Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

Kotanidou,Eleni P.; Kosvyra,Alexandra; Mouzaki,Konstantina; Giza,Styliani; Tsinopoulou,Vasiliki Rengina; Serbis,Anastasios; Chouvarda,Ioanna; Galli‑Tsinopoulou,Assimina

doi:10.3892/etm.2023.12160

Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?

Authors:
- Eleni P. Kotanidou
- Alexandra Kosvyra
- Konstantina Mouzaki
- Styliani Giza
- Vasiliki Rengina Tsinopoulou
- Anastasios Serbis
- Ioanna Chouvarda
- Assimina Galli‑Tsinopoulou
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Affiliations: Second Department of Pediatrics, Unit of Pediatric Endocrinology and Metabolism, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, AHEPA University Hospital, 54636 Thessaloniki, Greece, Laboratory of Computing, Medical Informatics and Biomedical Imaging Technologies, Faculty of Health Sciences, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
Published online on: August 8, 2023 https://doi.org/10.3892/etm.2023.12160
Article Number: 461

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Abstract

DNA methylation of cytosine‑guanine sites (CpGs) is associated with type 1 diabetes (T1D). The sequence of methylated and non‑methylated sites in a specific genetic region constitutes its methyl‑haplotype. The aim of the present study was to identify insulin gene promoter (IGP) methyl‑haplotypes among children and adolescents with T1D and suggest a predictive model for the discrimination of cases and controls according to methyl‑haplotypes. A total of 40 individuals (20 T1D) participated. The IGP region from peripheral whole blood DNA of 40 participants (20 T1D) was sequenced using next‑generation sequencing, sequences were read using FASTQ files and methylation status was calculated by python‑based pipeline for targeted deep bisulfite sequenced amplicons (ampliMethProfiler). Methylation profile at 10 CpG sites proximal to transcription start site of the IGP was recorded and coded as 0 for unmethylation or 1 for methylation. A single read could result in ‘1111111111’ methyl‑haplotype (all methylated), ‘000000000’ methyl‑haplotype (all unmethylated) or any other combination. Principal component analysis was applied to the generated methyl‑haplotypes for dimensionality reduction, and the first three principal components were employed as features with five different classifiers (random forest, decision tree, logistic regression, Naive Bayes, support vector machine). Naive Bayes was the best‑performing classifier, with 0.9 accuracy. Predictive models were evaluated using receiver operating characteristics (AUC 0.96). Methyl‑haplotypes ‘1111111111’, ‘1111111011’, ‘1110111111’, ‘1111101111’ and ‘1110101111’ were revealed to be the most significantly associated with T1D according to the dimensionality reduction method. Methylation‑based biomarkers such as IGP methyl‑haplotypes could serve to identify individuals at high risk for T1D.

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