# Resting energy expenditure in the risk assessment of anticancer treatments

Anne Jouinot, Clara Vazeille, Jean Philippe Durand, Olivier Huillard, Pascaline Boudou-Rouquette, Romain Coriat, Jeanne Chapron, Nathalie Neveux, Jean Pascal de Bandt, Jerome Alexandre, et al.

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Anne Jouinot, Clara Vazeille, Jean Philippe Durand, Olivier Huillard, Pascaline Boudou-Rouquette, et al.. Resting energy expenditure in the risk assessment of anticancer treatments. Clinical Nutrition, 2017, 37 (2), pp.558-565. 10.1016/j.clnu.2017.01.007. hal-05410064

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Resting energy expenditure in the risk assessment of anticancer treatments

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Authors and affiliations:

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Anne Jouinot¹, Clara Vazeille¹, Jean Philippe Durand¹, Olivier Huillard¹, Pascaline Boudou-Rouquette¹, Romain

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Coriat², Jeanne Chapron³, Nathalie Neveux⁴,⁵, Jean Pascal De Bandt⁴,⁵, Jerome Alexandre¹, Luc Cynober⁴,⁵,

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Francois Goldwasser¹

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1. Medical Oncology Department, Cancer Research for Personalized Medicine (CARPEM), Paris Centre

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Teaching Hospitals, Paris Descartes University, USPC, Paris, France

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2. Gastro-Enterology Department, Paris Centre Teaching Hospitals, AP-HP, Paris Descartes University, USPC,

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Paris, France

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3. Pneumology Department, Paris Centre Teaching Hospitals, AP-HP, Paris Descartes University, USPC, Paris,

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France

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4. Clinical Chemistry, Paris Centre Teaching Hospitals, AP-HP, Paris Descartes University, USPC, Paris, France

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5. EA 4466 PRETRAM, Pharmacy Faculty, Paris Descartes University, USPC, Paris, France

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Corresponding author: Anne Jouinot

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Medical Oncology, Cochin - Port Royal Hospital

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103 bd de Port Royal - 75014 Paris - France

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Tel: +33 1 58 41 14 54

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Fax: +33 1 58 41 17 53

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anne.jouinot@aphp.fr

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Abstract word count: 239

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Word count: 3247

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Keywords: malnutrition, cancer cachexia, toxicity, chemotherapy, targeted therapy
31 Abstract

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33 Background and aims: Alterations of nutritional and performance status (PS) are associated with higher risk of
34 chemotherapy toxicity. Increased resting energy expenditure (REE) is frequent in cancer patients and may
35 contributeto cachexia. We investigated whether abnormal energetic metabolism could predict early acute
36 limiting toxicities (ELT) of anticancer treatments.

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38 Methods: In this observational monocentric study, REE was measured by indirect calorimetry before treatment
39 initiation. Based on the ratio of measured REE to REE predicted by the Harris-Benedict formula, patients were
40 classified as hypometabolic (<90%), normometabolic (90-110%) or hypermetabolic (>110%). Body mass index,
41 weight loss, PS, albumin, transthyretin, C-reactive protein (CRP) and muscle mass (CT-scan) were studied. Were
42 defined as ELT any unplanned hospitalization or any adverse event leading to dose reduction or discontinuation
43 during the first cycle of treatment.

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45 Results: We enrolled 277 patients: 76% had metastatic disease; 89% received chemotherapy and 11% targeted
46 therapy; 29% were normometabolic, 51% hypermetabolic and 20% hypometabolic. Fifty-nine patients (21%)
47 experienced an ELT. Toxicity was associated with abnormal metabolism (vs normal: OR=2.37[1.13-4.94],
48 p=0.023), PS (2-3 vs 0-1: OR=2.04[1.12-3.74], p=0.023), albumin (<35 vs ≥35g/l: OR=2.39[1.03-5.54],
49 p=0.048), and inflammation (CRP ≥10 vs <10mg/l: OR=2.43[1.35-4.38], p=0.004). To predict toxicity, the most
50 sensitive parameter was the REE (83%) followed by PINI (63%), GPS (59%), CRP (55%), PS (41%), NRI
51 (37%), and albumin (16%). In multivariate analysis, elevated CRP was an independent predictor of toxicity
52 (p=0.047).

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54 Conclusion: Abnormal basal energy metabolism identifies patients at higher risk of treatment-related acute
55 complications.
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# Introduction

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Malnutrition is a common complication in cancer patients and is associated with an increased risk of acute toxicity following anticancer treatment, as well as with a reduced response to anticancer agents and shorter survival(1,2). A negative balance between energy intake and energy expenditure contributes to the development of malnutrition(3). The resting energy expenditure (REE) is often altered in cancer patients(3,4). Elevated R EE appears as the most common alteration in cancer patients compared to healthy subjects. Around 50% of cancer patients show elevated REE (4). Decreased REE is also observed and may concern 30% of cancer patients(5) depending on the tumor type, site or stage. Elevated REE is a determinant in the development of cancer induced malnutrition, as it is frequently higher than dietary intakes(3). Therefore, the REE measurement is potentialtool for patient management. Presently, indirect calorimeters have the expected characteristics to allow routine evaluation of alterations in energy expenditure in large cohorts of patients as they offer precise, reproducible, noninvasive, portable and rapid measurements of REE(4,6).

Since anticancer therapies have a narrow therapeutic index and potentially life-threatening toxicities, but are prescribed to with comorbidities or elderly patients, the clinical research on toxicity risk assessment is a rapidly emerging field. Deterioration of the performance status (PS) and/or alteration of the nutritional status are parameters associated with increased incidence of early acute toxicities (7,8). Several scores, such as the Glasgow Prognostic Score (GPS), the Nutritional Risk Index (NRI) or the Prognostic Inflammatory and Nutritional Index (PINI) help to identify inflammatory or malnourished patients at risk for complications(9-11). Nevertheless, treatment tolerance among patients with an apparently good medical condition remains highly variable.

We hypothesized that alterations in energy metabolism, especially hypermetabolism, as an early feature of precachexia, favoring malnutrition, might be associated with an increased risk of acute toxicity. In this study, we investigated whether REE measurement before initiating anticancer treatments could predict the occurrence of early limiting toxicity.
## 81 Patients and methods

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### 83 Patients

We studied a prospective cohort of consecutive cancer patients with solid tumors, who came in our institution in

the outpatient setting for a routine evaluation - including REE measurement – prior to initiate anticancer

treatment between June 2012 and April 2014. We included patients who started chemotherapy or targeted

therapy within one month. We excluded patients who received hormonal therapy or best supportive care alone.

Other exclusion criteria were age under 18 years, psychiatric disorders, respiratory insufficiency, surgery or

anticancer treatment during the last month.

Patients were followed until the date of their death, their last examination or the completion of a follow-up

period of 18 months after the end of the study.

This study was approved by the local Review Board for Oncology according to the declaration of Helsinki.

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### 94 Anthropometric measurements, medical and nutritional assessment

Weight was measured with a medical balance and height was measured with a stadiometer. Body mass index

(BMI) was calculated as weight (kg)/height (m²). Weight loss (WL) and Eastern Cooperative Oncology Group

(ECOG) Performance Status were determined by a physician.

Routine biological tests included C-reactive protein (CRP, liquid turbidimetry, Cobas, Roche Diagnostic), alpha

1-acid glycoprotein (α1-GP), serum albumin and transthyretin levels (nephelometry, BN II, Siemens).

GPS was calculated using albumin and CRP levels(9):

GPS 2: CRP >10 mg/L and albumin <35 g/L;

GPS 1: CRP >10 mg/L or albumin <35 g/L;

GPS 0: CRP ≤10 mg/L and albumin ≥35 g/L.

NRI was computed as: $1.519 \times \text{serum albumin (g/L)} + \{41.7 \times \text{present weight (kg)} / \text{basal weight (kg)}\}$

with risk of complications related to malnutrition when NRI <97.5, as previously described (10).

PINI was calculated as: $\{\text{CRP (mg/L)} \times \alpha1-\text{GP (mg/L)}\} / \{\text{Albumin (g/L)} \times \text{Transthyretin (mg/L)}\}$

with risk of complications(7) when PINI >1 (11).

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### 109 Resting energy expenditure measurement
Measured REE (mREE, kcal/d) was determined by indirect calorimetry using a face mask system (Fitmate VM®, Cosmed, Italy). The measurement was carried out under standard resting conditions (after a 12 h fasting, complete bed rest for 15 min, in a thermoneutral environment). A first 5 min measurement was performed to reach the steady state - which was defined by an average oxygen consumption (VO2) variation less than 10% - and was then followed by a 10 minute measurement for REE assessment. The calorimeter was calibrated before each measure.

In order to evaluate the extent of the perturbation in energy metabolism compared to a healthy situation, predicted REE (pREE, kcal/d) was calculated using revised Harris-Benedict (HB) formula(12):

- in males: $pREE (kcal/d) = 88,362 + 13,397 \times W + 479,9 \times H - 5,677 \times A$

- in females: $pREE (kcal/d) = 447,593 + 9,247 \times W + 309,8 \times H - 4,33 \times A$

where: W is weight in kilograms, H is height in centimeters, and A is age in years.

While several formulas may be used for the calculation of REE(13,14), the HB formula was chosen as it has a good score for accurate prediction of REE in a relatively large range of BMI(14).

Based on the ratio of mREE to pREE, patients were classified according to the standards of Boothby et al.(15) as hypometabolic (mREE < 90% pREE), normometabolic (90-110% pREE) or hypermetabolic (> 110% pREE).

## Muscle mass measurement

Muscle mass was evaluated by the measurement of muscle tissue areas on CT-scan images(16), with the third lumbar vertebra (L3) considered as a reference landmark(17). CT scans were performed for diagnostic or follow-up purposes less than 30 days before REE measurement. L3 images were analyzed with ImageJ software v1.46r (National Institutes of Health, https://imagej.nih.gov/ij).

Muscles were delimited on their anatomic features, and their area was quantified based on established thresholds of muscle radiation attenuation (-29 to +150 Hounsfield units(16)). Cross-sectional areas (cm²) of all muscles were computed and the mean value for two consecutive L3 images was calculated for each patient. These values were normalized for stature and expressed in cm²/m². Sex-specific cutoff values were used to define sarcopenia (55.4 cm²/m² for males and 38.9 cm²/m² for females)(18). Lean body mass (LBM) was estimated from muscle cross-sectional areas(17): $LBM (kg) = 0.30 \times (L3 skeletal muscle area (cm²)) + 6.06$.

## Treatment and toxicity assessment
139 Treatment choice was determined by the treating physician in agreement with national and international guidelines. Chemotherapy or targeted therapy was started within a month after the multidisciplinary evaluation.

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141 Targeted therapies were defined as any agent that blocks the growth and spread of cancer cells by interfering with specific molecular targets. Due to the high-risk toxicity profile, patients treated with antiangiogenics tyrosine kinase inhibitors (acting on VEGF-receptor) were analyzed separately from patients treated with other tyrosine kinase inhibitors. Corticosteroids therapy - which may increase REE - was also recorded. Toxicity was assessed at each visit, every one to three weeks, depending on the treatment plan. Toxicity was obtained for all cycles but only first-cycle toxicity was analyzed for this study due to great variability among regimens and durations and to dose reductions for subsequent cycles in patients with severe toxicity, and to the number of patients-related factors or disease-related parameters susceptible to change several months after the assessment.

149 The primary endpoint was the occurrence of early limiting toxicities, defined as any unplanned hospitalization (including emergency unit visit) or any adverse event leading to decrease in dose-intensity (dose reduction, treatment delay > 7 days or discontinuation) during the first cycle of treatment.

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153 Statistical analysis

154 Descriptive statistics were used for qualitative (number and percentage) and quantitative variables (mean and standard deviation or median and range).

155 The correlation between quantitative variables was assessed by the Pearson coefficient.

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157 Overall survival was determined as time elapsed between evaluation and death or last follow-up visit. The proportional hazards assumption was tested for each model constructed. Survival curves were obtained with Kaplan-Meier estimates and compared with log-rank test.

160 Comparisons between groups were assessed with Wilcoxon test for quantitative variables and with Fischer's exact test for qualitative variables. All p-values were two-sided, and the level of significance was set at p<0.05.

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162 Sensitivity and specificity were calculated for each parameter. We performed a multivariable analysis using logistic regression, including only non-redundant parameters predicting the occurrence of toxicities with a p-value<0.05 by univariable analysis.

165 Calculations were performed using R statistical software (R Stats Package).
166 **Results**

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168 **Patients' characteristics**

169 From May 2012 to April 2014, 404 patients underwent routine evaluation with REE measurement prior to anticancer treatment initiation, among which 277 (69%) met all criteria for analysis (Figure 1).

171 Patients' baseline characteristics are summarized in Table 1. All patients had documented malignant solid tumor, mostly of gastro-intestinal (25%), genito-urinary (23%) and thoracic (22%) origins. Most of the patients (n=219; 79%) had locally advanced or metastatic disease and 222 (80%) were naive of anticancer treatment. At the time of the risk assessment, 113 (41%) patients already displayed a weight loss greater than 5%, and among the 95 evaluable patients for muscle mass, 57 (60%) were sarcopenic.

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177 **Resting energy expenditure**

178 Mean mREE was 1556 kcal/d (sd 427 kcal/d). Study patients showed a higher mREE than expected from HB formula, with mean mREE/pREE of 108% (sd 24%), corresponding to 29% normometabolic, 51% hypermetabolic and 20% hypometabolic patients.

181 There was a strong correlation (r=0.88, p < 10⁻¹⁶) between mREE/pREE and mREE/LBM (Supplemental Fig 1), with higher mREE/LBM in hypermetabolic patients (40.5 kcal/kg/d) compared to normometabolic (33.7 kcal/kg/d, p < 10⁻⁸) and hypometabolic patients (25.1 kcal/kg/d, p < 10⁻¹⁰).

184 There were no significant difference in mREE (1659 vs 1549 kcal/d, p=0.35), mREE/body weight (23.2 vs 22.5 kcal/kg/d, p=0.76) and mREE/LBM (37.8 vs 35.8 kcal/kg/d, p=0.82) among patients receiving corticosteroids.

186 Comparison of baseline characteristics of normo-, hyper- and hypometabolic patients is shown in Suppl Table 1, Suppl Fig 2 and 3. Hyper- and hypometabolism were more frequent in patients with altered PS. Hypometabolic patients were frequently overweight and experienced less weight loss than hypermetabolic patients (Suppl Table 1). Hypermetabolism was more frequent in thoracic than in gastro-intestinal or genito-urinary malignancies (Suppl Fig 2). Energy metabolism was not associated with cancer stage (Suppl Table 1). Hypermetabolism was associated with systemic inflammation, high GPS, high PINI and low NRI (Suppl Table 1).

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193 **Survival analysis**

194 Survival was determined 18 months after the completion of the study. Overall, 19 patients (7%) were lost to follow-up.
For metastatic cancer patients, the median overall survival was 20.8 months (95% CI: 17.3-25.8).

Hypermetabolic patients showed a worst prognosis with a median overall survival of 17.3 months (95% CI: 11.8-22.7), compared to normometabolic (median not-reached, log-rank p=0.004, Figure 2A). Hypometabolic patients displayed an intermediate prognosis (median OS 22 months).

In the subset of patients with an apparently good clinical condition (PS 0-1), the median overall survival was 20.8 months for hypermetabolic, 28.4 months in hypometabolic and non-reached in normometabolic patients (Figure 2B). No further comparison was made due to violation of proportional hazard assumption.

## Early limiting toxicities

More patients were treated with chemotherapy (246, 89%) than targeted therapy (31, 11%). Platinum-based chemotherapy was the most common regimen (156, 56%).

Out of 277 patients, 59 (21%) experienced an early limiting toxicity. Patients treated with antiangiogenic drugs experienced more early limiting toxicities (10/21, 48%, p=0.012) than those treated with chemotherapy (47/246, 19%) or with another tyrosine kinase inhibitor (2/10, 20%).

Early limiting toxicities are detailed in Table 2. Thirteen patients were referred to emergency unit and 36 were hospitalized. Anticancer treatment had to be delayed more than 7 days for 17, reduced for 13 and discontinued for 15 patients following the first cycle.

Univariable analysis of baseline factors associated with early limiting toxicities is given in Figure 4. Baseline characteristics associated with toxicity occurrence were poor PS (2-3 vs 0-1: OR=2.04 [1.12-3.74], p=0.023), low albumin (<35 vs ≥35 g/L: OR=2.39 [1.03-5.54], p=0.048), inflammation (CRP ≥10 vs <10 mg/L: OR=2.48 [1.37-4.47], p=0.004), high GPS (1-2 vs 0: OR=2.61 [1.44-4.72], p=0.002), low NRI (≤97.5 vs >97.5: OR=1.88 [1.02-3.47], p=0.048), high PINI (≥1 vs <1: OR=3.08 [1.54-6.19], p=0.002) and abnormal metabolism (hyper- and hypo- vs normometabolism: OR= 2.36 [1.13-4.94], p = 0.023). Hyper- and hypometabolic patients exhibited similar hazard ratios for toxicity (2.43 and 2.20 respectively) compared to normometabolic patients.

There was no significant association between age, sex, tumor stage, BMI, weight loss or sarcopenia and the occurrence of early limiting toxicity (Figure 3).

REE was associated with a higher sensitivity (83%) than PINI (63%), GPS (59%), CRP (55%), PS (41%), NRI (37%), and albumin (16%) to predict early limiting toxicities (Table 3). Indeed, abnormal metabolism was frequent among patients with PS 0-1, normal albumin or normal CRP, representing more than 60% of patients in
225 these populations (Suppl. Fig 3). Most of the toxicities occurred in patients with normal albumin and CRP but

226 abnormal metabolism (Suppl. Fig 4).

227 In multivariable analysis, elevated CRP was the only independent predictor of early acute toxicities (p = 0.047,

228 Table 3).
229 **Discussion**

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This prospective analysis is the first study evaluating the relationship between REE and anticancer treatment

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toxicity in cancer patients. Hyper- and hypometabolic patients experienced more early limiting toxicity than

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normometabolic patients. Abnormal metabolism was the most sensitive parameter for predicting the occurrence

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of early limiting toxicity.

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Baseline characteristics of patients included in this study were similar to those observed in previous analyses of

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REE in cancer patients(3–5). Indeed hypermetabolic, normometabolic and hypometabolic patients represented

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51%, 29% and 20% respectively, of all patients.

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A high proportion of patients developed early limiting toxicity - 21% during the first cycle - probably because

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they represent a real-life population, meaning a less-selected population, frail and prone to develop severe

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toxicity in comparison to the patients included in clinical trials. In agreement with previous studies, altered

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performance status, low albumin levels, inflammation, low NRI, high GPS and high PINI were associated with

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higher risk of toxicity(7,19). Weight loss was not sufficient *per se* to identify patients at high risk of toxicity.

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This may be explained in part by the fact that weight loss was recorded independently of its duration. Moreover,

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less toxic regimens could have been offered to patients who already experienced a significant weight loss. There

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was also no association between sarcopenia and occurrence of early limiting toxicity. Sarcopenia alters

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pharmacokinetics and increases exposure to anticancer drugs. It was previously described as a risk factor for

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chemotherapy(20,21) and targeted therapy toxicity(22,23). The negative result in our study may be due, on the

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one hand, to the small number of patients with body composition data, since CT-scans were often performed

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outside of our institution, and on the other hand, to the heterogeneity of our cohort in terms of cancer types and

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treatments.

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In this study, abnormal metabolism - especially hypermetabolism - was associated with the occurrence of early

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limiting toxicity. Hypometabolic patients, as well as hypermetabolic ones, represent a frail population, but with

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different underlying mechanisms. The increased toxicity in hypermetabolic cancer patients could be explained

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by the association between hypermetabolism and inflammation. Several studies(24,25) have shown that

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hypermetabolism and weight loss are related to the presence of a systemic inflammatory response as reflected by

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enhanced levels of inflammatory mediators, such as TNF-α and Interleukin-6. In the present study, CRP was

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higher in hypermetabolic than in normometabolic patients (data not shown) and was the only independent

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predictor of toxicity in multivariate analysis.
259 Hypermetabolism was associated with poor survival in this study. This result is consistent with a previous short
260 report(26) and raises the question of an early management of hypermetabolism before the development of
261 cachexia.

262 Intriguingly 20% of patients presented hypometabolism. The underlying mechanisms of hypometabolism in
cancer patients remain poorly understood. The main hypothesis is that hypometabolism may be related to body
composition. REE is mainly dependent on lean body mass (LBM) and the HB equations tend to overestimate
265 REE for obese patients, especially in case of sarcopenic obesity(27). The mREE/pREE ratio could be
underestimated in these patients, which would then be classified as hypometabolic. Prado et al.(18) studied 2115
patients with solid tumors of the respiratory or gastrointestinal tract and showed that sarcopenic obesity is
associated with poor prognosis and chemotherapy toxicity(20,21). However, in the current study, the
mREE/LBM ratio was still significantly lower in hypometabolic than in hypermetabolic and normometabolic
patients (data not shown), suggesting that other mechanisms are involved in hypometabolism. Alternatively,
hypometabolism might be an adequate down-regulation in response to a reduction of the ingestas to maintain the
energetic balance equilibrated.
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273 The development of portable indirect calorimeters is now compatible with routine evaluation of REE. Indirect
274 calorimetry measures oxygen consumption and provides a rapid and accurate estimation of REE. It is now
275 considered as a valid surrogate for the gold standard of direct calorimetry, which measures heat generated by the
276 subject in a closed calorimetric chamber(28). Ventilated hood, face-mask and mouthpiece plus noseclips are the
277 three main devices for indirect calorimetry. These techniques showed similar accuracy(29,30). For the sake of
278 portability, we used Fitmate® indirect calorimeter with face-mask, a recent and compact (20 x 24 cm) metabolic
279 analyzer, demonstrating good accuracy and reliability for measuring resting energy expenditure in adults(31,32).
280 REE measurement was integrated to multidisciplinary evaluation with nutritional assessment for all new
281 patients. This evaluation could be integrated to diagnosis announcement procedure. Such a multidisciplinary
282 assessment should be performed in all hospitals.
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The limitations of the present study include the retrospective collection of toxicities - which could result in recall
bias - and the heterogeneity of patients and treatments - which are prone to confusion biases. Moreover, some
factors that could influence REE were not taken into account in our analysis. We found that REE was not
significantly different among patients receiving corticosteroids but diabetes, tobacco consumption or
uncontrolled pain could also increase REE. Additional fully prospective studies are needed to validate these
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288 results and identify which cancer types and which treatments could benefit most from REE evaluation and further of nutritional care.
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290 In the aim of personalized medicine, REE measurement should be used as a gold standard for adapting nutritional support rather than HB formula which is only an estimate(13,14). Hypermetabolic cancer patients might benefit from specific interventions. Even in extreme catabolic and inflammatory situations such as severe burn injury, enhanced nutritional support may counteract hypermetabolism and muscular loss(33). Dedicated pharmacologic interventions, with non-steroidal anti-inflammatory drugs or beta-blockers, seem also promising.
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295 Maccio et al.(34) have conducted a randomized phase III of combined treatment for cachexia in 104 patients with advanced gynecological cancer. Patients were randomly assigned to receive for 4 months either megestrol acetate, l-carnitine, anti-COX2 celecoxib, and antioxidants or megestrol acetate alone. REE significantly decreased in the combined arm and increased in the control arm. Another study(35) suggested that adrenergic factors are important mediators behind elevated REE in weight-losing cancer patients. Adrenoceptor blockade with propranolol improved skeletal muscle protein net balance in patients with severe burns during acute hypermetabolic phase of the injury(33). Hyltander et al. have studied the effect of propranolol in 13 cancer patients and shown a decreased of 18% in REE(36). Recently, Stewart Coats et al. have tested beta-blocker espindolol in a randomized phase II trial in cachectic patients with advanced colorectal or lung cancer and showed that espindolol reversed weight loss and improved fat-free mass(37). Further clinical trials are required to determine whether such interventions could stop the evolution to cachexia, reduce treatment toxicity and improve outcome in the specific subset of hypermetabolic cancer patients.
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307 In conclusion, our results highlight the emerging role of REE measurement in the risk assessment of anticancer treatments. The measurement of REE with indirect calorimeters is readily compatible with clinical routine. The REE measurement could improve the detection of patients at higher risk for toxicity who would be missed with a standard clinical evaluation and thus help to provide optimal supportive care for this undisclosed frail population.
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311
312 Acknowledgements

313

314 The authors wish to thank the nurses, residents and clinical research staff of the Cochin Teaching Hospital.

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317 Statement of Authorship

318 Anne Jouinot, Jean-Philippe Durand, Luc Cynober and François Goldwasser designed the study. Anne Jouinot

319 and Clara Vazeille collected the data. All of the authors participated in analysis and interpretation of data. All of

320 the authors participated in critical revision of the manuscript. All of the authors gave final approval of the

321 version to be submitted.

322

323

324 Conflict of Interest Statement and Funding sources

325 Francois Goldwasser participated to a board of experts of Fresenius Kabi. All other authors have no conflict of

326 interest to declare.

327 This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-

328 profit sectors.
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424 Tables

425

426 Table 1. Baseline characteristics

427

428 Table 2. Early limiting toxicities

429

430 Table 3. Sensitivity, specificity and multivariable analysis of factors associated with occurrence of early limiting toxicities

431

432
<table><caption>433 Table 1. Baseline characteristics</caption><thead><tr><th></th><th>N of 277</th><th>N (%)</th></tr></thead><tbody><tr><td>Age (years) median (range)</td><td>277</td><td>61,8 (13,4)</td></tr><tr><td>Sex</td><td>277</td><td></td></tr><tr><td>Female</td><td></td><td>123 (44)</td></tr><tr><td>Male</td><td></td><td>154 (56)</td></tr><tr><td>Performance status</td><td>276</td><td></td></tr><tr><td>0-1</td><td></td><td>196 (71)</td></tr><tr><td>2</td><td></td><td>62 (22)</td></tr><tr><td>3</td><td></td><td>18 (6)</td></tr><tr><td>Primary tumor</td><td>277</td><td></td></tr><tr><td>Gastro-intestinal</td><td></td><td>70 (25)</td></tr><tr><td>Genito-urinary</td><td></td><td>63 (23)</td></tr><tr><td>Thoracic</td><td></td><td>60 (22)</td></tr><tr><td>Gynaecologic</td><td></td><td>43 (16)</td></tr><tr><td>Sarcoma</td><td></td><td>27 (10)</td></tr><tr><td>Others</td><td></td><td>9 (3)</td></tr><tr><td>Cancer stage</td><td>277</td><td></td></tr><tr><td>Localized</td><td></td><td>58 (21)</td></tr><tr><td>Locally advanced</td><td></td><td>49 (18)</td></tr><tr><td>Metastatic</td><td></td><td>170 (61)</td></tr><tr><td>Number of prior anticancer therapies</td><td>277</td><td></td></tr><tr><td>0</td><td></td><td>222 (80)</td></tr><tr><td>≥ 1</td><td></td><td>55 (20)</td></tr><tr><td>Weight loss</td><td>276</td><td></td></tr><tr><td>&lt; 5%</td><td></td><td>163 (59)</td></tr><tr><td>≥ 5%</td><td></td><td>113 (41)</td></tr><tr><td>Albumin (g/l) mean (sd)</td><td>275</td><td></td></tr><tr><td>&lt; 35</td><td></td><td>40.6 (4.4)</td></tr><tr><td>≥ 35</td><td></td><td>27 (10)</td></tr><tr><td>≥ 35</td><td></td><td>248 (90)</td></tr><tr><td>Transthyretin (g/l) mean (sd)</td><td>264</td><td></td></tr><tr><td>&lt; 0.2</td><td></td><td>0.27 (0.09)</td></tr><tr><td>≥ 0.2</td><td></td><td>53 (20)</td></tr><tr><td>≥ 0.2</td><td></td><td>211 (80)</td></tr><tr><td>CRP (mg/l) mean (sd)</td><td>275</td><td></td></tr><tr><td>&lt; 10</td><td></td><td>17.5 (29.8)</td></tr><tr><td>≥ 10</td><td></td><td>170 (62)</td></tr><tr><td>≥ 10</td><td></td><td>105 (38)</td></tr><tr><td>GPS</td><td>274</td><td></td></tr><tr><td>0</td><td></td><td>164 (60)</td></tr><tr><td>1</td><td></td><td>89 (32)</td></tr><tr><td>2</td><td></td><td>21 (8)</td></tr><tr><td>PINI</td><td>207</td><td></td></tr><tr><td>≤ 1</td><td></td><td>122 (59)</td></tr><tr><td>&gt; 1</td><td></td><td>85 (41)</td></tr><tr><td>NRI</td><td>274</td><td></td></tr><tr><td>&lt; 97.5</td><td></td><td>75 (27)</td></tr><tr><td>≥ 97.5</td><td></td><td>199 (73)</td></tr><tr><td>Muscle mass</td><td>95</td><td></td></tr><tr><td>Sarcopenic</td><td></td><td>57 (60)</td></tr><tr><td>Non-sarcopenic</td><td></td><td>38 (40)</td></tr><tr><td>mREE (kcal/d) mean (sd)</td><td>277</td><td>1556 (427)</td></tr><tr><td>mREE/body weight (kcal/kg/d) mean (sd)</td><td>277</td><td>22,5 (5,7)</td></tr><tr><td>mREE/LBM (kcal/kg/d) mean (sd)</td><td>95</td><td>36,0 (7,3)</td></tr><tr><td>mREE/pREE (%) mean (sd)</td><td>277</td><td></td></tr><tr><td>Normometabolism</td><td></td><td>108.1 (24.3)</td></tr><tr><td>Hypermetabolism</td><td></td><td>81 (29)</td></tr><tr><td>Hypometabolism</td><td></td><td>141 (51)</td></tr><tr><td></td><td></td><td>55 (20)</td></tr></tbody></table>

434 CRP, C reactive protein; GPS, Glasgow Prognostic Score; PINI, Prognostic Inflammatory and Nutritional Index; NRI, Nutritional Risk Index; mREE, measured REE; pREE, predicted REE
<table><caption>437 Table 2. Early limiting toxicities</caption><thead><tr><th>Event</th><th>N (%)*</th></tr></thead><tbody><tr><td>Unscheduled hospitalization</td><td>44 (75)</td></tr><tr><td>Dcrease of dose-intensity</td><td>39 (66)</td></tr><tr><td><strong>Main toxicities</strong></td><td></td></tr><tr><td>Fever and infections</td><td>24 (41)</td></tr><tr><td style="padding-left: 2em;">including septic shock</td><td>1 (2)</td></tr><tr><td style="padding-left: 2em;">including febrile neutropenia</td><td>5 (8)</td></tr><tr><td>Haemorrage</td><td>5 (8)</td></tr><tr><td style="padding-left: 2em;">including hemorragic shock</td><td>2 (3)</td></tr><tr><td>Grade 3 nausea or vomiting</td><td>4 (7)</td></tr><tr><td>Diarrhea/Colitis</td><td>4 (7)</td></tr></tbody></table>

438

439

440 * of 59 patients with early limiting toxicities

441
442 Table 3. Sensitivity, specificity and multivariable analysis of factors associated with occurrence of early

443 limiting toxicities

444

<table><thead><tr><th rowspan="2"></th><th colspan="4">Univariable analysis</th><th colspan="2">Multivariable analysis</th></tr><tr><th>OR (95% CI)</th><th>p</th><th>Sensitivity</th><th>Specificity</th><th>OR (95% CI)</th><th>p</th></tr></thead><tbody><tr><td>PS 2-3 vs 0-1</td><td>2.04 [1.12-3.74]</td><td><strong>0.023</strong></td><td>41%</td><td>74%</td><td>1.34 [0.69-2.60]</td><td>0.393</td></tr><tr><td>Albumin &lt; 35 vs ≥ 35 g/L</td><td>2.39 [1.03-5.54]</td><td><strong>0.048</strong></td><td>16%</td><td>92%</td><td>0.51 [0.67-4.16]</td><td>0.271</td></tr><tr><td>CRP &gt; 10 vs ≤ 10 mg/L</td><td>2.48 [1.37-4.47]</td><td><strong>0.004</strong></td><td>55%</td><td>67%</td><td>1.92 [1.01-3.68]</td><td><strong>0.047</strong></td></tr><tr><td>GPS 1-2 vs 0</td><td>2.61 [1.44-4.72]</td><td><strong>0.002</strong></td><td>59%</td><td>65%</td><td>-</td><td>-</td></tr><tr><td>NRI &lt; 97.5 vs ≥ 97.5</td><td>1.88 [1.02-3.47]</td><td><strong>0.048</strong></td><td>37%</td><td>75%</td><td>-</td><td>-</td></tr><tr><td>PINI ≥ 1 vs &lt; 1</td><td>3.08 [1.54-6.19]</td><td><strong>0.002</strong></td><td>63%</td><td>65%</td><td>-</td><td>-</td></tr><tr><td>Hyper- and hypo- vs normometabolism</td><td>2.36 [1.13-4.94]</td><td><strong>0.023</strong></td><td><strong>83%</strong></td><td>32%</td><td>1.84 [0.86-3.95]</td><td>0.116</td></tr></tbody></table>

445 CRP, C reactive protein; GPS, Glasgow Prognostic Score; PINI, Prognostic Inflammatory and Nutritional Index; NRI,

446 Nutritional Risk Index
447 **Figures**

448

449 **Figure 1.** Patients' selection for analysis

450

451 **Figure 2.** Kaplan-Meier estimates of overall survival according to energy metabolism in metastatic patients (A)

452 and in metastatic patients with PS 0-1 (B)

453

454 **Figure 3.** Forest plot of factors associated with occurrence of early limiting toxicities
<caption>**Figure 1: Patients' selection for analysis**</caption>

456

457

458

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Kaplan-Meier estimates of overall survival according to energy metabolism in metastatic patients (A) and in metastatic patients with PS 0-1 (B)</figcaption></figure>

<figure><img src="image_5.png" /></figure>
463

**Figure 3. Forest plot of factors associated with occurrence of early limiting toxicites**

<figure><img src="image_6.png" /></figure>

464

465

466 PS, performance status; BMI, body mass index; GPS, Glasgow Prognostic Score; PINI, Prognostic Inflammatory and

467 Nutritional Index; NRI, Nutritional Risk Index; mREE, measured REE; pREE, predicted REE