Martínez, L. Effect of a Dynamic Exercise Program in Combination With Mediterranean Diet on Quality of Life in Women With Rheumatoid Arthritis. JCR J. Clin. Rheumatol. 2019. [Google Scholar] [CrossRef]
  98. Surapaneni, P.; Scherber, R.M. Integrative Approaches to Managing Myeloproliferative Neoplasms: The Role of Nutrition, Exercise and Psychological Interventions. Curr. Hematol. Malig. Rep. 2019, 14. [Google Scholar] [CrossRef]
  99. Huberty, J.; Eckert, R.; Gowin, K.; Mitchell, J.; Dueck, A.C.; Ginos, B.F.; Larkey, L.; Mesa, R. Feasibility study of online yoga for symptom management in patients with myeloproliferative neoplasms. Haematologica 2017, 36, 308–312. [Google Scholar] [CrossRef]
  100. Pedersen, K.M.; Zangger, G.; Brochmann, N.; Grønfeldt, B.M.; Zwisler, A.-D.; Hasselbalch, H.C.; Tang, L.H. The effectiveness of exercise-based rehabilitation to patients with myeloproliferative neoplasms-An explorative study. Eur. J. Cancer Care (Engl.) 2018, e12865. [Google Scholar] [CrossRef]
  101. Eckert, R.; Huberty, J.; Gowin, K.; Mesa, R.; Marks, L. Physical Activity as a Nonpharmacological Symptom Management Approach in Myeloproliferative Neoplasms: Recommendations for Future Research. Integr. Cancer Ther. 2017, 16, 439–450. [Google Scholar] [CrossRef] [PubMed]
  102. Scherber, R.M.; Langlais, B.T.; Geyer, H.; Dueck, A.; Kosoriek, H.; Johnston, C.; Padrnos, L.; Palmer, J.; Fleischman, A.G.; Mesa, R.A. Nutrition and Supplement Use Characteristics in the Myeloproliferative Neoplasms: Results from the Nutrient Survey. Blood 2017, 130, 2193. [Google Scholar]
  103. Wild, D.; Grove, A.; Martin, M.; Eremenco, S.; McElroy, S.; Verjee-Lorenz, A.; Erikson, P. Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: Report of the ISPOR Task Force for Translation and Cultural Adaptation. Value Health 2005, 8, 94–104. [Google Scholar] [CrossRef]
  104. Brochmann, N.; Flachs, E.M.; Christensen, A.I.; Andersen, C.L.; Juel, K.; Hasselbalch, H.C.; Zwisler, A.D. A nationwide population-based cross-sectional survey of health-related quality of life in patients with myeloproliferative neoplasms in Denmark (MPNhealthSurvey): Survey design and characteristics of respondents and nonrespondents. Clin. Epidemiol. 2017, 9, 141–150. [Google Scholar] [CrossRef] [PubMed]
  105. Schmidt, M.; Alba, S.; Schmidt, J.; Sandegaard, J.L.; Ehrenstein, V.; Pedersen, L.; Toft Sørensen, H. The Danish National Patient Registry: A review of content, data quality, and research potential. Clin. Epidemiol. 2015, 7, 449–490. [Google Scholar] [CrossRef] [PubMed]
  106. Schmidt, M.; Pedersen, L.; Sørensen, H.T. The Danish Civil Registration System as a tool in epidemiology. Eur. J. Epidemiol. 2014, 29, 541–549. [Google Scholar] [CrossRef] [PubMed]
  107. National Committee on Health Reserch Ethics. Act on Research Ethics Review of Health Research Projects. Available online: (accessed on 9 June 2020).
  108. Consultation, W.H.O. Obesity: Preventing and managing the global epidemic: Report of a WHO consultation. World Health Organ. 2000, 894, 8–9. [Google Scholar]
  109. Gelfond, J.; Goros, M.; Hernandez, B.; Bokov, A. A System for an Accountable Data Analysis Process in R Jonathan. R J. 2018, 10, 6–21. [Google Scholar] [CrossRef]
Figure 1. Association between Body Mass Index (BMI) and Total Symptom Score (TSS). TSS was calculated for all respondents completing at least six of 10 TSS survey items. A high score represents high total symptom burden: (a) combined data; (b) by study.
Figure 1. Association between Body Mass Index (BMI) and Total Symptom Score (TSS). TSS was calculated for all respondents completing at least six of 10 TSS survey items. A high score represents high total symptom burden: (a) combined data; (b) by study.
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Figure 2. Association between BMI and TSS by MPN subtype (a) and gender (b), respectively. TSS was calculated for all respondents completing at least six of 10 TSS survey items. A high score represents high total symptom burden.
Figure 2. Association between BMI and TSS by MPN subtype (a) and gender (b), respectively. TSS was calculated for all respondents completing at least six of 10 TSS survey items. A high score represents high total symptom burden.
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Table 1. MPN patients’ demographics by study.
Table 1. MPN patients’ demographics by study.
DemographicsMPN Health Survey
n = 2044
The Fatigue Study
n = 1070
n = 3114
Age (mean, sd)69.0 (12.3)58.8 (11.6)65.5 (13.0)<0.0001
Gender (N, %) <0.0001
Female1150 (56.3)702 (66.0)1852 (59.6)
Male894 (43.7)362 (34.0)1256 (40.4)
MPN subtype (n, %) <0.0001
ET697 (34.1)350 (32.8)1047 (33.7)
PV878 (43.0)423 (39.6)1301 (41.8)
MF68 (3.3)262 (24.6)330 (10.6)
MPN-U401 (19.6)32 (3.0)433 (13.9)
Disease duration (n, %) <0.0001
<½ years13 (0.6)54 (5.1)67 (2.2)
½–1 years136 (6.7)43 (4.0)179 (5.8)
1–3 years407 (19.9)216 (20.2)623 (20.0)
>3 years1488 (72.8)754 (70.7)2242 (72.1)
BMI (BMI, sd) <0.0001
<18.554 (2.6)18 (1.7)72 (2.3)
18.5–24.91072 (52.4)495 (46.3)1567 (50.3)
25.0–29.9648 (31.7)335 (31.3)983 (31.6)
≥30270 (13.2)222 (20.7)492 (15.8)
MPN: Philadelphia-chromosome negative myeloproliferative neoplasms.
Table 2. Symptom severity by BMI (mean, sd).
Table 2. Symptom severity by BMI (mean, sd).
MPN-SAF ItemUnderweight
n = 72
Normal Weight
n = 1567
n = 983
n = 492
Fatigue (BFI score)4.36 (3.39)4.08 (3.14)4.26 (3.14)5.25 (3.11)<0.001
Early satiety3.86 (3.05)2.58 (2.70)2.56 (2.62)2.91 (2.70)<0.001
Abdominal pain1.75 (2.63)1.27 (2.17)1.34 (2.17)1.80 (2.40)<0.001
Abdominal discomfort2.26 (2.66)1.78 (2.45)1.93 (2.44)2.33 (2.64)<0.001
Inactivity3.12 (3.00)2.38 (2.63)2.65 (2.72)3.66 (2.89)<0.001
Headache1.90 (2.87)1.76 (2.57)1.88 (2.58)2.38 (2.90)<0.001
Concentration problems3.09 (3.12)2.69 (2.82)2.92 (2.88)3.59 (3.12)<0.001
Dizziness2.71 (2.68)2.24 (2.59)2.32 (2.57)2.82 (2.88)0.001
Numbness2.83 (3.24)2.25 (2.82)2.34 (2.85)3.19 (3.15)<0.001
Insomnia3.54 (3.25)3.04 (2.99)3.19 (3.11)3.97 (3.22)<0.001
Sad mood2.67 (2.74)2.40 (2.71)2.49 (2.71)3.17 (2.88)<0.001
Sexuality problems3.29 (3.85)3.46 (3.61)3.91 (3.66)4.37 (3.79)<0.001
Cough2.56 (2.98)1.61 (2.36)1.80 (2.46)2.37 (2.80)<0.001
Night sweats2.33 (3.00)2.40 (2.83)2.72 (3.02)3.28 (3.16)<0.001
Itching1.91 (2.48)2.09 (2.74)2.52 (2.96)3.03 (3.12)<0.001
Bone pain2.40 (3.17)1.84 (2.70)2.08 (2.86)2.96 (3.18)<0.001
Fever0.42 (1.12)0.31 (1.13)0.32 (1.03)0.42 (1.30)0.057
Weight loss3.41 (3.52)1.24 (2.34)0.61 (1.66)0.54 (1.71)<0.001
Quality of life3.61 (2.71)2.90 (2.44)3.08 (2.51)3.50 (2.49)<0.001
Total Symptom Score 26.65 (17.27)21.39 (16.38)22.57 (16.66)27.95 (17.06)<0.001
BMI: Body Mass Index, sd: standard deviation.
Table 3. Symptom prevalence 1 by BMI (n, %).
Table 3. Symptom prevalence 1 by BMI (n, %).
MPN-SAF ItemUnderweight
(n = 72)
Normal Weight
(n = 1567)
(n = 983)
(n = 492)
Fatigue (BFI score)54 (78.3)1238 (80.3)795 (82.1)434 (89.5)<0.001
Early satiety53 (76.8)961 (62.4)610 (62.7)331 (67.7)0.018
Abdominal pain31 (44.9)590 (38.2)397 (41.0)244 (50.1)<0.001
Abdominal discomfort41 (59.4)773 (50.0)521 (53.8)295 (60.5)<0.001
Inactivity45 (69.2)932 (61.5)609 (64.4)366 (76.7)<0.001
Headache31 (44.9)741 (47.7)492 (50.4)280 (57.4)0.002
Concentration problems48 (68.6)979 (63.5)645 (67.0)352 (72.6)0.003
Dizziness49 (70.0)937 (60.7)618 (63.5)326 (66.5)0.057
Numbness41 (59.4)843 (54.5)542 (55.8)328 (67.2)<0.001
Insomnia49 (71.0)1054 (68.2)671 (68.9)374 (76.8)0.004
Sad mood50 (71.4)957 (62.0)612 (62.9)356 (72.7)<0.001
Sexuality problems36 (55.4)943 (62.7)642 (67.7)340 (71.0)0.001
Cough45 (64.3)726 (47.0)488 (50.2)285 (58.2)<0.001
Night sweats37 (52.9)918 (59.4)598 (61.5)342 (69.5)<0.001
Itching38 (54.3)822 (53.4)581 (59.8)327 (66.6)<0.001
Bone pain35 (50.0)706 (45.9)461 (47.6)296 (60.9)<0.001
Fever14 (20.3)191 (12.4)146 (15.1)77 (15.8)0.047
Weight loss44 (62.0)493 (31.9)174 (17.8)78 (16.0)<0.001
Quality of life58 (82.9)1224 (79.2)783 (80.6)411 (84.0)0.123
1 Prevalence was defined as a symptom score greater than or equal to 1.
Table 4. Differences in symptom severity by BMI adjusted for possible confounders (age, gender, and MPN subtype).
Table 4. Differences in symptom severity by BMI adjusted for possible confounders (age, gender, and MPN subtype).
MPN-SAF ItemUnderweight 1Overweight 1Obese 1
Difference (%)Mean Difference (CI95%)p-Value (Mean)Difference (%)Mean Difference (CI95%)p-Value (Mean)Difference (%)Mean Difference (CI95%)p-Value (Mean)
Fatigue (BFI score)10.00.41 (−0.32, 1.13)0.2694.90.20 (−0.04, 0.45)0.09823.30.95 (0.65, 1.26)<0.001
Early satiety48.41.25 (0.61, 1.9)<0.0010.40.01 (−0.20, 0.23)0.89312.80.33 (0.05, 0.6)0.02
Abdominal pain37.80.48 (−0.05, 1.01)0.0787.90.10 (−0.07, 0.28)0.25136.20.46 (0.23, 0.69)<0.001
Abdominal discomfort27.00.48 (−0.11, 1.07)0.1110.70.19 (−0.01, 0.39)0.06225.80.46 (0.21, 0.71)<0.001
Inactivity31.90.76 (0.09, 1.43)0.02610.50.25 (0.03, 0.48)0.02551.31.22 (0.94, 1.5)<0.001
Headache9.10.16 (−0.44, 0.77)0.5998.00.14 (−0.06, 0.35)0.16422.70.4 (0.15, 0.66)0.002
Concentration problems17.10.46 (−0.21, 1.13) (0.00, 0.45)0.05426.40.71 (0.42, 1)<0.001
Dizziness20.50.46 (−0.17, 1.09)0.1495.80.13 (−0.08, 0.35)0.21423.20.52 (0.25, 0.79)<0.001
Numbness24.40.55 (−0.14, 1.25) (−0.10, 0.37)0.26740.00.9 (0.6, 1.2)<0.001
Insomnia14.10.43 (−0.3, 1.16)0.2468.20.25 (0.01, 0.50)0.04225.30.77 (0.46, 1.08)<0.001
Sad mood13.80.33 (−0.31, 0.98)0.3114.20.10 (−0.12, 0.32)0.37526.70.64 (0.37, 0.92)<0.001
Sexuality problems−1.4−0.05 (−0.96, 0.85)0.919.80.34 (0.04, 0.64)0.02625.70.89 (0.52, 1.27)<0.001
Cough57.80.93 (0.33, 1.52)0.00211.20.18 (−0.02, 0.38)0.08147.80.77 (0.52, 1.03)<0.001
Night sweats−5.0−0.12 (−0.82, 0.58)0.72815.80.38 (0.14, 0.62)0.00233.80.81 (0.51, 1.11)<0.001
Itching−8.6−0.18 (−0.86, 0.5)0.60621.10.44 (0.21, 0.67)<0.00141.10.86 (0.57, 1.15)<0.001
Bone pain29.30.54 (−0.13, 1.21)0.11416.80.31 (0.08, 0.54)0.00753.30.98 (0.69, 1.26)<0.001
Fever38.70.12 (−0.15, 0.39)0.3760.00.00 (−0.09, 0.09)0.93929.00.09 (−0.03, 0.2)0.142
Weight loss176.62.19 (1.69, 2.68)<0.001−53.2−0.66 (−0.83, −0.49)<0.001−52.4−0.65 (−0.86, −0.44)<0.001
Quality of life25.90.75 (0.16, 1.33)0.0136.60.19 (0.00, 0.39)0.05517.90.52 (0.27, 0.77)<0.001
Total Symptom Score25.15.37 (1.47, 9.27)0.0076.41.36 (0.04, 2.67)0.04426.85.73 (4.07, 7.39)<0.001
1 Compared with Normal weight.
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