Longitudinal changes in gene expression during islet autoimmunity (IA) may provide insight into biological processes that explain progression to type 1 diabetes (T1D). We identified individuals from Diabetes Autoimmunity Study in the Young (DAISY) who developed IA, autoantibodies present on two or more visits. Illumina’s NovaSeq 6000 was used to quantify gene expression in whole blood. With linear mixed models we tested for changes in expression after IA that differed across individuals who progressed to T1D (progressors) (n = 25), reverted to an autoantibody-negative stage (reverters) (n = 47), or maintained IA positivity but did not develop T1D (maintainers) (n = 66). Weighted gene coexpression network analysis was used to identify coexpression modules. Gene Ontology pathway analysis of the top 150 differentially expressed genes (nominal P < 0.01) identified significantly enriched pathways including leukocyte activation involved in immune response, innate immune response, and regulation of immune response. We identified a module of 14 coexpressed genes with roles in the innate immunity. The hub gene, LTF, is known to have immunomodulatory properties. Another gene within the module, CAMP, is potentially relevant based on its role in promoting β-cell survival in a murine model. Overall, results provide evidence of alterations in expression of innate immune genes prior to onset of T1D.

Objective: Illumina BeadChip arrays are commonly used to generate DNA methylation data for large epidemiological studies. Updates in technology over time create challenges for data harmonization within and between studies, many of which obtained data from the older 450K and newer EPIC platforms. The pre-processing pipeline for DNA methylation is not trivial, and influences the downstream analyses. Incorporating different platforms adds a new level of technical variability that has not yet been taken into account by recommended pipelines. Our study evaluated the performance of various tools on different versions of platform data harmonization at each step of pre-processing pipeline, including quality control (QC), normalization, batch effect adjustment, and genomic inflation. We illustrate our novel approach using 450K and EPIC data from the Diabetes Autoimmunity Study in the Young (DAISY) prospective cohort.Results: We found normalization and probe filtering had the biggest effect on data harmonization. Employing a meta-analysis was an effective and easily executable method for accounting for platform variability. Correcting for genomic inflation also helped with harmonization. We present guidelines for studies seeking to harmonize data from the 450K and EPIC platforms, which includes the use of technical replicates for evaluating numerous pre-processing steps, and employing a meta-analysis.

Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference > 10°) and 2 concordant (Cobb angle difference ≤ 2°). Genome-wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper- or hypo-methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS.

Reproducibility of measurements This study investigates the reliability and standard error of measurement of spine and thoracic height radiographic measurements in patients with early onset scoliosis (EOS). Spine and thoracic height radiographic measurements are often used as a surrogate for pulmonary development in patients with EOS. There is limited literature validating the reliability of spine and thoracic height measurements in the EOS population. Using pilot data, we determined measuring 49 unique radiographs would provide 80% power to obtain a 95% confidence interval width of 0.05 for the interclass correlation coefficients (ICC). A random sampling strategy, stratified by underlying diagnosis from the Classification of Early Onset Scoliosis (C-EOS), was used to distribute the diagnoses in the study sample. Two attending pediatric spine surgeons, two pediatric orthopaedic fellows, and two research assistants measured coronal spine (T1–S1) and thoracic (T1–T12) height on digital radiographs using imaging software (Surgimap®; Nemaris, Inc; New York) on two separate occasions at least three weeks apart. Order of images was randomized for the second iteration. Linear mixed model regression analyses were used to estimate interrater and intrarater reliability. The study sample included subjects (N = 48) with idiopathic (N = 17, 35%), congenital (N = 16, 33%, 1 patient excluded), neuromuscular (N = 11, 23%), and syndromic (N = 4, 8%) scoliosis. Overall interrater reliability estimates for spine height [ICC: 0.894, 95% CI: 0.847–0.932] and thoracic height [ICC: 0.890, 95% CI: 0.844–0.929] were excellent. Intrarater reliability estimates for spine height [ICC: 0.906, 95% CI: 0.830–0.943] and thoracic height [ICC: 0.898, 95% CI: 0.817–0.938] were also excellent. There is excellent interrater and intrarater reliability for radiographic measurements of spine and thoracic height in the EOS population at our institution. Level of Evidence: 2