Optimizing Nutritional Care with Machine Learning: Identifying Sarcopenia Risk Through Body Composition Parameters in Cancer Patients-Insights from the NUTritional and Sarcopenia RIsk SCREENing Project (NUTRISCREEN)

Abstract

Background/Objectives: Cancer and related treatments can impair body composition (BC), increasing the risk of malnutrition and sarcopenia, poor prognosis, and Health-Related Quality of Life (HRQoL). To enhance BC parameter interpretation through Bioelectrical Impedance Analysis (BIA), we developed a predictive model based on unsupervised approaches including Principal Component Analysis (PCA) and k-means clustering for sarcopenia risk in cancer patients at the Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (Naples). Methods: Sarcopenia and malnutrition risks were assessed using the NRS-2002 and SARC-F questionnaires, anthropometric measurements, and BIA. HRQoL was evaluated with the EORTC QLQ-C30 questionnaire. PCA and clustering analysis were performed to identify different BC profiles. Results: Data from 879 cancer patients (mean age: 63 ± 12.5 years) were collected: 117 patients (13%) and 128 (15%) were at risk of malnutrition and sarcopenia, respectively. PCA analysis identified three main components, and k-means determined three clusters, namely HMP (High Muscle Profile), MMP (Moderate Muscle Profile), and LMP (Low Muscle Profile). Patients in LMP were older, with a higher prevalence of comorbidities, malnutrition, and sarcopenia. In the multivariable analysis, age, lung cancer site, diabetes, and malnutrition risk were significantly associated with an increased risk of sarcopenia; among the clusters, patients in LMP had an increased risk of sarcopenia (+62%, p = 0.006). Conclusions: The NUTRISCREEN project, part of the ONCOCAMP study (ClinicalTrials.gov ID: NCT06270602), provides a personalized nutritional pathway for early screening of malnutrition and sarcopenia. Using an unsupervised approach, we provide distinct BC profiles and valuable insights into the factors associated with sarcopenia risk. This approach in clinical practice could help define risk categories, ensure the most appropriate nutritional strategies, and improve patient outcomes by providing data-driven care.

Keywords: body composition; cancer prognosis; health-related quality of life; machine learning; sarcopenia risk.

Figure 1

Schematic representation of the study workflow.

Figure 2

Correlation analysis among the BIA parameters. The horizontal axis indicates the direction and intensity of the correlation coefficient between the two variables intersecting at the single cell. Cross refers to not significant Person’s correlation coefficients (ρs). PhA, Phase Angle; BCMI, Body Cell Mass Index; FMI, Fat Mass Index; FFMI, Fat-free Mass Index; SMI, Skeletal Muscle Index; ASMI, appendicular skeletal muscle mass; and ECW:ICW ratio, Extracellular Water–Intracellular Water ratio.

Figure 3

PCA results and k-means clustering outputs for the most important dimensions ((a) 1st and 2nd dimensions; (b) 2nd and 3rd dimensions). C1 = HMP (High Muscle Profile), C2 = MMP (Moderate Muscle Profile), and C3 = LMP (Low Muscle Profile). The overall explained variance was almost 97%. PhA, Phase Angle; BCMI, Body Cell Mass Index; ASMI, Appendicular Skeletal Muscle Index; SMI, Skeletal Muscle Index; FFMI, Fat-free Mass Index; FMI, Fat Mass Index; and ECW-ICW ratio, Extracellular Water–Intracellular Water ratio.