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

• Published in: Scientific data 2018-11, Vol.5 (1), p.180250-180250, Article 180250
• Main Authors: Lamichhane, Santosh, Ahonen, Linda, Dyrlund, Thomas Sparholt, Siljander, Heli, Hyöty, Heikki, Ilonen, Jorma, Toppari, Jorma, Veijola, Riitta, Hyötyläinen, Tuulia, Knip, Mikael, Orešič, Matej
• Format: Article
• Language: English
• Subjects: 631/1647/320; 631/92/608; 692/699/249/1313/1418; Age; Autoantibodies; Autoantibodies - blood; Autoimmunity; Child, Preschool; Children; Data Descriptor; Datasets; Diabetes; Diabetes mellitus; Diabetes mellitus (insulin dependent); Diabetes Mellitus, Type 1 - blood; Diabetes Mellitus, Type 1 - diagnosis; Diabetes Mellitus, Type 1 - genetics; Disease Progression; Finland; Genetic Predisposition to Disease; Humanities and Social Sciences; Humans; Infant; Islets of Langerhans - immunology; Islets of Langerhans - pathology; Lipids - blood; Lipids - physiology; Longitudinal Studies; Medicin och hälsovetenskap; Metabolism; Metabolomics; multidisciplinary; Science
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/sdata.2018.250

Early prediction and prevention of type 1 diabetes (T1D) are currently unmet medical needs. Previous metabolomics studies suggest that children who develop T1D are characterised by a distinct metabolic profile already detectable during infancy, prior to the onset of islet autoimmunity. However, the specificity of persistent metabolic disturbances in relation T1D development has not yet been established. Here, we report a longitudinal plasma lipidomics dataset from (1) 40 children who progressed to T1D during follow-up, (2) 40 children who developed single islet autoantibody but did not develop T1D and (3) 40 matched controls (6 time points: 3, 6, 12, 18, 24 and 36 months of age). This dataset may help other researchers in studying age-dependent progression of islet autoimmunity and T1D as well as of the age-dependence of lipidomic profiles in general. Alternatively, this dataset could more broadly used for the development of methods for the analysis of longitudinal multivariate data. Design Type(s) disease state design • observational design • longitudinal study design Measurement Type(s) lipid Technology Type(s) liquid chromatography-tandem mass spectrometry Factor Type(s) age • disease stage • autoantibody measurement Sample Characteristic(s) Homo sapiens • blood plasma Machine-accessible metadata file describing the reported data (ISA-Tab format)