Next Article in Journal
The Combination Effect of the Red Blood Cell Distribution Width and Prognostic Nutrition Index on the Prognosis in Patients Undergoing PCI
Next Article in Special Issue
Correlation between the Quantity and Type of Dietary Fiber with the Activity in Mexican Patients with Ulcerative Colitis (UC)
Previous Article in Journal
Royal Jelly Exerts a Potent Anti-Obesity Effect in Rats by Activating Lipolysis and Suppressing Adipogenesis
Previous Article in Special Issue
Impact of Omega-3 Fatty Acid Supplementation in Parenteral Nutrition on Inflammatory Markers and Clinical Outcomes in Critically Ill COVID-19 Patients: A Randomized Controlled Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 18
10.3390/nu16183175
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer

by
Agnes Denise de Lima Bezerra
1,
Jarson Pedro da Costa Pereira
2,
Ingryd Fernandes de Macedo Soares
3,
Glaucia Mardrini Cassiano Ferreira
1,
Ana Lúcia Miranda
1,
Galtieri Otávio Cunha de Medeiros
1,
Sara Maria Moreira Lima Verde
3 and
Ana Paula Trussardi Fayh
1,4,*
1
Postgraduate Program in Health Sciences, Health Sciences Centre, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
2
Postgraduate Program in Nutrition, Department of Nutrition, Federal University of Pernambuco, Recife 50670-901, Brazil
3
Postgraduate Program in Nutrition and Health, State University of Ceara, Fortaleza 60714-903, Brazil
4
PesqClin Lab, Onofre Lopes University Hospital, Brazilian Company of Hospital Services (EBSERH), Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
*
Author to whom correspondence should be addressed.
Nutrients 2024, 16(18), 3175; https://doi.org/10.3390/nu16183175
Submission received: 23 August 2024 / Revised: 18 September 2024 / Accepted: 19 September 2024 / Published: 19 September 2024
(This article belongs to the Special Issue Inflammation and Nutritional Therapy)
Browse Figures
Versions Notes

Abstract

:
Background/Objectives: Increasing evidence indicates that body composition can significantly influence prognosis in women with breast cancer. However, alterations in body composition, particularly among young women (<40 years), remain largely unknown and underexplored. This study aimed to investigate the relationship of computed tomography (CT)-derived body composition with mortality rates among young women recently diagnosed with breast cancer, identifying the best-correlated cutoff value. Methods: This is a bi-set cohort study with retrospective data collection. Women newly diagnosed with ductal invasive breast cancer, aged 20 to 40 years, treated in reference oncology units were included. Body composition was assessed using CT scans at the third lumbar vertebra (L3) level, including muscle and adipose compartments. The outcome of interest was the incidence of overall mortality. A maximally selected log-rank Cox-derived analysis was employed to assess the cutoffs associated with mortality. Results: A total of 192 women were included before any form of treatment (median age of 35 years, IQ range: 31–37). Overall mortality occurred in 12% of the females. Stages III–IV were the most frequent (69.5%). Patients who died had a significantly lower muscle area index. CT-derived muscle area was inversely associated with mortality. Each 1 cm2/m2 decrease in skeletal muscle index increased the mortality hazard by 9%. Higher values of adiposity compartments were independently associated with higher mortality. Conclusions: Our study highlights the predictive significance of skeletal muscle area and adipose tissue in predicting survival among young women recently diagnosed with breast cancer.

Graphical Abstract

1. Introduction

Breast cancer is the most prevalent type of cancer worldwide, significantly affecting females in Brazil [1,2]. Breast cancer contributes to mortality rates, with a risk equivalent to 16.5 deaths per 100 thousand women [1,2,3]. The incidence of breast cancer is rising due to several factors, including demographic aging, overdiagnosis consequent to excessive screening, and lifestyle factors, such as excess weight and its features [4,5]. Evidence indicates that excess weight or weight gain following diagnosis correlates with elevated risks of breast cancer mortality, all-cause mortality, and cancer recurrence [6,7,8].
Although evidence often connects aging and excess weight to both breast cancer development and specific mortality [8,9], a troubling trend has emerged: the rising incidence of breast cancer among young women [10,11,12]. In contrast, there is a paradoxical debate suggesting that excess weight (e.g., overweight or obesity) may be associated with a more favorable prognosis in this clinical population [13,14]. These results are controversial because the impact of obesity may vary depending on the stage of breast cancer disease (early vs. advanced). One study found that obesity was associated with improved overall survival in advanced breast cancer, yet correlated with poorer overall survival in early-stage disease [14]. However, most studies use body mass index (BMI) to diagnose obesity [14]. While BMI is a widely used indicator of adiposity, it does not differentiate between fat and skeletal muscle (SM) or account for body fat distribution [15]. BMI may overlook critical aspects of body composition, such as muscle depletion or excess visceral fat, both of which may significantly impact cancer outcomes [15]. It is also recognized that individuals with excess weight (higher BMI) may develop greater SM mass to support the added weight [15]. This increased SM could partially explain the paradoxical effect observed in some studies [15].
In this context, the relationship of body composition with breast cancer survival remains to be explored [16,17]. This is particularly relevant among young women diagnosed with breast cancer, who are often assumed to be at their peak body composition [18]. While body composition abnormalities are more commonly associated with aging, they can also occur at younger ages [18]. Although much of the research has focused on older populations, growing evidence suggests that these abnormalities may predict poor outcomes regardless of age [19]. This is critical, given the significant metabolic roles that both muscle and fat compartments play, potentially affecting treatment response and overall prognosis [20].
Given that young women with the same BMI can exhibit significant differences in body composition [21], such an evaluation becomes particularly relevant. In oncology, patients can benefit from opportunistic body composition assessment through computed tomography (CT), which provides precise measurements of various body compartments, including SM quantity, muscle composition, and adipose tissue distribution [22,23,24]. This method allows for a more comprehensive understanding of how body composition impacts cancer prognosis, beyond the limitations of BMI alone. As mentioned before, muscle and adipose tissues may be relevant predictors of prognosis in cancer, affecting clinical outcomes such as chemotoxicity and mortality [25,26].
Although previous studies have included a wide age range and demonstrated that body composition abnormalities can affect clinical outcomes in cancer regardless of age, there remains a significant gap in studies focusing on younger patients newly diagnosed with breast cancer. This lack of targeted studies limits our understanding of how body composition influences prognosis in this population, highlighting the need for further investigation in this area. We hypothesized that body composition abnormalities could predict poor survival, even in younger populations with breast cancer. If our hypothesis is confirmed, this detection could potentially facilitate the development of timely and tailored interventions aimed at enhancing prognosis. Thus, our study aimed to investigate the relationship between CT-derived body composition parameters and mortality rates among young women recently diagnosed with breast cancer, in addition to identifying the best cutoff values that predict this outcome.

2. Methods

2.1. Study Design and Subjects

This is a cohort study with retrospective data collection. A cohort of young females, aged between 20–40 years, newly diagnosed with invasive ductal breast cancer (stages I to IV) were conveniently sampled. They were under outpatient care in two reference oncology centers in Northeast Brazil. An additional inclusion criterion was the availability of abdominal CT scans for diagnosis or staging, without a previous history of cancer. Data collection occurred between January 2022 and July 2023. Patients with clinical or anatomical conditions that hindered CT evaluations were excluded from this analysis (e.g., ascites or anasarca). Those with missing anthropometric data in electronic medical records, CT scans > 90 days from the date of diagnosis, and patients who already underwent any type of treatment were also excluded. This study initially screened 262 women potentially eligible. A total of 70 participants were excluded due to missing data, and for presenting CT > 90 days from the date of diagnosis. A flowchart of patients’ selection is presented in Figure 1. This study was approved by the institutions’ Research Ethics Committees (CAAE: 43488621.4.0000.5528). Informed consent was waived.

2.2. Outcomes and Covariates

Sociodemographic, clinical, and anthropometric information was obtained from the electronic medical records. Data on age, staging, histological subtype, hormonal receptors, and long-term mortality were registered for analysis. On anthropometry, body weight (kg) and height (m) were registered to calculate BMI (kg/m2), classified according to the World Health Organization [27]. Anthropometric data were measured by the nutrition team at the respective units.

2.3. Body Composition from Computed Tomography Scans

Body composition was assessed at the third lumbar vertebra (L3) level, which is commonly used in oncology studies due to its strong correlation with whole-body composition and its reliability in evaluating muscle and adipose tissue [22]. L3 images were analyzed using the Slice-O-Matic software (v.4.3, Tomovision®, Montreal, QC, Canada), which allows specific tissue demarcation to be specified using Hounsfield unit thresholds: from −29 to +150 for SM (psoas, erector spinal, quadratus lumborum, transversus abdominis, internal and external obliques, rectus abdominis), from −150 to −50 for visceral adipose tissue (VAT), and from −190 to −30 for subcutaneous and intramuscular adipose tissue (SAT and IMAT, respectively). Total adipose tissue (TAT) was determined by the sum of the VAT, SAT, and IMAT measures. The skeletal muscle index (SMI) was determined by the SMA value, corrected by height in m2. All CT images were analyzed by a single trained researcher with anatomical expertise (G. O. C. M.). The intraclass correlation coefficient (ICC) and its 95% confidence interval (CI) were assessed using the same 15 images, analyzed over six distinct months. This evaluation was based on a single measurement, employing the absolute agreement, 2-way mixed-effects model. The ICC and CI for repeated measurements were 1.0 (95% CI: 1.0–1.0).

2.4. Statistical Analyses

Data were analyzed using the R studio version 4.3.2, and the Statistical Package for the Social Sciences (SPSS), version 20.0 (SPSS Inc., Chicago, IL, USA). Data normality was assessed using the Shapiro–Wilk test. Normally distributed data were described using mean ± standard deviation (SD), compared through independent Student’s t-tests. Non-normally distributed data were described using medians and interquartile (IQ) ranges, compared through the Mann–Whitney U test. Categorical variables were described as absolute (N) and relative (%) frequencies, compared using Pearson’s χ2 test or Fisher’s exact test. Adhering to Schoenfeld residual assumptions, Cox proportional hazards analysis was conducted to investigate the relationship between body composition and mortality. The body composition variables that independently correlated with mortality (adjusted hazards) underwent maximally selected log-rank analysis to determine optimal cutoff points. Adjustments were based on the prognostic criterion (TNM stage) and variations in BMI, to minimize the influence of body size variations. To estimate the mortality risk associated with a decrease in body composition parameters (continuous variable), the reciprocal of the hazard ratio (1/HR) was calculated. Statistical significance was established at p < 0.05 for all analyses.
Although it is a debatable approach, a post-hoc sample power analysis was also conducted using R Studio version 4.3.2. This analysis was based on the significant differences in SMI between survivors (47.1 ± 7.4 cm2/m2) and non-survivors (42.5 ± 8.0 cm2/m2), with sample sizes of 169 and 23, respectively. Effect size was calculated using Cohen’s method, and the test was performed with a 5% significance level and a two-tailed approach. The results indicated a power (1 − β) of 76.2%.

3. Results

A total of 192 young women were included in this analysis (Figure 1). Most of them identified as non-Caucasian (85.4%) and lived with a partner (61.9%). The median age at the time of diagnosis was 35 years (IQ range: 31–37). Up to 6 years of follow-up, the mortality incidence occurred in 12% (n = 23). A detailed overview of the sociodemographic and clinical features of the patients is provided in Table 1. Table 2 presents the nutritional assessment of patients, complemented by visual representation in Figure 2. The patients who died presented with both lower SM (cm2) (p = 0.009) and SMI (cm2/m2) (p = 0.015), with no differences for the other body composition compartments.
Table 1. Characteristics of young females newly diagnosed with breast cancer (N = 192).
Table 1. Characteristics of young females newly diagnosed with breast cancer (N = 192).
VariablesTotalSurvivorsNon-Survivorsp
N, %192169 (88.0)23 (12.0)
Ethnicity, n (%) 0.09
Caucasian28 (14.6)22 (78.6)6 (21.4)
Non-Caucasian164 (85.4)147 (89.6)17 (10.4)
Educational level, n (%) 0.84
Elementary66 (35.5)57 (86.4)9 (13.6)
Secondary85 (45.7)76 (89.4)9 (10.6)
Post-secondary35 (18.8)31 (88.6)4 (11.4)
Marital status, n (%) 0.51
With partner117 (61.9)102 (87.2)15 (12.8)
No partner72 (38.1)65 (90.3)7 (9.7)
Age at diagnosis, y (median IQR)35 (31–37)35 (31–37)34 (31–37)0.63
Menarche, y (median IQR)13 (12–14)13 (12–14)13 (11.5–14.5)0.89
Presence of comorbidities, n (%)
Type 2 diabetes mellitus3 (1.6)3 (100.0)0 (0.00)0.52
Hypertension21 (89.1)18 (85.7)3 (14.3)0.73
Dyslipidemia3 (1.6)3 (100.0)0 (0.00)0.52
TNM stage, n (%) 0.014
I and II58 (30.5)56 (96.6)2 (3.4)
III and IV132 (69.5)112 (84.8)20 (15.2)
Lymph node status at diagnosis, n (%) 0.046
Positive153 (79.9)129 (85.4)22 (14.6)
Negative36 (19.0)36 (100)0 (0.0)
Unknown2 (1.1)1 (50.0)1 (50.0)
Estrogen receptor status, n (%) 0.30
Positive112 (60.5)102 (91.1)10 (8.9)
Negative73 (39.5)63 (86.3)10 (13.7)
Progesterone receptor status, n (%) 0.20
Positive107 (58.2)98 (91.6)9 (8.4)
Negative77 (41.8)66 (85.7)11 (14.3)
HER2 status, n (%) 0.64
Positive61 (33.0)55 (90.2)6 (9.8)
Negative124 (67.0)109 (87.9)15 (12.1)
Immunophenotype, n (%) 0.77
Luminal A14 (8.9)13 (12.9)1 (7.1)
Luminal B54 (34.2)48 (88.9)6 (11.1)
Overexpression of ER2-HER2+15 (9.5)42 (89.4)5 (10.6)
Triple-negative47 (29.7)14 (93.3)1 (6.7)
Unknown28 (17.7)23 (82.1)5 (17.9)
Ki67 status, n (%) 0.75
≤2045 (28.5)39 (86.7)6 (13.3)
>20113 (71.5)100 (88.5)13 (11.5)
Data in frequency (n, %—compared using χ2 test or Fisher’s exact test) or median and IQR: interquartile range (compared using Mann–Whitney U test). Notes and sample size: educational level (n = 186); estrogen receptor status (n = 185); HER2: human epidermal growth factor receptor 2 (n = 185); immunophenotype (n = 158); ki67 status (n = 158); lymph nodes (n = 189); marital status (n = 189); menarche (n = 181); progesterone receptor status (n = 184). Bold is used to emphasize significant p-values.
Table 2. Anthropometric and body composition assessment of young females newly diagnosed with breast cancer (N = 192).
Table 2. Anthropometric and body composition assessment of young females newly diagnosed with breast cancer (N = 192).
VariablesTotalSurvivorsNon-Survivorsp
BMI (kg/m2), (median IQR)26.2 (23.5–29.8)26.2 (23.5–29.7)27.0 (21.7–30.1)0.80 a
Underweight, n (%)3 (1.6)3 (1.8)0 (0.0)0.77 b
Normal range, n (%)71 (37.0)61 (36.1)10 (43.5)
Overweight, n (%)72 (37.5)65 (38.5)7 (30.4)
Obesity, n (%)46 (24.0)40 (23.7)6 (26.1)
CT-derived body composition, (median IQR)
SAT (cm2)190.3 (142.7–259.3)189.0 (142.5–254.6)235.9 (149.3–283.8)0.22 a
VAT (cm2)67.5 (42.4–107.5)67.6 (39.3–106.7)67.5 (43.6–116.9)0.90 a
TAT (cm2)276.3 (197.7–367.5)272.9 (198.3–367.4)319.0 (183.8–384.0)0.36 a
IMAT (cm2)7.25 (4.57–10.01)7.24 (4.57–9.99)7.36 (4.44–11.05)0.87 a
SMD (HU)38.2 (35.3–43.1)38.4 (35.4–43.1)37.8 (34.6–41.2)0.25 a
SM (cm2)114.3 ± 18.8115.6 ± 18.1104.8 ± 21.10.029 c
SMI (cm2/m2)46.5 ± 7.647.1 ± 7.442.5 ± 8.00.015 c
a Mann–Whitney U test (median and IQR: interquartile range); b Pearson’s χ2 test (frequencies, N %); c independent Student’s t-test (mean ± standard deviation). Abbreviations: BMI: body mass index; CT: computed tomography; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; TAT: total adipose tissue; IMAT: intramuscular adipose tissue; SMD: skeletal muscle radiodensity in Hounsfield Units (HU); SM: skeletal muscle; SMI: skeletal muscle index in cm2/m2. Bold is used to emphasize significant p-values.
Nutrients 16 03175 g002
Figure 2. Comparative analysis of CT-based body composition between young women (20–40 years) with breast cancer who survived (blue plots) and those who did not survive (red plots). (A) Means muscle compartments: SM, SMI, SMD, IMAT. (B) Means fat compartments: SAT, VAT, TAT. Mann–Whitney U test for medians, independent Student’s t-test for means. Abbreviations: SM: skeletal muscle; SMI: skeletal muscle index in cm2/m2; SMD: skeletal muscle radiodensity in Hounsfield Units (HU); IMAT: intramuscular adipose tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; TAT: total adipose tissue.
Figure 2. Comparative analysis of CT-based body composition between young women (20–40 years) with breast cancer who survived (blue plots) and those who did not survive (red plots). (A) Means muscle compartments: SM, SMI, SMD, IMAT. (B) Means fat compartments: SAT, VAT, TAT. Mann–Whitney U test for medians, independent Student’s t-test for means. Abbreviations: SM: skeletal muscle; SMI: skeletal muscle index in cm2/m2; SMD: skeletal muscle radiodensity in Hounsfield Units (HU); IMAT: intramuscular adipose tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; TAT: total adipose tissue.
Nutrients 16 03175 g002
Cox regression analysis using continuous variables demonstrated that only SM, SMI, SAT, and TAT were independently associated with mortality. Each decrease in one cm2 and cm2/m2 of SM and SMI, respectively, were associated with a 3–9% increase in the hazard of mortality. In addition, higher values of both SAT and TAT were independently associated with a high mortality rate. A statistical tendency towards SMD was observed, and it also underwent maximally selected log-rank cutoffs. Table 3 illustrates detailed cutoffs and hazard coefficients for mortality.

4. Discussion

This study is the first to explore associations between CT-based body composition and mortality in a cohort of young women newly diagnosed with breast cancer in Latin America, providing survival-specific cutoff values related to body composition. Our results show that increases in both SM and SMI independently decrease the hazard of mortality, while increases in SAT and TAT are associated with a higher mortality risk. These preliminary findings emphasize the prognostic importance of body composition in predicting survival outcomes in young women with breast cancer.
Our findings are consistent with previous studies showing that low SM and excess adiposity are linked to higher mortality in breast cancer patients, regardless of the TNM stage [25]. While this relationship is well-established in older populations, our study highlights that similar associations hold true for younger women. Although our hazard estimates for mortality associated with adiposity were lower, this may be due to the relatively small sample size. Nevertheless, even small risks could have clinical relevance, reinforcing the importance of addressing body composition abnormalities.
Based on our body composition results, our study reinforces the inadequacy of relying solely on BMI for nutritional assessment, as it overlooks subtle or critical variations in body composition. Although only three patients were classified as underweight and none died, more than 60% were categorized as overweight or with obesity. Despite the ‘obesity paradox’ reported in similar populations [13,28], our findings show that low SM and higher adiposity are independent risk factors for mortality, even among those with higher body mass. Therefore, the early detection of body composition abnormalities could significantly improve survival outcomes and nutritional status.
Our additional results identified new mortality-related cutoffs for SM (<94.2 cm2) and SMI (<39.0 cm2/m2), closely aligning with previously established thresholds in patients with breast cancer [26,29]. However, our study exclusively focuses on young women aged 20–40, a population underrepresented in previous research. The validation of cutoff values specific to this demographic is important, as social, epidemiological, and biological factors can vary by age. The consistency of our results with prior studies supports potential generalizability to similar populations. In addition to SMI, we evaluated SM in absolute terms (cm2), which is rarely reported without height adjustment. This approach introduces a novel dimension to assessing low SM concerning mortality. However, SM alone may not fully account for individual anatomical variations, and further studies are needed to refine this method.
Few studies have examined the relationship between body composition and survival in patients with breast cancer, most of them focusing on metastatic disease or mixed-age populations [25,29,30,31,32,33]. For instance, a large study of 3283 non-metastatic breast cancer patients reported that 34% had low SMI (<40 cm2/m2), which was associated with higher mortality [30]. Similarly, a study of 3241 women found that low SM correlated with increased mortality [29]. These findings align with our results, where each 1 cm2/m2 decrease in SMI increased the mortality hazard by 9%.
This study also reinforces the theoretical aspect of SM as a functional, endocrine, and immunological organ [34,35,36]. The precise mechanisms linking muscle to survival following a cancer diagnosis remain unclear, but they may occur through various physiological and metabolic pathways. This includes inflammation and behavioral pathways, including reduced physical activity due to deconditioning and fatigue [26,37]. Muscle has local autocrine, paracrine, and endocrine effects, but also secretes cytokines and other myokines (including IL-6, IL-8, IL-15, and leukemia inhibitory factor), leading to systemic effects [36,38]. Therefore, higher amounts of muscle may diminish the impact of inflammation, while lower levels of muscle can lead to local or aggravate systemic inflammation [39], potentially affecting outcomes.
Systemic inflammation can also contribute to persistent muscle wasting in patients with cancer [40,41]. Consequently, a dual pathway to mortality may ensue: cancer-induced muscle wasting may occur, alongside muscle wasting exacerbating clinical conditions in cancer [26,40]. Muscle loss can also disrupt the oxidative pathways, stimulating tumor growth [42], and is associated with insulin resistance [43]. Low SM not only affects survival post-cancer diagnosis but also impacts critical factors, such as surgical complications, treatment toxicity, and the potential need for chemotherapy dose reductions or interruptions [44,45]. These factors can compromise the effectiveness of adjuvant therapies administered with curative intent [26,46]. Moreover, these substantial muscle abnormalities may extend, potentially leading to increased functional dependence, frailty, reduced autonomy, and a decline in overall quality of life [47,48].
This study is not without limitations. The retrospective data collection cohort design introduces the potential for selection bias. This may have occurred due to the exclusion of patients with incomplete data or those whose CT scans were performed more than 90 days after diagnosis. Such exclusions could result in a sample that is not fully representative of the broader breast cancer population, particularly younger patients or those with more advanced disease, who might have experienced delays in imaging. However, these time points were chosen to minimize the impact of changes in body composition over time. Additionally, this study’s retrospective design means that data collection was not standardized, which may introduce further variability and bias. The exclusive reliance on clinical records for data collection also introduces the potential for information bias and missing or incomplete data, such as detailed information on treatment procedures and comprehensive clinical characteristics.
The relatively small sample size, with limited statistical power (76%), although close to the commonly accepted threshold of 80%, may limit the generalizability of our findings. This limitation persists despite this study being conducted in two large cancer treatment centers, highlighting the need for cautious interpretation when applying these results to broader populations. This caution is particularly important, as our results may not fully represent the broader breast cancer Brazilian population, which can vary across different healthcare settings (primary, secondary, tertiary care), private and public hospitals, as well as socioeconomic factors. These variations could influence the outcomes and limit the generalizability of our findings to other patient populations. Furthermore, some unmeasured factors could have impacted the results in the regression model. Despite these challenges, the small sample size may be additionally influenced by the relatively lower incidence of breast cancer in younger ages, which, in turn, strengthens the relevance of our findings. As a result, our study provides valuable insights into a population that is often underrepresented in breast cancer research.
Additional positive aspects of our study include a relatively homogeneous population, consisting of young women recently diagnosed with both metastatic and non-metastatic breast cancer. These patients exhibited no significant clinical differences in the initiation of systemic treatment. Furthermore, our cohort was followed up for 5 years across two large reference centers for oncological treatment, in addition to the utilization of CT scans—for detailing body composition, reinforcing the precision and accuracy of our findings. As future implications and applicability, we recommend that clinicians (dietitians, oncologists) should, whenever feasible, assess CT scans to evaluate body composition of these patients. This approach may assist in diagnosing abnormal nutritional statuses (i.e., sarcopenia, sarcopenic obesity, and malnutrition), and aid in prognostication. However, we also acknowledge that this depends on the opportunistic availability of CT images and the training required to use specialized software for body composition analysis.
Despite the potential of our findings, future prospective studies with larger sample sizes and comprehensive prospective data collection methods are necessary to further elucidate the relationship between body composition and mortality in young patients with breast cancer. These studies could further explore the relationship between body composition and clinical outcomes by accounting for differences in ethnicity, as these factors may vary, particularly concerning tumor hormone receptor subtype distribution [49]. Such differences could potentially influence the results, highlighting the importance of considering ethnic variations in future research to better understand their impact on cancer outcomes. When CT scans are not available, studies could benefit from investigating BMI and other anthropometric markers of body composition, such as waist circumference and BMI-adjusted calf circumference [50,51]. These measures may provide useful estimates of obesity and low SM, offering an alternative approach to body composition-related prognosis.

5. Conclusions

This study demonstrates that abnormalities in body composition predict survival in a population of young women newly diagnosed with breast cancer. Our findings particularly highlight the importance of skeletal muscle and adiposity as predictors of precocious death in this cohort. Despite the need for further studies to validate our findings, our results are relevant for patient care. Clinicians and researchers working with similar populations can use our identified cutoffs for comparison and to assist in nutritional diagnosis. This is particularly important, given that body composition is a modifiable risk factor for mortality, highlighting the critical role of early detection and timely intervention to improve patient outcomes.

Author Contributions

Conceptualization, A.D.d.L.B., I.F.d.M.S., A.L.M., S.M.M.L.V. and A.P.T.F.; formal analysis, J.P.d.C.P. and G.O.C.d.M.; investigation, A.D.d.L.B., G.M.C.F., A.L.M. and S.M.M.L.V.; methodology, A.D.d.L.B., I.F.d.M.S., G.M.C.F., A.L.M., G.O.C.d.M., S.M.M.L.V. and A.P.T.F.; project administration, A.P.T.F.; resources, S.M.M.L.V. and A.P.T.F.; software, J.P.d.C.P., G.O.C.d.M. and A.P.T.F.; supervision, S.M.M.L.V. and A.P.T.F.; validation, A.P.T.F.; visualization, A.P.T.F.; writing—original draft, A.D.d.L.B.; writing—review and editing, J.P.d.C.P., I.F.d.M.S., G.M.C.F., S.M.M.L.V. and A.P.T.F. All authors have read and agreed to the published version of the manuscript.

Funding

This study was partially funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (Finance Code 001) and the Brazilian National Council for Scientific and Technological Development (CNPq). APTF received a productivity scholarship from CNPq. The supporting sources have no involvement or restrictions regarding this publication.

Institutional Review Board Statement

This study was approved by the institutions’ Research Ethics Committees (CAAE: 43488621.4.0000.5528) on 29 March 2021.

Informed Consent Statement

Informed consent was waived due to the retrospective nature of the data collection.

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors acknowledge the Cancer Institute of Ceara, Fortaleza, CE, Brazil for providing support in data collection.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F.; Bsc, M.F.B.; Me, J.F.; Soerjomataram, M.I.; et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Instituto Nacional de Câncer (Brasil). Estimativa 2023: Incidência de Câncer no Brasil; Instituto Nacional de Câncer, INCA: Rio de Janeiro, Brazil, 2022; 160p. [Google Scholar]
  3. Migowski, A.; Silva, G.A.E.; Dias, M.B.K.; Diz, M.D.P.E.; Sant’Ana, D.R.; Nadanovsky, P. Guidelines for early detection of breast cancer in Brazil. II—New national recommendations, main evidence, and controversies. Cad. Saúde Pública 2018, 34, e00074817. [Google Scholar] [PubMed]
  4. Lauby-Secretan, B.; Sccianti, C.; Loomis, D.; Grosse, Y.; Bianchini, F.; Straif, K. Body Fatness and Cancer—Viewpoint of the IARC Working Group. N. Engl. J. Med. 2016, 375, 794–798. [Google Scholar] [CrossRef] [PubMed]
  5. Queiroz, S.A.; Sousa, I.M.; Silva, F.; Lyra, C.D.; Fayh, A.P. Nutritional and environmental risk factors for breast cancer: A case-control study. Soc. Sci. Med. 2018, 28, 28723. [Google Scholar] [CrossRef]
  6. Protani, M.; Coory, M.; Martin, J.H. Effect of obesity on survival of women with breast cancer: Systematic review and meta-analysis. Breast Cancer Res. 2010, 123, 627–635. [Google Scholar] [CrossRef]
  7. Chan, D.S.M.; Vieira, A.R.; Aune, D.; Bandera, E.V.; Greenwood, D.C.; McTiernan, U.M.; Rosenblatt, D.N.; Thune, I.; Vieira, R.; Norat, T. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann. Oncol. 2014, 25, 1901–1914. [Google Scholar] [CrossRef]
  8. Pang, Y.; Wei, Y.; Kartsonaki, C. Associations of adiposity and weight change with recurrence and survival in breast cancer patients: A systematic review and meta-analysis. Breast Cancer 2022, 29, 575–588. [Google Scholar] [CrossRef]
  9. Kimmick, G.G.; Balducci, L. Breast cancer and aging. Clinical interactions. Hematol. Oncol. Clin. N. Am. 2000, 14, 213–234. [Google Scholar] [CrossRef]
  10. Anastasiadi, Z.; Lianos, G.D.; Ignatiadou, E.; Harissis, H.V.; Mitsis, M. Breast cancer in young women: An overview. Updates Surg. 2017, 69, 313–317. [Google Scholar] [CrossRef]
  11. Rossi, L.; Mazzara, C.; Pagani, O. Diagnosis and Treatment of Breast Cancer in Young Women. Curr. Treat. Options Oncol. 2019, 20, 86. [Google Scholar] [CrossRef]
  12. Alvarez-Bañuelos, M.T.; Segura-Jaramillo, K.A.; Gómez-Rivera, E.D.C.; Alarcón-Rojas, C.A.; Morales-Romero, J.; Sampieri, C.L.; Guzmán-García, R.E. Age Under 30 Years As a Predictor of Poor Survival in a Cohort of Mexican Women with Breast Cancer. Cancer Control 2021, 28, 10732748211047408. [Google Scholar] [CrossRef] [PubMed]
  13. Lee, D.H.; Giovannucci, E.L. The Obesity Paradox in Cancer: Insights and Epidemiological Perspectives. Curr. Nutr. Rep. 2019, 8, 175–181. [Google Scholar] [CrossRef]
  14. Modi, N.D.; Tan, J.Q.E.; Rowland, A.; Koczwara, B.; Abuhelwa, A.Y.; Kichenadasse, G.; McKinnon, R.A.; Wiese, M.D.; Sorich, M.J.; Hopkins, A.M. The obesity paradox in early and advanced HER2 positive breast cancer: Pooled analysis of clinical trial data. NPJ Breast Cancer 2021, 7, 30. [Google Scholar] [CrossRef] [PubMed]
  15. Cappellari, G.G.; Guillet, C.; Poggiogalle, E.; Pomar, M.D.B.; Batsis, J.A.; Boirie, Y.; Breton, I.; Frara, S.; Genton, L.; Gepner, Y.; et al. Sarcopenic obesity research perspectives outlined by the sarcopenic obesity global leadership initiative (SOGLI)—Proceedings from the SOGLI consortium meeting in Rome November 2022. Clin. Nutr. 2023, 42, 687–699. [Google Scholar] [CrossRef] [PubMed]
  16. Shachar, S.S.; Williams, G.R. The Obesity Paradox in the Cancer Movement Beyond the Response to BMI. Cancer Epidemiol. Biomark. Prev. 2017, 26, 981. [Google Scholar] [CrossRef]
  17. Caan, B.J.; Feliciano, E.M.C.; Kroenke, C.H. The importance of body composition in explaining the paradox of excess weight in the counterpoint of cancer. Cancer Res. 2018, 78, 1906–1912. [Google Scholar] [CrossRef]
  18. Cruz-Jentoft, A.J.; Gonzalez, M.C.; Prado, C.M. Sarcopenia ≠ low muscle mass. Eur. Geriatr. Med. 2023, 14, 225–228. [Google Scholar] [CrossRef]
  19. Bezerra, M.R.O.; Sousa, I.M.; Miranda, A.L.; Ferreira, G.M.C.; Chaves, G.V.; Verde, S.M.M.L.; Maurício, S.F.; Pereira, J.P.d.C.; Gonzalez, M.C.; Prado, C.M.; et al. Age-adjusted Charlson comorbidity index and its association with body composition and overall survival in patients with colorectal cancer. Support. Care Cancer 2024, 16, 517. [Google Scholar] [CrossRef]
  20. Baracos, V.E.; Arribas, L. Sarcopenic obesity: Hidden muscle wasting and its impact for survival and complications of cancer therapy. Rev. Ann. Oncol. 2018, 29, ii1–ii9. [Google Scholar] [CrossRef]
  21. Prado, C.M.; Gonzalez, M.C.; Heymsfield, S.B. Body composition phenotypes and obesity paradox. Curr. Opin. Clin. Nutr. Metab. Care 2015, 18, 535–551. [Google Scholar] [CrossRef]
  22. Fayh, A.P.T.; Sousa, I.M.; Gonzalez, M.C. New insights on how and where to measure muscle mass. Curr. Opin. Support. Palliat. Care 2020, 14, 316–323. [Google Scholar] [CrossRef] [PubMed]
  23. Prado, C.M.; Ford, K.L.; Gonzalez, M.C.; Murnane, L.C.; Gillis, C.; Wischmeyer, P.E.; Morrison, C.A.; Lobo, D.N. Nascent to novel methods to evaluate malnutrition and frailty in the surgical patient. J. Parenter. Enter. Nutr. 2023, 47, S54–S68. [Google Scholar] [CrossRef]
  24. Liguori, A.A.L.; Fayh, A.P.T. Computed tomography: An efficient, opportunistic method for assessing body composition and predicting adverse outcomes in cancer patients. Radiol. Bras. 2023, 56, VIII–IX. [Google Scholar] [CrossRef] [PubMed]
  25. Caan, B.J.; Feliciano, E.M.C.; Prado, C.M.; Alexeeff, S.; Kroenke, C.H.; Bradshaw, P.; Quesenberry, C.P.; Weltzien, E.K.; Castillo, A.L.; Olobatuyi, T.A.; et al. Association of Muscle and Adiposity Measured by Computed Tomography with Survival in Patients with Nonmetastatic Breast Cancer. JAMA Oncol. 2018, 4, 798–804. [Google Scholar] [CrossRef] [PubMed]
  26. Feliciano, E.C.; Chen, W.Y. Clinical implications of low skeletal muscle mass in early-stage breast and colorectal cancer. Proc. Nutr. Soc. 2018, 77, 382–387. [Google Scholar] [CrossRef]
  27. WHO. Physical Status: The Use and Interpretation of Anthropometry; Report of a WHO Expert Committee; World Health Organization Technical Report Series; WHO: Geneva, Switzerland, 1995; Volume 854, pp. 1–452. [Google Scholar]
  28. Shachar, S.S.; Williams, G.R. The Obesity Paradox in Cancer-Moving Beyond BMI. Cancer Epidemiol. Biomark. Prev. 2017, 26, 13–16. [Google Scholar] [CrossRef]
  29. Villaseñor, A.; Ballard-Barbash, R.; Baumgartner, K.; Baumgartner, R.; Bernstein, L.; McTiernan, A.; Neuhouser, M.L. Prevalence and prognostic effect of sarcopenia in breast cancer survivors: The HEAL Study. J. Cancer Surviv. 2012, 6, 398–406. [Google Scholar] [CrossRef]
  30. Shachar, S.S.; Deal, A.M.; Weinberg, M.; Nyrop, K.A.; Williams, G.R.; Nishijima, T.F.; Benbow, J.M.; Muss, H.B. Skeletal muscle measures as predictors of toxicity, hospitalization, and survival in patients with metastatic breast cancer receiving taxane-based chemotherapy. Clin. Cancer Res. 2017, 23, 658–665. [Google Scholar] [CrossRef]
  31. Del Fabbro, E.; Parsons, H.; Warneke, C.L.; Pulivarthi, K.; Litton, J.K.; Dev, R.; Palla, S.L.; Brewster, A.; Bruera, E. The relationship between body composition and response to neoadjuvant chemotherapy in women with operable breast cancer. Oncologist 2012, 17, 1240–1245. [Google Scholar] [CrossRef]
  32. Prado, C.M.M.; Baracos, V.E.; McCargar, L.J.; Reiman, T.; Mourtzakis, M.; Tonkin, K.; Mackey, J.R.; Koski, S.; Pituskin, E.; Sawyer, M.B. Sarcopenia as a determinant of chemotherapy toxicity and time to tumor progression in metastatic breast cancer patients receiving capecitabine treatment. Clin. Cancer Res. 2009, 15, 2920–2926. [Google Scholar] [CrossRef]
  33. Rier, H.N.; Jager, A.; Sleijfer, S.; Rosmalen, J.V.; Kock, M.C.J.M.; Levin, M.D. Low muscle attenuation is a prognostic factor for survival in metastatic breast cancer patients treated with first line palliative chemotherapy. Breast 2017, 31, 9–15. [Google Scholar] [CrossRef] [PubMed]
  34. Nagaraju, K. Immunological capabilities of skeletal muscle cells. Acta Physiol. Scand. 2001, 171, 215–223. [Google Scholar] [CrossRef] [PubMed]
  35. Giudice, J.; Taylor, J.M. Muscle as a paracrine and endocrine organ. Curr. Opin. Pharmacol. 2017, 34, 49–55. [Google Scholar] [CrossRef] [PubMed]
  36. Severinsen, M.C.K.; Pedersen, B.K. Muscle-Organ Crosstalk: The Emerging Roles of Myokines. Endocr. Rev. 2020, 41, 594–609. [Google Scholar] [CrossRef] [PubMed]
  37. Adams, S.C.; Segal, R.J.; McKenzie, D.C.; Vallerand, J.R.; Morielli, A.R.; Mackey, J.R.; Gelmon, K.; Friedenreich, C.M.; Reid, R.D.; Courneya, K.S. Impact of resistance and aerobic exercise on sarcopenia and dynapenia in breast cancer patients receiving adjuvant chemotherapy; a multicenter randomized controlled trial. Breast Cancer Res. Treat. 2016, 158, 497–507. [Google Scholar] [CrossRef]
  38. Pratesi, A.; Tarantini, F.; Bari, M.D. Skeletal muscle: An endocrine organ. Clin. Cases Miner. Bone Metab. 2013, 10, 11–14. [Google Scholar] [CrossRef]
  39. Kalinkovich, A.; Livshits, G. Sarcopenic obesity or obese sarcopenia: A cross talk between age-associated adipose tissue and skeletal muscle inflammation as a main mechanism of the pathogenesis. Ageing Res. Rev. 2017, 35, 200–221. [Google Scholar] [CrossRef]
  40. Feliciano, E.M.C.; Kroenke, C.H.; Meyerhardt, J.A.; Prado, C.M.; Bradshaw, P.T.; Kwan, M.L.; Xiao, J.; Alexeeff, S.; Corley, D.; Weltzien, E.; et al. Association of systemic inflammation and sarcopenia with survival in nonmetastatic colorectal cancer: Results from the C SCANS study. JAMA Oncol. 2017, 3, e172319. [Google Scholar] [CrossRef]
  41. Malietzis, G.; Johns, N.; Al-Hassi, H.O.; Cavaleiro, S.C.; Kennedy, R.H.; Fearon, K.C.H.; Aziz, O.P.; Jenkins, J.T.M. Low muscularity and myosteatosis is related to the host systemic inflammatory response in patients undergoing surgery for colorectal cancer. Ann. Surg. 2016, 263, 320–325. [Google Scholar] [CrossRef]
  42. Argiles, J.M.; Busquets, S.; Stemmler, B.; López-Soriano, F.J. Cachexia and sarcopenia: Mechanisms and potential targets for intervention. Curr. Opin. Pharmacol. 2015, 22, 100–106. [Google Scholar] [CrossRef]
  43. Srikanthan, P.; Hevener, A.L.; Karlamangla, A.S. Sarcopenia exacerbates obesity-associated insulin resistance and dysglycemia: Findings from the National Health and Nutrition Examination Survey III. PLoS ONE 2010, 5, e10805. [Google Scholar] [CrossRef] [PubMed]
  44. Carvalho, A.L.M.; Gonzalez, M.C.; Sousa, I.M.; das Virgens, I.P.A.; Medeiros, G.O.C.; Oliveira, M.N.; Dantas, J.C.A.D.S.; Trussardi Fayh, A.P. Low skeletal muscle radiodensity is the best predictor for short-term major surgical complications in gastrointestinal surgical cancer: A cohort study. PLoS ONE 2021, 16, e0247322. [Google Scholar] [CrossRef] [PubMed]
  45. Rocha, I.M.G.; Marcadenti, A.; Medeiros, G.O.C.; Bezerra, R.A.; Rêgo, J.F.M.; González, M.C.; Fayh, A.P.T. Is cachexia associated with chemotherapy toxicities in gastrointestinal cancer patients? A prospective study. J. Cachexia Sarcopenia Muscle 2019, 10, 445–454. [Google Scholar] [CrossRef]
  46. Veja, M.C.M.D.; Laviano, A.; Pimentel, G.D. Sarcopenia and chemotherapy-mediated toxicity. Einstein 2016, 14, 580–584. [Google Scholar]
  47. Ryan, A.M.; Prado, C.M.; Sullivan, E.S.; Power, D.G.; Daly, L.E. Effects of weight loss and sarcopenia on response to chemotherapy, quality of life, and survival. Nutrition 2019, 67–68, 110539. [Google Scholar] [CrossRef]
  48. Souza, A.P.S.; Silva, L.C.; Fayh, A.P.T. Nutritional Intervention Contributes to the Improvement of Symptoms Related to Quality of Life in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy: A Randomized Clinical Trial. Nutrients 2021, 13, 589. [Google Scholar] [CrossRef]
  49. Bandera, E.V.; Maskarinec, G.; Romieu, I.; John, E.M. Racial and ethnic disparities in the impact of obesity on breast cancer risk and survival: A global perspective. Adv. Nutr. 2015, 6, 803–819. [Google Scholar] [CrossRef]
  50. Poggio, F.; Blondeaux, E.; Tagliamento, M.; Perachino, M.; Nardin, S.; Conte, B.; Giuliano, M.; Arpino, G.; De Laurentiis, M.; Gravina, A.; et al. Efficacy of adjuvant chemotherapy schedules for breast cancer according to body mass index: Results from the phase III GIM2 trial. ESMO Open 2024, 9, 103650. [Google Scholar] [CrossRef]
  51. Pereira, J.P.C.; Gonzalez, M.C.; Prado, C.M.; Cabral, P.C.; Nascimento, T.G.; do Nascimento, M.K.; da Silva Diniz, A.; Ramiro, C.P.S.P.; Fayh, A.P.T. Body mass index-adjusted calf circumference is associated with mortality in hospitalized older patients with excess weight. Nutrition 2024, 125, 112505. [Google Scholar] [CrossRef]
Nutrients 16 03175 g001
Figure 1. Study flowchart.
Figure 1. Study flowchart.
Nutrients 16 03175 g001
Table 3. Cox regression analysis: Associations between body composition and mortality of young females newly diagnosed with breast cancer (N = 192).
Table 3. Cox regression analysis: Associations between body composition and mortality of young females newly diagnosed with breast cancer (N = 192).
VariablesHRcrude (95% CI)pHRadjusted (95% CI)pCutoffs
SM (cm2)0.97 (0.95;0.99)0.0260.97 (0.94;0.99)0.014<94.2
SMI (cm2/m2)0.93 (0.88;0.99)0.0180.91 (0.85;0.97)0.004<39
SMD (HU)0.93 (0.88;0.99)0.0290.94 (0.88;1.01)0.07<42.5
IMAT (cm2)1.03 (0.96;1.11)0.461.01 (0.92;1.10)0.90
SAT (cm2)1.00 (0.99;1.01)0.291.01 (1.00;1.02)0.030>216
VAT (cm2)1.00 (0.99;1.01)0.371.00 (0.99;1.02)0.26
TAT (cm2)1.00 (0.99;1.00)0.271.01 (1.00;1.01)0.021>273
Adjusted models mean that all body composition variables were adjusted for TNM stage and body mass index. Abbreviations: SM: skeletal muscle; SMI: skeletal muscle index in cm2/m2; SMD: skeletal muscle radiodensity in Hounsfield Units (HU); IMAT: intramuscular adipose tissue; SAT: subcutaneous adipose tissue; VAT: visceral adipose tissue; TAT: total adipose tissue. Bold is used to emphasize significant p-values.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

de Lima Bezerra, A.D.; da Costa Pereira, J.P.; de Macedo Soares, I.F.; Ferreira, G.M.C.; Miranda, A.L.; de Medeiros, G.O.C.; Verde, S.M.M.L.; Fayh, A.P.T. Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer. Nutrients 2024, 16, 3175. https://doi.org/10.3390/nu16183175

AMA Style

de Lima Bezerra AD, da Costa Pereira JP, de Macedo Soares IF, Ferreira GMC, Miranda AL, de Medeiros GOC, Verde SMML, Fayh APT. Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer. Nutrients. 2024; 16(18):3175. https://doi.org/10.3390/nu16183175

Chicago/Turabian Style

de Lima Bezerra, Agnes Denise, Jarson Pedro da Costa Pereira, Ingryd Fernandes de Macedo Soares, Glaucia Mardrini Cassiano Ferreira, Ana Lúcia Miranda, Galtieri Otávio Cunha de Medeiros, Sara Maria Moreira Lima Verde, and Ana Paula Trussardi Fayh. 2024. "Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer" Nutrients 16, no. 18: 3175. https://doi.org/10.3390/nu16183175

APA Style

de Lima Bezerra, A. D., da Costa Pereira, J. P., de Macedo Soares, I. F., Ferreira, G. M. C., Miranda, A. L., de Medeiros, G. O. C., Verde, S. M. M. L., & Fayh, A. P. T. (2024). Influence of Body Composition Assessed by Computed Tomography on Mortality Risk in Young Women with Breast Cancer. Nutrients, 16(18), 3175. https://doi.org/10.3390/nu16183175

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop