A longitudinal plasma lipidomics dataset from children who developed islet autoimmunity and type 1 diabetes

Area of Science:

• Metabolomics
• Pediatric Endocrinology
• Immunology

Background:

• Early prediction and prevention of type 1 diabetes (T1D) remain significant unmet medical needs.
• Prior metabolomics research indicates that children developing T1D exhibit unique metabolic profiles in infancy, preceding islet autoimmunity.
• The specificity of these persistent metabolic disturbances concerning T1D progression requires further investigation.

Purpose of the Study:

• To present a longitudinal plasma lipidomics dataset for analyzing early metabolic changes associated with type 1 diabetes development.
• To facilitate research into the age-dependent progression of islet autoimmunity and T1D.
• To support the development of analytical methods for longitudinal multivariate data.

Main Methods:

• Collected longitudinal plasma lipidomics data from three groups of children: those progressing to T1D, those developing single islet autoantibodies without T1D, and matched controls.
• Data were collected at six time points: 3, 6, 12, 18, 24, and 36 months of age.
• The dataset includes 40 children in each group, totaling 120 participants.

Main Results:

• The study provides a comprehensive dataset detailing plasma lipidomic profiles over the first three years of life.
• This data captures metabolic variations in children who develop T1D, islet autoimmunity, or remain healthy.
• The dataset allows for the examination of age-specific lipidomic alterations.

Conclusions:

• The presented dataset is valuable for investigating the early metabolic signatures of type 1 diabetes.
• It enables further research into the relationship between lipid metabolism, islet autoimmunity, and T1D onset during early childhood.
• The data can also serve as a resource for advancing statistical and computational methods for analyzing complex longitudinal biological data.