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Introduction to the DISRUPT postprandial database: subjects, studies and methodologies.

Kim G Jackson, Dave T Clarke, Peter Murray

Genes & Nutrition

|September 30, 2009

Summary

This summary is machine-generated.

This study created the comprehensive DISRUPT database, analyzing postprandial lipid and glucose metabolism. Preliminary findings highlight gender and age impacts on triacylglycerol responses, advancing understanding of metabolic variability.

Keywords:

Cardiovascular disease Genotype Glucose Mathematical modelling Postprandial lipid metabolism Single and multiple test meals

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Area of Science:

Metabolic Physiology
Nutritional Science
Cardiovascular Disease Research

Background:

Postprandial lipid and glucose metabolism dysregulation is a key risk factor for cardiovascular disease and type 2 diabetes.
Understanding inter-individual variability in metabolic responses is crucial for disease prevention and management.

Purpose of the Study:

To establish a comprehensive, standardized database of postprandial studies (DISRUPT) for in-depth analysis.
To investigate physiological factors influencing postprandial lipid and glucose responses.
To identify determinants of inter-individual variability in metabolic profiles.

Main Methods:

Data collation from 467 subjects participating in single and sequential meal postprandial studies.
Inclusion of subject attributes (age, gender, BMI, etc.) and detailed postprandial measurements (TAG, glucose, insulin, lipoprotein composition).
High-frequency blood sampling (10-13 samples per assessment) and standardized test meal protocols.

Main Results:

The DISRUPT database is the world's most extensive collection of postprandial metabolism data.
Preliminary analysis of pooled sequential meal data revealed significant impacts of gender and age on postprandial triacylglycerol (TAG) response.
Identified both confirmatory and novel observations regarding metabolic responses.

Conclusions:

The DISRUPT database provides a powerful resource for studying postprandial metabolism.
Further analysis using advanced statistical and modeling techniques will significantly enhance understanding of metabolic variability.
Insights gained can inform strategies for preventing cardiovascular disease and type 2 diabetes.