代谢综合征相关脂蛋白脂肪酶基因（LPL）单核苷酸多态性（SNP）个体的2型糖尿病和心血管疾病预测

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

作者信息

Fojas Esphie Grace, Haidery Ahmad, Naseeb Samina, Naemi Roozbeh

机构信息

University of Staffordshire, School of Health, Education, Policing and Sciences, Stoke-on-Trent, England, United Kingdom.

出版信息

J Diabetes Complications. 2025 Jun;39(6):109003. doi: 10.1016/j.jdiacomp.2025.109003. Epub 2025 Mar 24.

DOI:10.1016/j.jdiacomp.2025.109003

PMID:40179477

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.

摘要

目的

代谢综合征（MetS）可预测2型糖尿病（T2D）和心血管疾病（CVC）风险增加。脂蛋白脂肪酶基因（LPL）单核苷酸多态性（SNP）可能对T2D和CVC的最终诊断具有重要意义。本研究旨在预测携带LPL SNP rs268、rs11542065、rs116403115、rs118204057、rs118204061、rs144466625和rs547644955的个体中T2D和CVC的诊断情况。

方法

这是一项使用英国生物银行数据的回顾性研究。从数据集中选择与MetS、T2D和CVC相关的变量。队列中的受试者总数为12872人（平均年龄56岁±8.1岁，90.0%为英国种族，53.9%为女性）。采用逻辑回归评估基于LPL SNP的存在和一些临床指标能否预测T2D和CVC。

结果

使用临床参数的预测模型在预测T2D和CVC诊断方面显示出良好的曲线下面积（AUC）（在受试者工作特征（ROC）分析中，T2D的曲线下面积（AUC）=0.959，CVC的AUC=0.772）。添加多基因风险评分（PRS/s）后，两者的诊断准确性均有所提高（T2D和CVC的AUC分别为0.961和0.790）。进一步添加SNP后，AUC进一步增加（T2D和CVC的AUC分别为0.965和0.837）。PRS和LPL SNP的累加效应在CVC模型中比在T2D模型中更明显。对T2D和CVC诊断均具有重要意义的变异是rs547644955（AUC分别为1.0和0.910）。SNP rs116403115和rs118204057在T2D诊断中的AUC均为1.0。