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Clinical nutrition, body composition and oncology: A critical literature review of the synergies

Critical Reviews in Oncology/Hematology, Volume 84, Issue 1, October 2012, Pages 37 - 46

Highlights

► Body composition should replace weight loss to define the nutritional status. ► Obesity and cachexia may occur simultaneously. ► Sarcopenia is the only significant predictor of chemotoxicity. ► Body composition techniques have a demonstrated positive impact in cancer treatment. ► Nutrition is not a routine care, needs to be patient-specific and fine-tuned.

Abstract

Purpose of the research

Review the oncology and clinical nutrition literature to highlight the synergies between those two subjects. This review focuses on diagnostic of lean body wasting and the recent improvements in measuring body composition to monitor the response to nutrition during optimal oncology treatment.

Principal results

Nutrition support in cancer patients has made major progresses. A variety of advanced tools allow monitoring and explaining weight loss, body composition changes and metabolic alterations. Body composition is more accurate than body surface area to determine chemotherapeutic drug dosing. As with any therapeutic approach, clinical nutrition has a better risk-benefit ratio if implemented when indicated rather than used routinely. Body composition measurements are helpful for a better understanding of the host-tumor interactions during cancer treatment and nutrition support.

Major conclusions

Nutrition support based on body composition analysis may significantly contribute to optimize current oncology treatment and clinical outcomes.

Keywords: Cancer, Nutrition, Body composition, Chemotoxicity, Body surface area.

1. Introduction

Weight loss in cancer patients is often considered as unavoidable and is linked with reduced tolerance to anticancer therapy [1] and shorter survival [2] . The understanding of pathophysiological mechanisms of cancer-related weight loss has greatly improved during the last two decades. These findings made it possible to better define the anorexia–cachexia syndrome [3] which integrates the special dynamic and changes in body composition occurring in cancer patients. This syndrome also highlights the differences between weight loss observed in cancer and non-cancer patients. Body composition analyses, together with measurements of energy expenditure and plasma cytokines, have brought new insides about the interactions between the host and the tumor. This allowed the identification of the main nutritional therapy assets for the metabolic support in cancer treatments.

Many nutrition studies in cancer patients were published since the early seventies. Unfortunately, most of them did not integrate advanced nutrition assessment and/or cancer-specific nutrition strategies as recommended by recent guidelines. In addition, only few studies have included critical parameters such as tumor type and extension, chemotherapy and/or radiotherapy protocols, associated diagnostics such as infections or organ dysfunctions, physical functioning and quality of life. These methodological flaws explain most of the failures of nutritional interventions in oncology patients.

This review aims at evaluating recent studies addressing the issues mentioned above and discussing ongoing unsolved questions.

2. Cachexia definitions and classification: an international consensus on the key role of body composition and muscle strength

Weight loss and cachexia are not synonymous [4] . Table 1 lists the latest definitions of cachexia [2], [5], and [6] which are quite similar as they focus on a multifactorial syndrome defined by an ongoing loss of muscle mass with or without loss of fat mass. Fearon et al. add the notion of “cachexia cannot be fully reversed by conventional nutritional support” [3] . Indeed classification of cancer cachexia have emerged and recognized as a continuum with three stages of clinical relevance: precachexia, cachexia and refractory cachexia. Cachexia can be clinically refractory as a result of very advanced cancer or the presence of rapidly progressive cancer unresponsive to anticancer therapy. Cachexia is associated with active catabolism, or the presence of factors that render active management of weight loss no longer possible or appropriate [3] .

Table 1 Cachexia definitions.

European Palliative Care Research Collaborative Organization Cachexia is a multifactorial syndrome defined by a negative protein and energy balance driven by a variable combination of reduced food intake and abnormal metabolism. A key defining feature is ongoing loss of skeletal muscle mass which cannot be fully reversed by conventional nutritional support, leading to progressive functional impairment
 
Special Interest Group on cachexia and anorexia of the European Society for Clinical Nutrition and Metabolism (ESPEN) Cachexia is a multifactorial syndrome characterized by severe body weight loss, muscle and fat loss and increased protein catabolism due to underlying disease(s)
 
Evans et al. [6] and [32] Cachexia is a complex metabolic syndrome associated with underlying illness and characterized by loss of muscle with or without loss of fat mass, often associated with anorexia, inflammation, insulin resistance and increased muscle protein breakdown. Cachexia is distinct from starvation, age-related loss of muscle mass, primary depression, malabsorption and hyperthyroidism and is associated with increased mortality

Fearon et al. included the appendicular skeletal muscle index among diagnosis criteria for cachexia [3] . This can be assessed with lumbar skeletal muscle index determined by CT imaging (men < 55 cm2/m2; women < 39 cm2/m2). We have to be aware that some cachexia definitions refer to cachexia in general [6] whereas others [3] are cancer-specific. Cachexia definitions are being constantly updated and the most recent ones focus on muscle mass. A recent clinical study [7] suggests that the number and size of intramyocellular lipid droplets is increased in the presence of cancer and changes in the adipose tissue could be the first step in the process of cancer-cachexia.

With the most recent definition [3] , assessment for classification and clinical management should include muscle mass and strength, functional and psychosocial impairment.

These definitions played a key role in the shift from the use of weight loss to body composition to define the nutritional status in oncology.

3. Body composition

Body weight can be split into basic compartments like fat mass (FM) and fat free mass (FFM), described in Fig. 1 . These compartments are gathered into standard aggregates, depending on the technical capability of the tools used to determine body composition. Fig. 1 also illustrates the typical variations of body composition in healthy, cancer or elderly patients.

gr1

Fig. 1 Body compartments and composition illustration.

Skeletal muscles and internal organs are the metabolically active components of FFM. They are crucial in the ability to cope with stress that leads to tissue loss because of chronic inflammatory activity [8] . Body composition is thus important as body weight will not always diminish proportionally to tissue solids due to fluid retention. Magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA) and computerized tomography (CT) can be used for the in vivo measurement of the metabolically active components of FFM [9] and [10] but they have some unwanted effects like irradiation or cost. Other tools like bioelectrical impedance analysis (BIA) provide results with fewer details but are easier to implement.

Body composition modifications – in terms of FM, FFM or lean body mass – are needed to assess the impact of cancer on body mass status and to understand the dynamic of malnutrition, refeeding or to evaluate the impact of oncology treatment on the disease progression. A good understanding of the underlying causes of cachexia is critical when wanting to achieve the nutritional support goal. Thus, body composition is a key factor to reduce chemotoxicity and ultimately increase survival and quality of life.

3.1. Body composition: a valuable tool to determine energy expenditure and cachexia

Cachexia features reduction of some lean body mass compartments, whereas visceral proteins content are relatively preserved [6] and [11]. It is also frequently associated with FM loss.

The impact of the mass represented by the highly metabolically active organs on resting energy expenditure (REE) and the development of cancer cachexia has been suggested by Jebb et al. [12] . Increase in mass of high metabolic rate tissues such as liver or tumor may contribute to cachexia-associated weight loss [13] . This study demonstrates that colorectal cancer patients experience a progressive change in body composition with an exponential increase of the liver size and hepatic metastases with concurrent accelerations of muscle and fat loss. One year before death, liver (2.3 ± 0.7 kg) and spleen (0.32 ± 0.2 kg) were larger than reference values for normal healthy adult. The estimated proportion of FFM occupied by the liver increased from 4.5% to 7.0% over the last year of life time. This change was mirrored by a concurrent muscle (4.2 kg) and fat (3.5 kg) loss. So, mass of liver and metastases would be a significant determinant of resting metabolic rate. This fact is confirmed by Müller et al. [14] who have calculated a REE for brain, heart, liver and kidney (6% of total FFM) of 468 kcal/kg when skeletal muscles only need 15 kcal/kg (44% of total FFM), bones and extracellular mass (50% of total FFM) less than 1 kcal/kg.

A study [15] suggests that determining metabolically active tissue in cancer patients with weight loss thanks to total body water measurement leads to significantly overestimated results (+20%) and thus, to underestimated REE that may account for the continuing controversy over the importance of hypermetabolism in cancer [16] .

One hypothesis is that BIA does not distinguish skeletal muscles from other soft lean tissues [15] and [17]. Hopefully, new generation image-based body composition methods, especially CT and magnetic resonance imaging, permits separation of specific skeletal muscles and adipose tissue [18] and [19] and thus very accurate REE evaluation [14] . A lumbar vertebral landmark (L3) has been validated as the best area because in this region, skeletal muscle and adipose tissue correspond to whole-body tissue quantities [20] .

Illner et al. [10] demonstrate that only skeletal muscle and liver significantly contribute to REE and that prediction of REE on the basis of individual organ masses matches measured REE. Staal-van den Brekel et al. [21] demonstrate that small cells lung cancer weight loss is associated with a greater increase in REE adjusted for FFM than in non small cells lung cancer. This suggests that the difference is linked with a change in the metabolism. This change also affects the outcome as weight loss in lung cancer patients is associated with higher treatment-related toxicity and shorter survival depending on the cancer type [22] . But more than weight loss, type of lost masses is very important as obesity may hide low muscle mass.

3.2. Sarcopenia, cachexia and body composition

Aging and obesity are among the main challenges for health care strategy in industrialized countries. Aging causes a progressive loss of muscle mass independently of any disease process [23] and [24] and is reported as sarcopenia. Around the fifth decade of life, muscle mass decreases at an annual rate of 1–2% [25] . Sarcopenia is one of the four main reasons for loss of muscle mass, the others being anorexia, dehydration and cachexia [26] . Sarcopenia, as a multifactorial geriatric syndrome in which loss of muscle mass is the key feature, is a distinct entity from cachexia. To designate the disease-associated reduction of muscle mass, the word “myopenia” has been suggested [27] . In elderly cancer patients, the overlapping of both conditions frequently occurs and can act synergistically to induce the loss of muscle mass. Most of the patients with cachexia present sarcopenia, whereas most of the sarcopenic patients do not have cachexia. In clinical practice, sarcopenia may also pre-exist to cachexia. Sarcopenia features frailty, falls, fractures, extended hospitalization, disability, reduced ability to cope with major illness stress, infectious and non-infectious complications and ultimately mortality [28] and [29].

Body composition measurements are useful to identify the degree of muscle loss and may be assessed with CT or BIA:

  • - Lumbar skeletal muscle mass index determined by CT imaging should be under 52.4 cm2/m2 for men, 38.5 cm2/m2 for women [30] .
  • - The median FFM index (=FFM/height2) measured by BIA were 18.9 kg/m2 in young (18–34 years) males and 15.4 kg/m2 in young females [31] .

Unfortunately, the word sarcopenia is used for both cachexia-related and age-related muscle loss, which often creates confusion. This is an issue as the metabolic consequences and the underlying pathophysiology of the two types of muscle losses differ somewhat [32] and [33]:

  • - In cachexia-related muscle loss, there is an increase in muscle protein degradation, basal metabolic rate and total energy expenditure and either a decrease or no change in FM.
  • - In age-related muscle loss (true sarcopenia) muscle protein degradation is unchanged, basal metabolic rate and total energy expenditure are reduced and are often associated in elderly people with an increase in FM which may culminate in sarcopenic obesity.

This distinction is important as treatment will differ. Table 2 illustrates the main differences between cachexia and sarcopenia.

Table 2 Main differences between sarcopenia and cachexia.

  Sarcopenia Cachexia
Geriatric syndrome +++
Multifactorial syndrome + +
Metabolic syndrome +/− +
Inflammatory markers +/− +++
Muscle mass ↓↓ ↓↓
Functional impact (reduced walking speed, impaired mobility) ↓↓↓
Weight loss +/− +++
Anorexia +/− +++
Body composition (BIA, DXA, CT) +++ +++
Molecular pathophysiology of muscle wasting (Ubiquitin Proteasome System) Up or down regulation Up regulation
Risk factors Constitutional factors

Ageing process

Lifestyle

Chronic health conditions

Living conditions
Chronic health conditions (liver, renal, respiratory, heart failure, cancer, AIDS, chronic inflammatory disease, etc.)

+ to +++: importance scale; ↓ to ↓↓↓: variation scale.

Sarcopenia is a first step that only identifies the muscle mass alteration. In the future, we ultimately need to diagnose “dynapenia” by assessing the muscle function and loss of strength. Recent evidences do question the relationship between sarcopenia and dynapenia in elderly [34] . Thus, we need clinical trials that compare static and dynamic measures with the prognostic in cancer patients.

3.3. Body composition: a method to diagnose cachexia in obese patients

Patients are becoming increasingly obese and cachexia in this population will be difficult to identify and manage. The newly diagnosed patients with incurable cancer commonly have a body mass index (BMI) above 25 [35] and [36]. When such obese patients lose weight, the nature of their risk may differ from the severely wasted patients. Therefore, the type and goal of nutritional intervention needs to be adapted to the situation.

Obese patients are not protected from cachexia because they are overweight and because cachexia usually induces a weight loss. In fact, a small proportion of total weight loss can mask a proportionally higher – and thus dramatic from the chemotoxicity point of view – loss of skeletal muscle mass [37] . So, the growing prevalence of obesity may hinder the diagnosis of cachexia.

Sarcopenic obesity population and the new definition of cachexia do question the efficiency of widely spread inductors like BMI or weight loss percentage as a criterion for diagnosis, treatment and prognosis. Only body composition can help having a clear view on the various body compartments and thus to diagnose cachexia and adapt nutrition protocols.

3.4. Body composition and survival

BIA is an easy-to-use, non invasive and reproducible technique to evaluate changes in body composition. BIA has been validated for the assessment of body composition and nutritional status in a variety of patient population including cancer patients [38] . BIA measures the body tissue impedance. This impedance is split into its two core components, resistance and reactance. Then, standardized prediction equations are used to calculate FFM, total body water and other body composition compartments [39] .

Bioelectric resistance is the pure opposition of a biological conductor to the flow of an alternating electric current, whereas capacitance is the resistive effect due to capacitance produced by tissue interfaces and cell membranes. Capacitance causes the current to lag behind the voltage creating a phase shift. This shift is quantified geometrically as the angular transformation of the capacitance: resistance ratio or phase angle. The phase angle is calculated as the angle between the impedance and the resistance and is expressed in degrees. The relationship between capacitance and Resistance® is interesting because it reflects different electrical properties that are affected in various ways by disease.

Gupta et al. [40] have demonstrated that phase angle is a strong prognostic indicator in advanced pancreatic cancer. Patients with phase angle greater than 5.0° had a median survival time of 10.2 months while the other ones had only 6.3 months left. The same group [41] found that BIA-derived phase angle was an independent prognosis indicator in patients with stage IIIB and IV in non small cell lung cancer: patients with phase angle above 5.3° had a median survival of 7.6 months versus 5.3 months for the other group. Other studies do confirm these results [42], [43], and [44]. This is probably the most important application of BIA for cancer patients.

4. Body metrics and treatment

As anticancer drugs have a narrow therapeutic index, good predictors of individual variation in toxicity represent a key success factor of a chemotherapy treatment. A study [22] demonstrates that weight loss as a symptom of lung cancer predicts for toxicity from treatment and shorter survival. In non small cells lung cancer (NSCLC) weight loss is associated with the delivery of fewer cycles of chemotherapy and more treatment delays, together with an increased incidence of anemia as toxicity. This is the first study to examine the relationship between weight loss, toxicity, delivery of chemotherapy, response to treatment and prognosis in patients with lung cancer and mesothelioma. In NSCLC weight loss is associated with the delivery of fewer cycles, of chemotherapy and more treatment delays. Furthermore, patients whose weight stabilized on treatment had significantly better progression free and overall survival than those with continued weight loss. In contrast, weight loss associated with SCLC neither affected the number of patients completing at least three cycles of chemotherapy, the incidence of toxicity nor the response rate. Thus different patterns of outcomes were identified for patients with NSCLC compared to those with SCLC. The hypothesis of these differences is suggested by a study indicated that weight loss in SCLC is associated with a greater increase in resting energy expenditure adjusted for FFM than NSCLC associated weight loss. This suggests that body composition and other body metrics may help to understand the mechanism of weight loss and to minimize the value of the Dose Limiting Toxicity (DLT).

4.1. Body surface area and treatment toxicity

Body surface area (BSA) was introduced in oncology in order to derive a safe starting dose. Dose is calculated as a function of BSA, which developed because of convention, rather than as a result of scientific research. The use of BSA as the means to individualize the dose of chemotherapy has been questioned [45], [46], and [47]. Many cytotoxic drugs are largely metabolized and excreted by the liver and the BSA is not a good indicator of this function [46] . While renal function does correlate to some extent with body size, particularly BSA, few drugs have renal excretion as the sole mechanism for drug elimination. The pharmacokinetics (PK) of carboplatin, although almost exclusively handled by the glomerulus, are better described by glomerul filtration rate than by BSA [48] . BSA is the usual basis of normalization for stature and weight for the administration of many chemotherapy agents and by estimated lean body mass. Prado et al. Finding that estimated FFM showed a poor association with body-surface-area [30] . Assuming that FFM represents the volume of distribution of many cytotoxic chemotherapy drug, they estimated that individual variation in FFM could account for up to three-times variation in effective volume of distribution for chemotherapy administered per unit body surface area, in this population. But how relevant is BSA to optimize drug prescription? An editorial of the Journal of Clinical Oncology title: Body-surface area as a basis for dosing of anticancer agents: science, myth, or habit [49] ? This article more specifically highlights the fact that we switch from the basic BSA-based dosing in phase I trials to the BSA-based dosing of virtually all drugs prescribed by oncologists without looking for a better dose predictor. Does BSA-based dosing make pharmacologic sense? We could expect that measure of body size would be correlated to organ size and function but is BSA, a function of height and weight, being the best correlate? This is not a new story and the distribution of fat-soluble drugs (such as anesthetics) in the fat mass is well known by the anesthesiologists. The measure of body size that has been suggested to be the best correlate to clearance is LBM, which has been shown to be better than BSA for multiple agents [50] .

Another study about Epirubicin [51] confirms that BSA is questionable and that liver function tests may give a better indication. The primary aim was to test whether BSA or any measure of body size correlated with the effects of epirubicin. There was no correlation between either BSA or weight and any PK parameter or toxicity. Both neutropenia and epirubicin PK independently correlated with various measures of liver function. Further, many cytotoxic drugs are largely metabolized and excreted by the liver and the BSA of individuals does not correlate with the capacity of these processes. This prospective study found no significant correlation of BSA with epirubicin PK or the severity of neutropenia resulting from epirubicin treatment. So, why BSA is such a widely spread method to determine treatment dosing? Probably because it is intuitive to derive the dose from a factor linked to body size. However, this is not enough to make BSA the only one or the best predictor.

4.2. Body mass index, treatment completion and treatment toxicity

Is BMI a better predictor than BSA? Dignam et al. [52] focus on the association between BMI, chemotherapy toxicity and completion of treatment. Although several studies have established a link between obesity and colon cancer risk, little is known about the effect of obesity on outcomes after diagnosis. To examine whether mortality and recurrence hazard differences by BMI could be related to differences in treatment tolerance or completion, they examined whether BMI was associated with toxicity events from chemotherapy or the completion of the chemotherapy regimen. They demonstrate that obese and very obese patients have lower rates of neutropenia and stomatitis than normal body weight patients. On the other side, underweight patients have higher incidence of stomatitis. They examined whether dose “capping” was more frequent among obese patients, as expected, capping was more frequent among heavier patients with 55% of the obese compared with 7% of normal weight patients. Secondly they examined whether BMI was associated with the number of chemotherapy courses begun. Underweight patients were more likely than normal weight patients to receive less than the planned dose, whereas obese and very obese patients were not.

Jeffrey et al. [53] study the relationship between body mass index and treatment-related toxicities in patients with rectal cancer. They evaluated a nested cohort of 1688 patients with stages II and III rectal cancer. During chemotherapy-only treatments increasing BMI was inversely related with grade 3 or 4 neutropenia and any grade 3 or 4 toxicity. During combination of chemotherapy and radiation therapy treatment, only grade 3 or 4 leukopenia and neutropenia were significantly less when comparing normal weight patients with obese patients.

As a conclusion, BMI and treatment toxicity are somehow linked, but the mechanisms remains unclear and hard to use, at least alone.

4.3. Lean body mass and treatment toxicity

Is there a better treatment toxicity predictor than BSA and BMI?

Some studies suggest that LBM may be useful to normalize doses of chemotherapy.

Antoun et al. [54] has focused on body composition as a potential determinant of toxicity in response to commonly used antineoplastic agents. Sorafenib is applied at a constant dose of 800 mg/day, without adjustment for body weight. Renal cell carcinoma patients have strikingly heterogeneous weight and body mass index (BMI; 15.2–38.1 kg/m2). And thus body size could be a potential source of variation in drug concentration and metabolism. They analyzed a sample of the TARGET trial, a phase III study of Sorafenib versus placebo for RCC. The objective was to test for relationship between BMI and muscle mass and the presence of DLT, defined here as toxicity leading to dose reduction (to 400 mg/day) or to termination of treatment. Regional adipose tissues (visceral and sub cutaneous) and muscle tissues were assessed on CT images in the patient record, which had been done for diagnostic and follow-up purposes. They analyzed the toxicity data in function of both BMI and L3 skeletal muscle index, on the basis of the idea that at a flat dose of 800 mg, people with smaller body weight would have a higher dose per unit body weight and also that sarcopenia may be a risk factor for toxicity. All patients considered, the mean BMI of patients with DLT was significantly lower than in those who were able to continue at 800 mg/day Sorafenib (23.1 versus 26.0 kg/m2). They showed that in patients with renal carcinoma who received Sorafenib, the DLT was observed for 41% of sarcopenic patients whose BMI was under 25 kg/m2 and only for 13% of non-sarcopenic and/or overweight or obese patients. Toxicity was prevalent in sarcopenic male patients with BMI lower than 25 kg/m2, with 71% being unable to continue treatment at 800 mg/day. Considering sarcopenia per se, 37% of males who were sarcopenic experienced DLT and only 5% of male who were not sarcopenic had a DLT (p < 0.04). The worst case scenario is the males who were simultaneously below BMI 25 and sarcopenic: 5 out of 7,71% experienced DLT. The most important is the fact that with the higher proportion of cancer patients presented as overweight or obese, these high weights obscure a loss of muscle mass, and if one employs cut point validated in the literature [30] , 54% patients in the study were sarcopenic, while only 7% would be considered clinically underweight by accepted criteria (BMI > 18.5).

Prado et al. [30] demonstrated that sarcopenic obesity (defined through lumbar CT images), was a predictor of survival with a median survival (10 months) diminished by half. One hypothesis is that in obesity, a very small degree of weight loss can mask a proportionally higher loss of skeletal muscle mass [26] and thus lead to a chemotherapy dose above the toxicity limit when the dose is calculated on the total weight without body composition correction elements.

Data from a prospective study of colon cancer patients treated with 5-FU and Leucovorin [55] , showed that women who had a low proportion of skeletal muscle in relation to their BSA had a higher incidence of DLT (OR = 16.73; p = 0.021). In this study a cut off point of 20 mg 5-FU per kg of lean body mass was a predictor of 5-FU toxicity.

Recent works have focused on body composition as a potential determinant of pharmacokinetic of commonly used antineoplastic agents. A study [56] demonstrates that among women with metastatic breast cancer receiving Capecitabine treatment, 25% were classified as sarcopenic (defined using published cut-point [30] ) and toxicity was present in 50% of sarcopenic patients compared with only 20% of non-sarcopenic patients. Time to tumor progression was shorter in sarcopenic patients (101.4 days; confidence interval, 59.2–142.9) compared with non-sarcopenic patients (173.3 days; confidence interval, 126.1–220.5; p = 0.05). Variables known to relate to chemotherapy toxicity (age, BSA, performance status and albumin levels) were further modeled using logistic regression. Sarcopenia was the only significant predictor of toxicity (hazard ratio, 4.1; p = 0.04) even after controlling for performance status, albumin and age.

All these results highlight the importance of muscle mass, independently of weight or of BMI. Several mechanisms may be hypothesized: alteration in the distribution, metabolism and clearance of antineoplastic agents, increased propensity of nosocomial infections, systemic inflammation, etc.

As a conclusion, body composition, especially LBM, is determinant in chemotherapy toxicity and time to tumor progression. Therefore, using body composition techniques will provide synergetic advantage to the classical cancer treatment and help to reach the best efficiency, especially in the area of chemotherapy dose determination. A practical approach would be to use the initial CT (used for cancer diagnosis) to precisely determine the optimal chemotherapy dose. Then, routine BIA should be performed in order to early detect the anorexia–cachexia syndrome, to establish a survival prognosis with the phase angle and to monitor the nutrition efficiency, especially for what concerns fine-tuning of the nutritional support. This concept has been reviewed in details by Thibault et al. [57] .

5. Conclusion

The latest cachexia definitions highlight the importance of body composition as a critical diagnostic criterion of this syndrome. Body composition is more accurate than BSA to determine the optimal chemotherapeutic drug dosing. This is particularly true for patients with unusual body composition such as obese or elderly patients.

Nutritional support has to be considered rather as synergetic and neo-adjuvant to oncological treatment than as a separated action. Nutrition is not a routine care and patients-specific fine-tuned prescription is mandatory.

The next step is to develop the tools that will allow this practice to become standard in cancer treatment. Future studies need to integrate body composition in order to determine the role and the best timing pattern for nutrition support to improve the tolerance to oncology treatments, benefit-risk and cost-benefit ratios.

Fig. 2 illustrates a recommended nutrition intervention scheme.

gr2

Fig. 2 Recommended nutritional intervention in oncology.

Reviewers

Jann Arends, M.D., Albert Ludwigs University, Department of Medical Oncology, Tumor Biology Center, Freiburg, Germany.

Pr. Alessandro Laviano, Sapienza University, Department of Clinical Medicine, viale dell’Università 37, I-00185 Rome, Italy.

Conflict of interest statement

There are no financial or other aspects that could lead to a conflict of interest.

Funding

None of the authors received funding for this study.

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Nathalie Jacquelin-Ravel is Medical Doctor at the Multidisciplinary Oncology Institute of Clinique de Genolier, Switzerland. Certificate of Advanced Studies in Clinical Nutrition, Swiss Clinical Nutrition Society (SSNC), Diploma in Clinical Nutrition and Metabolism (The European Society for Clinical Nutrition and Metabolism). Research activity and in charge of the “OncoNut” quality project at Geneva University Hospital.

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Claude PICHARD is Professor of Nutrition and Head of Clinical Nutrition at the Geneva University Hospital, Switzerland. He has published over 240 peer-review papers, and is currently the chairman of the European Society for Clinical Nutrition and Metabolism (ESPEN).

Footnotes

a Clinical Nutrition, Clinique de Genolier, Genolier, Switzerland

b Clinical Nutrition, Geneva University Hospital, Geneva, Switzerland

Corresponding author at: Clinical Nutrition, Clinique de Genolier, Genolier, Switzerland. Tel.: +41 78 84 84 194.


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