(OR07-04-24) Improving Body Composition Analysis in Oncology: Do Measures From Multicompartment, 2C and CT Agree? Implications for Monitoring Sarcopenia Risk

Saturday, June 29, 2024

5:24 PM – 5:36 PM CT

Location: W184

Presenting Author(s)

Jonathan P. Bennett, PhD

Researcher
University of Hawai'i Cancer Center
Honolulu, Hawaii, United States

Disclosure(s): No relevant financial relationship(s) with ineligible companies to disclose.

Co-Author(s)

KF

Katherine Ford

University of Waterloo, Canada
MS

Mario Siervo

Curtin University, Australia
MG

Maria Cristina Gonzalez

Federal University of Pelotas, Brazil
HL

Henry C. Lukaski

University of North Dakota, United States
MS

Michael B. Sawyer

University of Alberta, Canada
MM

Marina Mourtzakis

University of Waterloo, Canada
Nicolaas Deutz, MD, PhD (he/him/his)

Director for the Center of Translational Research in Aging and Longevity
Texas A&M University
College Station, Texas, United States
JS

John A. Shepherd

University of Hawai'i Cancer Center, United States
Carla M. Prado, PhD RD

Assistant Professor; Campus Alberta Innovate Program Chair in Nutrition, Food and Health; Director, Human Nutrition Research Unit
University of Alberta
Edmonton, Alberta, Canada

Objectives: Assessment of body composition as a marker of nutritional status is common in cancer, primarily for monitoring changes in fat-free mass (FFM) versus fat (FM) and/or risk of sarcopenia. While computed tomography (CT) scans are opportunistically used for low muscle assessment at diagnosis, CT is not a feasible approach to longitudinal assessment of changes in body composition. We sought to assess the accuracy of body composition measures of FFM by CT and clinically accessible methods of bioelectrical impedance analysis (BIA), air displacement plethysmography (ADP), and dual energy X-ray absorptiometry (DXA) to a criterion multicompartment (4C) model. Additionally, we compared appendicular lean soft tissue index (ALSTI) measures between those predicted by CT and DXA to determine the effect on identification of low muscle mass phenotypic criteria.

Methods: Patients with colorectal cancer were recruited to complete DXA, ADP, and BIA. The measures were used alone, or in combination to construct the criterion 4C FFM. Patients also received abdominal CT scans as part of routine clinical assessment. Agreement of each method with 4C was analyzed using mean constant error (CE = criterion – alternative), root mean square error (RMSE), Bland-Altman limits of agreement (LoA) and mean percentage difference (MPD). Total skeletal muscle mass from CT and DXA were compared in terms of absolute agreement and agreement of low muscle classification.

Results: In total, 45 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 %). However, CT cutoffs for skeletal muscle index or CT-estimated ALSTI were lower than DXA ALSTI (average 1.0 ± 1.2 kg/m2) with CT misclassifying 24.0-32.0% of patients as low muscle compared to DXA.

Conclusions: Despite discrepancies between clinical methods of body composition assessment when compared to criterion, DXA provides strong agreement to 4C. Disagreement between DXA and CT for classification of low muscle mass for sarcopenia diagnosis requires further evaluation to the measures and cutoffs used with each technique to improve our ability to monitor low muscle risk in patients with cancer.

Funding Sources: This work was partially supported by the Government of Alberta.