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
  • View Affiliations

  • Published online on: August 8, 2023     https://doi.org/10.3892/etm.2023.12160
  • Article Number: 461
Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


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.
View Figures
View References

Related Articles

Journal Cover

October-2023
Volume 26 Issue 4

Print ISSN: 1792-0981
Online ISSN:1792-1015

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Kotanidou EP, Kosvyra A, Mouzaki K, Giza S, Tsinopoulou VR, Serbis A, Chouvarda I and Galli‑Tsinopoulou A: Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?. Exp Ther Med 26: 461, 2023.
APA
Kotanidou, E.P., Kosvyra, A., Mouzaki, K., Giza, S., Tsinopoulou, V.R., Serbis, A. ... Galli‑Tsinopoulou, A. (2023). Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?. Experimental and Therapeutic Medicine, 26, 461. https://doi.org/10.3892/etm.2023.12160
MLA
Kotanidou, E. P., Kosvyra, A., Mouzaki, K., Giza, S., Tsinopoulou, V. R., Serbis, A., Chouvarda, I., Galli‑Tsinopoulou, A."Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?". Experimental and Therapeutic Medicine 26.4 (2023): 461.
Chicago
Kotanidou, E. P., Kosvyra, A., Mouzaki, K., Giza, S., Tsinopoulou, V. R., Serbis, A., Chouvarda, I., Galli‑Tsinopoulou, A."Methylation haplotypes of the insulin gene promoter in children and adolescents with type 1 diabetes: Can a dimensionality reduction approach predict the disease?". Experimental and Therapeutic Medicine 26, no. 4 (2023): 461. https://doi.org/10.3892/etm.2023.12160