Abstract
BACKGROUND: Kiwifruit is widely recognized for its nutritional value and health benefits, yet reliable and objective methods for assessing kiwifruit intake in populations remain limited. OBJECTIVE: This study aimed to identify urinary biomarkers of kiwifruit intake and develop an optimal biomarker panel for differentiating consumers within days. METHODS: A randomized, controlled, crossover dietary intervention was conducted among 17 healthy volunteers. The intervention included four phases: run-in, single-exposure, repeat-exposure, and follow-up. Urine samples at multiple time-point and fruit samples were prepared and analyzed using untargeted metabolomics via dual-column ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). Candidate biomarkers were identified through a systematic statistical strategy on kinetic profiles within 24 h, and annotated for potential fruit-derived origin through spectral matching. Machine learning algorithms were employed to establish an optimal biomarker panel for assessing kiwifruit intake under habitual diet conditions. RESULTS: Twenty-three urinary metabolites showed significantly elevated kinetic profiles, among which 15 were matched to compounds detected in the original fruit or in vitro digestion samples. These metabolites mainly included polyphenol-related metabolites and plant-derived amino acid derivatives. The excretion of many metabolites turned to be delayed compared to those typically observed for other fruits. For example, 2-isopropylmalic acid usually peaked in urine or blood within 6 h of consuming other fruits, but in our study urinary level at 24 h was much higher compared to 6 h. Most of the selected candidates are not specific to kiwifruit based on existing literature, such as hippuric acid. In this regard, an XGBoost algorithm-based model using 7 metabolites achieved substantial discriminative performance (accuracy = 0.88) in predicting kiwifruit intake within two days. CONCLUSIONS: This study identified potential biomarkers of kiwifruit and developed a prediction model that may differentiate consumers. Further validation is necessary to confirm the reliability and generalizability of our findings. TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100048279. Registered on July 5, 2021.