Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Paula Ravasco
 -- 2663 2023-12-19 11:43:57 |
2 Reference format revised.
Lindsay Dong
+ 6 word(s) 2669 2023-12-22 01:54:08 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Branco, M.G.; Mateus, C.; Capelas, M.L.; Pimenta, N.; Santos, T.; Mäkitie, A.; Ganhão-Arranhado, S.; Trabulo, C.; Ravasco, P. Bioelectrical Impedance Analysis for Body Composition Assessment. Encyclopedia. Available online: https://encyclopedia.pub/entry/52923 (accessed on 09 July 2024).
Branco MG, Mateus C, Capelas ML, Pimenta N, Santos T, Mäkitie A, et al. Bioelectrical Impedance Analysis for Body Composition Assessment. Encyclopedia. Available at: https://encyclopedia.pub/entry/52923. Accessed July 09, 2024.
Branco, Mariana Garcia, Carlota Mateus, Manuel Luís Capelas, Nuno Pimenta, Teresa Santos, Antti Mäkitie, Susana Ganhão-Arranhado, Carolina Trabulo, Paula Ravasco. "Bioelectrical Impedance Analysis for Body Composition Assessment" Encyclopedia, https://encyclopedia.pub/entry/52923 (accessed July 09, 2024).
Branco, M.G., Mateus, C., Capelas, M.L., Pimenta, N., Santos, T., Mäkitie, A., Ganhão-Arranhado, S., Trabulo, C., & Ravasco, P. (2023, December 19). Bioelectrical Impedance Analysis for Body Composition Assessment. In Encyclopedia. https://encyclopedia.pub/entry/52923
Branco, Mariana Garcia, et al. "Bioelectrical Impedance Analysis for Body Composition Assessment." Encyclopedia. Web. 19 December, 2023.
Bioelectrical Impedance Analysis for Body Composition Assessment
Edit
This entry is adapted from the peer-reviewed paper 10.3390/nu15224792

Bioelectrical Impedance Analysis (BIA) is a reliable, non-invasive, objective, and cost-effective body composition assessment method, with high reproducibility. 

cancer body composition bioelectrical impedance analysis (BIA)

1. Introduction

Nutritional deterioration and progressive unintentional weight loss are prevalent conditions in patients diagnosed with cancer, and are widely associated with poor outcomes and complications through the course of the disease and treatments [1][2].
Cancer leads to metabolic alterations that contribute to depletion in nutritional status resulting in changes in body composition, which in turn are negative predictors of therapy toxicity, clinical outcomes, quality of life and survival. Therefore, it is critical to timely identify and treat malnutrition in order to enhance clinical outcomes. Thus, body composition should be part of nutritional assessment in these patients. Nevertheless, it remains a challenge due to a variety of methods and tools to assess nutritional status and body composition in patients with cancer [1].
Methods for body composition assessment include anthropometry, bioelectrical impedance analysis (BIA), air displacement plethysmography (ADP), dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI) [1][2]. While DXA has traditionally been regarded as the “gold standard” for assessing body composition, research has also emphasized the validity and reliability of body composition evaluation through CT and MRI [3][4]. Nonetheless, these methods are costly, may involve radiation (CT), and are not commonly performed, rendering them often inaccessible [5]. In contrast, Bioelectrical Impedance Analysis (BIA) emerges as a dependable, non-invasive, objective, and cost-effective alternative with high reproducibility and minimal training requirements [1]. Recent studies have even indicated potential advantages of utilizing BIA for body composition assessment in patients with cancer [6][7].
BIA estimates total body water (TBW) by measuring impedance (Z), which results from resistance (R) and reactance (Xc) components [1][8]. This relationship can be expressed using the equation Z2 = R2 + Xc2 [9]. R represents the body’s opposition to the flow of an alternating current, while reactance gauges the electrical charge stored in cell membranes. Impedance measurements can be obtained using single or multiple current frequencies [1][10]. BIA calculates an individual’s resistance to a weak electric current, enabling the estimation of a two-compartment model of body composition, which includes fat mass (FM) and fat-free mass (FFM), using empirically derived equations [5][11]. Lower BIA-derived FFM has been associated with malnutrition in hospitalized patients and linked to unfavorable clinical outcomes, such as prolonged hospital stays and increased 28-day mortality in intensive care settings [12][13][14].
In the context of medical oncology, BIA-derived body composition measures can be clinically relevant. Two crucial BIA-derived parameters are Bioelectrical Impedance Vector Analysis (BIVA) and Phase Angle (PhA), both of which provide insights into nutritional and hydration status [15]. BIVA assesses hydration and cell mass independently of body size and has been used to explore the connections between hydration status, symptoms, and survival in persons with advanced cancer [16][17]. By converting BIVA measurements to z-scores, researchers can compare body composition across different study populations, considering variables like cancer type, stage, gender, and ethnicity [18][19].
PhA serves as an indicator of cell membrane integrity and water distribution inside and outside the cell membrane [20]. It typically ranges from 5 to 7 in healthy populations but tends to decrease with age due to muscle mass loss and declining body fluid proportions [21]. PhA has been linked to increased mortality and morbidity in various patient groups and has demonstrated prognostic value in malignancies and chronic diseases, including CRC and PC [22][23][24].
However, in persons with advanced cancer, factors like dehydration or ascites can lead to imprecise BIA-derived FFM measurements. Discrepancies have been observed in advanced LC and CRC patients [3]. Additionally, BIA may inaccurately gauge FFM or FM in patients with breast and gynecological malignancies due to lymphedema [1].
To ensure the most accurate assessment, it is essential to determine whether BIA is being used as an indicator of nutritional and metabolic health or to assess risk based on derived body composition measurements [10]. Several studies have underscored BIA’s role in predicting preoperative risk and postoperative complications, identifying patients with sarcopenia in routine clinical practice, and providing valuable information on body composition changes in patients with cancer [5][16][25][26][27][28].
Therefore, BIA has demonstrated itself as a precise and valid tool with substantial clinical relevance, offering valuable insights that could have a significant impact on clinical outcomes [29][30][31].

2. Bioelectrical Impedance Analysis for Body Composition Assessment

2.1. Head and Neck Cancer

Persons diagnosed with head and neck cancer (HNC) often face a heightened risk of malnutrition due to the location of the tumor and the impact of oncological treatments on their ability to consume food. This challenge is particularly pronounced among those with oral, oropharyngeal, and hypopharyngeal cancers. Consequently, there is a need for tools to identify malnutrition in this specific patient group.
Almada-Correia et al. [2] conducted a comprehensive review of the existing literature on body composition assessment in patients with HNC to determine the most appropriate method for this population. Their review considered various methods for assessing body composition in clinical settings, including anthropometry, BIA, CT, and DXA. The findings indicated that CT and DXA were the established standards for evaluating body composition in patients with cancer, although they are not routinely used in the management of HNC cases [2].
Malecka-Massalska et al. [32] also emphasized the relevance of BIA, particularly BIVA, as a method that could provide objective measures to enhance clinical decision-making and predict outcomes in HNC patients. Similarly, Axelsson et al. [33] highlighted three different factors derived from BIA variables: Fat-Free Mass Index (FFMI), PhA and Standardized Phase Angle (SPA), adjusted for age and sex. A PhA cutoff value at 5.95° was identified as the most accurate predictor of 5-year survival. Both PhA and SPA were considered valuable prognostic tools for patients with advanced HNC [33].

2.2. Breast Cancer

Breast cancer (BC) has emerged as the predominant cause of cancer incidence globally. The development of BC has been associated with the accumulation of adipose tissue in adulthood over the years. Recommendations for patients with BC include weight loss for those who are obese and a reduction in adiposity reserves.
In these patients, undetected or unaddressed malnutrition can lead to severe adverse outcomes. The existing body of literature demonstrates that malnutrition is linked to elevated morbidity and mortality, and the nutritional status plays a pivotal role in the prognosis of BC, potentially influencing the progression of the disease [34].
Nutrition-related symptoms, such as nausea, vomiting, loss of appetite, constipation, diarrhea, stomach pain, altered taste perception, sore mouth, and difficulty swallowing, can arise from the tumor itself or as side effects of treatment. These symptoms adversely affect dietary intake and elevate the risk of malnutrition. Malnourished patients with BC can tolerate fewer treatment cycles, rendering them more vulnerable to treatment-related toxicities and increased hospitalizations [34][35].
The impact of body composition on BC risk and outcomes in BC survivors is well-documented. Therefore, the integration of body composition assessment into the comprehensive care of these patients is essential. A scoping review involving persons with BC [36] aimed to investigate changes in weight and body composition, suggesting the need for further investigations with long-term prospective designs and consistent assessment of weight and body composition using the same measurement tools. BIA was mentioned as a cost-effective and user-friendly tool that could contribute to standardizing measurements [36].

2.3. Oesophageal Cancer

Somewhat similarly to HNC, patients with EC are at nutritional risk due to the location of the disease and side effects of anticancer therapies and/or surgery. Consequently, most patients with EC become malnourished. Thus, these patients represent a group of persons with cancer who are nutritionally compromised, due to dysphagia and oncological treatments. In addition, patients undergoing surgery for cancer are at particular risk of post-operative complications [37]. BIA may offer an additional method of identifying patients at risk of post-operative morbidity.
A recent review [38] on current literature regarding the assessment of body composition in EC patients could not agree on the best tool, due to inconsistencies in methods of assessing and reporting body composition, although authors recognized its usefulness regarding decision-making support in patients with EC.
Powell et al. [26] conducted a study with the objective of establishing a connection between low muscle volume (LMV) defined by Bioelectrical Impedance Analysis (BIA) in patients undergoing EC surgery and clinical outcomes.

2.4. Hepatocellular Cancer

Although common, malnutrition is frequently an underdiagnosed condition in patients with hepatocellular carcinoma (HCC). These patients are at a special increased risk for malnutrition as the liver is the central organ involved in nutrients metabolism [39].
In a prospective study conducted by Lee et al. [30], the impact of various factors, including Bioelectrical Impedance Analysis (BIA)-derived PhA, the presence of sarcopenia, and edema index, was evaluated in relation to postoperative complications in patients with hepatocellular carcinoma (HCC). The study found that BIA offered valuable additional clinical insights into the occurrence of postoperative complications in patients with HCC scheduled for surgery. However, it should be noted that due to a relatively short follow-up duration, the precise role of BIA in predicting short-term survival remained unclear [30].

2.5. Pancreatic Cancer

PC has a poor overall prognosis, with a low 5-year survival rate. However, patients with early tumor resection have a higher chance of more successful treatments. Patients with PC often are malnourished and suffer from cancer cachexia. PC has been characterized as a highly catabolism inducer, with rapid depletion of the host’s body compartments. In fact, a large proportion of these patients have already lost 10% of body weight at the diagnosis. Early detection of wasting is central in the clinical approach of these patients [40]. Therefore, it is crucial to early assess nutritional status to identify and treat malnutrition and also to prevent or counteract cachexia. In clinical practice, anthropometric methods have been used but are not ideal: they are time-consuming and difficult to perform, especially in bed-ridden patients. Additionally, objective indicators such as serum albumin and transferrin are difficult to interpreter due to non-nutritional factors [41].
Regarding specifically PC, the only available systematic review on this topic was conducted by Bundred et al. [41], who summarized the existing literature on body composition assessment in patients with PC and assessed its impact on perioperative outcomes and long-term survival.

2.6. Gastric Cancer

GC leads to important depletion of muscle and fat tissue due to surgical interventions, chemo- and radiotherapy. In line with this, GC patients are at high nutritional risk and malnutrition-related to malignant disease, leading to lower compliance with treatment and complications during surgery. Complete surgical resection remains the only curative modality for early-stage GC. These patients in the perioperative period first consume LBM, which might not be evident from BMI nor other nutritional scores. BIA can overcome these difficulties [42].
In a cross-sectional study conducted by Gao et al. [43], the accuracy of BIA in estimating visceral fat area (VFA) in individuals with GC was explored. The study’s findings revealed a significant correlation and satisfactory reliability between VFA measurements obtained by CT and BIA [43].

2.7. Colorectal Cancer

Excess body weight and metabolic alterations have been identified as risk factors for cancer of the colon-rectum. Patients with advanced disease stage can develop a wasting-like phenotype of nutritional status, and gastrointestinal cancers globally show high prevalence of malnutrition.
In a prospective study led by Jones et al. [44], the primary objective was to assess the agreement between BIA and MAMC in comparison to CT scans for the measurement of muscle mass and the identification of sarcopenia in patients with CRC. The study’s conclusion revealed that both BIA and MAMC were found to be inadequate methods for detecting reduced muscle mass in patients with CRC when compared to the measurements of CT-derived muscle mass at L3 [44]. Another study [31] explored the relationships between a single cross-sectional area of skeletal muscle at the lumbar region (L3) measured by CT and total body SMM assessed by BIA in primary CRC patients. Kim et al. [31] identified BIA as an alternative method to CT scans, offering a non-invasive and cost-effective tool for assessing body composition status, including SMM, in patients with CRC.

2.8. Lung Cancer

Hansen et al. [45] conducted a study to explore the agreement between BIA assessment of body composition and software analysis of CT scans in patients with non-small cell LC. The study’s conclusion indicated that both methods were not directly comparable for body composition measurements. Furthermore, BIA was found to overestimate SMM and underestimate FM [45]. In another study, Kovarik et al. [46] provided a summary of recent evidence concerning various methods, including BIA, for assessing changes in body composition in LC patients. The study underscored the importance of BIA and BIA-derived PhA in predicting outcomes for LC patients. Similarly, Gupta et al. [47] observed that BIA-derived PhA served as an independent prognostic indicator in patients with stage IIIB and IV non-small cell LC [45][46][47].

2.9. Skin Cancer

A prospective study conducted by Zopfs et al. [29] evaluated the correlation between simple, planimetric measurements in CT slices and measurements of patient body composition and anthropometric data, performed with BIA and metric clinical assessments. The study concluded that simple measurements in a single axial CT slice could determine body composition parameters, with high clinical relevance [29].

2.10. All Cancer Types

Adequate body composition has been proved essential for neoplastic disease outcome. BIA has been found to be a prognostic indicator in several chronic conditions, including cancer [24].
Cereda et al. [48] conducted a study with the aim of investigating the potential independent prognostic roles of FFMI, BMI, and weight loss (WL), and their associations with quality of life (QoL) in a substantial cohort of patients with cancer. While acknowledging the versatility and non-invasive nature of BIA for bedside assessments, the study emphasized the necessity for additional confirmatory studies to validate the usefulness and prognostic value of BIA-derived FFMI in people diagnosed with cancer [48].
In a cross-sectional study by Mueller et al. [49], the validity of BIA as a diagnostic tool in patients with malignancies, both with and without malnutrition, was investigated. BIA was recognized as a valid diagnostic tool for assessing muscle and FM, and its use was recommended for the early detection and short-term follow-up of malnutrition and cachexia [49].

2.11. General Considerations for the Use of BIA

Bioelectric impedance analysis (BIA) is a safe [38], non-invasive [1][2][7][15][41][50][51][52], easy-to-use [1][2][15][41][51], reproducible [1][2], indirect method [2] for measuring body composition. It appears to show good correlations, when compared to other methods [50], despite being considered less accurate than radiological assessment methods [38].
Although it has good application consistency and is considered a useful tool for assessing the nutritional status of persons with cancer [2][52], BIA can underestimate FFM in patients with advanced cancer, compared with DXA [1]. Moreover, considering BIA depends on water volume, it has limited use in people with advanced-staged cancer and patients with BC and is not capable of distinguishing tumor or lymphedema in the lean and fat tissue depots [1]. While BIA is recognized as a validated tool to assess body composition in patients with cancer [2][50][52], some studies reported some inconsistent findings, with poorer accuracy and precision in obese and edematous individuals [41][51]. As a practical and objective assessment method [1][41][51][52], BIA is also considered to be relatively inexpensive, when compared to more sophisticated methods like DXA, CT or MRI [1][2][7][38][50], although it is usually more expensive than anthropometric measures [1].
BIA is a portable [1][7][15][50], time and cost-effective technique [15], that requires little training [1]. However, this tool is not routinely available outside the research setting [38]. Many prediction equations using linear regression rely on BIA to estimate body composition, based on variables that may differ between different populations and were derived from healthy individuals [1][2][15][50]. Nonetheless, BIA-derived PhA, a prognostic factor of patient survival [15], which, along with BIVA, is considered to reflect both nutritional and hydration status [7][15], does not depend on regression equations to be calculated [50]. BIA-derived measures (FFM, FM, body weight, BMI) are correlated with an increased risk of developing colon cancer and potentially other cancers [7] and may serve as early indicators for improvement in nutritional and health status [7][52], but can also be useful to evaluate and predict outcomes, such as post-operative complications [10].

3. Conclusions

The identified studies have considered BIA to be a suitable and valid method for the assessment of body composition in oncology. BIA-derived measures have shown good potential and relevant clinical value in preoperative risk evaluation, in the reduction of postoperative complications and hospital stay and as an important prognostic indicator in patients with cancer. Hence, research encourages the implementation of this method in the nutritional assessment at a larger extent.

References

  1. Di Sebastiano, K.M.; Mourtzakis, M. A critical evaluation of body composition modalities used to assess adipose and skeletal muscle tissue in cancer. Appl. Physiol. Nutr. Metab. 2012, 37, 811–821.
  2. Almada-Correia, I.; Neves, P.M.; Mäkitie, A.; Ravasco, P. Body Composition Evaluation in Head and Neck Cancer Patients: A Review. Front. Oncol. 2019, 9, 1112.
  3. Mourtzakis, M.; Prado, C.M.; Lieffers, J.R.; Reiman, T.; McCargar, L.J.; Baracos, V.E. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl. Physiol. Nutr. Metab. 2008, 33, 997–1006.
  4. Yip, C.; Dinkel, C.; Mahajan, A.; Siddique, M.; Cook, G.; Goh, V. Imaging body composition in cancer patients: Visceral obesity, sarcopenia and sarcopenic obesity may impact on clinical outcome. Insights Imaging 2015, 6, 489–497.
  5. Grossberg, A.J.; Rock, C.D.; Edwards, J.; Mohamed, A.S.; Ruzensky, D.; Currie, A.; Rosemond, P.; Phan, J.; Gunn, G.B.; Frank, S.J.; et al. Bioelectrical impedance analysis as a quantitative measure of sarcopenia in head and neck cancer patients treated with radiotherapy. Radiother. Oncol. 2021, 159, 21–27.
  6. Mulasi, U.; Kuchnia, A.J.; Cole, A.J.; Earthman, C.P. Bioimpedance at the Bedside: Current Applications, Limitations, and Opportunities. Nutr. Clin. Pract. 2015, 30, 180–193.
  7. Grundmann, O.; Yoon, S.L.; Williams, J.J. The value of bioelectrical impedance analysis and phase angle in the evaluation of malnutrition and quality of life in cancer patients—A comprehensive review. Eur. J. Clin. Nutr. 2015, 69, 1290–1297.
  8. Kyle, U.G.; Bosaeus, I.; De Lorenzo, A.D.; Deurenberg, P.; Elia, M.; Gomez, J.M.; Heitmann, B.L.; Kent-Smith, L.; Melchior, J.-C.; Pirlich, M.; et al. Bioelectrical impedance analysis? Part I: Review of principles and methods. Clin. Nutr. 2004, 23, 1226–1243.
  9. Jager-Wittenaar, H.; Dijkstra, P.U.; Earthman, C.P.; Krijnen, W.P.; Langendijk, J.A.; van der Laan, B.F.; Pruim, J.; Roodenburg, J.L. Validity of bioelectrical impedance analysis to assess fat-free mass in patients with head and neck cancer: An exploratory study. Head Neck 2013, 36, 585–591.
  10. Matthews, L.; Bates, A.; Wootton, S.; Levett, D. The use of bioelectrical impedance analysis to predict post-operative complications in adult patients having surgery for cancer: A systematic review. Clin. Nutr. 2021, 40, 2914–2922.
  11. Ellis, K.J. Innovative Non-or Minimally-Invasive Technologies for Monitoring Health and Nutritional Status in Mothers and Young Children Selected Body Composition Methods Can Be Used in Field Studies 1. J. Nutr. 2001, 131, 1589–1595.
  12. Kyle, U.G.; Morabia, A.; Slosman, D.O.; Mensi, N.; Unger, P.; Pichard, C. Contribution of body composition to nutritional assessment at hospital admission in 995 patients: A controlled population study. Br. J. Nutr. 2001, 86, 725–731.
  13. Pichard, C.; Kyle, U.G.; Morabia, A.; Perrier, A.; Vermeulen, B.; Unger, P. Nutritional assessment: Lean body mass depletion at hospital admission is associated with an increased length of stay. Am. J. Clin. Nutr. 2004, 79, 613–618.
  14. Thibault, R.; Makhlouf, A.-M.; Mulliez, A.; Gonzalez, M.C.; Kekstas, G.; Kozjek, N.R.; Preiser, J.-C.; Rozalen, I.C.; Dadet, S.; Krznaric, Z.; et al. Fat-free mass at admission predicts 28-day mortality in intensive care unit patients: The international prospective observational study Phase Angle Project. Intensive Care Med. 2016, 42, 1445–1453.
  15. Mantzorou, M.; Tolia, M.; Poultsidi, A.; Pavlidou, E.; Papadopoulou, S.K.; Papandreou, D.; Giaginis, C. Can Bioelectrical Impedance Analysis and BMI Be a Prognostic Tool in Head and Neck Cancer Patients? A Review of the Evidence. Cancers 2020, 12, 557.
  16. Lundberg, M.; Nikander, P.; Tuomainen, K.; Orell-Kotikangas, H.; Mäkitie, A. Bioelectrical impedance analysis of head and neck cancer patients at presentation. Acta Oto-Laryngol. 2017, 137, 417–420.
  17. Nwosu, A.C.; Mayland, C.R.; Mason, S.; Cox, T.F.; Varro, A.; Ellershaw, J. The Association of Hydration Status with Physical Signs, Symptoms and Survival in Advanced Cancer—The Use of Bioelectrical Impedance Vector Analysis (BIVA) Technology to Evaluate Fluid Volume in Palliative Care: An Observational Study. PLoS ONE 2016, 11, e0163114.
  18. Piccoli, A.; Pillon, L.; Dumler, F. Impedance vector distribution by sex, race, body mass index, and age in the United States: Standard reference intervals as bivariate Z scores. Nutrition 2002, 18, 153–167.
  19. Nwosu, A.C.; Mayland, C.R.; Mason, S.; Cox, T.F.; Varro, A.; Stanley, S.; Ellershaw, J. Bioelectrical impedance vector analysis (BIVA) as a method to compare body composition differences according to cancer stage and type. Clin. Nutr. ESPEN 2019, 30, 59–66.
  20. Barbosa-Silva, M.C.G.; Barros, A.J.; Wang, J.; Heymsfield, S.B.; Pierson, R.N. Bioelectrical impedance analysis: Population reference values for phase angle by age and sex. Am. J. Clin. Nutr. 2005, 82, 49–52.
  21. Norman, K.; Stobäus, N.; Pirlich, M.; Bosy-Westphal, A. Bioelectrical phase angle and impedance vector analysis—Clinical relevance and applicability of impedance parameters. Clin. Nutr. 2012, 31, 854–861.
  22. Norman, K.; Stobäus, N.; Zocher, D.; Bosy-Westphal, A.; Szramek, A.; Scheufele, R.; Smoliner, C.; Pirlich, M. Cutoff percentiles of bioelectrical phase angle predict functionality, quality of life, and mortality in patients with cancer. Am. J. Clin. Nutr. 2010, 92, 612–619.
  23. Gupta, D.; Lammersfeld, C.A.; Burrows, J.L.; Dahlk, S.L.; Vashi, P.G.; Grutsch, J.F.; Hoffman, S.; Lis, C.G. Bioelectrical Impedance Phase Angle in Clinical Practice: Implications for Prognosis in Advanced Colorectal Cancer. Am. J. Clin. Nutr. 2004, 80, 1634–1638.
  24. Gupta, D.; Lis, C.G.; Dahlk, S.L.; Vashi, P.G.; Grutsch, J.F.; Lammersfeld, C.A. Bioelectrical impedance phase angle as a prognostic indicator in advanced pancreatic cancer. Br. J. Nutr. 2004, 92, 957–962.
  25. Lundberg, M.; Dickinson, A.; Nikander, P.; Orell, H.; Mäkitie, A. Low-phase angle in body composition measurements correlates with prolonged hospital stay in head and neck cancer patients. Acta Oto-Laryngol. 2019, 139, 383–387.
  26. Powell, A.; Mulla, M.; Eley, C.; Patel, N.; Abdelrahman, T.; Blake, P.; Barlow, R.; Bailey, D.; Lewis, W. Prognostic significance of low muscle volume in patients undergoing surgery for oesophageal cancer. Clin. Nutr. ESPEN 2020, 40, 220–225.
  27. Skroński, M.; Andrzejewska, M.; Fedosiejew, M.; Ławiński, M.; Włodarek, D.; Ukleja, A.; Nyckowski, P.; Słodkowski, M. Assessment of changes in the body composition in patients qualified for the operational treatment of the primary and metastatic liver tumors with the use of bioelectric impedance. Ann. Surg. 2018, 90, 1–5.
  28. Mikamori, M.; Miyamoto, A.; Asaoka, T.; Maeda, S.; Hama, N.; Yamamoto, K.; Hirao, M.; Ikeda, M.; Sekimoto, M.; Doki, Y.; et al. Postoperative Changes in Body Composition After Pancreaticoduodenectomy Using Multifrequency Bioelectrical Impedance Analysis. J. Gastrointest. Surg. 2015, 20, 611–618.
  29. Zopfs, D.; Theurich, S.; Hokamp, N.G.; Knuever, J.; Gerecht, L.; Borggrefe, J.; Schlaak, M.; dos Santos, D.P. Single-slice CT measurements allow for accurate assessment of sarcopenia and body composition. Eur. Radiol. 2019, 30, 1701–1708.
  30. Lee, G.H.; Cho, H.J.; Lee, G.; Kim, H.G.; Wang, H.J.; Kim, B.-W.; Lee, M.Y.; Yoon, S.Y.; Noh, C.-K.; Seo, C.W.; et al. Bioelectrical impedance analysis for predicting postoperative complications and survival after liver resection for hepatocellular carcinoma. Ann. Transl. Med. 2021, 9, 190.
  31. Kim, E.Y.; Kim, S.R.; Won, D.D.; Choi, M.H.; Lee, I.K. Multifrequency Bioelectrical Impedance Analysis Compared With Computed Tomography for Assessment of Skeletal Muscle Mass in Primary Colorectal Malignancy: A Predictor of Short-Term Outcome After Surgery. Nutr. Clin. Pract. 2019, 35, 664–674.
  32. Malecka-Massalska, T.; Smolen, A.; Morshed, K. Body composition analysis in head and neck squamous cell carcinoma. Eur. Arch. Oto-Rhino-Laryngol. 2013, 271, 2775–2779.
  33. Axelsson, L.; Silander, E.; Bosaeus, I.; Hammerlid, E. Bioelectrical phase angle at diagnosis as a prognostic factor for survival in advanced head and neck cancer. Eur. Arch. Oto-Rhino-Laryngol. 2018, 275, 2379–2386.
  34. Adam, R.; Haileselassie, W.; Solomon, N.; Desalegn, Y.; Tigeneh, W.; Suga, Y.; Gebremedhin, S. Nutritional status and quality of life among breast Cancer patients undergoing treatment in Addis Ababa, Ethiopia. BMC Women’s Health 2023, 23, 1–12.
  35. Mohammadi, S.; Sulaiman, S.; Koon, P.B.; Amani, R.; Hosseini, S.M. Association of Nutritional Status with Quality of Life in Breast Cancer Survivors. Asian Pac. J. Cancer Prev. 2013, 14, 7749–7755.
  36. Pedersen, B.; Delmar, C.; Lörincz, T.; Falkmer, U.; Grønkjær, M. Investigating Changes in Weight and Body Composition Among Women in Adjuvant Treatment for Breast Cancer: A Scoping Review. Cancer Nurs. 2019, 42, 91–105.
  37. Jordan, T.; Mastnak, D.M.; Palamar, N.; Kozjek, N.R. Nutritional Therapy for Patients with Esophageal Cancer. Nutr. Cancer 2017, 70, 23–29.
  38. Boshier, P.R.; Heneghan, R.; Markar, S.R.; E Baracos, V.; E Low, D. Assessment of body composition and sarcopenia in patients with esophageal cancer: A systematic review and meta-analysis. Dis. Esophagus 2018, 31, doy047.
  39. Schütte, K.; Tippelt, B.; Schulz, C.; Feneberg, A.; Seidensticker, R.; Arend, J.; Malfertheiner, P. Malnutrition is a prognostic factor in patients with hepatocellular carcinoma (HCC). Clin. Nutr. 2014, 34, 1122–1127.
  40. Emanuel, A.; Krampitz, J.; Rosenberger, F.; Kind, S.; Rötzer, I. Nutritional Interventions in Pancreatic Cancer: A Systematic Review. Cancers 2022, 14, 2212.
  41. Bundred, J.; Kamarajah, S.K.; Roberts, K.J. Body composition assessment and sarcopenia in patients with pancreatic cancer: A systematic review and meta-analysis. HPB 2019, 21, 1603–1612.
  42. Kirac, I.; Fila, J.; Misir, Z.; Čugura, J.F.; Žaja, A.; Benčić, I.; Štefančić, L. Nutritional evaluation in the perioperative period of gastric cancer patients using bioelectrical impedance analysis (BIA). Libr. Oncol. Croat. J. Oncol. 2019, 47, 13–16.
  43. Gao, B.; Liu, Y.; Ding, C.; Liu, S.; Chen, X.; Bian, X. Comparison of visceral fat area measured by CT and bioelectrical impedance analysis in Chinese patients with gastric cancer: A cross-sectional study. BMJ Open 2020, 10, e036335.
  44. Jones, D.J.; Lal, S.; Strauss, B.J.; Todd, C.; Pilling, M.; Burden, S.T. Measurement of Muscle Mass and Sarcopenia Using Anthropometry, Bioelectrical Impedance, and Computed Tomography in Surgical Patients with Colorectal Malignancy: Comparison of Agreement Between Methods. Nutr. Cancer 2019, 72, 1074–1083.
  45. Hansen, C.; Tobberup, R.; Rasmussen, H.H.; Delekta, A.M.; Holst, M. Measurement of body composition: Agreement between methods of measurement by bioimpedance and computed tomography in patients with non-small cell lung cancer. Clin. Nutr. ESPEN 2021, 44, 429–436.
  46. Kovarik, M.; Hronek, M.; Zadak, Z. Clinically relevant determinants of body composition, function and nutritional status as mortality predictors in lung cancer patients. Lung Cancer 2014, 84, 1–6.
  47. Gupta, D.; Lammersfeld, C.A.; Vashi, P.G.; King, J.; Dahlk, S.L.; Grutsch, J.F.; Lis, C.G. Bioelectrical Impedance Phase Angle in Clinical Practice: Implications for Prognosis in Stage IIIB and IV Non-Small Cell Lung Cancer. BMC Cancer 2009, 9, 1–6.
  48. Cereda, E.; Pedrazzoli, P.; Lobascio, F.; Masi, S.; Crotti, S.; Klersy, C.; Turri, A.; Stobäus, N.; Tank, M.; Franz, K.; et al. The prognostic impact of BIA-derived fat-free mass index in patients with cancer. Clin. Nutr. 2021, 40, 3901–3907.
  49. Mueller, T.C.; Reik, L.; Prokopchuk, O.; Friess, H.; Martignoni, M.E. Measurement of body mass by bioelectrical impedance analysis and computed tomography in cancer patients with malnutrition—A cross-sectional observational study. Medicine 2020, 99, e23642.
  50. Ferrão, B.; Neves, P.M.; Santos, T.; Capelas, M.L.; Mäkitie, A.; Ravasco, P. Body composition changes in patients with head and neck cancer under active treatment: A scoping review. Support. Care Cancer 2020, 28, 4613–4625.
  51. Kamarajah, S.K.; Bundred, J.; Tan, B.H.L. Body composition assessment and sarcopenia in patients with gastric cancer: A systematic review and meta-analysis. Gastric Cancer 2018, 22, 10–22.
  52. Małecka-Massalska, T.; Powrózek, T.; Mlak, R. Handbook of Famine, Starvation, and Nutrient Deprivation; Springer Science and Business Media LLC: Dordrecht, The Netherlands, 2017; pp. 1–23. ISBN 9783319400075.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Oncology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mariana Garcia Branco
,
Carlota Mateus
,
Manuel Luís Capelas
,
Nuno Pimenta
,
Teresa Santos
,
Antti Mäkitie
,
Susana Ganhão-Arranhado
,
Carolina Trabulo
,
Paula Ravasco
View Times: 220
Revisions: 2 times (View History)
Update Date: 22 Dec 2023
Table of Contents
    1000/1000
    Hot Most Recent
    Video Production Service
    Feedback