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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: Health Psychol. 2019 Aug 1;38(10):866–877. doi: 10.1037/hea0000775

Interleukin-6 and Body Mass Index, Tobacco Use, and Sleep in Gynecologic Cancers

Elizabeth L Kacel 1, Janae L Kirsch 2, Timothy S Sannes 3, Seema Patidar 4, Rachel Postupack 5, Sally Jensen 6, Shan Wong 7, Stephanie Garey 8, Stacy Dodd 9, Chantel M Ulfig 10, Christina S McCrae 11, Michael E Robinson 12, Jacqueline Castagno 13, Gregory Schultz 14, Deidre B Pereira 15
PMCID: PMC6746588  NIHMSID: NIHMS1042900  PMID: 31368718

Abstract

Objective:

Elevated body mass index (BMI), tobacco use, and sleep disturbance are common health concerns among women with gynecologic cancers. The extent to which these factors are associated with systemic inflammation in gynecologic cancers is unknown. This is a significant literature gap given that (1) chronic, systemic inflammation may mediate relationships between behavioral health factors and cancer outcomes, and (2) elevated BMI, tobacco use, and sleep disturbances can be modified via behavioral interventions. This study examined IL-6 relations with BMI, tobacco use history, and sleep disturbances in patients undergoing surgery for suspected gynecologic cancer.

Methods:

Participants were 100 women (M age =58.42 years, SD =10.62 years) undergoing surgery for suspected gynecologic cancer. Smoking history was determined by participant self-report. Sleep quality/disturbance was assessed via the Pittsburgh Sleep Quality Index. BMI was abstracted from electronic health records. Presurgical serum IL-6 concentrations were determined using Enzyme-Linked Immunosorbent Assay.

Results:

Controlling for the cancer type and stage, regression analyses revealed higher BMI, β = 0.258, p = .007, and former/current smoking status, β = 0.181, p = .046, were associated with higher IL-6. IL-6 did not differ between former and current smokers, β = 0.008, p = .927. Global sleep quality, sleep latency, and sleep efficiency were not associated with IL-6.

Conclusions:

Higher BMI and any history of tobacco use predicted higher IL-6 among women undergoing surgery for suspected gynecologic cancers. Cognitive-behavioral interventions targeting primary and secondary obesity and tobacco use prevention may reduce systemic inflammation and optimize cancer outcomes in this population.

Keywords: Psychoneuroimmunology, nterleukin-6, tobacco use, obesity, sleep, gynecologic oncology

Introduction

Epidemiology of Gynecologic Cancers

Gynecologic cancers account for significant morbidity and mortality among women in the United States. Incidence and mortality rates vary by cancer site. Endometrial cancer is one of the most common cancers among women, with an incidence rate of 26.2 per 100,000. However, mortality rates are relatively low, with only 4.6 deaths per 100,000. In comparison, ovarian cancer affects fewer women than endometrial cancer (i.e., incidence rate of 11.3 per 100,000); however, it has a higher mortality rate than endometrial cancer, resulting in 7.2 deaths per 100,000 (U.S. Cancer Statistics Working Group, 2017). Indeed, ovarian cancer tends to be diagnosed at later stages, demonstrate resistance to treatment, have high recurrence rates, and have relatively low 5-year survival rates (American Cancer Society, 2017; Jemal et al., 2008; National Cancer Institute, 2017). Overall, these statistics suggest that gynecologic cancers represent a significant public health problem in the United States. As such, it is important to identify biobehavioral factors that contribute to the pathogenesis of these cancers.

Inflammation and Cancer Pathogenesis

Chronic inflammatory responses at systemic and tumor microenvironmental levels promote cancer initiation, progression, recurrence, metastasis, and treatment resistance, including in gynecologic cancers (Taniguchi & Karin, 2014). IL-6 is one of the most potent promoters of inflammation and tumorigenesis in humans. IL-6 is a cytokine produced by immune cells, fibroblasts, and epithelial/malignant cells. It activates signaling pathways that stimulate the proliferation, survival, differentiation, and chemo-resistance of cancer cells. In addition, IL-6 is capable of inducing an epithelial-mesenchymal transition (EMT), which can promote tumor invasion, migration, and metastasis. IL-6 also promotes the supply of blood to tumor cells, a process known as angiogenesis. Consistent with these known biological mechanisms, IL-6 is high in many cancers, including gastrointestinal, breast, genitourinary, and gynecologic cancers (Taniguchi & Karin, 2014).

In ovarian cancer, IL-6 is a known inflammatory mediator of tumor initiation, progression, angiogenesis, metastasis, and chemo-resistance (Savant, Sriramkumar, & O’Hagan, 2018). Ovarian epithelial cells, ovarian cancer cells, macrophages, mesothelial cells, and ascites (malignant fluid surrounding the tumor) secrete IL-6, which then promotes a pro-inflammatory tumor microenvironment, tumor angiogenesis, and tumor metastasis (Savant, Sriramkumar, & O’Hagan, 2018). In ovarian cancer, higher levels of IL-6 are linked to faster disease progression (Kotowicz, Fuksiewicz, Jonska-Gmyrek, Bidzinski, & Kowalska, 2016; Lane, Matte, Rancourt, & Piché, 2011; Zakrzewska & Poznanski, 2001), faster tumor growth, and shorter survival (Guo, Xu, Lu, Duan, & Zhang, 2012; Scambia et al., 1995). Elevated IL-6 is also associated with non-home discharge after primary debulking of advanced stage ovarian cancer, surgical complications, and longer hospital stay. Similar associations exist in endometrial and cervical cancers. Serum IL-6 is associated with cervical cancer progression (Chopra, Dinh, & Hannigan, 1998) and presence of poor prognosis endometrial cancer subtypes (Bellone et al., 2005). In summary, the link between elevated IL-6 and poorer cancer outcomes is robust enough that researchers are beginning to explore whether blocking IL-6 signaling pathways is an effective cancer treatment (Taniguchi & Karin, 2014).

Behavioral Health Concerns, Inflammation, and Gynecologic Cancers

Furthering knowledge about modifiable predictors of inflammation may be a crucial component of comprehensive cancer treatment approaches. This research may have the greatest utility by targeting behavioral health factors that are (a) common in cancer, (b) associated with clinically-significant cancer outcomes, and (c) potentially modifiable via behavioral intervention strategies. Three of the most common behavioral health concerns among those with cancer are obesity (Calle, Rodriguez, Walker-Thurmond, & K., 2003), tobacco use (Kelemen, Warren, Koziak, Kobel, & Steed, 2016; Kim et al., 2017), and sleep disturbance/poor sleep quality (Sandadi et al., 2011; Savard, Ivers, Villa, Caplette-Gingras, & Morin, 2011), all of which meet the criteria above for critical targets for future research.

The Obesity Medicine Association defines obesity as “a chronic, relapsing, multifactorial, neurobehavioral disease” that is marked by an increase in body fat and adipose tissue dysfunction and results in negative metabolic and psychosocial health outcomes (Obesity Medicine Association, 2017). Obesity is commonly operationalized as body mass index (BMI), which represents an individual’s weight in kilograms divided by height in square meters. Overweight is defined as a BMI between 25.0 and 29.9 kg/m2, while obesity is defined as a BMI over 30.0 kg/m2. Obesity is a well-established cause of chronic, systemic inflammation. Adipose tissue releases IL-6 into the body (Fain, Madan, Hiler, Cheema, & Bahouth, 2004; Orban, Remaley, Sampson, Trajanoski, & Chrousos, 1999), which may be one mechanism through which obesity is associated with elevated risk of endometrial cancer (Calle & Kaaks, 2004; Reeves et al., 2007) and higher risk of death from endometrial cancer (Calle, Rodriguez, Walker-Thurmond, & Thun, 2003). Notably, the relationship between elevated BMI and higher IL-6 may be moderated by other maladaptive behavioral health factors, such as greater sleep disturbances (Arnardottir et al., 2012; Prather et al., 2009; Vgontzas et al., 1997).

Another behavioral health factor that has been consistently related to greater systemic inflammation is smoking (Aldaham, Foote, Chow, & Hakim, 2015; Bermudez, Rifai, Buring, Manson, & Ridker, 2002; Frost-Pineda et al., 2011; Liu et al., 2011; Panoulas et al., 2009; Wannamethee et al., 2005). Cigarette smoke contains immunomodulatory toxins, such as nicotine, carbon monoxide, acrolein, and reactive oxidant substances (Lee, Taneja, & Vassallo, 2012). These toxins cause a cascade of effects that result in inflammatory gene activation and chronic inflammation. Cigarette smoke also suppresses innate immunity, as well as T-helper (Th) 1 adaptive immunity, and impairs immune responses to infections (Arnson, Shoenfeld, & Amital, 2010; Cui & Li, 2010; Lee et al., 2012; Sopori, 2002). Notably, tobacco use is associated with higher levels of IL-6 even decades after quitting usage (Yanbaeva, Dentener, Creutzberg, Wesseling, & Wouters, 2007).

A third behavioral health factor associated with greater systemic inflammation is sleep difficulty. Sleep difficulties occur among 30 – 50% of individuals with newly diagnosed cancer (Savard & Morin, 2001). Women with gynecologic cancers are among the most affected by clinical insomnia (29 – 44%) and sub-threshold insomnia symptoms (39%) at the point of diagnosis (Palesh et al., 2010; Savard et al., 2011). Importantly, sleep disturbance and poor sleep quality are associated with higher IL-6 levels in both healthy and cancer samples. In healthy samples, higher levels of IL-6 are seen among those with clinical sleep disorders characterized by excessive daytime sleepiness (Vgontzas et al., 1997; Zisapel, 2007), as well as among individuals without clinical disorders who still suffer from sleep loss and shorter sleep duration (Mullington, Simpson, Meier-Ewert, & Haack, 2010). Within cancer populations, improvement in overall sleep (e.g., sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances) is significantly associated with decreases in IL-6 at pre-surgery and at 1-year follow-up in women diagnosed with ovarian cancer (Clevenger et al., 2012). Similar findings in head and neck cancers have been observed. Specifically, better Medical Outcomes Study (MOS) scores (Hays, Sherbourne, & Mazel, 1995), which assess global sleep quality, are significantly associated with lower circulating IL-6 (Duffy et al., 2013).

Very few studies have explored how multiple behavioral factors are associated with IL-6 in cancer samples. This is a significant gap in the literature due to the fact that poor health behaviors are often comorbid with each other in individuals affected by cancer. For instance, Duffy and colleagues (Duffy et al., 2013) examined obesity, tobacco use, and IL-6 in head and neck cancer patients. They found that current and former smokers had higher IL-6 than never smokers. In addition, while BMI was associated with higher IL-6 in univariate analyses, this relationship was no longer significant in multivariate analyses controlling for demographic and health-related variables. This latter study, in particular, highlights the importance of modeling the effects of multiple, comorbid behavioral risk factors on IL-6 in cancer.

Purpose of the Present Study

The present study examined the extent to which BMI, smoking history, and sleep disturbance/poor sleep quality were associated with serum IL-6 in women undergoing surgery for suspected gynecologic cancers. It was hypothesized that higher BMI and greater sleep disturbance/poorer sleep quality would be associated with higher IL-6. It was also hypothesized that current and former smokers would have higher IL-6 than never smokers, while current smokers would have higher IL-6 than former smokers. In line with research suggesting an interaction between sleep disturbances and BMI on IL-6 (Arnardottir et al., 2012; Prather et al., 2009; Vgontzas et al., 1997), an exploratory aim evaluated whether greater sleep disturbances moderated the relationship between higher BMI and higher IL-6.

Methods

Participants

Participants were women with suspected gynecologic cancers enrolled in one of two biobehavioral oncology research projects at the University of Florida. The first parent study was a nonexperimental, longitudinal study (N = 134) of PNI relationships in endometrial cancer conducted from 2004 to 2009. The second parent study was a randomized clinical trial (RCT) (N = 115) examining psychological intervention effects on sleep, pain, mood, cortisol, and cytokines in gynecologic cancers; this study was conducted from 2009 to 2017 (ClinicalTrials.gov Identifier: ).

Inclusion criteria for enrollment at pre-surgery across both studies were: (a) suspected primary gynecologic cancer1 scheduled to undergo surgical intervention, (b) at least 18 years of age, and (c) fluency in English. Exclusion criteria for enrollment at pre-surgery across both studies were: (a) recurrent gynecologic cancer, (b) metastases to the reproductive organs from another primary cancer site, (c) pre-operative chemotherapy or radiotherapy, and/or (d) severe psychopathology. An additional inclusion criterion for the RCT was a positive screen for sleep disturbance/poor sleep quality at pre-surgery. An additional exclusion criterion for the RCT was a medical record documented history of seizure disorder and/or obstructive sleep apnea (OSA), conditions for which a behavioral intervention for insomnia would be unsafe or ineffective. Participants meeting pre-surgical eligibility criteria were included in the current analyses if they had (a) surgically confirmed primary gynecologic cancer, borderline ovarian tumor, or complex endometrial hyperplasia with atypia (endometrial pre-cancer), and (b) viable serum at pre-surgery for quantitation of IL-6. Forty-four women from the nonexperimental, longitudinal study, and 65 women from the RCT (total N = 109) met these criteria. One woman from the nonexperimental study had medical record documented OSA, while five women from the RCT were ultimately diagnosed with OSA. These participants were excluded from analyses. Of the remaining 103 participants, 3 had missing tobacco use data. Therefore, analyses were performed on 100 participants (nonexperimental study: N = 40, 40.00%; RCT: N = 60, 60.00%).

Procedures

All participants from both parent studies were recruited from the Gynecologic Oncology Clinic at the University of Florida. Patients who were potentially eligible based on medical record review were identified during their preoperative consultation visit with their oncology surgeon and nursing staff. Interested potential participants met in a private room with a study team member who provided an overview of the study. All study procedures were conducted according to the rules and regulations set forth by the University of Florida Institutional Review Board (IRB).

Following consent, participants underwent a brief psychiatric screening to rule out current psychotic symptoms and current/recent suicidal ideation, intent, or plan. If the assessment was negative, a preoperative psychosocial assessment was scheduled and questionnaires were provided. After completing study interviews and written questionnaires, participants in both studies completed a preoperative blood draw by a certified phlebotomist.

Measures

Psychological Screening Measures.

To be enrolled in either of the two parent studies, patients were required to complete two types of screeners. First, patients completed a brief assessment for severe psychopathology as evaluated by the Structured Clinical Interview for DSM Disorders (SCID) (First, 1997) and the Beck Suicide Scale (BSS) (A. T. Beck, Steer, & Ranieri, 1988). The SCID is a multimodal, interview-based survey that was used to evaluate potential participants in both parent studies for the presence of current or previous mania, hypomania, and/or psychotic symptoms. Patients with these disorders were excluded from participation. The BSS is a 21-item questionnaire designed to measure respondents’ current and/or history of suicidal ideation, planning, and/or suicide attempts. Patients with any current suicidal ideation were more fully assessed by the study team to evaluate immediate patient safety and provide referrals if needed. These patients were not eligible to participate in the parent studies. Patients with no current suicidal ideation but a history of ideation or attempt were also further evaluated. Those with a remote history of suicidality who, based on the clinical judgment of the Study PI, a licensed psychologist, were determined to be low risk for future suicide attempt, were retained in the parent studies.

Sleep Quality/Disturbance Assessment.

The PSQI was used to assess sleep quality/disturbance. The PSQI provides a global score, as well as seven subscale scores (e.g., subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction). Higher scores indicate greater self-reported sleep dysfunction.

The PSQI has been used among diverse clinical groups, including cancer populations (S. L. Beck, Schwartz, Towsley, Dudley, & Barsevick, 2004; Carpenter & Andrykowski, 1998). The PSQI had good internal consistency in two samples of heterogeneous cancer patients (Cronbach’s alpha=0.770–0.808) (S. L. Beck et al., 2004), which is consistent with results obtained in the original psychometric validation study (Cronbach’s alpha=0.83) (Buysse et al., 1989). Similar results were found in bone marrow transplant and breast cancer patients (Cronbach’s alpha=0.80) (Carpenter & Andrykowski, 1998), which offers further evidence that the PSQI is a valid and reliable measure of sleep disturbance/sleep quality in cancer populations.

As noted previously, RCT participants were required to screen positive for sleep disturbances/poor sleep quality using a Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989) global score > 5 and/or a brief insomnia screener based on the American Academy of Sleep Medicine’s clinical research diagnostic criteria for insomnia (Edinger, 2004). Participants in the nonexperimental study completed the PSQI at pre-surgery, as well. However, a PSQI Global Index Score > 5 was not required for enrollment.

The PSQI global score and the PSQI sleep latency and sleep efficiency subscales were selected as predictors for the current analyses for several reasons. First, the PSQI sleep latency subscale was selected because prolonged sleep latency is a common feature of insomnia and maps closely onto the required criterion “A” for insomnia in the Diagnostic and Statistical Manual of Mental Disorders (DSM 5) (Diagnostic and statistical manual of mental disorders: DSM-5, 2013). In addition, sleep onset latency (SOL) and wake after sleep onset (WASO) comprise the construct of total wake time (TWT), which is a major target of behavioral sleep treatments. Second, the PSQI sleep efficiency subscale was selected, as sleep efficiency percentage (total sleep time/time in bed × 100) is a major target of behavioral sleep treatments.

Smoking History Assessment.

Participants were classified as never smokers (“0”), former smokers (“1”), or current smokers (“2”). Former smokers with very low exposure to cigarette smoking equivalent to a pack year history equal to 0 (e.g., 1 cigarette per year for 8 years) were considered to have negative lifetime histories of smoking.

Sociodemographic Assessment.

Information regarding participants’ race, ethnicity, education, marital status, and other demographic factors was captured via the MacArthur Sociodemographic Questionnaire (MSQ; (Adler, Epel, Castellazzo, & Ickovics, 2000)). The MSQ is a self-report survey developed by the MacArthur Network on SES and Health to assess subjective social status, educational attainment, occupational status, income, assets, and racial/ethnic background. Participants completed the MSQ during the preoperative baseline assessment.

BMI Assessment.

BMIs (in kg/m2) were abstracted from post-surgical discharge summaries.

Control Variable Assessment.

Presence of ovarian cancer or fallopian tube cancer (poor prognosis cancer types) and presence of Stage III or IV (advanced) cancer were selected as a priori control variables based upon the known relationship between these cancer types and greater inflammation/higher IL-6 (Anglesio et al., 2011; Macciò & Madeddu, 2012; Scambia et al., 1995; Tempfer et al., 1997). These variables were abstracted from post-surgical discharge summaries.

IL-6 Assessment.

IL-6 was obtained via a pre-operative blood draw completed by a hospital phlebotomist. Samples were centrifuged at 1000 × g for 15 minutes, aliquoted, and stored at − 80°C until assaying. Standardized ELISA assays were used for quantification of IL-6 concentrations (R&D Systems, Minneapolis, MN) at the University of Florida’s Institute for Wound Research laboratory under the direction and supervision of the Institute’s Director (G. Schultz). Values were expressed in pg/mL.

Statistical Analyses

Analyses were conducted using IBM SPSS Version 25. Descriptive statistics were performed to assess normality assumptions for parametric analyses. IL-6 was non-normally distributed and Blom-transformed to normalize the data. The smoking history variable was then recoded as two orthogonal vectors using Helmert coding under the condition of unequal smoking history subgroup sizes. Vector 1 tested whether IL-6 was higher among former and current smokers compared to never smokers (never versus former/current smoking status). Vector 2 tested whether IL-6 was higher among current smokers compared to former smokers (former versus current smoking status). Bivariate correlations were conducted to explore univariate relationships among the variables of interest. Then, three multiple linear regression analyses were performed with IL-6 as the criterion. In each equation, Block 1 included the a priori control variables. Block 2 included the predictors of interest: BMI, both smoking variables, and then one of the following: (Equation 1) PSQI global score, (Equation 2) PSQI sleep latency subscale score, or (Equation 3) PSQI sleep efficiency subscale score. For the exploratory aim, the product of PSQI global scores and BMI values was added to Equation 1, Block 2.

Results

Participants were primarily middle-aged (M age = 58.42 years, SD = 10.62 years), married (N = 54, 54.0%), non-Hispanic (N = 67, 67.0%), and Caucasian (N = 82, 82.0%) women with some college education (M education = 13.63 years, SD = 2.94 years). Most participants had endometrial cancer (N = 72, 72.0%) and early-stage (Stage I-II) disease (N = 71, 71.0%). Following receipt of tumor pathology reports, nine participants (9.0%) had confirmed complex endometrial hyperplasia with atypia (a benign but precancerous condition of the endometrium); these participants were retained in the analyses. Non-transformed IL-6 levels ranged from 0 pg/mL to 22.00 pg/mL (M = 3.10 pg/mL, SD = 3.68 pg/mL). Further information on demographic and disease characteristics for our sample is reported in Table 1.

Table 1.

Sample Demographics and Health Characteristics

Variable M SD N %
Age (Years) 58.42 10.62
Education (Years) 13.63 2.94
Race
Caucasian 82 82
African-American/Black 12 12
Asian 2 2
American Indian/Native Alaskan 2 2
Missing 2 2
Ethnicity
Non-Hispanic 67 67
Hispanic 10 10
Missing 23 23
Marital Status
Married 54 54
Never Married 9 9
Separated 3 3
Divorced 17 17
Widowed 11 11
Missing 6 6
Cancer Type
Ovarian/Fallopian Tube, Invasive 10 10
Endometrial, Invasive 72 72
Squamous Cell Carcinoma of the 5 5
Vulva, Cervix, or Vagina, Invasive
Complex Endometrial Hyperplasia 9 9
Borderline Ovarian Tumor 4 4
Tumor Stagea
Precancerb 9 9
Stage I 55 55
Stage II 16 16
Stage III 18 18
Stage IV 2 2
BMI (kg/m2) 35.33 10.37
Underweight 1 1
Normal Weight 15 15
Overweight 21 21
Obese 63 63
Smoking History
Never Smoker 53 53
Former Smoker 34 34
Current Smoker 13 13
PSQI Global Score 9.03 4.22
PSQI Global score > 5 (Buysse et al., 1989) 86 86
PSQI Global score > 8 (Carpenter & Andrykowski, 1998) 46 46
PSQI Sleep Latency Subscale Score 1.34 1.15
PSQI Sleep Efficiency Subscale Score 1.39 1.19
IL-6 (pg/mL)c 3.10 3.68
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a

Tumor staging applies to invasive tumors and borderline ovarian tumors.

b

Complex endometrial hyperplasia.

c

Mean and standard deviation of Blom-transformed scores = 0.00 and 1.00, respectively.

Forty-seven (47.0%) of participants endorsed a history of smoking, 13 of whom (13.0% of entire sample) were current smokers. BMI values ranged from 17.85 kg/m2 (Underweight) to 72.62 kg/m2 (Class III Obesity) (M = 35.33 kg/m2, SD = 10.37 kg/m2). Sixty-three (63.0%) of participants had BMI values in the obese range (≥ 30 kg/m2) (Table 1). Mean PSQI global score was 9.03 (SD = 4.22). Consistent with differing eligibility criteria for the two parent studies, participants in the RCT had significantly higher PSQI global index scores (M = 10.23, SD = 3.91) than those in the nonexperimental study (M = 7.22, SD = 4.07), F(1,98) = 13.753, p <.001, ηp2=.123. However, PSQI global index scores did not differ significantly by study origin among participants screening positive for sleep disturbance/poor sleep quality using either a cutoff of 5 (Buysse et al., 1989), F(1,86) = .934, p = .337, ηp2=.011, or a modified cutoff of 8 suggested for cancer patients (Carpenter & Andrykowski, 1998), F(1,46) = 3.234, p = .079, ηp2=.068. In the total sample, mean PSQI sleep latency and sleep efficiency subscale scores were 1.34 (SD = 1.15) and 1.39 (SD = 1.19), respectively.

Inter-correlations among a priori control variables, predictors, and IL-6 revealed that, as hypothesized, higher IL-6 was significantly associated with presence of ovarian/fallopian tube cancer, r(100)= 0.380, p < .001, and advanced cancer (Stage III-IV), r(100) = 0.340, p = .001 (Table 2). There were nonsignificant, small-to-medium effect size relationships between IL-6 and the following predictors: BMI, r(100) = 0.154, p = .127; never smoker versus former/current smoker status, r(100) = 0.136, p = .177; PSQI global index scores, r(100) = 0.155, p = .125; and PSQI sleep latency subscale scores, r(100) = 0.113, p = .267. IL-6 was unrelated to former versus current smoker status, r(100) = −0.022, p = .830 and PSQI sleep efficiency subscale scores, r(100) = .081, p = .423 (Table 2).

Table 2.

Summary of Inter-correlation Matrix among Control Variables, Predictors, and Presurgical IL-6 in Women Undergoing Surgery for Suspected Gynecologic Cancer

Variable 1 2 3 4 5 6 7 8 9
1.Ovarian/ Fallopian Tube Cancer -- 0.500*** −0.139 −0.114 −0.090 0.117 0.104 0.116 0.380**
2.Advanced Cancer 0.500*** -- −0.282** −0.120 0.005 0.080 0.091 0.089 0.340**
3.BMI −0.139 −0.282** -- 0.068 −0.059 0.085 −0.001 −0.085 0.154
4.Never Smoker Vs. Former and Current Smokers −0.114 −0.120 0.068 -- 0.000 0.084 0.004 −0.141 0.136
5. Former Vs. Current Smokers −0.090 0.005 −0.059 0.000 -- 0.161 0.157 0.034 −0.022
6.PSQI Global Score 0.117 0.080 0.085 0.084 0.161 -- 0.706** 0.632** 0.155
7. PSQI Sleep Latency Subscale Score 0.104 0.091 −0.001 0.004 0.157 0.706** -- 0.399** 0.113
8. PSQI Sleep Efficiency Subscale Score 0.116 0.089 −0.085 −0.141 0.034 0.632** 0.399** -- 0.081
9. IL-6 (Blom– Transformed ) 0.380** 0.340** 0.154 0.136 −0.022 0.155 0.113 0.081 --
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*

p ≤ .050,

**

p ≤ .010,

***

p ≤ .001.

Hierarchical linear regression analyses were used to predict IL-6 with a priori control variables in step 1 and predictors of interest in step 2. Three equations were tested. Equation 1 included PSQI global index scores as the sleep quality/disturbance variable of interest, while Equations 2 and 3 included PSQI sleep latency and PSQI sleep efficiency subscale scores, respectively.

In the first equation (Table 3), step 1 revealed that presence of ovarian/fallopian tube cancer was significantly associated with higher IL-6, β = 0.280, p = .010, while presence of advanced cancer was marginally associated with higher IL-6, β = 0.201, p = .063. These variables accounted for 17.5% of the variance in IL-6, F of ΔR2 = 10.265, p < .001. In step 2, higher BMI, β = 0.258, p = .007, and never smoker versus former/current smoker status, β = 0.181, p = .046, were associated with higher IL-6. As depicted in Figure 1, IL-6 estimated marginal means were higher among former/current smokers than never smokers. However, neither former versus current smoker status, β = 0.008, p = .927, nor PSQI global scores, β = 0.060, p = .513, were associated with IL-6. Step 2 accounted for 10.5% of the variance in IL-6 above and beyond a priori control variables, F of ΔR2 = 3.396, p = .012.

Table 3.

BMI, Smoking History, and Sleep Quality/Disturbances Predicting IL-6 in Women Undergoing Surgery for Suspected Gynecologic Cancer

Predictor IL-6
B SE B β 95% CI
for B 		P ΔR2
Step 1 .175***
 Ovarian/Fallopian Tube Cancer 0.912 .347 0.280 [0.223–1.601] .010
 Advanced Cancer 0.490 .260 0.201 [−0.027–1.007] .063
Step 2 .105*
 BMI 0.024 .009 0.258 [0.007–0.042] .007
 Never Vs. Former/Current Smokers 0.004 .002 0.181 [0.000 – 0.007] .046
 Former Vs. Current Smokers 0.001 .006 0.008 [−0.012 – 0.013] .927
 PSQI Global Score 0.014 .021 0.060 [−0.028–0.056] .513
R2 .280**
F 6.023**
N 100
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*

p <.050,

**

p < .010,

***

p < .001.

Figure 1. Relationship between IL-6 and Smoking Status in Women Undergoing Surgery for Suspected Gynecologic Cancer.

Figure 1

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Note. Figure 1a depicts Blom-transformed IL-6 estimated marginal means and 95% confidence intervals for Never Smokers and Former/Current Smokers. To facilitate interpretation of the results presented in Figure 1a, Blom-transformed IL-6 values were back-transformed to approximate, raw IL-6 values and plotted for Never Smokers and Former/Current Smokers. These results are depicted in Figure 1b for illustrative purposes.

Similar relationships emerged in Equations 2 and 3, which substituted PSQI sleep latency and sleep efficiency subscale scores for PSQI global scores. Neither PSQI sleep latency subscale scores in Equation 2, β = 0.053, p = .561, nor PSQI sleep efficiency subscale scores in Equation 3, β = 0.072, p = .423, predicted IL-6 (not shown).

For the exploratory aim, the product of PSQI global scores and BMI values was added to Equation 1, Block 2. However, the interaction term did not significantly predict IL-6, β = 0.012, p = .901 (not shown).

Discussion

The current study examined whether BMI, smoking history, and sleep disturbance/poor sleep quality were associated with IL-6 above and beyond the presence of poor prognosis cancer types (e.g., ovarian/fallopian tube cancers) and advanced cancer (i.e., Stages III-IV) in women undergoing surgery for suspected gynecologic cancer. Results revealed that, as hypothesized, IL-6 was significantly associated with presence of ovarian/fallopian tube cancer and marginally associated with the presence of Stage III-IV cancer. Together, these variables predicted 17.5% of the variance in IL-6. These findings are consistent with previous research implicating advanced stage (Esfandi, Mohammadzadeh Ghobadloo, & Basati, 2006; Guo et al., 2012; Kaminska et al., 2005) and cancer type (Anglesio et al., 2011; Macciò & Madeddu, 2012; Scambia et al., 1995; Tempfer et al., 1997) as important factors in elevated IL-6 levels.

Consistent with hypotheses, greater BMI and former/current smoking status were significantly associated with higher IL-6. Obesity and tobacco use represent behavioral health factors that: (a) tend to co-occur (Grandner et al., 2016; Hussaini, Nicholson, Shera, Stettler, & Kinsman, 2011; Kauffman, Farris, Alfano, & Zvolensky, 2017), (b) increase risk for cancer and cardiovascular disease (Koene, Prizment, Blaes, & Konety, 2016), and (c) are modifiable via evidence-based behavioral health interventions (Kushner & Ryan, 2014; Morin, 2006; Sallit, Ciccazzo, & Dixon, 2009). Recently, overweight and obesity have been associated with higher risk of at least 13 cancer types, with these cancers accounting for 40% of all cancers diagnosed in 2014. Endometrial and ovarian cancers are two of these 13 types. Every 1 kg/m2 increase in BMI is associated with an 8% increase in risk for endometrial cancer and a 1% increase in risk for ovarian cancer (Steele et al., 2017). In our sample of 100 women undergoing surgery for suspected gynecologic cancer, 63% of participants had BMI values in the obese range. The high prevalence of obese women in our sample is consistent with the prevalence of overweight and obese women (66.9%) in the United States population (National Institute of Diabetes and Digestive and Kidney Diseases, 2017).

The American College of Obstetricians and Gynecologists (ACOG) has recognized the high prevalence and negative consequences of obesity among gynecology patients. As such, they recently created physician toolkits which offer specific guidelines and resources for managing obesity (Crowe, Gregg, & DeFrancesco, 2016). While publications and guidelines such as these may be useful if implemented, a recent study examining attitudes toward weight management among gynecologic cancer survivors found that only a small percentage of patients received weight management counseling, even though the vast majority of patients felt that it was appropriate for their oncologist to discuss behavioral health and weight loss topics with them (Zaleta, Neff, McCann, O’Malley, & Carpenter, 2017). This is significant for several reasons. First, obesity may soon be associated with more cancer deaths than tobacco use (Ligibel et al., 2014). Second, existing guidelines and empirically supported lifestyle programs exist for weight management among cancer patients (Rutledge, 2016) and the general population (Jensen et al., 2014). Third, as suggested by the current study, obesity is associated with higher IL-6, which may have deleterious downstream consequences on perioperative and other cancer outcomes (He, Wang, Bian, Deng, & Wang, 2017; Huo, Smith, Giordano, Reece, & Tina Shih, 2016).

Slightly less than half of participants (47.0%) reported a positive lifetime history of tobacco use. This is relatively consistent with other studies which have shown that 52.8% of women with ovarian cancer report a history of cigarette use (Rossing, Cushing-Haugen, Wicklund, & Weiss, 2008). The health risks of cigarette smoke have received considerable attention since being identified as a cause of lung cancer. While this line of research has developed significant inquiry and important knowledge into direct mechanisms through which smoking is associated with cancer outcomes, particularly in lung and head and neck cancers, more research is needed on the indirect pathways through which tobacco use may affect treatment outcomes and prognoses among individuals with other types of cancers.

It is well known that tobacco cessation upon cancer diagnosis confers immediate health benefits (Sitas et al., 2014), while, in contrast, continued tobacco use is associated with negative outcomes such as tumor progression, decreased response to chemotherapy, worse quality of life, and greater pain-related functional impairment (Florou, Gkiozos, Tsagouli, Souliotis, & Syrigos, 2014). However, the current study found that any lifetime history of smoking was associated with higher IL-6 in women with gynecologic cancers, suggesting that both primary and secondary prevention of tobacco use should be key public health priorities for optimizing health outcomes in gynecologic cancers. Decades of research on tobacco prevention and cessation have resulted in strong evidence for the efficacy of individual (Lancaster & Stead, 2017) and group-based (Stead, Carroll, & Lancaster, 2017) behavioral interventions. Furthermore, ACOG has developed toolkits for smoking cessation in gynecology patients (Crowe et al., 2016) which allows known interventions to be tailored to the specific needs of women with gynecologic cancers.

Contrary to hypotheses, current smokers did not have significantly greater IL-6 than former smokers. This finding may be attributed to several factors. The current study did not have complete information on pack year smoking history or length of time between quitting tobacco use and assessment of tobacco use in this study. It is possible that individuals who were categorized as past smokers had quit smoking fairly recently and therefore may have appeared more similar to the current smoker group in terms of IL-6 levels. Previous findings have shown that at the time of cancer diagnosis, approximately 20–40% of individuals are current smokers (Burke, Miller, Saad, & Abraham, 2009; Schnoll et al., 2003; Walker, Larsen, Zona, Govindan, & Fisher, 2004). Burke and colleagues found that, among the 20% of patients who were smoking at the time of diagnosis, 44% reported smoking cessation following their cancer diagnosis (Burke et al., 2009). In patients with newly diagnosed cancer, it is likely important to assess cumulative tobacco exposure among current and former smokers, as well as length of abstinence among former smokers.

In addition, contrary to hypotheses, PSQI measures of global sleep quality/disturbance, sleep latency, and sleep efficiency were unrelated to IL-6 in this sample. The PSQI assesses sleep disturbance/poor sleep quality rather than presence or severity of clinical insomnia. It is possible that it is the construct of insomnia per se and/or the degree of insomnia severity that is associated with elevated IL-6 in cancer, rather than simply poor sleep quality. In addition, the current study excluded individuals diagnosed with other sleep disorders marked by excessive daytime sleepiness, such as obstructive sleep apnea (Vgontzas et al., 1997; Zisapel, 2007). Maintaining a more homogeneous sleep-disordered sample may have contributed to discordant findings between the current study and prior published research.

While the current study modeled the effects of behavioral health factors on IL-6, it is important to note that the relationship between health behaviors and inflammation is likely to be bidirectional. IL-6 and other pro-inflammatory cytokines may induce subjective feelings of illness and so-called “sickness behaviors,” such as depressed mood, sleep disturbances, impaired appetite, and fatigue (Dantzer, 2009). In a study of ovarian cancer patients, Costanzo and colleagues found that greater IL-6 was associated with compromised physical and functional well-being and lower social support (Costanzo et al., 2005). Similarly, in another study examining the relationship between IL-6 and mood in ovarian cancer patients at pre-surgery, 6 month, and 1 year follow-up, declines in IL-6 were associated with reductions in depression and fatigue across time (Schrepf et al., 2013). Therefore, while obesity and tobacco use may have effects on IL-6, elevated IL-6 may have indirect effects on obesity and tobacco use through poorer mood and quality of life.

Strengths of the study include the fact that the sample was comprised of participants from two large, NIH-funded, longitudinal studies with well-characterized samples. Participants represented a wide range of gynecologic pathologies and treatment interventions, which allows for enhanced generalizability to the overall gynecologic oncology population.

However, study results must be interpreted in light of several limitations. Our sample was cross-sectional and somewhat homogenous in terms of sociodemographic characteristics. Patients were mostly married, well-educated, middle-aged, and Caucasian. Additionally, both parent studies were conducted at an academic medical center serving a large rural population. It is possible that regional differences in patterns of behavioral health and/or inflammation may limit our ability to generalize results more broadly. Second, there are several important limitations of using BMI as a measure of abnormal fat accumulation. BMI does not discern among fat, muscle, and bone mass or provide information on body fat distribution, such as central adiposity. Future research, particularly with post-menopausal women, should use methods such as dual-energy x-ray absorptiometry, which provides a direct and objective measure of body fat (Banack, Wactawski-Wende, Hovey, & Stokes, 2018). Third, given some inconsistencies in smoking history data (e.g., some participants reported different smoking histories at different study time points and/or did not complete all parts of the self-report smoking history questionnaire), we did not have pack-year history data and thus could not examine how cumulative tobacco exposure was associated with IL-6. Relatedly, 28.26% of non-smoking adult cancer survivors are exposed to secondhand smoke (95% CI: 24.97%–31.55%), with even higher rates occurring among individuals (a) from racial/ethnic minority groups, (b) affected by poverty, and (c) with a smoking history (Akinboro et al., 2017). This highlights the importance of understanding the relationship between passive smoking and inflammation among current, former, and never smokers. Fourth, we had a relatively small number of current smokers, and it is possible that low statistical power precluded the ability to detect significant differences between current smokers and former smokers. Fifth, the current study capitalized on the commonalities between the eligibility criteria of two NCI-funded parent studies. Using a PSQI global index score cutoff of 8 recommended by Carpenter and Andrykowski (Carpenter & Andrykowski, 1998) for cancer patients, the resultant sample contained almost equivalent numbers of individuals with and without clinically-significant sleep disturbance/poor sleep quality. While this was a strength of the study, it is possible that there are differences between the parent study samples that confounded the emergence of a true relationship between sleep and IL-6. Lastly, the current study did not assess chronotype or individual sleep-wake preferences ranging from morningness to eveningness (Horne & Ostberg, 1976; Roennenberg, 2012). Chronotype impacts sleep behavior and a growing body of research has started to explore the ways in which chronotype, along with disruptions in natural circadian rhythm, may be associated with cancer risk and outcomes (Cash et al., 2015; Innominato et al., 2012; Papantoniou et al., 2015).

In summary, future research should continue to examine the bidirectional associations between behavioral health factors and inflammation in gynecologic cancers. Behavioral health factors should include pre-surgical BMI, tobacco use and secondhand smoke exposure, and insomnia symptoms/severity. In addition, future research should explore the extent to which these behavioral health factors are associated with clinical outcomes, such as post-surgical complications, treatment resistance, cancer recurrence, and survival in women with gynecologic cancers. In particular, this research should explore the extent to which cognitive-behavioral interventions for primary and secondary obesity and tobacco use prevention may reduce systemic inflammation and optimize cancer outcomes in this population.

Acknowledgments

This work was supported by the National Cancer Institute (NCI; R03 CA117480; PI Deidre B. Pereira, PhD) and the National Institutes of Health (NIH; R01 CA138808; PI Deidre B. Pereira, PhD). The authors report no financial disclosures or conflicts of interest.

Footnotes

1

The non-experimental study was limited to endometrial cancers, while the RCT was open to all gynecologic cancers. The majority of gynecologic cancers in the RCT were endometrial cancers.

Contributor Information

Elizabeth L. Kacel, Department of Clinical and Health Psychology, University of Florida

Janae L. Kirsch, Department of Clinical and Health Psychology, University of Florida

Timothy S. Sannes, Department of Clinical and Health Psychology, University of Florida;

Seema Patidar, Department of Clinical and Health Psychology, University of Florida;.

Rachel Postupack, Department of Clinical and Health Psychology, University of Florida.

Sally Jensen, Department of Clinical and Health Psychology, University of Florida.

Shan Wong, Department of Clinical and Health Psychology, University of Florida.

Stephanie Garey, Department of Clinical and Health Psychology, University of Florida.

Stacy Dodd, Department of Clinical and Health Psychology, University of Florida.

Chantel M. Ulfig, Department of Clinical and Health Psychology, University of Florida

Christina S. McCrae, Department of Clinical and Health Psychology, University of Florida

Michael E. Robinson, Department of Clinical and Health Psychology, University of Florida

Jacqueline Castagno, Department of Obstetrics and Gynecology, University of Florida.

Gregory Schultz, Department of Obstetrics and Gynecology, University of Florida.

Deidre B. Pereira, Department of Clinical and Health Psychology, University of Florida

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