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Background <p>Kiwifruit is widely recognized for its nutritional value and health benefits, yet reliable and objective methods for assessing kiwifruit intake in populations remain limited.</p> Objective <p>This study aimed to identify urinary biomarkers of kiwifruit intake and develop an optimal biomarker panel for differentiating consumers within days.</p> Methods <p>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&#xa0;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.</p> Results <p>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&#xa0;h of consuming other fruits, but in our study urinary level at 24&#xa0;h was much higher compared to 6&#xa0;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.</p> Conclusions <p>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.</p> Trial registration <p>Chinese Clinical Trial Registry, ChiCTR2100048279. Registered on July 5, 2021.</p>

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Discovery of urinary biomarkers of kiwifruit intake in a randomized intervention study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Zilin Xiao,
  • Wanning Shang,
  • Peiyu Li,
  • Nan Wang,
  • Tongtong Li,
  • Yunan Liu,
  • Ying Chen,
  • Ying Wang,
  • Hao Ma,
  • Xuan Wang,
  • Han Han,
  • Geng Zong

摘要

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.