
==== Front
ESMO Open
ESMO Open
ESMO Open
2059-7029
Elsevier

S2059-7029(24)01435-2
10.1016/j.esmoop.2024.103666
103666
Original Research
Bioimpedance-derived body composition parameters predict mortality and dose-limiting toxicity: the multicenter ONCO-BIVA study
Cereda E. 1
Casirati A. 1
Klersy C. 2
Nardi M. 3
Vandoni G. 4
Agnello E. 5
Crotti S. 1
Masi S. 1
Ferrari A. 6
Pedrazzoli P. 67
Caccialanza R. r.caccialanza@smatteo.pv.it
1∗
on behalf of the
ONCO-BIVA Collaborative Group†Caccialanza Riccardo 1
Cereda Emanuele 1
Casirati Amanda 1
Crotti Silvia 1
Masi Sara 1
Klersy Catherine 2
Ferrari Alessandra 3
Pedrazzoli Paolo 34
Gavazzi Cecilia 5
Vandoni Giulia 6
Farina Gabriella 6
La Verde Nicla 7
Zagonel Vittorina 8
Nardi Maria Teresa 9
Baldan Ilaria 9
Di Costanzo Francesco 10
Mascheroni Annalisa 11
Trestini Ilaria 12
Valoriani Filippo 13
Lucchin Lucio 14
Aprile Giuseppe 15
De Francesco Antonella 16
Agnello Elena 17
Giovanardi Filippo 18
1 Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
2 Biometry and Clinical Epidemiology Service, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
3 Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
4 Department of Internal Medicine, University of Pavia, Pavia, Italy
5 Clinical Nutrition Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
6 Medical Oncology Department, ASST Fatebenefratelli-Sacco, Milan, Italy
7 Department of Medical Oncology, Luigi Sacco University Hospital, ASST Fatebenefratelli Sacco, Milan, Italy
8 Department of Oncology, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
9 Nutritional Support Unit, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy
10 Medical Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
11 Clinical Nutrition and Dietetics Unit-ASST Melegnano e Martesana, Melegnano (Milan), Italy
12 Dietetics Service, Medical Direction, University Hospital of Verona, Verona, Italy
13 Division of Metabolic Diseases and Clinical Nutrition, Department of Specialistic Medicines, University Hospital of Modena, Modena, Italy
14 Dietetics and Clinical Nutrition, Bolzano Health District, Bolzano, Italy
15 Department of Oncology, San Bortolo General Hospital, ULSS 8 Berica-Vicenza, Vicenza, Veneto, Italy
16 Dietetics and Clinical Nutrition, Department of Medicine, A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy
17 Dietetics and Clinical Nutrition, Department of Medicine, A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy
18 Department of Oncology, Guastalla Hospital, Guastalla, Italy

1 Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia
2 Biostatistics & Clinical Trial Center, Fondazione IRCCS Policlinico San Matteo, Pavia
3 Nutritional Support Unit, Veneto Institute of Oncology IOV-IRCCS, Padua
4 Clinical Nutrition, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milan
5 Dietetic and Clinical Nutrition Unit, Città della Salute e della Scienza Hospital, Turin
6 Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia
7 Department of Internal Medicine, University of Pavia, Pavia, Italy
∗ Correspondence to: Dr Riccardo Caccialanza, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy r.caccialanza@smatteo.pv.it
† The ONCO-BIVA Collaborative Group members are listed in the Acknowledgements section.

12 8 2024
8 2024
12 8 2024
9 8 103666© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

In patients with cancer, lean body mass loss is frequent and associated with worse outcomes, including reduced treatment tolerance and survival. Bioelectrical impedance analysis (BIA) is a popular method for body composition assessment. We evaluated the value of BIA-derived body composition parameters in predicting mortality and, for the first time, dose-limiting toxicity (DLT).

Patients and methods

We conducted a prospective multicenter (n = 12) observational study in adult patients with solid neoplastic disease and receiving primary systemic treatment. We collected information on BIA-derived parameters: phase angle (PhA) <5th percentile of age and gender-specific normative values; standardized PhA (SPA) <−1.65; Nutrigram® <660 mg/24 h/m and <510 mg/24 h/m for males and females, respectively. The primary outcome and the key secondary were 1-year mortality and DLT (any-type severe toxicity requiring a delay in systemic treatment administration or a reduction of its dosage), respectively.

Results

In total, 640 patients were included. At 12 months, death occurred in 286 patients (47.6%). All BIA-derived body composition parameters were independently associated with death: SPA, hazard ratio (HR) = 1.59 [95% confidence interval (CI) 1.30-1.95] (P < 0.001); PhA, HR = 1.38 (95% CI 1.13-1.69) (P = 0.002); Nutrigram®, HR = 1.71 (95% CI 1.42-2.04) (P < 0.001). DLT occurred in 208 patients (32.5%) and body composition parameters were associated with this outcome, particularly SPA: odds ratio = 6.37 (95% CI 2.33-17.44) (P < 0.001).

Conclusions

The study confirmed that BIA-derived body composition parameters are independently associated not only with survival but also with DLT. Although our findings were limited to patients receiving first-line systemic treatment, the evidence reported may have important practice implications for the improvement of the clinical work-up of cancer patients.

Highlights

• In patients with cancer, reduced lean body mass loss is frequent.

• Reduced lean body mass loss is associated reduced treatment tolerance and survival.

• BIA is a popular and bedside method for body composition assessment.

• BIA-derived body composition parameters predicted reduced survival.

• BIA-derived body composition parameters were associated with DLT.

Key words

phase angle
bioelectrical impedance vectorial analysis
body composition
mortality
dose-limiting toxicity
cancer
==== Body
pmcIntroduction

Disease-related malnutrition and changes in body composition are frequent in patients with cancer due to multiple factors including reduced food intake, increased metabolic demands, tumor-induced inflammatory and catabolic responses and treatment-related side-effects.1 It is well established that nutritional derangements—such as low body weight and unintentional weight loss (WL)—adversely affect quality of life and survival, but more recent research has clearly emphasized that particularly lean body mass loss is associated with reduced treatment tolerance, which in turn results in poor response to treatments and lower survival.2 Therefore, integrating the assessment of body composition in the clinical work-up of cancer patients has become of pivotal importance.3,4 Some methodologies of common use in the oncologic setting—such as computed tomography (CT) and magnetic resonance imaging (MRI)—are clearly of proven and superior accuracy in providing information on muscle quantity and quality.5 However, they cannot be routinely carried out except at certain time points of the clinical history, and well-established age- and sex-specific cut-offs are not yet available.

Bioelectrical impedance analysis (BIA) is a popular method to assess body composition due to its ease of use, portability, rapidity, relatively low cost and non-invasiveness for patients.6 However, BIA does not directly measure body compartments—namely fat and fat-free mass—whose estimation relies on the use of regression equations.7

In a clinical context, it may be preferable to use the electrical properties of tissues and thus measured raw bioelectrical impedance parameters: resistance (R) and reactance (Xc). From their combination, a single parameter called phase angle (PhA)—reflecting cellular mass and health (soft-tissue quantity and quality)—has been proposed and validated against multiple outcomes.6 However, age, sex, body mass index (BMI) and ethnicity substantially influence PhA and the need for reference values for different populations has been clearly emphasized.8, 9, 10

To overcome these limitations, standardization of PhA by reference values of age, sex and BMI strata from the general healthy population has been considered to develop Z-scores [standardized PhA (SPA)].11 Nonetheless, to account also for the length of impedance vector, a strong quantitative correlate of whole-body soft-tissue mass, a new body composition parameter (Nutrigram®), has been implemented and validated in patients with cancer.12

However, while a consistent mass of data has been collected on the association between these parameters and outcomes (e.g. quality of life, functional status and mortality) in patients with cancer,13 no evidence is available on the risk of treatment-related complications and reduced treatment feasibility. On the other hand, reduced skeletal muscle mass, as assessed by accurate imaging techniques—such as CT and MRI—has been consistently demonstrated as a strong predictor of dose-limiting toxicities (DLTs),2 potentially due to altered pharmacokinetics and distribution of chemotherapy agents in the lean body mass—of which muscle mass is the major contributor—and higher blood levels.14

Therefore, the objective of this prospective cohort study, conducted in patients with cancer candidate to first-line systemic therapy, was to evaluate not only the prognostic value of the most important bioimpedence-derived body composition parameters against mortality, but also to investigate their utility in the prediction of DLT.

Patients and methods

Study design

We designed a prospective multicenter (n = 12) observational study (enrollment from March 2021 to May 2022; end of follow-up March 2023). Adult (age ≥18 years) patients with solid neoplastic disease and receiving primary systemic treatment (either adjuvant or first-line systemic therapy for unresectable or metastatic disease, employed by investigators’ choice within the framework of good clinical practice and in agreement with current Italian Association of Medical Oncology guidelines)15 were eligible of inclusion, and provided a signed informed consent. Patients with implanted pacemakers or defibrillators or presenting with peripheral edema were excluded to avoid interference with the assessment of body composition.

Assessments

The following information were recorded:

Clinical data—cancer site and stage (American Joint Committee on Cancer stage groupings), previous surgery, performance status (Karnofsky scale)16 and treatment regimen and related tolerance/feasibility (according to the Common Terminology Criteria for Adverse Events version 5.0).17

Anthropometry—body weight (to the nearest 0.1 kg) and height (to the nearest 0.5 cm) were measured and used for the calculation of BMI [weight (kg)/height (m)2] and 6-month previous unintentional WL (based on retrospective evaluation).18

Nutritional risk—using data on BMI, WL, recent self-reported reduction of food intake, age and disease according to the Nutritional Risk Screening (NRS) 2002 tool. Patients with a score ≥3 were considered at nutritional risk.19

Body composition—assessed by BIA, keeping the patients in the supine position with arms and legs abducted from the body after an overnight fast. In all centers, a phase-sensitive impedance device (NUTRILAB, Akern Srl, Florence, Italy), injecting an alternating sinusoidal electric current of 400 microamperes at 50 kHz, was used. Resistance (R) and reactance (Xc) were measured and used to derive PhA, SPA and the new body composition parameter Nutrigram®. The following threshold values were considered to define impaired body composition: SPA <−1.6511; PhA <5th percentile of age- and gender-specific normative values10; Nutrigram® <660 mg/24 h/m and <510 mg/24 h/m for males and females, respectively.12,13

Study endpoints

The primary endpoint was 1-year all-cause mortality. Particularly, survival—with vital status ascertained by means of active follow-up (in-office visits, inquiries by telephone or mail to participants or proxy respondents and linkage to municipal registries)—was defined as the time between the date of enrollment and the date of death or the date of last contact (censoring).

The key secondary endpoint was DLT defined as any-type severe toxicity requiring a delay in systemic treatment administration or a reduction of its dosage or definitively to discontinue the treatment. Discontinuation secondary to disease progression was not accounted for DLT definition.

Ethics

The local Institutional ethics committees approved the study and written informed consent was obtained from every patient [Coordinating Center (Fondazione IRCCS Policlinico San Matteo, Pavia, Italy) first approval prot. 20160000398].

Statistical analysis

Descriptive statistics for continuous [mean and standard deviation (SD) or median and interquartile range (IQR)] and categorical variables (count and percentage) were provided. For the primary endpoint, hazard ratio (HR) and 95% confidence interval (CI) for SPA, PhA and Nutrigram® were estimated using Cox regression models (one for each nutritional parameters), while adjusting for age, gender, disease stage, Karnofsky performance status [categorized into tertiles (<80, 80-90, >90)], DLT and nutritional risk (NRS-2002 score ≥3). We computed Harrell’s c statistic for discrimination. For the secondary endpoint, we used multivariable logistic regression models to derive odds ratios (ORs) and 95% CI, while adjusting for the same potential confounders. We computed the model area under the ROC curve (AUC-ROC) statistic for discrimination. For all models, Huber–White robust standard errors were computed, while clustering on center to account for lack of independence within center. A two-sided level of probability <0.05 was adopted as significant. All statistical analyses were carried out using STATA 18 statistical software (Stata Corporation, College Station, TX).

Results

In total, 640 patients (males, 62%) were included (Table 1) and 39 were lost to follow-up after primary systemic treatment. About 70% of patients presented with a stage-IV disease, over 60% of them had gastrointestinal malignancies and one patient out of four had undergone surgery before systemic treatment. As far as the nutritional domain is concerned, we report that more than half of the cohort was at nutritional risk (NRS-2002 score ≥3, 56.9%). Frequency of impaired body composition by BIA-derived parameters was: SPA, 17.7%; PhA, 52.3%; Nutrigram®, 21.4%. After 12 months of follow-up, death had occurred in 286 patients [47.6%; rate per 100 person-years, 62.9 (95% CI 55.9-70.6)]. Multivariable Cox regression models of candidate mortality predictors is reported in Table 2. Specifically, all BIA-derived body composition parameters were independently associated with death (Figure 1): SPA, HR = 1.59 (95% CI 1.30-1.95) (P < 0.001); PhA, HR = 1.38 (95% CI 1.13-1.69) (P = 0.002); Nutrigram®, HR = 1.71 (95% CI 1.42-2.04) (P < 0.001). Other predictors of outcome were disease stage, performance status, previous surgery and nutritional risk (NRS-2002 score ≥3). All three multivariable models had a similar discrimination ability, with Harrell’s c statistic of 0.67-0.68.Table 1 Demographic and clinical of the study cohort

Characteristic	Overall cohort (N = 640)	
Gender (male), n (%)	397 (62.0)	
Age (years), mean (SD)	63.1 (11.3)	
 >65, n (%)	299 (46.7)	
BMI (kg/m2), mean (SD)	23.9 (4.3)	
6-month weight loss (%), mean (SD)	9.2 (7.3)	
 ≥10%, n (%)	249 (38.9)	
Cancer site, n (%)		
 Stomach	130 (20.3)	
 Esophagus	63 (9.8)	
 Colon/rectum	86 (13.4)	
 Pancreas/biliary	119 (18.6)	
 Lung	120 (18.8)	
 Head and neck	48 (7.5)	
 Breast	20 (3.1)	
 Urogenital	17 (2.7)	
 Others	37 (5.8)	
Treatment regimens, n (%)		
 Chemotherapy	475 (75.4)	
 Chemo-immunotherapy	86 (13.7)	
 Immunotherapy	14 (2.2)	
 Chemo-radiotherapy	43 (6.8)	
 Chemo-radiotherapy + immunotherapy	5 (0.8)	
 Tyrosine kinase inhibitors	5 (0.8)	
 Tyrosine kinase inhibitors + immunotherapy	2 (0.3)	
Cancer stage, n (%)		
 I-II	50 (7.8)	
 III	148 (23.1)	
 IV	442 (69.1)	
Karnofsky performance status score, mean (SD)	86.7 (13.0)	
 <80, n (%)	248 (38.8)	
 80-90, n (%)	196 (30.6)	
 >90, n (%)	196 (30.6)	
Previous surgery, n (%)	159 (24.8)	
NRS-2002 score, mean (SD)	2.7 (1.2)	
 ≥3, n (%)	364 (56.9)	
Phase angle (°), mean (SD)	4.99 (2.12)	
 Low,an (%)	335 (52.3)	
SPA, median (25th-75th)	−0.40 (1.52)	
 <−1.65, n (%)	113 (17.7)	
Nutrigram® (mg/24 h/m), mean (SD)	730.9 (203.3)	
 Low,bn (%)	137 (21.4)	
BMI, body mass index; NRS, Nutritional Risk Screening; SD, standard deviation; SPA, standardized phase angle.

a <5th percentile of age- and gender-specific normative values.

b Males, <660 mg/24 h/m; females, <510 mg/24 h/m.

Table 2 BIA-derived body composition parameters and 1-year mortality

Characteristic	Unadjusted risk HR (95% CI)	P value	Model 1 (SPA)
Adjusted risk HR (95% CI)	P value	Model 2 (PhA)
Adjusted risk HR (95% CI)	P value	Model 3 (Nutrigram)
Adjusted risk HR (95% CI)	P value	
Model discrimination (Harrell’s c)			0.68		0.67		0.68		
SPA		<0.001		<0.001	—	—	—	—	
 ≥−1.65	1 (reference)		1 (reference)						
 <−1.65	1.89 (1.52-2.36)		1.59 (1.30-1.95)						
Phase angle		<0.001	—	—		0.002	—	—	
 High	1 (reference)				1 (reference)				
 Lowa	1.59 (1.25-2.02)				1.38 (1.13-1.69)				
Nutrigram®		<0.001	—	—	—	—		<0.001	
 High	1 (reference)						1 (reference)		
 Lowb	2.06 (1.71-2.48)						1.71 (1.42-2.04)		
Sex	—	—		0.77		0.42		0.19	
 Female			1 (reference)		1 (reference)		1 (reference)		
 Male			1.04 (0.81-1.32)		1.11 (0.86-1.43)		1.18 (0.92-1.51)		
Age	—	—		0.069		0.37		0.35	
 ≤65			1 (reference)		1 (reference)		1 (reference)		
 >65			1.19 (0.99-1.43)		1.09 (0.91-1.31)		1.09 (0.91-1.29)		
Cancer stage	—	—		<0.001c		<0.001c		<0.001c	
 I-II			1 (reference)		1 (reference)		1 (reference)		
 III			1.43 (0.53-3.82)		1.48 (0.56-3.92)		1.48 (0.59-3.71)		
 IV			2.64 (0.95-7.34)		2.71 (0.94-7.88)		2.74 (1.01-7.46)		
Karnofsky performance status score	—	—		<0.001c		<0.001c		<0.001c	
 <80			1.78 (1.44-2.20)		1.80 (1.47-2.21)		1.88 (1.52-2.33)		
 80-90			1.23 (0.97-1.56)		1.25 (0.98-1.60)		1.29 (1.04-1.59)		
 >90			1 (reference)		1 (reference)		1 (reference)		
Previous surgery	—	—		0.001		0.001		0.001	
 No			1 (reference)		1 (reference)		1 (reference)		
 Yes			0.68 (0.54-0.86)		0.68 (0.55-0.85)		0.69 (0.55-0.86)		
Dose-limiting toxicity	—	—		0.94		0.67		0.72	
 No			1 (reference)		1 (reference)		1 (reference)		
 Yes			1.01 (0.81-1.25)		1.06 (0.82-1.35)		1.04 (0.83-1.31)		
NRS-2002 score ≥3	—			<0.001		<0.001		<0.001	
 No		1 (reference)		1 (reference)		1 (reference)		
 Yes		1.42 (1.27-1.60)		1.42 (1.25-1.61)		1.33 (1.14-1.54)		
Univariable and multivariable Cox regression models [no. of deaths = 286; rate per 100 person-years, 62.9 (95% CI 55.9-70.6)].

BIA, bioelectrical impedance analysis; CI, confidence interval; HR, hazard ratio; NRS, Nutritional Risk Screening; SPA, standardized phase angle.

a <5th percentile of age- and gender-specific normative values.

b Males, <660 mg/24 h/m; females, <510 mg/24 h/m.

c For trend over risk categories.

Figure 1 Survival probability (Kaplan–Meier) according to BIA-derived body composition parameters. (A) Low standardized phase angle (<−1.65). (B) Low phase angle (<5th percentile of age- and gender-specific normative values). (C) Low Nutrigram® (<660 mg/24 h/m and <510 mg/24 h/m for males and females, respectively). BIA, bioelectrical impedance analysis.

During the primary systemic treatment, DLT occurred in 208 patients (32.5%) and in 135 out of these it was recorded as the need to delay treatment administration. Body composition parameters were all independently associated also with this outcome (Table 3), particularly SPA: OR = 6.37 (95% CI 2.33-17.44) (P < 0.001), though with a wide 95% CI. The other estimates for BIA-derived parameter correlates were: PhA, OR = 3.31 (95% CI 1.80-6.08) (P < 0.001); Nutrigram®, OR = 3.63 (95% CI 1.84-7.14) (P < 0.001). All three models had a similar discrimination ability (AUC-ROC 0.71-0.74).Table 3 BIA-derived body composition parameters and dose-limiting toxicity risk estimates

Characteristic	Unadjusted risk OR (95% CI)	Model 1 (SPA)
Adjusted risk OR (95% CI)	P value	Model 2 (PhA)
Adjusted risk OR (95% CI)	P value	Model 3 (Nutrigram)
Adjusted risk OR (95% CI)	P value	
Model discrimination (AUC-ROC)		0.74		0.72		0.71		
SPA			<0.001	—	—	—	—	
 ≥−1.65	1 (reference)	1 (reference)					
 <−1.65	6.47 (2.90-14.43)	6.37 (2.33-17.44)					
Phase angle		—	—		<0.001	—	—	
 High	1 (reference)			1 (reference)				
 Lowa	3.27 (2.06-5.19)			3.31 (1.80-6.08)				
Nutrigram®		—	—				<0.001	
 High	1 (reference)			1 (reference)		
 Lowb	3.51 (2.26-5.46)			3.63 (1.84-7.14)		
Sex	—		0.74		0.028		0.60	
 Female		1 (reference)		1 (reference)		1 (reference)		
 Male		1.07 (0.73-1.56)		1.34 (0.95-1.90)		1.49 (0.98-2.27)		
Age	—		0.59		0.52		0.64	
 ≤65		1 (reference)		1 (reference)		1 (reference)		
 >65		1.15 (0.69-1.94)		0.82 (0.46-1.49)		0.86 (0.45-1.62)		
Cancer stage	—		<0.001c		<0.001c		<0.001c	
 I-II		1 (reference)		1 (reference)		1 (reference)		
 III		3.34 (1.63-6.85)		4.03 (2.03-7.99)		3.98 (2.03-7.78)		
 IV		7.58 (3.84-14.98)		8.82 (4.38-17.75)		8.59 (4.38-16.84)		
Karnofsky performance status score	—		0.98c		0.97c		0.090c	
 <80, n (%)		0.99 (0.37-2.63)		0.98 (0.43-2.26)		1.14 (0.48-2.67)		
 80-90, n (%)		1.07 (0.46-2.50)		1.09 (0.51-2.33)		1.20 (0.50-2.87)		
 >90, n (%)		1 (reference)		1 (reference)		1 (reference)		
Previous surgery	—		0.11		0.077		0.066	
 No		1 (reference)		1 (reference)		1 (reference)		
 Yes		0.73 (0.50-1.07)		0.72 (0.50-1.04)		0.74 (0.53-1.02)		
NRS-2002 score ≥3	—		0.090		0.11		0.20	
 No		1 (reference)		1 (reference)		1 (reference)		
 Yes		1.36 (0.95-1.92)		1.31 (0.94-1.83)		1.24 (0.89-1.73)		
Multivariable logistic models (no. of events = 208).

AUC-ROC, area under the ROC curve; BIA, bioelectrical impedance analysis; CI, confidence interval; OR, odds ratio; NRS, Nutritional Risk Screening; SPA, standardized phase angle.

a <5th percentile of age- and gender-specific normative values.

b Males, <660 mg/24 h/m; females, <510 mg/24 h/m.

c For trend over risk categories.

Discussion

In the ONCO-BIVA study, BIA-derived body composition parameters were all associated with all-cause mortality and DLT, particularly SPA.

Indeed, as far as mortality is concerned, our analysis is confirmatory and consistent with the mass of data collected in the oncologic setting. Several authors have reported that the use of body composition parameters derived from measured raw bioelectrical impedance parameters (resistance and reactance) and reflecting the electrical properties of tissues—namely PhA, SPA and Nutrigram®—are valuable predictors of prognosis independently of tumor type.12,13

However, this was the first study investigating and reporting an association between BIA-derived body composition parameters and DLT, although a study addressing this issue in women with early breast cancer is currently ongoing.20 PhA, SPA and Nutrigram® are strongly correlated with and are used as surrogate measures of lean body mass, body cell mass and skeletal muscle mass, particularly in the presence of normal hydration status.6,21,22

Studies using more accurate imaging techniques for the pre-therapeutic assessment of muscle quantity and quality (radiodensity)—such as CT and MRI—have consistently demonstrated a strong prediction of DLT,2 which can be explained by altered pharmacokinetics and distribution of chemotherapy agents in the lean body mass.14 Nonetheless, pooled OR estimates reported in the literature range between 1.26 and 4.50—depending on treatment modalities, age, cancer site and extension, world region and quality of the studies—with an overall estimate of 1.47.

In our cohort, the OR estimates for DLT were 3.31, 3.63 and 6.37 for PhA, Nutrigram® and SPA, respectively, and the stronger association with SPA could be explained by the standardization for important factors influencing tissue impedance such as age, sex and BMI, though a lesser precision was observed as denoted by the large 95% CI.11 BIA, particularly vectorial analysis (BIVA), has been recently proposed as a tool for morpho-functional assessment.6 It is reasonable to argue that measuring the electrical properties of tissues reflecting the health of cells and the integrity of membranes would enable to identify patients susceptible to developing adverse toxicity reactions to anticancer treatments.

Recent literature has also highlighted how PhA could be a valuable marker of inflammation and oxidative stress, able to reflect subclinical derangements coming before important changes in body mass and structure.23

The results of the present study may have important clinical implications, particularly taking into account the feasibility of BIA and the recent recommendation by American Society of Clinical Oncology on the conduction of prospective studies to ‘explore the role of body composition in predicting DLTs’, particularly in obese patients, in whom whole body mass would not account for treatment kinetics related to lean body mass.24 Interestingly, among BIA-derived body composition parameters, SPA is the only parameter having a single cut-off (<−1.65°), regardless of sex and age, which has been substantially investigated and consistently validated against prognosis in cancer patients.25

Indeed, further research in this area is needed. Body composition assessment has become of pivotal importance in oncology. Methodologies such as CT and MRI are certainly more accurate, but are less feasible, more invasive and costly, and well-established cut-offs are not available. On the other hand, BIA is a bedside procedure with a larger mass of data and evidence collected resulting in specific cut-offs. Besides, into routine clinical practice, BIA-derived body composition parameters not only can provide valuable prognostic information, but can also guide personalized treatment strategies. Its regular monitoring throughout the disease course can help identify patients at higher risk of complications, enabling to set up timely intervention and to tailor nutritional support.26,27 For this purpose, a more detailed data collection on the type of adverse events—not considered in the present study—should be taken into account in the next research projects.

In conclusion, in the ONCO-BIVA study cohort, BIA-derived body composition parameters not only independently predicted mortality but also DLT. Indeed, our findings were limited to patients receiving primary systemic treatment, but the evidence reported may have important practice implications for the improvement of the clinical work-up as all the patients were assessed at the very beginning of their disease course. Nonetheless, being an easy-to-apply methodology, which does not require adaptations to the local practices, the study provides valuable perspectives and inputs for additional research. Future studies could reasonably address the value of serial assessments and the application to patients eligible for second- or third-line therapy, where treatment decisions are often more nuanced and complex. A focus on the predictive role associated with the disease setting (e.g. breast versus gastrointestinal cancer) and stage should be considered, as well as a direct comparison with standard imaging techniques. Particularly, further study on this last topic is currently ongoing.

Acknowledgements

The ONCO-BIVA Collaborative Group collaborators are the following:

Riccardo Caccialanza, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Emanuele Cereda, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Amanda Casirati, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Silvia Crotti, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Sara Masi, Clinical Nutrition and Dietetics Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Catherine Klersy, Biometry and Clinical Epidemiology Service, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Alessandra Ferrari, Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy; Paolo Pedrazzoli, Medical Oncology Unit, Fondazione IRCCS Policlinico San Matteo, Pavia and Department of Internal Medicine, University of Pavia, Pavia, Italy; Cecilia Gavazzi, Clinical Nutrition Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Giulia Vandoni, Clinical Nutrition Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Gabriella Farina, Medical Oncology Department, ASST Fatebenefratelli-Sacco, Milan, Italy; Nicla La Verde, Department of Medical Oncology, Luigi Sacco University Hospital, ASST Fatebenefratelli Sacco, Milan, Italy; Vittorina Zagonel, Department of Oncology, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy; Maria Teresa Nardi, Nutritional Support Unit, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy; Ilaria Baldan, Nutritional Support Unit, Veneto Institute of Oncology IOV-IRCCS, Padua, Italy; Francesco Di Costanzo, Medical Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy; Annalisa Mascheroni, Clinical Nutrition and Dietetics Unit-ASST Melegnano e Martesana, Melegnano (Milan), Italy; Ilaria Trestini, Dietetics Service, Medical Direction, University Hospital of Verona, Verona, Italy; Filippo Valoriani, Division of Metabolic Diseases and Clinical Nutrition, Department of Specialistic Medicines, University Hospital of Modena, Modena, Italy; Lucio Lucchin, Dietetics and Clinical Nutrition, Bolzano Health District, Bolzano, Italy; Giuseppe Aprile, Department of Oncology, San Bortolo General Hospital, ULSS 8 Berica-Vicenza, Vicenza, Veneto, Italy; Antonella De Francesco, Dietetics and Clinical Nutrition, Department of Medicine, A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy; Elena Agnello, Dietetics and Clinical Nutrition, Department of Medicine, A.O.U. Città della Salute e della Scienza di Torino, Turin, Italy; Filippo Giovanardi, Department of Oncology, Guastalla Hospital, Guastalla, Italy.

Funding

This work was supported partially by ACC [Bando Ricerca Corrente Reti IRCCS 2021, grant number RCR-2021-23671213 and Bando Ricerca Corrente Reti IRCCS 2022, grant number RCR-2022-23682293] and by an unconditional grant from Baxter SpA (no grant number).

Disclosure

RC and EC have served as speakers for Akern Srl, Florence, Italy. All other authors have declared no conflicts of interest.
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