Rochmawati Ike Dhiah, Deo Salil, Lees Jennifer S, Mark Patrick B, Sattar Naveed, Celis-Morales Carlos, Pell Jill P, Welsh Paul, Ho Frederick K
School of Health and Wellbeing, University of Glasgow, 90 Byres Road, Glasgow G12 8TB, UK.
Department of Clinical and Community Pharmacy, Faculty of Pharmacy, University of Surabaya, Jalan Raya Kalirungkut, Surabaya 60293, Indonesia.
Eur J Prev Cardiol. 2025 May 12;32(7):585-595. doi: 10.1093/eurjpc/zwae352.
This study aims to explore whether conventional and emerging biomarkers could improve risk discrimination and calibration in the secondary prevention of recurrent atherosclerotic cardiovascular disease (ASCVD), based on a model using predictors from SMART2 (Secondary Manifestations of ARTerial Disease).
In a cohort of 20 658 UK Biobank participants with medical history of ASCVD, we analysed any improvement in C indices and net reclassification index (NRI) for future ASCVD events, following addition of lipoprotein A (LP-a), apolipoprotein B, Cystatin C, Hemoglobin A1c (HbA1c), gamma-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase, and alkaline phosphatase (ALP), to a model with predictors used in SMART2 for the outcome of recurrent major cardiovascular event. We also examined any improvement in C indices and NRIs replacing creatinine-based estimated glomerular filtration rate (eGFR) with Cystatin C-based estimates. Calibration plots between different models were also compared. Compared with the baseline model (C index = 0.663), modest increments in C indices were observed when adding HbA1c (ΔC = 0.0064, P < 0.001), Cystatin C (ΔC = 0.0037, P < 0.001), GGT (ΔC = 0.0023, P < 0.001), AST (ΔC = 0.0007, P < 0.005) or ALP (ΔC = 0.0010, P < 0.001) or replacing eGFRCr with eGFRCysC (ΔC = 0.0036, P < 0.001) or eGFRCr-CysC (ΔC = 0.00336, P < 0.001). Similarly, the strongest improvements in NRI were observed with the addition of HbA1c (NRI = 0.014) or Cystatin C (NRI = 0.006) or replacing eGFRCr with eGFRCr-CysC (NRI = 0.001) or eGFRCysC (NRI = 0.002). There was no evidence that adding biomarkers modified calibration.
Adding several biomarkers, most notably Cystatin C and HbA1c, but not LP-a, in a model using SMART2 predictors modestly improved discrimination.
本研究旨在基于一个使用来自ARTerial疾病二级表现（SMART2）的预测因子的模型，探讨传统和新兴生物标志物是否能改善复发性动脉粥样硬化性心血管疾病（ASCVD）二级预防中的风险判别和校准。
在20658名有ASCVD病史的英国生物银行参与者队列中，我们分析了在将脂蛋白A（LP-a）、载脂蛋白B、胱抑素C、糖化血红蛋白（HbA1c）、γ-谷氨酰转移酶（GGT）、天冬氨酸转氨酶（AST）、丙氨酸转氨酶和碱性磷酸酶（ALP）添加到一个使用SMART2中用于复发性主要心血管事件结局的预测因子的模型后，未来ASCVD事件的C指数和净重新分类指数（NRI）的任何改善情况。我们还检查了用基于胱抑素C的估计值替代基于肌酐的估计肾小球滤过率（eGFR）后C指数和NRI的任何改善情况。还比较了不同模型之间的校准图。与基线模型（C指数 = 0.663）相比，添加HbA1c（ΔC = 0.0064，P < 0.001）、胱抑素C（ΔC = 0.0037，P < 0.001）、GGT（ΔC = 0.0023，P < 0.001）、AST（ΔC = 0.0007，P < 0.005）或ALP（ΔC = 0.0010，P < 0.001），或用基于胱抑素C的eGFR（eGFRCysC）替代基于肌酐的eGFR（eGFRCr）（ΔC = 0.0036，P < 0.001）或基于肌酐和胱抑素C的eGFR（eGFRCr-CysC）（ΔC = 0.00336，P < 0.001）时，观察到C指数有适度增加。同样，添加HbA1c（NRI = 0.014）或胱抑素C（NRI = 0.006），或用eGFRCr-CysC替代eGFRCr（NRI = 0.001）或用eGFRCysC替代eGFRCr（NRI = 0.002）时，观察到NRI有最显著的改善。没有证据表明添加生物标志物会改变校准。
在使用SMART2预测因子的模型中添加几种生物标志物，最显著的是胱抑素C和HbA1c，但不包括LP-a，可适度改善判别。
