Please use this identifier to cite or link to this item:
http://localhost:8080/xmlui/handle/123456789/5193
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | M Gibbons, Sean | - |
dc.contributor.author | Gurry, Thomas | - |
dc.contributor.author | W Lampe, Johanna | - |
dc.contributor.author | Chakrabarti, Anirikh | - |
dc.contributor.author | Dam, Veerle | - |
dc.contributor.author | Everard, Amandine | - |
dc.contributor.author | Goas, Almudena | - |
dc.contributor.author | Gross, Gabriele | - |
dc.contributor.author | Kleerebezem, Michiel | - |
dc.contributor.author | Lane, Jonathan | - |
dc.contributor.author | Maukonen, Johanna | - |
dc.contributor.author | Barretto Penna, Ana Lucia | - |
dc.contributor.author | Pot, Bruno | - |
dc.contributor.author | M Valdes, Ana | - |
dc.contributor.author | Walton, Gemma | - |
dc.contributor.author | Weiss, Adrienne | - |
dc.contributor.author | Cindya Zanzer, Yoghatama | - |
dc.contributor.author | V Venlet, Naomi | - |
dc.contributor.author | Miani, Michela | - |
dc.date.accessioned | 2023-08-01T03:53:55Z | - |
dc.date.available | 2023-08-01T03:53:55Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/5193 | - |
dc.description.abstract | Humans often show variable responses to dietary, prebiotic, and probiotic interventions. Emerging evidence indicates that the gut microbiota is a key determinant for this population heterogeneity. Here, we provide an overview of some of the major computational and experimental tools being applied to critical questions of microbiota-mediated personalized nutrition and health. First, we discuss the latest advances in in silico modeling of the microbiota-nutrition-health axis, including the application of statistical, mechanistic, and hybrid artificial intelligence models. Second, we address high-throughput in vitro techniques for assessing interindividual heterogeneity, from ex vivo batch culturing of stool and continuous culturing in anaerobic bioreactors, to more sophisticated organ-on-a-chip models that integrate both host and microbial compartments. Third, we explore in vivo approaches for better understanding of personalized, microbiota-mediated responses to diet, prebiotics, and probiotics, from nonhuman animal models and human observational studies, to human feeding trials and crossover interventions. We highlight examples of existing, consumerfacing precision nutrition platforms that are currently leveraging the gut microbiota. Furthermore, we discuss how the integration of a broader set of the tools and techniques described in this piece can generate the data necessary to support a greater diversity of precision nutrition strategies. Finally, we present a vision of a precision nutrition and healthcare future, which leverages the gut microbiota to design effective, individual-specific interventions. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Oxford University Press on behalf of the American Society for Nutrition 2022 | en_US |
dc.subject | : prebiotic, | en_US |
dc.subject | probiotic, | en_US |
dc.subject | diet, | en_US |
dc.subject | microbiome, | en_US |
dc.subject | microbiota, | en_US |
dc.subject | personalized nutrition, | en_US |
dc.subject | personalized healthcare, | en_US |
dc.subject | precision nutrition, | en_US |
dc.subject | precision healthcare | en_US |
dc.title | Perspective: Leveraging the Gut Microbiota to Predict Personalized Responses to Dietary, Prebiotic, and Probiotic Interventions | en_US |
dc.type | Article | en_US |
Appears in Collections: | VOL 13 NO 5 2022 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1450-1461.pdf | 931.44 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.