Errington Niamh, Zhou Li, Rhodes Christopher J, Fong Yiu-Lian, Zhou Lihan, Kariotis Sokratis, Harder Eileen, Waxman Aaron, Jatkoe Timothy, Wharton John, Thompson A A Roger, Condliffe Robin A, Kiely David G, Howard Luke S, Toshner Mark, He Cheng, Wang Dennis, Wilkins Martin R, Lawrie Allan
National Heart and Lung Institute, Imperial College London, United Kingdom (N.E., C.J.R., J.W., L.S.H., D.W., M.R.W., A.L.).
MiRXES Lab, Singapore, Republic of Singapore (Li Zhou, Lihan Zhou, C.H.).
Circ Genom Precis Med. 2025 Jun;18(3):e004862. doi: 10.1161/CIRCGEN.124.004862. Epub 2025 Apr 18.
BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs treatment. The heart failure biomarker NT-proBNP (N-terminal pro-B-type natriuretic peptide) is used to help inform risk but is not specific to PH or sub-classification groups. There are currently no other biomarkers in clinical use to help guide diagnosis or risk. METHODS: We profiled a retrospective cohort of 1150 patients from 3 expert centers with PH and 334 non-PH symptomatic controls (disease controls) from the United Kingdom to measure circulating levels of 650 microRNAs (miRNAs) in serum. NT-proBNP (ELISA) and 326 well-detected miRNAs (polymerase chain reaction) were prioritized by feature selection using multiple machine learning models. From the selected miRNAs, generalized linear models were used to describe miRNA signatures to differentiate PH and pulmonary arterial hypertension from the disease controls, and pulmonary arterial hypertension, PH due to left heart disease, PH due to lung disease, and chronic thromboembolic pulmonary hypertension from other forms of PH. These signatures were validated on a UK test cohort and independently validated in the prospective CIPHER study (A Prospective, Multicenter, Noninterventional Study for the Identification of Biomarker Signatures for the Early Detection of Pulmonary Hypertension) comprising 349 patients with PH and 93 disease controls. RESULTS: NT-proBNP achieved a balanced accuracy of 0.74 and 0.75 at identifying PH and pulmonary arterial hypertension from disease controls with a threshold of 254 and 362 pg/mL, respectively but was unable to sub-categorize PH subgroups. In the UK cohort, miRNA signatures performed similarly to NT-proBNP in distinguishing PH (area under the curve of 0.7 versus 0.78), and pulmonary arterial hypertension (area under the curve of 0.73 versus 0.79) from disease controls. MicroRNA signatures outperformed NT-proBNP in distinguishing PH classification groups. External testing in the CIPHER cohort demonstrated that miRNA signatures, in conjunction with NT-proBNP, age, and sex, performed better than either NT-proBNP or miRNAs alone in sub-classifying PH. CONCLUSIONS: We suggest a threshold for NT-proBNP to identify patients with a high probability of PH, and the subsequent use of circulating miRNA signatures to help differentiate PH subgroups.
背景：肺动脉高压（PH）患者根据疾病发病机制和血流动力学驱动因素进行分类。分类有助于指导治疗。心力衰竭生物标志物NT-proBNP（N端前B型利钠肽）用于辅助评估风险，但并非PH或亚分类组所特有。目前尚无其他临床可用的生物标志物来辅助指导诊断或评估风险。 方法：我们对来自英国3个专家中心的1150例PH患者和334例非PH症状对照（疾病对照）的回顾性队列进行分析，以测量血清中650种微小RNA（miRNA）的循环水平。使用多种机器学习模型通过特征选择对NT-proBNP（酶联免疫吸附测定）和326种检测良好的miRNA（聚合酶链反应）进行优先级排序。从选定的miRNA中，使用广义线性模型来描述miRNA特征，以区分PH和肺动脉高压与疾病对照，以及区分肺动脉高压、左心疾病所致PH、肺部疾病所致PH和慢性血栓栓塞性肺动脉高压与其他形式的PH。这些特征在英国测试队列中得到验证，并在前瞻性CIPHER研究（一项用于识别肺动脉高压早期检测生物标志物特征的前瞻性、多中心、非干预性研究）中进行独立验证，该研究包括349例PH患者和93例疾病对照。 结果：NT-proBNP在分别以254和362 pg/mL的阈值从疾病对照中识别PH和肺动脉高压时，平衡准确率分别为0.74和0.75，但无法对PH亚组进行分类。在英国队列中，miRNA特征在区分PH（曲线下面积为0.7对0.78）和肺动脉高压（曲线下面积为0.73对0.79）与疾病对照方面的表现与NT-proBNP相似。miRNA特征在区分PH分类组方面优于NT-proBNP。在CIPHER队列中的外部测试表明，miRNA特征与NT-proBNP、年龄和性别相结合，在对PH进行亚分类时比单独使用NT-proBNP或miRNA表现更好。 结论：我们提出了NT-proBNP的一个阈值，以识别PH高概率患者，并随后使用循环miRNA特征来辅助区分PH亚组。
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