Literature DB >> 33863802

Phospholipid Levels at Seroconversion Are Associated With Resolution of Persistent Islet Autoimmunity: The Diabetes Autoimmunity Study in the Young.

Patrick M Carry1, Lauren A Vanderlinden1, Randi K Johnson2, Teresa Buckner1, Oliver Fiehn3, Andrea K Steck4, Katerina Kechris5, Ivana Yang6, Tasha E Fingerlin1,5,7, Marian Rewers4, Jill M Norris8,4.   

Abstract

Reversion of islet autoimmunity (IA) may point to mechanisms that prevent IA progression. We followed 199 individuals who developed IA during the Diabetes Autoimmunity Study in the Young. Untargeted metabolomics was performed in serum samples following IA. Cox proportional hazards models were used to test whether the metabolites (2,487) predicted IA reversion: two or more consecutive visits negative for all autoantibodies. We conducted a principal components analysis (PCA) of the top metabolites; |hazard ratio (HR) >1.25| and nominal P < 0.01. Phosphatidylcholine (16:0_18:1(9Z)) was the strongest individual metabolite (HR per 1 SD 2.16, false discovery rate (FDR)-adjusted P = 0.0037). Enrichment analysis identified four clusters (FDR P < 0.10) characterized by an overabundance of sphingomyelin (d40:0), phosphatidylcholine (16:0_18:1(9Z)), phosphatidylcholine (30:0), and l-decanoylcarnitine. Overall, 63 metabolites met the criteria for inclusion in the PCA. PC1 (HR 1.4, P < 0.0001), PC2 (HR 0.85, P = 0.0185), and PC4 (HR 1.28, P = 0.0103) were associated with IA reversion. Given the potential influence of diet on the metabolome, we investigated whether nutrients were correlated with PCs. We identified 20 nutrients that were correlated with the PCs (P < 0.05). Total sugar intake was the top nutrient. Overall, we identified an association between phosphatidylcholine, sphingomyelin, and carnitine levels and reversion of IA.
© 2021 by the American Diabetes Association.

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Year:  2021        PMID: 33863802      PMCID: PMC8336007          DOI: 10.2337/db20-1251

Source DB:  PubMed          Journal:  Diabetes        ISSN: 0012-1797            Impact factor:   9.337


  55 in total

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Authors:  Mei Li Ng; Carol Wadham; Olga A Sukocheva
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9.  Meta-analysis of genome-wide association study data identifies additional type 1 diabetes risk loci.

Authors:  Jason D Cooper; Deborah J Smyth; Adam M Smiles; Vincent Plagnol; Neil M Walker; James E Allen; Kate Downes; Jeffrey C Barrett; Barry C Healy; Josyf C Mychaleckyj; James H Warram; John A Todd
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10.  Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets.

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1.  A Mediation Approach to Discovering Causal Relationships between the Metabolome and DNA Methylation in Type 1 Diabetes.

Authors:  Tim Vigers; Lauren A Vanderlinden; Randi K Johnson; Patrick M Carry; Ivana Yang; Brian C DeFelice; Alexander M Kaizer; Laura Pyle; Marian Rewers; Oliver Fiehn; Jill M Norris; Katerina Kechris
Journal:  Metabolites       Date:  2021-08-14
  1 in total

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