Food research international (Ottawa, Ont.) · 2026
Untargeted metabolomics-based approach to discover biomarkers of apple intake: A randomized controlled trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Du Yuewen, Xiao Zilin, Li Peiyu, Shang Wanning, Li Tongtong, Wang Nan, Han Han, Wang Ying, Zhang Huiping, Chen Yinhao, Wang Xuan, Ma Hao, Zong Geng
Аннотация
BACKGROUND: Biomarkers of food intake (BFIs) provide an objective strategy for dietary assessment and may help to reduce the biases of traditional self-reported methods. So far, existing biomarkers of apple intake remain limited in terms of practical applicability.
OBJECTIVE: This study aimed to identify urinary biomarkers of apple intake with extended detection windows, compare their postprandial kinetics, and develop a multi-biomarker panel for identifying recent apple consumers.
METHODS: A randomized, controlled, crossover dietary intervention was conducted in 18 healthy adults. After a 2-day standardized run-in period, participants completed single-exposure, repeat-exposure and follow-up phases. Urine samples collected at multiple time points, along with original fruit and in vitro digested samples, were analyzed using untargeted metabolomics via dual-column ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A systematic statistical pipeline based on postprandial kinetic profiles was applied to identify candidate biomarkers with prolonged postprandial responses in 24-h urine. Additionally, a complementary screening strategy focusing on the 0-6 h postprandial window was implemented to characterize early-responsive metabolites. XGBoost machine learning models were applied to establish an optimal multi-biomarker panel for assessing apple intake under free-living conditions.
RESULTS: Five distinct metabolites were identified as candidate urinary biomarkers for apple intake. These metabolites remained significantly elevated at 24 h. In parallel, 28 early-responsive metabolites were discovered within the 0-6 h postprandial phase, which included several previously reported apple biomarkers, such as phloretin-2'-O-glucuronide. Individual metabolites showed limited predictive performance under free-living conditions, whereas a four-metabolite panel achieved the best discriminative performance (AUC = 0.80) for predicting apple consumption within 1-2 days compared to other panels.
CONCLUSIONS: This study identified urinary biomarkers of apple intake with a detection window extending to at least 24 h and established a multi-biomarker panel for identifying recent apple intake.
OBJECTIVE: This study aimed to identify urinary biomarkers of apple intake with extended detection windows, compare their postprandial kinetics, and develop a multi-biomarker panel for identifying recent apple consumers.
METHODS: A randomized, controlled, crossover dietary intervention was conducted in 18 healthy adults. After a 2-day standardized run-in period, participants completed single-exposure, repeat-exposure and follow-up phases. Urine samples collected at multiple time points, along with original fruit and in vitro digested samples, were analyzed using untargeted metabolomics via dual-column ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A systematic statistical pipeline based on postprandial kinetic profiles was applied to identify candidate biomarkers with prolonged postprandial responses in 24-h urine. Additionally, a complementary screening strategy focusing on the 0-6 h postprandial window was implemented to characterize early-responsive metabolites. XGBoost machine learning models were applied to establish an optimal multi-biomarker panel for assessing apple intake under free-living conditions.
RESULTS: Five distinct metabolites were identified as candidate urinary biomarkers for apple intake. These metabolites remained significantly elevated at 24 h. In parallel, 28 early-responsive metabolites were discovered within the 0-6 h postprandial phase, which included several previously reported apple biomarkers, such as phloretin-2'-O-glucuronide. Individual metabolites showed limited predictive performance under free-living conditions, whereas a four-metabolite panel achieved the best discriminative performance (AUC = 0.80) for predicting apple consumption within 1-2 days compared to other panels.
CONCLUSIONS: This study identified urinary biomarkers of apple intake with a detection window extending to at least 24 h and established a multi-biomarker panel for identifying recent apple intake.