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


ABSTRACT: 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.

SUBMITTER: Lamichhane S 

PROVIDER: S-EPMC6233478 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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

Lamichhane Santosh S   Ahonen Linda L   Dyrlund Thomas Sparholt TS   Siljander Heli H   Hyöty Heikki H   Ilonen Jorma J   Toppari Jorma J   Veijola Riitta R   Hyötyläinen Tuulia T   Knip Mikael M   Orešič Matej M  

Scientific data 20181113


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  ...[more]

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