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Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
Published on: June 7, 2024
Incorporating CT-Derived Body Composition Analysis Into the Global Leadership Initiative on Malnutrition Criteria Is
Clodagh Scannell1, Erin Stella Sullivan2, Ross D Dolan3
1School of Food and Nutritional Sciences, College of Science, Engineering and Food Science, University College Cork, Cork, Ireland; Cancer Research @UCC University College Cork, Cork, Ireland.
Background:
Cancer-associated malnutrition is highly prevalent and linked to adverse outcomes. However, studies evaluating malnutrition using the Global Leadership Initiative on Malnutrition (GLIM) criteria with inclusion of computed tomography (CT)-derived muscle mass are limited, particularly in Caucasian oncology populations. Moreover, the prevalence of GLIM-defined malnutrition and its association with overall survival across different treatment intents remains insufficiently characterized.
Objective:
This study investigates whether incorporating CT-derived body composition assessment into the GLIM criteria improves the detection of malnutrition in oncology patients and evaluates its association with overall survival across different treatment intent groups.
Design:
Secondary analysis of data from 2 observational, prospective studies in Ireland and the United Kingdom that both examined the effect of nutritional and inflammatory status on survival in patients. GLIM phenotypic and etiologic criteria were retrospectively applied using data available from these studies. Reduced muscle mass was derived from CT.
Participants/Setting:
Data were available for 1405 patients enrolled between 2011 and 2016; 1118 had evaluable CT scans. Treatment intent included curative (n = 428), palliative active treatment (n = 873), and supportive care (n = 104).
Main Outcome:
Prevalence of GLIM-diagnosed malnutrition incorporating CT muscle assessment and its association with overall survival (months) by treatment intent. Survival analysis was carried out using Kaplan-Meier curves and Cox regression.
Statistical Analysis:
Descriptive statistics characterized clinical characteristics and nutritional status. Analyses were conducted in SPSS, and statistical significance was considered at P < .05.
Results:
GLIM-malnutrition was identified in 40.4% (22.6% moderate; 17.8% severe). Among those with CT scans, 42.2% had low muscle mass, and only 24.8% had experienced >5% weight loss. CT analysis identified GLIM malnutrition in 22.8% of patients missed by weight/body mass index (BMI) criteria. Median follow-up was 102.4 months. Median survival was 30.4 months for well-nourished patients vs 13.5 months (moderate) and 6.4 months (severe) for malnourished patients (P < .001). Adjusted hazard ratios for death were 1.379 (moderate malnutrition) and 1.799 (severe malnutrition) (P < .001).
Conclusion:
GLIM-diagnosed malnutrition, particularly when incorporating CT-derived muscle mass, is common and strongly predicts poor survival across treatment intents. CT-based assessment identifies patients with muscle wasting who may be missed by conventional criteria, supporting its integration into GLIM phenotyping.
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