Data Mining Framework For Discovering and Clustering Phenotypes of Atypical Diabetes

Supplemental tables and figures for "Data Mining Framework For Discovering and Clustering Phenotypes of Atypical Diabetes"</p

Rising Hemoglobin A1c in the Non-Diabetic Range Predicts Progression of Type 1 Diabetes As Well As Oral Glucose Tolerance Tests

   Objective: Biomarkers predicting risk of type 1 diabetes (Stage 3) among children with islet autoantibodies are greatly needed to prevent DKA and facilitate prevention therapies.  Research Design and Methods: Children in the prospective TEDDY study (n=707) with confirmed diabetes-associated autoantibodies (GADA, IA-2A and/or IAA) and ≥2 HbA1c measurements were followed to diabetes or median age 11.1 years. Once confirmed autoantibody positive, HbA1c was measured quarterly. Cox models and receiver operative characteristic (ROC) analyses revealed the prognostic utility for risk of Stage 3 on a relative HbA1c increase from the baseline visit or an OGTT 2-hr plasma glucose (2-hPG). This HbA1c approach was then validated in the TrialNet Pathway to Prevention study (n=1190). Results: A 10% relative HbA1c increase from baseline best marked increased risk of Stage 3 in TEDDY (74% sensitive; 88% specific). Significant predictors of risk for HbA1c change were age and HbA1c at baseline test, genetic sex, maximum number of autoantibodies and maximum rate of HbA1c increase by time of change. The multivariable model featuring a HbA1c >10% increase and these additional factors revealed increased risk of Stage 3 in TEDDY (HR=12.74, 95%CI 8.7-18.6, p>10% increase performed similarly to an OGTT 2-hPG composite model (TEDDY AUC=0.88 and 0.85, respectively) and to the HbA1c model in TrialNet (AUC=0.82). Conclusion: An increase of >10% in HbA1c from baseline is as informative as OGTT 2-hPG in predicting risk of Stage 3 in youth with genetic risk and diabetes-associated autoantibodies.</p

Six SNPs from two genomic regions significantly associated with celiac disease in Sweden.

<p>Five SNPs mapped to <i>PKIA</i> region and one SNP mapped <i>PFKFB3</i> region.</p

Novel associations with celiac disease or tissue transglutaminase autoantibody (tTGA) positivity (p<10⁻⁴).

<p>Novel associations with celiac disease or tissue transglutaminase autoantibody (tTGA) positivity (p<10⁻⁴).</p

Country-specific associations with risk of celiac disease.

<p>Kaplan-Meier plots of five SNPs mapped to the <i>PKIA</i> region and one SNP mapped to the <i>PFKFB3</i> region, in the Swedish TEDDY population (<b>A</b>) and in the other TEDDY countries (<b>B</b>). Kaplan-Meier plots clearly indicate country-specific differences. HRs and p-values are calculated using three possible genotypes and adjusted for family history of celiac disease, HLA-DR-DQ genotype, gender, <i>HLA-DPB1</i> and population stratification (ancestral heterogeneity).</p

Associations with risk of celiac disease in the Swedish population.

<p><b>A:</b> Manhattan plot of 133620 SNPs with MAF>0.01, displaying the <i>P</i>-values on the −log₁₀ scale for the SNPs associated with celiac disease in the Swedish TEDDY population. <b>B:</b> Regional association plots at the <i>PKIA</i> locus generated by LocusZoom, showing the significance of association and the recombination rate. Colors represent HapMap CEU linkage disequilibrium r² values with the most significantly associated SNP (rs117128341; shown in purple). <b>C:</b> Pairwise LD plot for five SNPs in the region of <i>PKIA</i>. The five most significant SNPs from this region are in high LD with each other.</p

SNPs in the previously reported celiac disease associated regions.

<p>Manhattan plot of <i>P</i>-values on the −log₁₀ scale for SNPs (±400kb) previously associated with celiac disease (<b>A</b>) and persistent tissue transglutaminase autoantibody (tTGA) positivity <b>(B</b>). HRs and p-values are calculated using three possible genotypes and adjusted for family history of celiac disease, HLA-DR-DQ genotype, gender, <i>HLA-DPB1</i>, population stratification (ancestral heterogeneity) and country of residence (as strata). The red dashed line represents <i>p</i> = 1x10⁻⁴. Kaplan-Meier plots of the three most significant SNPs associated with celiac disease (<b>C</b>) and tTGA (<b>D</b>) are plotted by dividing the subjects in two groups: (i) Major homozygous (black curves) and (ii) Heterozygous combined with minor homozygous (red curves).</p

Associations with risk of celiac disease and risk of persistent tissue transglutaminase autoantibody (tTGA) positivity.

<p>Manhattan plot of 133,620 SNPs with MAF>0.01, displaying the <i>P</i>-values on the −log₁₀ scale for SNP associations with celiac disease (<b>A</b>) and persistent tTGA positivity <b>(B</b>). HRs and p-values are calculated using three possible genotypes and adjusted for family history of celiac disease, HLA-DR-DQ genotype, gender, <i>HLA-DPB1</i>, population stratification (ancestral heterogeneity) and country of residence (as strata). The red dashed line represents <i>p</i> = 1x10⁻⁴, the red solid line represents Bonferroni correction threshold. Kaplan-Meier plots of selected SNPs associated with celiac disease (<b>C</b>) and persistent tTGA (<b>D</b>) are plotted by dividing the subjects in two groups: (i) Major homozygous (black curves) and (ii) Heterozygous combined with minor homozygous (red curves).</p

Associations with celiac disease or tissue transglutaminase autoantibody (tTGA) positivity (p<10⁻⁴), mapped to previously known regions.

<p>Associations with celiac disease or tissue transglutaminase autoantibody (tTGA) positivity (p<10⁻⁴), mapped to previously known regions.</p

Flow chart of study participants.

<p>The Environmental Determinants of Diabetes in the Young (TEDDY) is an international multicenter study that screened over 420,000 newborns from the general population in four different countries. The present study genotyped 195,806 SNPs on ImmunoChip in 6,010 TEDDY children to identify potential genetic factors responsible for the development of CD and country-specific differences in genetic predisposition. As shown in flow chart, a total of 6,010 subjects were included in the analysis of time-to-CD, and 5379 subjects were included in the analysis of time-to-tTGA.</p