Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models
•4C body composition modeling reveals hydration variations in patients with cancer.•Compared to a 4C model, DXA is preferred for clinical body composition.•Regional lean soft tissue by DXA is higher than that predicted by abdominal CT scan.•Differences between DXA and CT result in a lower muscle mas...
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| creator | Bennett, Jonathan P. Ford, Katherine L. Siervo, Mario Gonzalez, Maria Cristina Lukaski, Henry C. Sawyer, Michael B. Mourtzakis, Marina Deutz, Nicolaas E.P. Shepherd, John A. Prado, Carla M. |
| description | •4C body composition modeling reveals hydration variations in patients with cancer.•Compared to a 4C model, DXA is preferred for clinical body composition.•Regional lean soft tissue by DXA is higher than that predicted by abdominal CT scan.•Differences between DXA and CT result in a lower muscle mass prevalence by DXA.•More research is needed to understand technology-specific thresholds for low muscle.
Measurement of body composition using computed tomography (CT) scans may be a viable clinical tool for low muscle mass assessment in oncology. However, longitudinal assessments are often infeasible with CT. Clinically accessible body composition technologies can be used to track changes in fat-free mass (FFM) or muscle, though their accuracy may be impacted by cancer-related physiological changes. The purpose of this study was to examine the agreement among accessible body composition method with criterion methods for measures of whole-body FFM measurements and, when possible, muscle mass for the classification of low muscle in patients with cancer.
Patients with colorectal cancer were recruited to complete measures of whole-body DXA, air displacement plethysmography (ADP), and bioelectrical impedance analysis (BIA). These measures were used alone, or in combination to construct the criterion multicompartment (4C) mode for estimating FFM. Patients also underwent abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C model was analyzed using mean constant error (CE = criterion – alternative), linear regression including root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Additionally, appendicular lean soft tissue index (ALSTI) measured by DXA and predicted by CT were compared for the absolute agreement, while the ALSTI values and skeletal muscle index by CT were assessed for agreement on the classification of low muscle mass.
Forty-five patients received all measures for the 4C model and 25 had measures within proximity of clinical CT measures. Compared to 4C, DXA outperformed ADP and BIA by showing the strongest overall agreement (CE = 1.96 kg, RMSE = 2.45 kg, MPD = 98.15 ± 2.38%), supporting its use for body composition assessment in patients with cancer. However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with 24.0% to 32.0% of patients having a different low muscle classification by CT when compa |
| doi_str_mv | 10.1016/j.nut.2024.112494 |
| format | Article |
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Measurement of body composition using computed tomography (CT) scans may be a viable clinical tool for low muscle mass assessment in oncology. However, longitudinal assessments are often infeasible with CT. Clinically accessible body composition technologies can be used to track changes in fat-free mass (FFM) or muscle, though their accuracy may be impacted by cancer-related physiological changes. The purpose of this study was to examine the agreement among accessible body composition method with criterion methods for measures of whole-body FFM measurements and, when possible, muscle mass for the classification of low muscle in patients with cancer.
Patients with colorectal cancer were recruited to complete measures of whole-body DXA, air displacement plethysmography (ADP), and bioelectrical impedance analysis (BIA). These measures were used alone, or in combination to construct the criterion multicompartment (4C) mode for estimating FFM. Patients also underwent abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C model was analyzed using mean constant error (CE = criterion – alternative), linear regression including root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Additionally, appendicular lean soft tissue index (ALSTI) measured by DXA and predicted by CT were compared for the absolute agreement, while the ALSTI values and skeletal muscle index by CT were assessed for agreement on the classification of low muscle mass.
Forty-five patients received all measures for the 4C model and 25 had measures within proximity of clinical CT measures. Compared to 4C, DXA outperformed ADP and BIA by showing the strongest overall agreement (CE = 1.96 kg, RMSE = 2.45 kg, MPD = 98.15 ± 2.38%), supporting its use for body composition assessment in patients with cancer. However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with 24.0% to 32.0% of patients having a different low muscle classification by CT when compared to DXA.
Despite discrepancies between clinical body composition assessment and the criterion multicompartment model, DXA demonstrates the strongest agreement with 4C. Disagreement between DXA and CT for low muscle mass classification prompts further evaluation of the measures and cutoffs used with each technique. Multicompartment models may enhance our understanding of body composition variations at the individual patient level and improve the applicability of clinically accessible technologies for classification and monitoring change over time.</description><identifier>ISSN: 0899-9007</identifier><identifier>ISSN: 1873-1244</identifier><identifier>EISSN: 1873-1244</identifier><identifier>DOI: 10.1016/j.nut.2024.112494</identifier><identifier>PMID: 38843564</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Accessibility ; Agreements ; bioelectrical impedance ; bioelectrical impedance analysis, BIA ; Bioelectricity ; Body composition ; Body mass index ; Cancer ; Cancer therapies ; Classification ; Clinical medicine ; Colorectal cancer ; Colorectal carcinoma ; colorectal neoplasms ; Computed tomography ; Criteria ; densitometry ; Dual energy X-ray absorptiometry ; DXA ; Electrocardiography ; Electrodes ; Error analysis ; Fat-free body mass ; Fat-free mass ; lean body mass ; Malnutrition ; Medical records ; Multicompartment model ; Muscles ; nutrition ; Nutrition research ; Oncology ; Patients ; Plethysmography ; Proteins ; regression analysis ; Regression models ; Root-mean-square errors ; Skeletal muscle ; Soft tissues ; Software</subject><ispartof>Nutrition (Burbank, Los Angeles County, Calif.), 2024-09, Vol.125, p.112494, Article 112494</ispartof><rights>2024 The Authors</rights><rights>Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>2024. The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-d4206e3bc094ef9318b312e1c56e55340ec639591a9916a2a13d7c35950b92813</citedby><cites>FETCH-LOGICAL-c387t-d4206e3bc094ef9318b312e1c56e55340ec639591a9916a2a13d7c35950b92813</cites><orcidid>0000-0002-5418-5851 ; 0000-0002-3609-5641 ; 0000-0002-8620-9360 ; 0000-0002-3901-8182 ; 0000-0003-1033-2343</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0899900724001436$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38843564$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Bennett, Jonathan P.</creatorcontrib><creatorcontrib>Ford, Katherine L.</creatorcontrib><creatorcontrib>Siervo, Mario</creatorcontrib><creatorcontrib>Gonzalez, Maria Cristina</creatorcontrib><creatorcontrib>Lukaski, Henry C.</creatorcontrib><creatorcontrib>Sawyer, Michael B.</creatorcontrib><creatorcontrib>Mourtzakis, Marina</creatorcontrib><creatorcontrib>Deutz, Nicolaas E.P.</creatorcontrib><creatorcontrib>Shepherd, John A.</creatorcontrib><creatorcontrib>Prado, Carla M.</creatorcontrib><title>Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models</title><title>Nutrition (Burbank, Los Angeles County, Calif.)</title><addtitle>Nutrition</addtitle><description>•4C body composition modeling reveals hydration variations in patients with cancer.•Compared to a 4C model, DXA is preferred for clinical body composition.•Regional lean soft tissue by DXA is higher than that predicted by abdominal CT scan.•Differences between DXA and CT result in a lower muscle mass prevalence by DXA.•More research is needed to understand technology-specific thresholds for low muscle.
Measurement of body composition using computed tomography (CT) scans may be a viable clinical tool for low muscle mass assessment in oncology. However, longitudinal assessments are often infeasible with CT. Clinically accessible body composition technologies can be used to track changes in fat-free mass (FFM) or muscle, though their accuracy may be impacted by cancer-related physiological changes. The purpose of this study was to examine the agreement among accessible body composition method with criterion methods for measures of whole-body FFM measurements and, when possible, muscle mass for the classification of low muscle in patients with cancer.
Patients with colorectal cancer were recruited to complete measures of whole-body DXA, air displacement plethysmography (ADP), and bioelectrical impedance analysis (BIA). These measures were used alone, or in combination to construct the criterion multicompartment (4C) mode for estimating FFM. Patients also underwent abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C model was analyzed using mean constant error (CE = criterion – alternative), linear regression including root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Additionally, appendicular lean soft tissue index (ALSTI) measured by DXA and predicted by CT were compared for the absolute agreement, while the ALSTI values and skeletal muscle index by CT were assessed for agreement on the classification of low muscle mass.
Forty-five patients received all measures for the 4C model and 25 had measures within proximity of clinical CT measures. Compared to 4C, DXA outperformed ADP and BIA by showing the strongest overall agreement (CE = 1.96 kg, RMSE = 2.45 kg, MPD = 98.15 ± 2.38%), supporting its use for body composition assessment in patients with cancer. However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with 24.0% to 32.0% of patients having a different low muscle classification by CT when compared to DXA.
Despite discrepancies between clinical body composition assessment and the criterion multicompartment model, DXA demonstrates the strongest agreement with 4C. Disagreement between DXA and CT for low muscle mass classification prompts further evaluation of the measures and cutoffs used with each technique. Multicompartment models may enhance our understanding of body composition variations at the individual patient level and improve the applicability of clinically accessible technologies for classification and monitoring change over time.</description><subject>Accessibility</subject><subject>Agreements</subject><subject>bioelectrical impedance</subject><subject>bioelectrical impedance analysis, BIA</subject><subject>Bioelectricity</subject><subject>Body composition</subject><subject>Body mass index</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Classification</subject><subject>Clinical medicine</subject><subject>Colorectal cancer</subject><subject>Colorectal carcinoma</subject><subject>colorectal neoplasms</subject><subject>Computed tomography</subject><subject>Criteria</subject><subject>densitometry</subject><subject>Dual energy X-ray absorptiometry</subject><subject>DXA</subject><subject>Electrocardiography</subject><subject>Electrodes</subject><subject>Error analysis</subject><subject>Fat-free body mass</subject><subject>Fat-free mass</subject><subject>lean body mass</subject><subject>Malnutrition</subject><subject>Medical records</subject><subject>Multicompartment model</subject><subject>Muscles</subject><subject>nutrition</subject><subject>Nutrition research</subject><subject>Oncology</subject><subject>Patients</subject><subject>Plethysmography</subject><subject>Proteins</subject><subject>regression analysis</subject><subject>Regression models</subject><subject>Root-mean-square errors</subject><subject>Skeletal muscle</subject><subject>Soft tissues</subject><subject>Software</subject><issn>0899-9007</issn><issn>1873-1244</issn><issn>1873-1244</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkb2O1DAURi0EYoeFB6BBlmhoMvg3saFarVhAWokGastx7oBHSTz4OoO24dnxTBYKCqjiSOc7kn0Iec7ZljPevt5v56VsBRNqy7lQVj0gG2462dQf9ZBsmLG2sYx1F-QJ4p4xxm1rH5MLaYySulUb8vNqOPo5xPkr7dNwR0OaDgljiWmmHhEQJ5gLjTM9-BLrEemPWL7RUEeQ39CbmLGcVz5HTDPStKMl--Gs8CM9QsYF6bSMJa5YORunNMCIT8mjnR8Rnt1_L8mXm3efrz80t5_ef7y-um2CNF1pBiVYC7IPzCrYWclNL7kAHnQLWkvFILTSasu9tbz1wnM5dEFqq1lvheHykrxavYecvi-AxU0RA4yjnyEt6CTXsuO6M-L_KGu1NYpZU9GXf6H7tOR66xNlhTBWipOQr1TICTHDzh1ynHy-c5y5U0e3d7WjO3V0a8e6eXFvXvoJhj-L3-Eq8HYF6iPCMUJ2GGqeAEPMEIobUvyH_hdjmK7V</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Bennett, Jonathan P.</creator><creator>Ford, Katherine 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body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models</title><author>Bennett, Jonathan P. ; Ford, Katherine L. ; Siervo, Mario ; Gonzalez, Maria Cristina ; Lukaski, Henry C. ; Sawyer, Michael B. ; Mourtzakis, Marina ; Deutz, Nicolaas E.P. ; Shepherd, John A. ; Prado, Carla M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-d4206e3bc094ef9318b312e1c56e55340ec639591a9916a2a13d7c35950b92813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accessibility</topic><topic>Agreements</topic><topic>bioelectrical impedance</topic><topic>bioelectrical impedance analysis, BIA</topic><topic>Bioelectricity</topic><topic>Body composition</topic><topic>Body mass index</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Classification</topic><topic>Clinical medicine</topic><topic>Colorectal 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Mario</au><au>Gonzalez, Maria Cristina</au><au>Lukaski, Henry C.</au><au>Sawyer, Michael B.</au><au>Mourtzakis, Marina</au><au>Deutz, Nicolaas E.P.</au><au>Shepherd, John A.</au><au>Prado, Carla M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models</atitle><jtitle>Nutrition (Burbank, Los Angeles County, Calif.)</jtitle><addtitle>Nutrition</addtitle><date>2024-09-01</date><risdate>2024</risdate><volume>125</volume><spage>112494</spage><pages>112494-</pages><artnum>112494</artnum><issn>0899-9007</issn><issn>1873-1244</issn><eissn>1873-1244</eissn><abstract>•4C body composition modeling reveals hydration variations in patients with cancer.•Compared to a 4C model, DXA is preferred for clinical body composition.•Regional lean soft tissue by DXA is higher than that predicted by abdominal CT scan.•Differences between DXA and CT result in a lower muscle mass prevalence by DXA.•More research is needed to understand technology-specific thresholds for low muscle.
Measurement of body composition using computed tomography (CT) scans may be a viable clinical tool for low muscle mass assessment in oncology. However, longitudinal assessments are often infeasible with CT. Clinically accessible body composition technologies can be used to track changes in fat-free mass (FFM) or muscle, though their accuracy may be impacted by cancer-related physiological changes. The purpose of this study was to examine the agreement among accessible body composition method with criterion methods for measures of whole-body FFM measurements and, when possible, muscle mass for the classification of low muscle in patients with cancer.
Patients with colorectal cancer were recruited to complete measures of whole-body DXA, air displacement plethysmography (ADP), and bioelectrical impedance analysis (BIA). These measures were used alone, or in combination to construct the criterion multicompartment (4C) mode for estimating FFM. Patients also underwent abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C model was analyzed using mean constant error (CE = criterion – alternative), linear regression including root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Additionally, appendicular lean soft tissue index (ALSTI) measured by DXA and predicted by CT were compared for the absolute agreement, while the ALSTI values and skeletal muscle index by CT were assessed for agreement on the classification of low muscle mass.
Forty-five patients received all measures for the 4C model and 25 had measures within proximity of clinical CT measures. Compared to 4C, DXA outperformed ADP and BIA by showing the strongest overall agreement (CE = 1.96 kg, RMSE = 2.45 kg, MPD = 98.15 ± 2.38%), supporting its use for body composition assessment in patients with cancer. However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with 24.0% to 32.0% of patients having a different low muscle classification by CT when compared to DXA.
Despite discrepancies between clinical body composition assessment and the criterion multicompartment model, DXA demonstrates the strongest agreement with 4C. Disagreement between DXA and CT for low muscle mass classification prompts further evaluation of the measures and cutoffs used with each technique. Multicompartment models may enhance our understanding of body composition variations at the individual patient level and improve the applicability of clinically accessible technologies for classification and monitoring change over time.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>38843564</pmid><doi>10.1016/j.nut.2024.112494</doi><orcidid>https://orcid.org/0000-0002-5418-5851</orcidid><orcidid>https://orcid.org/0000-0002-3609-5641</orcidid><orcidid>https://orcid.org/0000-0002-8620-9360</orcidid><orcidid>https://orcid.org/0000-0002-3901-8182</orcidid><orcidid>https://orcid.org/0000-0003-1033-2343</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Nutrition (Burbank, Los Angeles County, Calif.), 2024-09, Vol.125, p.112494, Article 112494 |
| issn | 0899-9007 1873-1244 1873-1244 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3153715782 |
| source | Elsevier ScienceDirect Journals |
| subjects | Accessibility Agreements bioelectrical impedance bioelectrical impedance analysis, BIA Bioelectricity Body composition Body mass index Cancer Cancer therapies Classification Clinical medicine Colorectal cancer Colorectal carcinoma colorectal neoplasms Computed tomography Criteria densitometry Dual energy X-ray absorptiometry DXA Electrocardiography Electrodes Error analysis Fat-free body mass Fat-free mass lean body mass Malnutrition Medical records Multicompartment model Muscles nutrition Nutrition research Oncology Patients Plethysmography Proteins regression analysis Regression models Root-mean-square errors Skeletal muscle Soft tissues Software |
| title | Advancing body composition assessment in patients with cancer: First comparisons of traditional versus multicompartment models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T07%3A22%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Advancing%20body%20composition%20assessment%20in%20patients%20with%20cancer:%20First%20comparisons%20of%20traditional%20versus%20multicompartment%20models&rft.jtitle=Nutrition%20(Burbank,%20Los%20Angeles%20County,%20Calif.)&rft.au=Bennett,%20Jonathan%20P.&rft.date=2024-09-01&rft.volume=125&rft.spage=112494&rft.pages=112494-&rft.artnum=112494&rft.issn=0899-9007&rft.eissn=1873-1244&rft_id=info:doi/10.1016/j.nut.2024.112494&rft_dat=%3Cproquest_cross%3E3065984098%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3092289322&rft_id=info:pmid/38843564&rft_els_id=S0899900724001436&rfr_iscdi=true |