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. 2019 Oct 15;9(1):14819.
doi: 10.1038/s41598-019-51251-4.

Metabolite-related dietary patterns and the development of islet autoimmunity

Collaborators, Affiliations

Metabolite-related dietary patterns and the development of islet autoimmunity

Randi K Johnson et al. Sci Rep. .

Abstract

The role of diet in type 1 diabetes development is poorly understood. Metabolites, which reflect dietary response, may help elucidate this role. We explored metabolomics and lipidomics differences between 352 cases of islet autoimmunity (IA) and controls in the TEDDY (The Environmental Determinants of Diabetes in the Young) study. We created dietary patterns reflecting pre-IA metabolite differences between groups and examined their association with IA. Secondary outcomes included IA cases positive for multiple autoantibodies (mAb+). The association of 853 plasma metabolites with outcomes was tested at seroconversion to IA, just prior to seroconversion, and during infancy. Key compounds in enriched metabolite sets were used to create dietary patterns reflecting metabolite composition, which were then tested for association with outcomes in the nested case-control subset and the full TEDDY cohort. Unsaturated phosphatidylcholines, sphingomyelins, phosphatidylethanolamines, glucosylceramides, and phospholipid ethers in infancy were inversely associated with mAb+ risk, while dicarboxylic acids were associated with an increased risk. An infancy dietary pattern representing higher levels of unsaturated phosphatidylcholines and phospholipid ethers, and lower sphingomyelins was protective for mAb+ in the nested case-control study only. Characterization of this high-risk infant metabolomics profile may help shape the future of early diagnosis or prevention efforts.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Data flow diagram summarizing selection and size of analysis population for all aims (numbered). From a nested case-control study in TEDDY, we tested the association of 853 metabolites with outcomes at the time of seroconversion to IA (sets = 352), the last sample prior to IA (sets = 366), and at infancy (sets = 253). We created dietary patterns explaining candidate mAb+ metabolites identified in infancy, when children (n = 529) were 9-months of age and autoantibody negative. All subjects with food records at 9-months in the full TEDDY cohort (n = 6,537) were scored on the dietary pattern, and the association with development of mAb+ tested. IA = islet autoimmunity, mAb+ = multiple autoantibody positive, sets = number of risk sets or matched strata, n = number of subjects.
Figure 2
Figure 2
Chemically similar metabolite sets identified as significantly associated with mAb+ by ChemRICH. Each row is an individual metabolite, grouped by ChemRICH set and sorted by log(OR) within each set. Log(OR) > 0 (red) indicates a positive association between metabolite and mAb+, whereas log(OR) < 0 (blue) indicates an inverse association between metabolite and mAb+. Phosphatidylcholines were significantly lower in cases compared to controls in infancy (9-month), just prior to seroconversion (PSV), and at seroconversion to primary IA (SV). Other phospholipids were significantly lower in cases only in infancy, while other metabolite groups, such as unsaturated triglycerides and amino acids, distinguished cases and controls at seroconversion. Metabolite groups identified as significant (group p-value < 0.05) in any cross-section are shown, along with the corresponding adjusted p-value for the group (group false discovery rate).
Figure 3
Figure 3
Food group loadings and metabolite weights for metabolite-related dietary patterns. In total, the three dietary patterns explained 8% of metabolite variation and 29.3% of food variation. For food, the radial axis indicates the loading on each dietary pattern (Range: −0.6 to 0.4), and is used to interpret which combinations of foods are influential in the dietary pattern. Similarly, the metabolite radial axis indicates the weight of each metabolite on each dietary pattern (Range: −0.6 to 0.9), indicating which combination of metabolites are explained by each dietary pattern. For example, subjects scoring high on dietary pattern 1 had diets higher in non-gluten containing cereals, onions, vegetable oils, and fat-free milk, and lower in breast milk. This diet corresponded to higher levels of PE (34:2).

References

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