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AArhus protein source
95A
They got different proteins: Cod, Gluten, Casein, Whey isolate
Start date
2008-06-25 00:00:00
Endpoint
Objectives
Conclusion
Exclusion
Inclusion
Inclusion
Country
Denmark
Consortium
Published (PubMed)
Researchdesign
Research design
Blinding
Yes
Blinding method
Crossover
Research design description
Recruitment
Recruitment start year
Recruitment end year
Number of volunteers
11
Number of volunteers terminated
Factors
Number of treatments
4
Number of factors
2
Number of arms
2
A personal microbiome-dependent glucose response in healthy young volunteers: a meal test study
MIGLUCOSE
The gut microbiome has combined with other person-specific information, such as blood parameters, dietary habits, anthropometrics, and physical activity been found to predict personalized postprandial glucose responses (PPGRs) to various foods. Yet, the contributions of specific microbiome taxa, measures of fermentation, and abiotic factors in the colon to glycemic control remain elusive. We tested whether PPGRs 60 min after a standardized breakfast was associated with gut microbial α-diversity (primary outcome) and explored whether postprandial responses of glucose and insulin were associated with specific microbiome taxa, colonic fermentation as reflected by fecal short-chain fatty acids (SCFAs), and breath hydrogen and methane exhalation, as well as abiotic factors including fecal pH, fecal water content, fecal energy density, intestinal transit time (ITT), and stool consistency. A single-arm meal trial was conducted. A total of 31 healthy (24 female and seven male) subjects consumed a standardized evening meal and a subsequent standardized breakfast (1,499 kJ) where blood was collected for analysis of postprandial glucose and insulin responses. PPGRs to the same breakfast varied across the healthy subjects. The largest inter-individual variability in PPGRs was observed 60 min after the meal but was not associated with gut microbial α-diversity. In addition, no significant associations were observed between postprandial responses and specific taxa of the gut microbiome, measures of colonic fermentation, ITT, or other abiotic factors. However, fasting glucose concentrations were negatively associated with ITT, and fasting insulin was positively associated with fasting breath hydrogen. In conclusion, the gut microbiome, measures of colonic fermentation, and abiotic factors were not shown to be significantly associated with variability in postprandial responses, suggesting that contributions of the gut microbiome, colonic fermentation, and abiotic factors to PPGRs may be subtle in healthy adults.
Start date
2018-10-12 00:00:00
Endpoint
Postprandial plasma glucose at 60 min as a function of gut microbial richness
Objectives
Conclusion
Exclusion
Inclusion
Inclusion
Country
Denmark
Consortium
Published (PubMed)
Researchdesign
Research design
Blinding
No
Blinding method
Research design description
single-arm meal study
Recruitment
Recruitment start year
2018
Recruitment end year
2018
Number of volunteers
31
Number of volunteers terminated
31
Factors
Number of treatments
1
Number of factors
1
Number of arms
1
Welcome to Squidr
The Squidr data repository is a secure research infrastructure for storing, sharing and (re)use of data from intervention, epidemiological, in vitro and animal studies. Squidr is compatible with GDPR legislation and supports sharing according to the FAIR principles.

Squidr is designed to make data sharing effortless for researchers worldwide while adhering to the FAIR Principles (Findable, Accessible, Interoperable, and Reusable) and respecting ethical guidelines and GDPR requirements.

01
WHAT SETS
Squidr apart

Current research databases fall into two categories:

Domain-specific databases e.g., genomic data repositories with fixed structures and ontologies.

General-purpose "data dumps" that allow raw data deposits but lack structure.

Squidr is providing a simple design for uploading and downloading curated datasets within a general-purpose structure.

The system allows all datasets from a study to be combined into a single coherent structure.

This allows Squidr to be used both as a datahub and as a FAIR data sharing tool.

02
Flexible uploads

Researchers can upload tabular data of all types, enabling versatility across disciplines.

The system can accommodate complex study designs,observational as well as experimental.

The system can accomodate practically any kind of measurements, including clinical data, 'omics', and questionnaires.

03
STRUCTURED DATA
A game-changing approach

Datasets are directly linked with their study design, making them intuitive to interpret and reuse for further analysis.

Searchable. Squidr allows researchers to search across studies with ease. Searching e.g. metabolomics features to discover where they were observed previously.

Reusable. Download public datasets for analysis in other contexts.

We imagine a future where every university and research institution runs its own Squidr server to curate and share data. These servers form a federated network, allowing researchers to search and retrieve data across institutions seamlessly. While access can be restricted as needed, we champion openness wherever possible to foster innovation.

Squidr bridges the gap between structure, usability, and openness—empowering researchers to collaborate and build upon each other’s work.

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