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Funder: Lund University, The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled 8676 children, 3-4 months of age, born with HLA-susceptibility genotypes for islet autoimmunity (IA) and type 1 diabetes (T1D). Whole-genome sequencing (WGS) was performed in 1119 children in a nested case-control study design. Telomere length was estimated from WGS data using five tools: Computel, Telseq, Telomerecat, qMotif and Motif_counter. The estimated median telomere length was 5.10 kb (IQR 4.52-5.68 kb) using Computel. The age when the blood sample was drawn had a significant negative correlation with telomere length (P = 0.003). European children, particularly those from Finland (P = 0.041) and from Sweden (P = 0.001), had shorter telomeres than children from the U.S.A. Paternal age (P = 0.019) was positively associated with telomere length. First-degree relative status, presence of gestational diabetes in the mother, and maternal age did not have a significant impact on estimated telomere length. HLA-DR4/4 or HLA-DR4/X children had significantly longer telomeres compared to children with HLA-DR3/3 or HLA-DR3/9 haplogenotypes (P = 0.008). Estimated telomere length was not significantly different with respect to any IA (P = 0.377), IAA-first (P = 0.248), GADA-first (P = 0.248) or T1D (P = 0.861). These results suggest that telomere length has no major impact on the risk for IA, the first step to develop T1D. Nevertheless, telomere length was shorter in the T1D high prevalence populations, Finland and Sweden.

Additional file 1: Figure S1. Seurat snRNAseq workflow quality control statistics. From left to right, first figure reports the number of expressed genes per cell, second figure reports the number of UMIs (library size) per cell, third figure shows the fraction of reads mapped to mitochondrial genes per cell. Figure S2. The initial average sequencing depth of 35 million paired-end (PE) reads were ´down-sampled´ and mean square errors between the ATPase and totRNAseq predicted fractions of Type I fibers were calculated. Figure S3. Principal Component Analysis (PCA) plot of gene exppression from human skeletal muscle snRNAseq colored by the cluster annotation, see Figure 1A, obtained from the Louvain clustering on the number of significant principal components (according to Seurat workflow). Figure S4. (a) Principal Component Analysis (PCA) plot, and (b) tSNE plot of gene expression from human skeletal muscle snRNAseq colored by the cell cycle annotation obtained from the CellCycleScoring function from Seurat workflow. No obvious cluster formation based on the cell cycle can be observed from neither the PCA nor the tSNE plot.

Our aim was to investigate the associations between erythrocyte fatty acids and the risk of islet autoimmunity in children. The Environmental Determinants of Diabetes in the Young Study (TEDDY) is a longitudinal cohort study of children at high genetic risk for type 1 diabetes (n = 8676) born between 2004 and 2010 in the U.S., Finland, Sweden, and Germany. A nested case–control design comprised 398 cases with islet autoimmunity and 1178 sero-negative controls matched for clinical site, family history, and gender. Fatty acids composition was measured in erythrocytes collected at the age of 3, 6, and 12 months and then annually up to 6 years of age. Conditional logistic regression models were adjusted for HLA risk genotype, ancestry, and weight z-score. Higher eicosapentaenoic and docosapentaenoic acid (n − 3 polyunsaturated fatty acids) levels during infancy and conjugated linoleic acid after infancy were associated with a lower risk of islet autoimmunity. Furthermore, higher levels of some even-chain saturated (SFA) and monounsaturated fatty acids (MUFA) were associated with increased risk. Fatty acid status in early life may signal the risk for islet autoimmunity, especially n − 3 fatty acids may be protective, while increased levels of some SFAs and MUFAs may precede islet autoimmunity.

Additional file 1: Supplementary Table S1. Clinical and biochemical characteristics of male subjects from study A. Supplementary Table S2. Clinical and biochemical characteristics of male subjects from study B. Supplementary Table S3. Genes of which expression levels in skeletal muscle were positively correlated with insulin sensitivity (1/HOMA-IR) in study A. Supplementary Table S4. Genes of which expression levels in skeletal muscle were inversely correlated with insulin sensitivity (1/HOMA-IR) in study A. Supplementary Table S5. Significantly enriched Gene Ontology (GO) categories in the 70 genes whose expression level in skeletal muscle positively correlated with insulin sensitivity in Study A, analyzed with the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). Supplementary Table S6. Significantly enriched Wikipathways, in the 70 genes whose expression level in skeletal muscle positively correlated with insulin sensitivity in Study A, analyzed with the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). Supplementary Table S7. Significantly enriched Gene Ontology (GO) categories in the 110 genes whose expression level in skeletal muscle was inversely correlated with insulin sensitivity in Study A, analyzed with the WEB-based GEne SeT AnaLysis Toolkit (WebGestalt).