The association of body composition phenotypes before chemotherapy with epithelial ovarian cancer mortality

Evan W Davis; Kristopher Attwood; Joseph Prunier; Gyorgy Paragh; Janine M Joseph; André Klein; Charles Roche; Nancy Barone; John Lewis Etter; Andrew D Ray; Britton Trabert; Matthew B Schabath; Lauren C Peres; Rikki Cannioto

doi:10.1093/jnci/djae112

. 2024 May 28;116(9):1513–1524. doi: 10.1093/jnci/djae112

The association of body composition phenotypes before chemotherapy with epithelial ovarian cancer mortality

Evan W Davis ¹, Kristopher Attwood ², Joseph Prunier ³, Gyorgy Paragh ⁴, Janine M Joseph ⁵, André Klein ⁶, Charles Roche ⁷, Nancy Barone ⁸, John Lewis Etter ⁹, Andrew D Ray ^10,¹¹, Britton Trabert ^12,¹³, Matthew B Schabath ¹⁴, Lauren C Peres ¹⁵, Rikki Cannioto ^16,^✉

¹ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

² Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

³ Lake Erie College of Osteopathic Medicine, Elmira, NY, USA

⁴ Department of Dermatology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁵ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁶ Department of Research Information Technology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁷ Department of Diagnostic Radiology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁸ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

⁹ Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA

¹⁰ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹¹ Department of Rehabilitation, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

¹² Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA

¹³ Huntsman Cancer Institute at the University of Utah, Cancer Control and Population Sciences, Salt Lake City, UT, USA

¹⁴ Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

¹⁵ Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA

¹⁶ Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

^✉

Correspondence to: Rikki A. Cannioto, PhD, EdD, Department of Cancer Prevention & Control, Roswell Park Comprehensive Cancer Center, Elm & Carlton Streets, Buffalo, NY 14263, USA (e-mail: rikki.cannioto@roswellpark.org).

Roles

Evan W Davis: MPH, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - original draft, Writing - review & editing

Kristopher Attwood: PhD, Data curation, Visualization, Writing - review & editing

Joseph Prunier: MS, Data curation, Writing - review & editing

Gyorgy Paragh: MD, PhD, Writing - review & editing

Janine M Joseph: MS, MBA, Writing - review & editing

André Klein: PhD, Writing - review & editing

Charles Roche: MD, Data curation, Writing - review & editing

Nancy Barone: BS, Data curation, Project administration, Writing - review & editing

John Lewis Etter: PhD, Writing - review & editing

Andrew D Ray: PT, PhD, Writing - review & editing

Britton Trabert: PhD, Writing - review & editing

Matthew B Schabath: PhD, Writing - review & editing

Lauren C Peres: PhD, Writing - review & editing

Rikki Cannioto: PhD, EdD, Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing

PMCID: PMC11378317 PMID: 38802116

Abstract

Background

The association of body composition with epithelial ovarian carcinoma (EOC) mortality is poorly understood. To date, evidence suggests that high adiposity is associated with decreased mortality (an obesity paradox), but the impact of muscle on this association has not been investigated. Herein, we define associations of muscle and adiposity joint-exposure body composition phenotypes with EOC mortality.

Methods

Body composition from 500 women in the Body Composition and Epithelial Ovarian Cancer Survival Study was dichotomized as normal or low skeletal muscle index (SMI), a proxy for sarcopenia, and high or low adiposity. Four phenotypes were classified as fit (normal SMI and low adiposity; reference; 16.2%), overweight or obese (normal SMI and high adiposity; 51.2%), sarcopenia and overweight or obese (low SMI and high adiposity; 15.6%), and sarcopenia or cachexia (low SMI and low adiposity; 17%). We used multivariable Cox models to estimate associations of each phenotype with mortality for EOC overall and high-grade serous ovarian carcinoma (HGSOC).

Results

Overweight or obesity was associated with up to 51% and 104% increased mortality in EOC and HGSOC [Hazard Ratio (HR)] = 1.51, 95% CI = 1.05 to 2.19 and HR = 2.04, 95% CI = 1.29 to 3.21). Sarcopenia and overweight or obesity was associated with up to 66% and 67% increased mortality in EOC and HGSOC (HR = 1.66, 95% CI = 1.13 to 2.45 and HR = 1.67, 95% CI = 1.05 to 2.68). Sarcopenia or cachexia was associated with up to 73% and 109% increased mortality in EOC and HGSOC (HR = 1.73, 95% CI = 1.14 to 2.63 and HR = 2.09, 95% CI = 1.25 to 3.50).

Conclusions

Overweight or obesity, sarcopenia and overweight or obesity, and sarcopenia or cachexia phenotypes were each associated with increased mortality in EOC and HGSOC. Exercise and dietary interventions could be leveraged as ancillary treatment strategies for improving outcomes in the most fatal gynecological malignancy with no previously established modifiable prognostic factors.

The impact of obesity and body composition on cancer outcomes is currently an area of immense clinical interest and scientific deliberation (1-7), with many studies suggesting that excess adiposity is associated with improved survival or immunotherapy outcomes (an overweight or obesity paradox) (8-10). Although obesity is an established risk factor for developing epithelial ovarian cancer (EOC) (11), the relationship between obesity and EOC survival remains poorly understood (8,10,12,13).

In recent years, two meta-analyses summarizing associations of obesity [Body Mass Index (BMI) ≥30 kg/m²] (10) and excess adiposity (8) with EOC mortality have been published, but neither report solidified our understanding of the associations of excess adiposity with EOC survival. Petrelli et al. showed that obesity (BMI ≥30) was not associated with all-cause mortality, cancer-specific mortality, or disease recurrence in EOC (10). However, reliance on BMI as a proxy for adiposity was a potential limitation of this work.

Most recently, Cheng et al. published a meta-analysis reporting associations of image-based adiposity with EOC outcomes (8). This analysis revealed that higher vs low adiposity was suggestively associated with decreased mortality and decreased disease progression in EOC (8). However, an important limitation to this work was a lack of consideration of skeletal muscle mass, a potentially independent prognostic factor in EOC (14-21). Despite some evidence suggesting that low skeletal muscle index (SMI) is associated with worse EOC survival and normal SMI is associated with improved survival (14-21), 7 prior reports of the associations of image-assessed adiposity with EOC survival have been published (22-28), and none have considered the role of skeletal muscle mass. Thus, the impact of skeletal muscle mass on the association of excess adiposity with EOC mortality remains a fundamental gap in knowledge.

To address this important knowledge gap, we leveraged image-based body composition and clinical data from the Body Composition and Epithelial Ovarian Cancer Survival (BComES) Study (Principal Investigator: Cannioto) at Roswell Park Comprehensive Cancer Center (Roswell Park) to define the association of excess adiposity before chemotherapy with EOC mortality while accounting for skeletal muscle mass using three epidemiological methods. We hypothesized that methods of analyses represented in the current literature (ie, comparing high vs low adiposity in relationship to EOC mortality) would inadvertently include sarcopenic patients in the reference group, which could attenuate associations of excess adiposity with EOC mortality or falsely induce an obesity paradox. Conversely, we hypothesized that analyses excluding sarcopenia patients from the reference group would show that excess adiposity is associated with increased EOC mortality.

To test this hypothesis, we conducted a series of primary and secondary analyses that account for muscle mass in three different ways. In primary analyses, we defined associations of muscle and adiposity joint-exposure body composition phenotypes with EOC mortality, identifying the “fit” phenotype (normal SMI and low adiposity) as the comparison (reference) group. In secondary analyses, we defined associations of high vs low adiposity with EOC mortality, accounting for SMI in two ways. First, we defined associations of high vs low adiposity with EOC mortality with adjustment for SMI. Second, we defined associations of high vs low adiposity with EOC mortality within subgroups according to low SMI (sarcopenia) vs normal SMI. For all analyses, we examined associations in the overall study population and in patients diagnosed with high-grade serous ovarian carcinoma (HGSOC), the most common and fatal EOC histotype.

Methods

Study population and data collection

The BComES Study is a survival cohort comprising 744 histologically confirmed invasive EOC patients receiving first-line chemotherapy at Roswell Park, who also have a high-quality Computed Tomography (CT) scan available either before chemotherapy onset or up to 6 months after chemotherapy completion. BComES participants meeting the following criteria were included in the current investigation: 1) provided written, informed consent allowing Roswell Park Shared Resources to link clinical data from the electronic health record with follow-up data from the Cancer Registry for research purposes; 2) completed first-line treatment without significant treatment delays between 2006 and 2023; 3) had clinically measured height and weight recorded before first-line chemotherapy; 4) had a high-quality CT image in the peri-diagnosis period (before chemotherapy onset) in the Picture Archiving and Communication System (PACS); the median time between diagnosis and CT scan was 17 days [interquartile range (IQR) = 28.5 days]; and 5) had updated follow-up data including vital status and date of last contact from the Cancer Registry at Roswell Park. A total of 244 patients did not have pre-chemotherapy CT scans available in PACS; thus, 500 invasive EOC patients met the inclusion criteria for the current analysis. Approval to initiate the BComES Cohort was obtained in June 2019 from the Roswell Park Institutional Review Board.

Body composition assessment and parameterization

Previous studies have established that adiposity and skeletal muscle surface area at the third lumbar vertebra (L3) are most representative of whole-body adiposity and muscle composition (29). For the current study, standard-of-care CT images were manually segmented by a trained rater whose procedural pipeline was standardized and validated under the supervision of a diagnostic radiologist and co-investigator (CR). We used sliceOmatic software to segment and quantify cross-sectional surface area (cm²) at L3 for intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and skeletal muscle area (30). Total adipose tissue (TAT) was derived as the sum of IMAT, VAT, and SAT at L3. Skeletal muscle index (SMI) was calculated as muscle mass cm²/height m²; patients with SMI less than 38.5 cm²/m² were classified as low SMI (a proxy for sarcopenia), and patients with SMI equal to or greater than 38.5 cm²/m² were classified as normal SMI (31). As no standard cut-points for adiposity at L3 exist, and tertile 1 coincided with the approximate percentage of normal-weight patients in our cohort according to BMI, we used the cut-point for the lowest tertile of adiposity to classify low vs high adiposity. Next, adiposity and muscle joint-exposure body composition phenotypes were classified as fit (normal SMI and low adiposity); overweight or obese (normal SMI and high adiposity); sarcopenia and overweight or obese (low SMI and high adiposity); and sarcopenia or cachexia (low SMI and low adiposity).

Outcome assessment

Hazard of all-cause mortality was the primary analytic outcome, an excellent proxy for EOC-specific mortality (32). Clinical follow-up data were obtained from the Cancer Registry at Roswell Park, an accredited Commission on Cancer registry surpassing all minimal standards for annual and 5-year follow-up, 80% and 90%, respectively. Follow-up methods include annual reports, continuous review of patient medical records, phone calls and letters to patient and/or family homes, obituary and national death index searches, and review of HEALTHeLINK, a western NY clinical information exchange portal.

Statistical analysis

In primary analyses, multivariable Cox proportional hazard regression models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) representing the association of joint-exposure body composition phenotypes with mortality. We first assessed the association of the overall joint-exposure phenotype construct with EOC mortality by defining associations of the combined at-risk phenotypes (overweight or obese, sarcopenia and overweight or obese, and sarcopenia or cachexia) vs the fit phenotype with EOC mortality. Next, we defined independent associations of overweight or obese, sarcopenia and overweight or obese, and sarcopenia or cachexia vs the fit phenotype with EOC mortality for each adipose depot.

In secondary analyses, we first estimated associations of higher vs low adiposity with mortality for each adipose depot with adjustment for SMI in multivariable models; this method by default includes sarcopenic patients in the reference group. Second, we estimated associations of higher vs lower adiposity with EOC mortality within subgroups of patients with normal and low SMI. This method of analysis allows for a comparison of associations of high vs low adiposity with EOC mortality when sarcopenia patients are in the reference group (ie, within low SMI subgroup) vs when sarcopenia patients are excluded from the comparison group (ie, within normal SMI subgroup). All statistical tests were two-sided, and a P-value less than .05 was considered statistically significant. All analyses were performed using SAS 9.4 (SAS Institute, Cary, NC). This report follows the Strengthening the Reporting of Observational studies in Epidemiolgoy (STROBE) reporting guidelines (33).

Covariates and assessment of confounding. For all multivariable analyses, we a priori defined age at diagnosis, tumor stage, and residual disease as important adjustment factors given they are well-established prognostic factors for EOC. To identify additional confounders for adjustment in multivariable models, we applied established conceptual (34) and empirical methods for identifying confounders including the use of directed acyclic graphs (DAGs) (Supplementary Figure 1, available online) (35), the change-in-estimate method (36), and stepwise regression (37).

Potential covariates to be considered included self-identified race (White, Black, and Other, which included American Indian and/or Alaska Native, Asian, and Native Hawaiian and/or Other Pacific Islander), tumor grade at diagnosis, treatment regimen, diabetes, hypertension, smoking status, alcohol use, EOC histotype, disease dissemination pattern (ie, miliary vs non-miliary) (38), and presence of ascites; where appropriate, we also assessed the influence of mutually adjusting for remaining independent body composition indices, height, weight or BMI. Based on the DAG, several mediating factors were identified as colliders based on restricting the study design to women diagnosed with EOC. Among the putative classical confounders, final multivariable models were adjusted for age at diagnosis, tumor stage at diagnosis, surgical debulking status, tumor grade, smoking status, alcohol use, and SMI where appropriate; adjustment for self-identified race had no impact on observed estimates.

To assess potential bias from unmeasured confounding, we calculated the E-value associated with observed estimates from primary analyses (39). The E-value represents the minimum magnitude of association needed for unmeasured confounder(s) to have with both the exposure and the outcome to explain away observed associations (39). Lastly, we used standard diagnostic methods to detect departures from model assumptions that may have influenced our estimates, including examining residuals and log-log survival plots.

Results

Table 1 summarizes the descriptive and clinical characteristics of the study population. The average age of EOC diagnosis was 63 years, and most patients self-identified as White (91.8%). Additionally, most patients were diagnosed with advanced-stage (61.6%) and HGSOC (69.2%) tumors. Moreover, the majority of patients were overweight or obese according to BMI (69%), 32.6% had low SMI (sarcopenia) (31), and based on total adipose tissue phenotypes, 16.2% were classified as fit, 51.2% were classified as overweight or obese, 15.6% were classified as sarcopenia and overweight or obese, and 17% were classified as sarcopenia or cachexia. During the follow-up period, we observed 289 deaths; the median follow-up time for those not experiencing an event was 42 months.

Table 1.

Clinical and epidemiological characteristics of epithelial ovarian cancer patients in the Body Composition and Epithelial Ovarian Cancer Survival (BComES) Study

Clinical and descriptive characteristics	Overall Cohort	Vital status
	Overall Cohort	Alive	Deceased
	(n = 500)	(n = 211, 42.2%)	(n = 289, 57.8%)
	No. (%)^a	Mean (SD)/N (%)	Mean (SD)/N (%)
Age at diagnosis	63.0 (±11.4)	61.1 (±10.5)	64.3 (±11.8)
Race
White	459 (91.8%)	182 (41.8%)	267 (58.2%)
Black	19 (3.8%)	7 (36.8%)	12 (63.2%)
Other^b	22 (4.4%)	12 (54.6%)	10 (45.5%)
Stage
I	54 (10.8%)	42 (77.8%)	12 (22.2%)
II	52 (10.4%)	37 (71.2%)	15 (28.9%)
III	199 (39.8%)	57 (28.6%)	142 (71.4%)
IV	109 (21.8%)	36 (33.0%)	73 (67.0%)
Unknown	86 (17.2%)	39 (45.4%)	47 (54.7%)
Grade
Well differentiated or Low grade	31 (6.2%)	19 (61.3%)	12 (38.7%)
Moderately differentiated	217 (43.4%)	56 (25.8%)	161 (74.2%)
Poorly or undifferentiated or High grade	189 (37.8%)	102 (54.0%)	87 (46.0%)
Undetermined	63 (12.6%)	34 (54.0%)	29 (46.0%)
Histotype
High-grade serous	346 (69.2%)	135 (39.0%)	211 (61.0%)
Low-grade serous	13 (2.6%)	7 (53.9%)	6 (46.2%)
Clear cell	28 (5.6%)	14 (50.0%)	14 (50.0%)
Endometrioid	15 (3.0%)	11 (73.3%)	4 (26.7%)
Mucinous	15 (3.0%)	6 (40.0%)	9 (60.0%)
Mixed	59 (1.8%)	28 (47.5%)	31 (52.5%)
Other	24 (4.8%)	10 (41.7%)	14 (58.3%)
Surgical debulking status
Complete (R0)	61 (12.2%)	35 (57.4%)	26 (42.6%)
Optimal (≤1 cm)	199 (39.8%)	75 (37.7%)	124 (62.3%)
Suboptimal (>1 cm)	66 (13.2%)	10 (15.2%)	56 (84.9%)
Unknown	174 (34.8%)	91 (52.3%)	83 (47.7%)
Disease dissemination pattern
Miliary	203 (40.6%)	57 (28.1%)	146 (71.9%)
Non-miliary	230 (46.0%)	127 (55.2%)	103 (44.8%)
Unknown	67 (13.4%)	27 (40.3%)	40 (59.7%)
Ascites (Yes or No)
Yes	251 (50.2%)	77 (30.7%)	174 (69.3%)
No	228 (45.6%)	122 (53.5%)	106 (46.5%)
Unknown	21 (4.2%)	12 (57.1%)	9 (42.9%)
Chemotherapy regimen
Adjuvant	395 (79.0%)	164 (41.5%)	231 (58.5%)
Neoadjuvant	105 (21.0%)	47 (44.5%)	58 (55.2%)
Diabetes status
Yes	58 (11.6%)	23 (39.7%)	35 (60.3%)
No	442 (88.4%)	188 (42.5%)	254 (57.5%)
Hypertension status
Yes	200 (40.0%)	80 (40.0%)	120 (60.0%)
No	300 (60.0%)	131 (43.7%)	169 (56.3%)
Alcohol use
Current	207 (41.4%)	91 (44.0%)	116 (56.0%)
Former	13 (2.6%)	6 (46.2%)	7 (53.9%)
Never	252 (50.4%)	95 (37.7%)	157 (62.3%)
Unknown	28 (5.6%)	19 (67.9%)	9 (32.1%)
Smoking status
Current	102 (20.4%)	37 (36.3%)	65 (63.7%)
Former	127 (25.4%)	56 (44.1%)	71 (55.9%)
Never	267 (53.4%)	115 (43.1%)	152 (56.9%)
Unknown	4 (0.8%)	3 (75.0%)	1 (25.0%)
Body mass index (BMI)
Underweight (BMI <18.5 kg/m²)	9 (1.8%)	3 (33.3%)	6 (6.7%)
Normal weight (BMI = 18.5-24.9)	146 (29.2%)	64 (43.8%)	82 (54.2%)
Overweight (BMI = 25.0-29.9)	160 (32.0%)	68 (42.5%)	92 (57.5%)
Obese (BMI ≥30.0)	185 (37.0%)	76 (41.1%)	109 (58.9%)
Skeletal Muscle Index (SMI)
Low SMI or Sarcopenia (<38.5 cm²/m²)	163 (32.6%)	53 (32.5%)	110 (67.5%)
Normal SMI (≥38.5 cm²/m²)	337 (67.4%)	158 (46.9%)	179 (53.1%)
Intermuscular Adipose Tissue (IMAT) body composition phenotypes
Normal SMI & low IMAT	104 (20.8%)	58 (55.8%)	46 (44.2%)
Low SMI & low IMAT	62 (12.4%)	26 (41.9%)	36 (58.1%)
Normal SMI & high IMAT	233 (46.6%)	100 (42.9%)	133 (57.1%)
Low SMI & high IMAT	101 (20.2%)	27 (26.7%)	74 (73.3%)
Visceral Adipose Tissue (VAT) body composition phenotypes
Normal SMI & low VAT	87 (17.4%)	43 (49.4%)	44 (50.6%)
Low SMI & low VAT	79 (15.8%)	24 (30.4%)	55 (69.6%)
Normal SMI & high VAT	250 (50.0%)	115 (46.0%)	135 (54.0%)
Low SMI & high VAT	84 (16.8%)	29 (34.5%)	55 (65.5%)
Subcutaneous Adipose Tissue (SAT) body composition phenotypes
Normal SMI & low SAT	84 (16.8%)	36 (42.9%)	48 (57.1%)
Low SMI & low SAT	82 (16.4%)	29 (35.4%)	53 (64.6%)
Normal SMI & high SAT	253 (50.6%)	122 (48.2%)	131 (51.8%)
Low SMI & high SAT	81 (16.2%)	24 (29.6%)	57 (70.4%)
Total Adipose Tissue (TAT) body composition phenotypes
Normal SMI & low TAT	81 (16.2%)	39 (48.2%)	42 (51.9%)
Low SMI & low TAT	85 (17.0%)	29 (34.1%)	56 (65.9%)
Normal SMI & high TAT	256 (51.2%)	119 (46.5%)	137 (53.5%)
Low SMI & high TAT	78 (15.6%)	24 (30.8%)	54 (69.2%)

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The number (No.) and percentage (%) of EOC patients in each group for each variable except for age at diagnosis, wherein the mean age and standard deviation are provided.

Patients with “Other” race include self-identified American Indian and/or Alaska Native, Asian, and Native Hawaiian and/or Other Pacific Islander.

Figure 1 summarizes primary analyses representing associations of the overall at-risk joint-exposure phenotypes vs the fit phenotype with EOC mortality. We observed consistent evidence showing that the combined at-risk phenotypes were associated with significant increases in mortality in EOC overall and in HGSOC. For example, considering total adiposity, risk phenotypes were associated with 55% increased mortality in EOC overall (HR = 1.55, 95% CI = 1.09 to 2.19, P = .01) and 94% increased mortality in HGSOC (HR = 1.94, 95% CI = 1.26 to 2.99, P = .003).

Figure 2 summarizes primary analyses representing associations of the independent at-risk joint-exposure body composition phenotypes (overweight or obese, sarcopenia and overweight or obese, sarcopenia or cachexia) vs the fit phenotype with mortality. In EOC overall (Figure 2, A), we observed consistent evidence suggesting that the individual risk phenotypes were associated with increased mortality, and we observed no evidence of an obesity paradox for the high-adiposity phenotypes. For instance, the overweight or obese phenotype was associated with up to 51% increased mortality (normal SMI and high TAT: HR = 1.51, 95% CI = 1.05 to 2.19, P = .03); the sarcopenia and overweight or obese phenotype was associated with up to 66% increased mortality (low SMI and high IMAT: HR = 1.66, 95% CI = 1.13 to 2.45, P = .01); and the sarcopenia or cachexia phenotype was associated with up to a 73% increase in mortality (low SMI and low TAT: HR = 1.73, 95% CI = 1.14 to 2.63, P = .01).

In HGSOC (Figure 2, B), we also observed consistent evidence suggesting that the individual risk phenotypes were associated with increased mortality, and we observed no evidence of an obesity paradox in the high-adiposity phenotypes. For example, the overweight or obese phenotype was associated with up to 104% increased mortality (normal SMI and high TAT: HR = 2.04, 95% CI = 1.29 to 3.21, P = .002); the sarcopenia and overweight or obese phenotype was associated with up to 67% increased mortality (low SMI and high IMAT: HR = 1.67, 95% CI = 1.05 to 2.68, P = .03); and the sarcopenia or cachexia phenotype was associated with up to 109% increased mortality (low SMI and low TAT: HR = 2.09, 95% CI= 1.25 to 3.50, P = .005). Importantly, the calculated E-values associated with these associations ranged from 2.39, representing the association of normal SMI and high TAT with mortality in EOC overall, to 3.60, representing the association of low SMI and low TAT with mortality in HGSOC.

Figure 3 summarizes secondary analyses representing multivariable associations of higher vs low adiposity with mortality with adjustment for SMI. Using this approach, which does not exclude sarcopenic patients from the reference group, we observed virtually no associations of high vs low VAT, SAT, and TAT with mortality in EOC overall (Figure 3, A). However, there was a suggestion that higher vs low IMAT was associated with increased mortality (middle vs low tertile: HR = 1.43, 95% CI = 1.05 to 1.93, P = .02 and high vs low tertile: HR = 1.23, 95% CI = 0.90 to 1.68, P = .20).

In HGSOC patients (Figure 3, B), no significant associations were observed for SAT and TAT, but there was a suggestion that that higher vs low IMAT and VAT were associated with increased mortality. Specifically, moderate vs low IMAT was associated with a 56% increased mortality (HR = 1.56, 95% CI = 1.08 to 2.25, p = .02), but the CI for high vs low IMAT included unity (HR = 1.31, 95% CI = 0.90 to 1.91, P = .16). Additionally, higher vs low VAT was suggestively associated increased mortality (moderate vs low VAT: HR = 1.28, 95% CI = 0.89 to 1.84, P = .19 and high vs low VAT: HR = 1.37, 95% CI = 0.94 to 2.00, P = .10).

Lastly, Figure 4 summarizes associations of higher vs low adiposity with EOC mortality in subgroups according to normal SMI and low SMI (31). As shown in Figure 4, A, we observed consistent evidence that high vs low adiposity was associated with increased EOC mortality in patients with normal SMI. For example, highest vs lowest IMAT was associated with a 46% increase in mortality (HR = 1.46, 95% CI = 0.98 to 2.16; P for trend = .08), highest vs lowest VAT was associated with a 60% increase in mortality (HR = 1.60, 95% CI = 1.07 to 2.40; P for trend = .03), highest vs lowest SAT was associated with a 48% increase in mortality (HR = 1.48, 95% CI = 1.00 to 2.18; P for trend = .047), and highest vs lowest TAT was associated with a 64% increase in mortality (HR = 1.64, 95% CI = 1.10 to 2.46; P for trend = .01). Conversely, as shown in Figure 4, B, associations of highest vs lowest adiposity with EOC mortality in patients with low SMI were mostly near or below unity, suggestive of either no association or an obesity paradox.

Lastly, for HGSOC patients with normal SMI (Figure 4, C), the associations of higher vs low adiposity with increased mortality were more robust. For example, we observed significant increases in mortality for patients with highest vs lowest IMAT (HR = 1.81, 95% CI = 1.12 to 2.90; P for trend = 0.02), highest vs lowest VAT (HR = 2.30, 95% CI = 1.42 to 3.72; P for trend = .001), highest vs lowest SAT (HR = 2.12, 95% CI = 1.30 to 3.44; P for trend = .003), and highest vs lowest TAT (HR = 2.35, 95% CI = 1.43 to 3.85; P for trend < .001). Conversely, in HGSOC patients with low SMI (Figure 4, D), associations of higher vs lower adiposity with mortality were suggestive of either no association with mortality for IMAT or an overweight or obesity paradox for VAT, SAT, and TAT.

Discussion

In this investigation of the association of body composition before chemotherapy with EOC mortality, we uncovered exciting new translational knowledge revealing that three “risk” body composition phenotypes (overweight or obese, sarcopenia and overweight or obese, and sarcopenia or cachexia) were associated with increased mortality in EOC and HGSOC. Importantly, through a sequence of secondary analyses, we demonstrated that excluding patients with low muscle mass from the reference group eliminated any suggestion of an obesity paradox and was essential for observing associations of excess adiposity with increased mortality.

Our findings showing that high vs low adiposity is associated with increased EOC mortality when sarcopenic patients are removed from the reference group conflicts with the extant epidemiological evidence, including data from the most recent meta-analysis of image-assessed adiposity, which showed a consistent suggestion of an obesity paradox for patients with ovarian cancer (8). For example, summary estimates from Cheng et al. show that highest vs lowest visceral adiposity was suggestively associated with decreased hazards of mortality (HR = 0.53, 95% CI = 0.11 to 2.66) and disease progression (HR = 0.76, 95% CI = 0.44 to 1.30). Similar associations were seen for highest vs lowest subcutaneous adiposity for mortality (HR = 0.65, 95% CI: = 0.19 to 2.19) and disease progression (HR = 0.76, 95% CI = 0.58 to 1.00) (8). However, this report and others have not jointly considered adiposity and skeletal muscle mass, a potentially independent prognostic factor for EOC (22-28). To this end, although some prior evidence suggests that low SMI is associated with worse EOC survival and normal SMI is associated with improved survival (14-21), three of the most recent reports of the independent associations of sarcopenia with EOC mortality were mostly inconclusive (16, 20, 21).

Our observation that the overweight or obese phenotype was strongly associated with increased mortality in EOC overall and in HGSOC is consistent with preclinical evidence demonstrating that obesity is associated with worse tumor outcomes. For instance, preclinical evidence using a variety of animal models for ovarian cancer and other solid tumors demonstrates that host obesity and physical inactivity are associated with an immunosuppressed tumor immune microenvironment (TIME), leading to increased tumor aggressiveness and worse outcomes (40-47).

To this end, our group has also previously reported that a lifestyle of physical inactivity before EOC diagnosis was associated with increased EOC mortality (48,49). Although the biological intermediaries underlying this association have yet to be elucidated, epidemiological paradigms center on body composition and immune and/or inflammatory pathways (50,51). Interestingly, for both EOC and HGSOC, the overweight or obese phenotype for IMAT and VAT were more robustly associated with mortality than the overweight or obese phenotype for SAT. This finding is noteworthy given that IMAT and VAT are known to be preferentially reduced with exercise (52), are more proximal to ovarian tumors, and may be associated with metabolic and immune dysregulation leading to chronic systemic inflammation and altered composition of the TIME, favoring tumor progression and more aggressive disease (41,3-63). Conversely, SAT may possess less inflammatory adipose tissue, is distally located to ovarian tumors, may serve as a nutrient reserve in times of stress and illness (the hibernation hypothesis), and may be more strongly associated with less aggressive EOC histotypes that have a better prognosis (6,54,61,64).

Nevertheless, the pathways mediating the observed associations of joint-exposure body composition phenotypes with EOC mortality are likely complex and multifactorial and have yet to be elucidated in patient populations. However, as body composition is known to be modifiable and the current report is based on body composition phenotypes before chemotherapy, we identified a modifiable exposure that could be targeted ancillary to standard therapies (65) to improve outcomes in EOC and HGSOC—the most common and fatal gynecological malignancy with no previously well-established modifiable prognostic factors (66-70). As shown in Figure 5, all patients undergoing cancer treatment with curative intent, regardless of body composition phenotype, are encouraged to meet the physical activity and dietary guidelines recommended for cancer survivors (65, 71-73). However, in the Comprehensive Cancer Center setting, patients suspected of low skeletal muscle mass (sarcopenia) may also benefit from medical nutrition therapy and an evaluation by a medical professional (ie, physical therapist or physiotherapist) or someone certified in exercise oncology to administer an exercise program (74,75).

Figure 5. — A summary of clinical and lifestyle interventions for improving EOC outcomes according to body composition phenotype in the peri-diagnosis period. A graphical schematic representing tailored clinical and lifestyle interventions for improving epithelial ovarian carcinoma (EOC) outcomes according to body composition phenotype. Summarized here are the prevalence of each body composition phenotype and associated increases in mortality in high-grade serous ovarian carcinoma (HGSOC) patients in the Body Composition and Epithelial Ovarian Cancer Survival Study (BComES). All patients receiving treatment with curative intent are encouraged to meet the Physical Activity Guidelines (PAGs) for aerobic and strength training and the American Institute for Cancer Research (AICR) lifestyle recommendations for cancer prevention. However, patients with low-muscle phenotypes may also benefit from physical therapy and medical nutrition therapy.

A potential limitation of our study includes reliance on manually segmented body composition at the third lumbar vertebra, which may not be representative of whole-body IMAT. However, automated frameworks have been shown to underestimate adiposity and associations with cancer outcomes (76), and many have not yet been optimized to assess IMAT, a potentially important prognostic factor reflecting muscle quality at L3. Nonetheless, in analyses ancillary to the current report, our group generated validity data showing that manually segmented BComES data strongly agrees with muscle, SAT, and VAT quantities derived from TotalSegmentator, a powerful pretrained deep learning model for volumetric 3D CT segmentation (77,78), and an nnU-Net algorithm (79,80) trained to assess muscle, SAT, VAT, and IMAT. As shown in Supplementary Figure 2 (available online), mean Dice coefficients (81), a metric that quantifies overlap between manual and machine learning outputs, were as high as 0.95 for muscle, 0.98 for SAT, 0.95 for VAT, and 0.78 for IMAT in our initial training set. We also acknowledge that emerging data suggest that both the quantity and quality (radiodensity) of skeletal muscle and adipose tissue may be important prognostic factors for cancer (17,82). However, the focus of the current report was cross-sectional tissue quantity at L3.

Moreover, although the sarcopenia or cachexia and overweight or obese phenotypes were consistently associated with significantly increased mortality relative to the fit phenotype, we cannot entirely account for factors contributing to somewhat attenuated associations of the sarcopenia and overweight or obese phenotype with mortality. However, this phenotype was the least prevalent among the four phenotypes, potentially contributing to suboptimal power to detect significant associations for this group. We also noted in sensitivity analyses that the sarcopenia or cachexia group had significantly lower mean BMI than the sarcopenia and overweight or obese group (22.4 [2.6] vs 28.6 [4.9]; P < .001), and the overweight or obese group had significantly higher BMI than the sarcopenia and overweight or obese group (33.14 [6.4] vs 28.6 [4.9]; P < .001). As the BMI-mortality relationship is known to be U-shaped (ie, the highest rates of mortality are seen for lower and higher BMI), this may explain why we observed slightly attenuated associations in the sarcopenia and overweight or obese group. However, it is important to note that this finding does not suggest that the sarcopenic and overweight or obese phenotype is protective. Rather, in comparison to the fit phenotype, the hazard ratios associated with the sarcopenia and overweight or obese phenotype were consistently above unity (ie, 30%-67% increased mortality).

Additionally, as the BComES Study includes a homogeneous population of EOC patients, these findings may not be generalizable to more demographically or clinically diverse populations. For example, to decrease the likelihood of a reverse causation bias and the potential influence of treatment toxicities or chemotherapy dose reductions on our findings, we focus on patients who completed chemotherapy without significant treatment delays. Therefore, it is possible that patients who were too sick to begin or complete chemotherapy were more likely to have at-risk body composition phenotypes and more rapidly fatal disease, which would strengthen our reported associations. Additionally, as most patients in the BComES cohort were diagnosed with HGSOC, we were not powered to examine the relationships of body composition phenotypes with mortality in patients diagnosed with less common EOC histotypes, including low-grade serous, endometrioid, mucinous, or clear cell tumors.

Furthermore, we cannot entirely rule out the possibility that residual confounding from measured or unmeasured factors influenced our observations. However, the calculated E-values for each of the primary findings reported in the results ranged from 2.39 to 3.60. Given that HRs of 2- and 3-fold are not commonly observed in biomedical literature as a whole, or in ovarian cancer epidemiology (32,49,83), an unmeasured variable that affects both the exposure and the outcome of interest by this magnitude would be even less common (39,84).

Lastly, all-cause mortality was the primary analytic outcome of interest because verification for cause-specific death is available only for patients who died at Roswell Park. However, as prior evidence from the Ovarian Cancer Association Consortium suggests, most EOC patients die from their disease (32), making all-cause mortality an excellent proxy for EOC-specific mortality.

Primary strengths of our study include the large, well-characterized population of EOC patients with detailed follow-up data from an accredited Cancer Registry and our comprehensive approach to analysis, including the ability to account for skeletal muscle mass and intermuscular adiposity in our analyses. Furthermore, the observed associations of overweight or obesity and sarcopenia or cachexia with increased EOC mortality were robust to adjustment for relevant confounders and prognostic factors and, based on the calculation of E-values, would also be robust to confounding caused from unmeasured factors. Additionally, we used well-established previously validated methods for quantification of body composition (29). Importantly, the temporality of body composition exposure data from before chemotherapy enhances the translational significance of this work by identifying a modifiable prognostic factor that could be targeted ancillary to standard therapies to improve outcomes for a highly fatal disease.

In this study of the association of body composition with EOC mortality, we observed compelling evidence that overweight or obese, sarcopenia or cachexia, and sarcopenia and overweight or obese joint-exposure body composition phenotypes before chemotherapy were associated with increased mortality in EOC and HGSOC. Additionally, we observed no evidence of an obesity paradox when sarcopenic patients were removed from the reference group of analyses. Future work aimed at elucidating the association of excess adiposity with cancer mortality in EOC and beyond should appropriately account for low skeletal muscle mass by excluding sarcopenic patients from the reference group. Importantly, as body composition is known to be modifiable (52,66-70,85-87), targeted interventions including exercise and dietary interventions (65) can be leveraged as safe, noninvasive, and feasible ancillary strategies for improving clinical outcomes in patients diagnosed with the most fatal gynecological cancer in the United States with no previously well-established modifiable prognostic factors.

Supplementary Material

djae112_Supplementary_Data

djae112_supplementary_data.pdf^{(188.8KB, pdf)}

Acknowledgments

Xavier Williams, Visual Translation Specialist at Roswell Park, created Figure 5 based on a concept developed by RC. Linda K. Leising, BS, RDN, CSO, CDN, Senior Clinical Dietitian at Roswell Park, and Sara M. Jank, MS, RDN, CDN, Clinical Dietitian at Roswell Park, consulted with RC to identify appropriate interventions for patients with low SMI.

Prior publication relating to the current report: We published the below cited abstract reporting the associations of body composition phenotype in the peri-diagnosis period with mortality in high-grade serous patients. Multivariable models reported in the abstract were adjusted for age, tumor stage, and surgical debulking status.

Evan W. Davis, Nancy Barone, Charles Roche, Gyorgy Paragh, André Klein, Rikki Cannioto; Abstract 3403: Disentangling the obesity paradox in high-grade serous epithelial ovarian cancer. Cancer Res. 2024;84(6 Suppl):3403. https://doi.org/10.1158/1538-7445.AM2024-3403.

The role of the funder: The funders had no role in the design of the study, the collection of data, the analysis of data, the interpretation of the data, the writing of the manuscript, or the decision to submit the manuscript for publication.

Contributor Information

Evan W Davis, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Kristopher Attwood, Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Joseph Prunier, Lake Erie College of Osteopathic Medicine, Elmira, NY, USA.

Gyorgy Paragh, Department of Dermatology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Janine M Joseph, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

André Klein, Department of Research Information Technology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Charles Roche, Department of Diagnostic Radiology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Nancy Barone, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

John Lewis Etter, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.

Andrew D Ray, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA; Department of Rehabilitation, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Britton Trabert, Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT, USA; Huntsman Cancer Institute at the University of Utah, Cancer Control and Population Sciences, Salt Lake City, UT, USA.

Matthew B Schabath, Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Lauren C Peres, Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Rikki Cannioto, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Data availability

The data supporting the findings of this study are not publicly available but may be shared upon reasonable request in a deidentified format for use in meta-analysis or pooled analyses. All data requests should be addressed to the corresponding author (rikki.cannioto@roswellpark.org) and will be dependent on an approved data sharing agreement with Roswell Park Comprehensive Cancer Center.

Author contributions

Evan W. Davis, MPH (Data curation; Formal analysis; Investigation; Methodology; Software; Validation; Visualization; Writing—original draft; Writing—review & editing), Kristopher Attwood, PhD (Data curation; Visualization; Writing—review & editing), Joseph Prunier, MS (Data curation; Writing—review & editing), Gyorgy Paragh, MD, PhD (Writing—review & editing), Janine M. Joseph, MS, MBA (Writing—review & editing), André Klein, PhD (Writing—review & editing), Charles Roche, MD (Data curation; Writing—review & editing), Nancy Barone, BS (Data curation; Project administration; Writing—review & editing), John Lewis Etter, PhD (Writing—review & editing), Andrew D. Ray, PT, PhD (Writing—review & editing), Britton Trabert, PhD (Writing—review & editing), Matthew B. Schabath, PhD (Writing—review & editing), Lauren C. Peres, PhD, MPH (Writing—review & editing), Rikki Ann Cannioto, PhD, EdD, MS (Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—original draft; Writing—review & editing)

Funding

This work was supported by Roswell Park Comprehensive Cancer Center and National Cancer Institute (NCI) grant P30CA016056, the Roswell Park - University of Pittsburgh Cancer Institute Ovarian Cancer SPORE 5P50CA159981, and the Roswell Park - University of Chicago Ovarian Cancer SPORE 5P50CA159981.

Conflicts of interest

E.W.D., K.A., G.P., J.M.J., A.K., C.R., A.D.R., J.P., N.B., J.L.E., and R.C. report no conflicts.

L.C.P. reports research funding unrelated to the current report from Bristol Myers Squibb and Karyopharm.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

djae112_Supplementary_Data

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Data Availability Statement

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PERMALINK

The association of body composition phenotypes before chemotherapy with epithelial ovarian cancer mortality

Evan W Davis, MPH

Kristopher Attwood, PhD

Joseph Prunier, MS

Gyorgy Paragh, MD, PhD

Janine M Joseph, MS, MBA

André Klein, PhD

Charles Roche, MD

Nancy Barone, BS

John Lewis Etter, PhD

Andrew D Ray, PT, PhD

Britton Trabert, PhD

Matthew B Schabath, PhD

Lauren C Peres, PhD

Rikki Cannioto, PhD, EdD

Roles

Abstract

Background

Methods

Results

Conclusions

Methods

Study population and data collection

Body composition assessment and parameterization

Outcome assessment

Statistical analysis

Results

Table 1.

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Discussion

Figure 5.

Supplementary Material

Acknowledgments

Contributor Information

Data availability

Author contributions

Funding

Conflicts of interest

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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