- 2025-11-03
- Nutrition journal 24(1)
- Zilin Xiao
- Wanning Shang
- Peiyu Li
- Nan Wang
- Tongtong Li
- Yunan Liu
- Ying Chen
- Ying Wang
- Hao Ma
- Xuan Wang
- Han Han
- Geng Zong
Study Design
- Type
- Randomized Controlled Trial (RCT)
- Population
- 17 healthy volunteers
- Methods
- A randomized, controlled, crossover dietary intervention with four phases: run-in, single-exposure, repeat-exposure, and follow-up; untargeted metabolomics via dual-column UHPLC-MS; machine learning algorithms for biomarker panel
- Blinding
- Open-label
- Funding
- Unclear
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.