Metabolic Syndrome (MetS)-Associated Lipoprotein Lipase Gene (LPL) Single Nucleotide Polymorphisms (SNPs) and Diagnosis, Prognosis and Management of Type 2 Diabetes and Cardiovascular Conditions in Adults: Data from 12,872 Participants from the UK Biobank

Metabolic syndrome (MetS) is the clustering of risk factors for type 2 diabetes (T2D) and cardiovascular conditions (CVC). MetS features include abdominal obesity, hyperglycemia, and dyslipidemia. The lipoprotein lipase (LPL) gene (LPL) provides instructions for making the enzyme LPL, hence is crucial for lipid regulation. LPL polymorphisms, including single nucleotide polymorphisms (SNPs), which have been previously associated with MetS, may be of particular significance to the progression of CVC and T2D. This study aimed to investigate the molecular pathogenesis of T2D and CVC amongst individuals with polymorphisms of LPL. SNPs rs268, rs11542065, rs116403115, rs118204057, rs118204061, rs144466625, and rs547644955, in particular, were investigated using data from the UK Biobank. Specifically, the confirmed diagnoses of T2D and CVC in the cohort with these SNPs were assessed. In addition, this study also aimed to predict the confirmed diagnosis of T2D and CVC in the cohort. Variables associated with MetS, T2D and CVC were selected from the dataset and were analysed using SPSS. The total number of participants analysed in the cohort was 12,872 (mean age 56 years +8.1; 90.0% were of British ethnicity; 53.9% were females). Significant (p < 0.05) associations between all the SNPs and diagnosis of both T2D and CVC were found. Statistically significant differences in weight, BMI, diastolic BP, total lipids in lipoprotein, HbA1c, WC, HDL, and LDL were found between SNPs. BMI and WC were significantly higher in individuals who were diagnosed with both T2D and CVC; when sexes were compared, men with T2D and CVC had slightly increased BMI and WC than women. Prediction models using clinical parameters showed good AUC for predicting the diagnosis of T2D and CVC in ROC analysis (AUC = .959 for T2D, AUC = .772 for CVC). The addition of Polygenic Risk Scores (PRSs) showed diagnosis prediction improvement for both (AUC = .961 for T2D, AUC = .790 for CVC), and further
addition of SNPs showed more AUC increase (AUC = .965 for T2D, AUC = .837 for CVC). This study shows that the investigated LPL SNPs are associated with the diagnosis of T2D and CVC. In addition, this study demonstrates that T2D and CVC diagnoses may be predicted by clinically available factors, which may be further enhanced by incorporating associated PRSs and SNPs, including the reported LPL SNPs. These results can have particular implications for T2D and CVC prevention and treatment with the utilisation of stratified and personalised medicine. In this light, pharmacogenetic investigations of T2D and CVC related to these LPL SNPs combined with current pharmacogenomics knowledge may pave the way for improved preventive and therapeutic clinical guidelines.