| 1. |
- Nikolic, Marina, et al.
(author)
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Improving the reporting quality of intervention trials addressing the inter-individual variability in response to the consumption of plant bioactives : quality index and recommendations
- 2019
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In: European Journal of Nutrition. - : Springer Science and Business Media LLC. - 1436-6207 .- 1436-6215. ; 58:S2, s. 49-64
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Research review (peer-reviewed)abstract
- Purpose: The quality of the study design and data reporting in human trials dealing with the inter-individual variability in response to the consumption of plant bioactives is, in general, low. There is a lack of recommendations supporting the scientific community on this topic. This study aimed at developing a quality index to assist the assessment of the reporting quality of intervention trials addressing the inter-individual variability in response to plant bioactive consumption. Recommendations for better designing and reporting studies were discussed. Methods: The selection of the parameters used for the development of the quality index was carried out in agreement with the scientific community through a survey. Parameters were defined, grouped into categories, and scored for different quality levels. The applicability of the scoring system was tested in terms of consistency and effort, and its validity was assessed by comparison with a simultaneous evaluation by experts’ criteria. Results: The “POSITIVe quality index” included 11 reporting criteria grouped into four categories (Statistics, Reporting, Data presentation, and Individual data availability). It was supported by detailed definitions and guidance for their scoring. The quality index score was tested, and the index demonstrated to be valid, reliable, and responsive. Conclusions: The evaluation of the reporting quality of studies addressing inter-individual variability in response to plant bioactives highlighted the aspects requiring major improvements. Specific tools and recommendations favoring a complete and transparent reporting on inter-individual variability have been provided to support the scientific community on this field.
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| 2. |
- Koistinen, Ville Mikael, et al.
(author)
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Interlaboratory coverage test on plant food bioactive compounds and their metabolites by mass spectrometry-based untargeted metabolomics
- 2018
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In: Metabolites. - : MDPI AG. - 2218-1989 .- 2218-1989. ; 8:3
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Journal article (peer-reviewed)abstract
- Bioactive compounds present in plant-based foods, and their metabolites derived from gut microbiota and endogenous metabolism, represent thousands of chemical structures of potential interest for human nutrition and health. State-of-the-art analytical methodologies, including untargeted metabolomics based on high-resolution mass spectrometry, are required for the profiling of these compounds in complex matrices, including plant food materials and biofluids. The aim of this project was to compare the analytical coverage of untargeted metabolomics methods independently developed and employed in various European platforms. In total, 56 chemical standards representing the most common classes of bioactive compounds spread over a wide chemical space were selected and analyzed by the participating platforms (n = 13) using their preferred untargeted method. The results were used to define analytical criteria for a successful analysis of plant food bioactives. Furthermore, they will serve as a basis for an optimized consensus method.
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| 3. |
- Low, Dorrain Yanwen, et al.
(author)
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Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
- 2021
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In: Food Chemistry. - : Elsevier BV. - 0308-8146 .- 1873-7072. ; 357
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Journal article (peer-reviewed)abstract
- Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.
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