(P03-052-24) Nutrimetabotyping: Predictive Model of Nasopharyngeal Carcinoma Survival in a Prospective Matched Case Control Study in Malaysia

Objectives: Nutrimetabotypes are groups of similar individuals based on combinations of specific nutritional metabolites. The application of nutrimetabotyping in predicting cancer risk and survival remains understudied. This study investigates the association between nutritional metabolites, anthropometric phenotypes, and nasopharyngeal carcinoma (NPC) survival.

Methods: This is a matched case-control study conducted in public hospitals in Malaysia among 300 histologically confirmed NPC cases and 300 cancer-free controls. Metabolomic profiling was conducted employing Nuclear Magnetic Resonance spectroscopy. Kaplan-Meier analysis and the Multivariable Cox proportional hazard model were used to predict NPC survival.

Results: The overall 5 years survival rate of NPC cases was 63.7%. After adjusting for confounders, the lowest quartile of leptin independently increased NPC mortality almost 10 times (AHR(Q1) =9.61, 95%CI=3.36,27.47). The highest quartile of TNFA significantly tripled the NPC mortality (AHR(Q4) =3.59, 95%CI=1.32,9.79). The third and the highest quartile of IL-6 conferred tripled independent mortality risk (AHR(Q3) =3.39, 95%CI=1.54, 7.48 and AHR(Q4) =3.04, 95%CI=1.48,6.25). Mortality among NPC cases had increased by 68% with high fasting blood glucose (AHR=1.68,95%CI=1.14,2.47). Low HDL levels had increased risk mortality among NPC cases by 82% (AHR=1.82,95%CI=1.20,2.77). Low albumin level doubled the risk of NPC deaths (AHR=2.39,95%CI=1.49,3.82). Median Calcium: Magnesium ratio increased NPC mortality by 82% (AHR=1.82,95%CI=1.18,2.80). Being underweight significantly increased NPC death risk by 82% (AHR=1.82,95%CI=1.05,3.15).

Conclusions: The predictive model indicates the importance of clustering these targeted nutritional metabolites, and anthropometric phenotypes have the potential to guide the development of personalized nutritional strategies, ultimately leading to improved patient prognosis.

Funding Sources: This research was funded by E-Science Fund from Ministry of Science, Technology and Innovation, Malaysia.