Rationale: Association between different components of metabolic syndrome and the rate of age-related telomere shortening was reported repeatedly, although some findings are inconsistent across studies, suggesting the need for further research on the topic. In the present study, we examined relationships between different components of metabolic syndrome (MetS); glucose tolerance reflected in 2-h post-load plasma glucose (2hPG) levels and age on the leukocyte telomere length (LTL) in Ukraine population. Methods: The study was conducted on the 115 adult individuals residing in the Kyiv region (Ukraine). Among them, 79 were diagnosed with MetS according to the International Diabetes Federation definition. LTL were determined by a qPCR-based method. Multivariate logistic regression (MLR) and artificial neural networks (ANN) modeling were used for the analysis of the results. ROC-analysis was also performed to compare the predictively values of this models. Results: MetS was associated with a high (OR = 3.0 CI 1.3-6.7; p = 0.01) risk of having shorter telomeres that remained significant after adjusting for age, gender and 2hPG levels. Fasting plasma glucose (FPG) levels and other MetS components did not affect the magnitude of the relationship and did not reveal the independent influence of these factors. The level of 2hPG in turn, demonstrated a significant relationship (OR = 1.3 CI 1.0-1.6 per 1 mmol/l; p = 0.04) with LTL regardless of the presence of MetS. The non-linearity of the interactions between age, gender and 2hPG level was revealed by neural network modeling (AUC = 0.76 CI 0.68-0.84). Conclusion: Our study found that impaired glucose tolerance, but not FPG levels, affected the association between LTL and MetS, which may be also indicative for pathophysiological differences in these hyperglycemia categories. 2hPG levels can provide an opportunity for a more accurate diagnostics of MetS and for evaluating the rate of aging in patients with MetS. Further research, however, is needed to verify this assumption.
Keywords: Ukraine; artificial neural networks; impaired glucose tolerance; metabolic syndrome; telomeres.