Type 2 diabetes and cardiovascular conditions prediction in individuals with metabolic syndrome-associated lipoprotein lipase gene (LPL) single nucleotide polymorphisms (SNPs)

Abstract

Objective

Metabolic syndrome (MetS) is predictive of increased risk of type 2 diabetes (T2D) and cardiovascular conditions (CVC). Lipoprotein lipase gene (LPL) single nucleotide polymorphisms (SNPs) may be of importance to the eventual diagnosis of T2D and CVC. This study aimed to predict the diagnosis of T2D and CVC amongst individuals with LPL SNPs rs268, rs11542065, rs116403115, rs118204057, rs118204061, rs144466625, and rs547644955.

Methods

This is a retrospective study using the UK Biobank data. Variables associated with MetS, T2D and CVC were selected from the data set. The total number of subjects in the cohort was 12,872 (mean age 56 years ± 8.1, 90.0 % were of British ethnicity, and 53.9 % were females). Logistic regression was used to assess whether the T2D and CVC can be predicted based on the presence of LPL SNPs and some of the clinical measures.

Results

Prediction models using clinical parameters showed good area under the curve (AUC) for prediction of T2D and CVC diagnosis (in receiver operating characteristic (ROC) analysis, area under the curve (AUC) = 0.959 for T2D, AUC = 0.772 for CVC). The addition of Polygenic Risk Scores (PRS/s) showed an improvement for diagnosis of both (AUC = 0.961 and 0.790 for TD and CVC, respectively). Further addition of SNPs showed more increase in AUC (AUC = 0.965 and 0.837 for T2D and CVC, respectively). The additive effect of the PRSs and LPL SNPs was more pronounced in the CVC than in the T2D model. The variant that had major significance for both T2D and CVC diagnoses was rs547644955 (AUC 1.0 and 0.910, respectively). The SNPs rs116403115 and rs118204057 both had an AUC of 1.0 for T2D diagnosis.

Conclusion

The prediction of T2D and CVC diagnoses with the use of clinically available factors may be enhanced with the addition of PRSs and SNPs, including LPL SNPs, which may have implications for stratified or personalised approaches for disease prevention or treatment.