近日，美国西北大学范伯格医学院Ciaran N. Kohli-Lynch团队研究了ApoB、非HDL-C和LDL-C目标在一级预防降脂治疗中的成本-效果。该项研究成果发表在2026年4月8日出版的《美国医学会志》上

载脂蛋白B（apoB）与低密度脂蛋白胆固醇（LDL-C）和非高密度脂蛋白胆固醇（non-HDL-C）相比，在接受降脂治疗（LLT）的患者中，是评估残留动脉粥样硬化性心血管疾病风险的更优标志物。然而，以LDL-C、non-HDL-C和apoB为目标的成本效果尚未确立。

为了确定基于LDL-C、non-HDL-C和apoB目标，对一级预防人群强化降脂治疗的相对成本效果，研究组采用计算机模拟模型，评估根据LDL-C、non-HDL-C或apoB目标，使用高强度他汀类药物或依折麦布强化LLT的成本效果。研究构建了一个包含250,000名符合他汀使用条件且无动脉粥样硬化性心血管疾病的美国成年人队列，数据来自2005–2016年国家健康与营养调查（参与者N=4,149）。个体在血脂筛查后开始模拟，并根据2018年美国心脏协会/美国心脏病学会指南接受他汀治疗。模型输入参数来源于全国调查数据、汇总的纵向队列研究及已发表文献。通过传统和概率敏感性分析评估不确定性。

若个体治疗后LDL-C水平未低于100 mg/dL、non-HDL-C未低于118 mg/dL或apoB未低于78.7 mg/dL，则强化降脂治疗。主要观测指标为终身质量调整生命年（QALYs）和成本（以2025年美元计），每年贴现3.0%。主要结局是增量成本效果比。若每获得一个QALY的成本低于120,000美元，则该策略被视为具有成本效果。

与LDL-C目标相比，采用non-HDL-C目标可增加965 QALYs（95%不确定区间[UI]，-3551至5341 QALYs），同时成本降低210万美元（95% UI，-9420万至9200万美元）。与non-HDL-C目标相比，采用apoB目标可增加1324 QALYs（95% UI，-2602至5669 QALYs），同时成本增加4020万美元（95% UI，-4360万至1.34亿美元），由此产生的增量成本效果比为每获得一个QALY需30,300美元。在每获得一个QALY愿意支付120,000美元的阈值下，apoB目标在65%的概率分析中为最优，non-HDL-C目标在25%的分析中为最优。apoB检测的成本可忽略不计；较高的成本反映了更长的预期寿命和更长时间的预防性治疗。

该计算机模拟研究的结果表明，apoB可作为具有成本效果的标志物，用于指导一级预防的降脂治疗，并改善人群健康。

附：英文原文

Title: Cost-Effectiveness of ApoB, Non–HDL-C, and LDL-C Goals for Primary Prevention Lipid-Lowering Therapy

Author: Samuel Luebbe, Allan D. Sniderman, Andrew E. Moran, John T. Wilkins, Ciaran N. Kohli-Lynch

Issue&Volume: 2026-04-08

Abstract:

Importance  Apolipoprotein B (apoB) is a superior marker of residual atherosclerotic cardiovascular disease risk in patients treated with lipid-lowering therapy (LLT) compared with low-density lipoprotein cholesterol (LDL-C) and non–high-density lipoprotein cholesterol (non–HDL-C). The cost-effectiveness of LDL-C, non–HDL-C, and apoB goals has not been established.

Objective  To determine the relative cost-effectiveness of intensifying LLT for primary prevention based on LDL-C, non–HDL-C, and apoB goals.

Design, Setting, and Participants  This economic evaluation used a computer simulation model to evaluate the cost-effectiveness of intensifying LLT with high-intensity statins or ezetimibe according to LDL-C, non–HDL-C, or apoB goals. A cohort of 250000 statin-eligible and atherosclerotic cardiovascular disease–free US adults was constructed from 2005 to 2016 National Health and Nutrition Examination Survey participants (N=4149). Individuals commenced the simulation after lipid screening and received statin therapy based on 2018 American Heart Association/American College of Cardiology guidelines. Model inputs were derived from national survey data, pooled longitudinal cohort studies, and published literature. Uncertainty was explored with traditional and probabilistic sensitivity analysis.

Exposures  Lipid-lowering therapy was intensified if individuals did not achieve treated LDL-C level less than 100 mg/dL, non–HDL-C level less than 118 mg/dL, or apoB level less than 78.7 mg/dL.

Main Outcomes and Measures  Lifetime quality-adjusted life-years (QALYs) and costs (in 2025 US dollars), discounted 3.0% annually. The primary outcome was the incremental cost-effectiveness ratio. Strategies were considered cost-effective if they cost less than $120000 per QALY gained.

Results  Compared with an LDL-C goal, 965 QALYs (95% uncertainty interval [UI], 3551 to 5341 QALYs) would be gained with a non–HDL-C goal, alongside a $2.1 million (95% UI, $94.2 million to $92.0 million) reduction in costs. Compared with a non–HDL-C goal, 1324 QALYs (95% UI, 2602 to 5669 QALYs) would be gained with an apoB goal, alongside a $40.2 million (95% UI, $43.6 million to $134 million) increase in costs, yielding an incremental cost-effectiveness ratio of $30300 per QALY gained. At a willingness-to-pay threshold of $120000 per QALY gained, an apoB goal was optimal in 65% of probabilistic analyses and a non–HDL-C goal was optimal in 25%. The cost of apoB testing was marginal; higher costs reflected longer life expectancy and prolonged preventive treatment.

Conclusions and Relevance  The results of this computer simulation study suggest that apoB can be used as a cost-effective marker to guide primary prevention LLT and improve population health.

DOI: 10.1001/jama.2026.2986

Source: https://jamanetwork.com/journals/jama/fullarticle/2847303