A Prediction Tool for Malnutrition and Sarcopenia in Patients with Gastroenteropancreatic Neuroendocrine Neoplasms: Results From NUTRIGETNE (GETNE-S2109) Study

Why It Matters

Early identification of malnutrition and sarcopenia can improve treatment tolerance and survival in GEP‑NEN patients, addressing a gap left by generic screening tools.

Key Takeaways

• 399 GEP‑NEN patients studied, 61.9% malnutrition, 15% sarcopenia.
• LASSO model predicts malnutrition with 65.3% accuracy, AUC 0.72.
• Sarcopenia model achieves 71.2% accuracy, AUC 0.78.
• Key predictors: poor differentiation, nausea/vomiting, walking difficulty, diabetes.
• Nomogram provides rapid bedside risk scoring for clinicians.

Pulse Analysis

Malnutrition and sarcopenia are increasingly recognized as critical determinants of outcomes in gastroenteropancreatic neuroendocrine neoplasms (GEP‑NENs). The disease’s hormonal secretions, anatomical disruptions, and treatment‑related side effects create a perfect storm for nutritional decline, with prior reports citing prevalence rates between 30% and 70%. By assembling the largest prospective cohort of 399 advanced‑stage GEP‑NEN patients, the NUTRIGETNE study quantifies this burden more precisely—61.9% of participants meet GLIM criteria for malnutrition and 15% fulfill EWGSOP2 sarcopenia definitions. These figures underscore the urgency of disease‑specific screening, as generic tools like MUST or SGA often miss subtle, tumor‑related risk factors.

The study’s methodological strength lies in its use of high‑dimensional data (478 variables) and a disciplined LASSO‑based variable selection process, which distilled the predictive signal to a handful of clinically intuitive factors. Poor tumor differentiation, nausea/vomiting, reduced walking capacity, and comorbid diabetes emerged as the strongest malnutrition drivers, while functional dependence and walking difficulty dominated sarcopenia risk. The resulting models deliver respectable discrimination—AUCs of 0.72 and 0.78—while remaining simple enough for bedside application. Compared with existing screening instruments, the malnutrition model offers higher sensitivity, and the sarcopenia model, though less sensitive, still provides a pragmatic first‑step assessment.

For clinicians, the nomogram translates statistical outputs into actionable risk scores, enabling timely referral to dietitians or physiotherapists before treatment‑related toxicities accrue. Health systems can leverage this tool to allocate nutritional resources more efficiently, potentially reducing hospitalizations and improving quality‑adjusted survival. Future work should focus on external validation across diverse geographic cohorts and on integrating the score into electronic health records for automated alerts. As the oncology community embraces precision supportive care, the NUTRIGETNE Score positions itself as a cornerstone for personalized nutrition management in GEP‑NENs.