WO2023083380A1 - 以血清中的花生四烯酸代谢组数据预测心衰患者预后的方法 - Google Patents

以血清中的花生四烯酸代谢组数据预测心衰患者预后的方法 Download PDF

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WO2023083380A1
WO2023083380A1 PCT/CN2022/132026 CN2022132026W WO2023083380A1 WO 2023083380 A1 WO2023083380 A1 WO 2023083380A1 CN 2022132026 W CN2022132026 W CN 2022132026W WO 2023083380 A1 WO2023083380 A1 WO 2023083380A1
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heart failure
detection kit
acid
detection
patients
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杜杰
李玉琳
马珂
张栩
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北京市心肺血管疾病研究所
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  • the invention belongs to the field of medical technology, and in particular relates to a method for predicting the prognosis of heart failure patients based on arachidonic acid metabolome data in serum.
  • Heart failure (heart failure) is the terminal stage of various cardiovascular diseases including coronary heart disease, myocardial infarction, hypertension, arrhythmia, viral myocarditis and hereditary cardiomyopathy. Once it happens, it is almost irreversible. Although considerable progress has been made in the management of heart failure drugs and equipment in recent decades, epidemiological surveys have shown that the prevalence of heart failure is still high, with about 22.5 million heart failure patients worldwide, and up to 22.5 million patients in China. 4.5 million, about 3 million people are admitted to hospital every year due to heart failure, and the prognosis is poor. The 5-year mortality rate of patients is 50%, and the 10-year mortality rate exceeds 90%.
  • Biomarkers are quantifiable, cost-effective, and convenient tools for identifying potential pathways to predict adverse outcomes in heart failure.
  • biomarkers such as natriuretic peptides provide prognostic value beyond clinical or imaging tests.
  • the heterogeneity of heart failure suggests that assessment of multiple biomarkers reflecting different pathophysiological pathways may better explain heart failure.
  • biomarkers are grouped into the following categories according to the major pathophysiological pathways they represent: myocardial stretch/stress (i.e., natriuretic peptides), cardiomyocyte injury/death (i.e., troponin), myocardial fibrosis (i.e., Namely galectin-3), neurohumoral activation (ie copeptin), renal insufficiency (ie LCN2) and so on.
  • myocardial stretch/stress i.e., natriuretic peptides
  • cardiomyocyte injury/death i.e., troponin
  • myocardial fibrosis i.e., Namely galectin-3
  • neurohumoral activation ie copeptin
  • renal insufficiency ie LCN2
  • Arachidonic acid is present in all mammalian cells and is one of the most abundant polyunsaturated fatty acids. AA derived a class of metabolites with different structures and signaling functions as well as different biological roles. Although experimental evidence suggests that AA and its metabolites are involved in multiple pathological processes of heart failure, including lipid metabolism, inflammatory response, oxidative stress, and cardiomyocyte apoptosis, there is little concern about the potential prognostic value of AA metabolites in patients with heart failure. Clinical data are still limited. Previous studies have shown that a low eicosapentaenoic acid to AA ratio is associated with higher mortality in hospitalized heart failure patients.
  • the present invention relates to the application of a group of serological markers in the preparation of detection kits for detecting the prognosis of patients with heart failure.
  • the serological markers are: metabolites of arachidonic acid; preferably, the arachidonic acid
  • the metabolites of enoic acid are:
  • 14,15-DHET 14,15-dihydroxyeicosatrienoic acid, 14,15-dihydroxyeicosatrienoic acid;
  • PGD2 prostaglandin D2, prostaglandin D2;
  • 9-HETE 9-hydroxyeicosatetraenoic acid, 9-hydroxyeicosatetraenoic acid.
  • the detection of the prognosis of patients with heart failure is as follows: the patients with heart failure are divided into: a high-risk group of all-cause death within 1 year, or a low-risk group of all-cause death within 1 year.
  • PGD2 higher than 0.26ng/mL
  • the detection kit contains reagents for quantifying the serological markers, and the quantitative reagents include but are not limited to: enzyme-linked immunosorbent reagents, colloidal gold reagents, chemiluminescence reagents, flow fluorescence quantitative reagents, mass spectrometry Quantitative reagents;
  • the detection kit is a detection kit for quantifying the serological markers by mass spectrometry.
  • the present invention also relates to the application of the detection kit in the further preparation of a combination type heart failure prognosis detection product, and the combination is:
  • the beneficial effect of the present invention is that,
  • the present invention uses single factor and multifactor COX regression analysis to screen serological metabolite groups, and confirms that a group of arachidonic acid metabolites can be used as a 1-year prognosis risk for patients with heart failure evaluation index;
  • the sample is selected as the Chinese population, and consistent results have been obtained in both the experimental cohort and the verification cohort.
  • Figure 1 Flow chart of patient enrollment and follow-up experiment.
  • Figure 3 Score chart of the predictive value of 20 AA metabolites on the 1-year incidence of all-cause mortality in the discovery cohort using the elastic network algorithm.
  • FIG. 4 Scoring model composed of data or ratios of 4 arachidonic acid metabolites (14,15-DHET, 14,15-DHET/14,15-EET, PGD2, 9-HETE), in the validation cohort Results plot for predicting patient all-cause mortality events. The results showed that the cutoff value of the scoring model was used to distinguish patients whose model calculation result was ⁇ cutoff value, and the probability of all-cause death within 12 months was significantly higher than that of patients whose model calculation result was ⁇ cutoff value.
  • FIG. 5A Mass spectrometry spectra of typical patients in different groups (with outcome events/no outcome events), Figure 5A, 14,15-DHET detection results; Figure 5B, 14,15-EET detection results; Fig. 5C, PGD2 detection results; Figure 5D, 9-HETE detection results.
  • Example 1 Screening of enrolled patients, processing of serum samples and detection of arachidonic acid metabolites
  • Serum was extracted by solid phase extraction (SPE). Before extraction, the Waters-Oasis HLB cartridge was washed with methanol (1 mL) and Milli-Q water (1 mL). Samples were spiked with isotope mixtures (5 ng each) and loaded into cassettes. Wash the cartridge with 1 mL of 5% methanol. Pull out the water plug from the SPE cassette under high vacuum and further dry under high vacuum for 20 min. Analytes were eluted into the cartridge with 1 mL of methanol. The eluate was then evaporated to dryness.
  • SPE solid phase extraction
  • Chromatographic separation involved the use of a UPLC BEH C18 column (1.7 ⁇ m, ID 100 ⁇ 2.1 mm) composed of ethylene-bridged hybrid particles (Waters, Milford, MA). The column was maintained at 25°C and the injection volume was set at 10 ⁇ L. Solvent A is water and solvent B is acetonitrile. The mobile phase flow rate was 0.6 mL/min. The chromatography was optimized to separate 32 AA metabolites within 9 min. The gradient is 0-1.5min, from 30% to 40%B; 1.5-6.5min to 60%B; 6.5-7.6min to 80%B, hold for 1min; 8.6-8.8min to 30%B and hold for 0.2min.
  • Targeted preparation of AA metabolites was performed using a 5500QTRAP hybrid triple quadrupole linear ion trap mass spectrometer (AB Sciex, Foster City, CA) equipped with a turbo ion spray electrospray ionization source.
  • the mass spectrometer was operated using Analyst 1.5.1 software. Analytes were detected by MRM scans in negative mode. A dwell time of 25 ms was used for all MRM experiments.
  • Example 2 analysis of serum AA metabolites in patients with heart failure
  • 14,15-DHET 14,15-dihydroxyeicosatrienoic acid, 14,15-dihydroxyeicosatrienoic acid;
  • PGD2 prostaglandin D2, prostaglandin D2;
  • 9-HETE 9-hydroxyeicosatetraenoic acid, 9-hydroxyeicosatetraenoic acid.
  • the elastic network algorithm was used to establish a scoring model consisting of four arachidonic acid metabolites or ratios—AA score. The results are shown in Figure 3 and verified in the validation cohort.
  • the concentration of four AA metabolites is expressed as: median (quartile), and the unit is ng/mL.
  • the AA scoring model was compared with BNP and existing clinical risk scores.
  • Table 2.1 Score table using AA (arachidonic acid metabolite) as the predictive parameter of all-cause death in patients with heart failure within 1 year (discovery cohort)
  • Table 2.2 Score table using AA (arachidonic acid metabolite) as the predictive parameter of all-cause death in patients with heart failure within 1 year (validation cohort)
  • the ML-based AA score column is the score of the four arachidonic acid metabolite scoring models established by the present invention alone;
  • BNP is the score of the detection results of commonly used blood markers in the cardiovascular field
  • the three columns of ADHERE, OPTIMIZE-HF, and GWTG-HF are the scores of the test results of the three clinical risk scoring models established in the first report: specific:
  • ADHERE Acute Decompensated Heart Failure National Registry
  • OPTIMIZE-HF Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure
  • the +ML-based AA score column is the scoring model used in conjunction with the four arachidonic acid metabolite scoring models established by the present invention and the BNP, ADHERE, OPTIMIZE-HF, and GWTG-HF models;
  • NRI net reclassification improvement
  • ⁇ AUC, NRI both evaluate the comparison between the newly established ML-based AA sore and the previous three clinical risk scores combined with the three previous scores alone, and the P values are all ⁇ 0.001, indicating that the four arachidonic sores established by the present invention
  • the scoring model composed of acid metabolites or ratios with previous scores the prognostic discrimination performance is better than that of clinical scores alone.

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Abstract

一种血清学标志物在制备检测心衰患者预后情况的检测试剂盒中的应用，血清学标志物为花生四烯酸的代谢产物，花生四烯酸的代谢组产物为：14,15-DHET：14,15-dihydroxyeicosatrienoic acid，14,15-二羟基二十碳三烯酸；14,15-EET：14,15-epoxyeicosatrienoic acid，14,15-环氧二十碳三烯酸；PGD2：prostaglandin D2，前列腺素D2；9-HETE：9-hydroxyeicosatetraenoic acid，9-羟基二十碳四烯酸。预测心衰患者预后情况为：预测心衰患者1年内发生全因性死亡的风险。

Description

以血清中的花生四烯酸代谢组数据预测心衰患者预后的方法 技术领域
本发明属于医疗技术领域，具体的，涉及一种以血清中的花生四烯酸代谢组数据预测心衰患者预后的方法。
背景技术
心力衰竭(心衰)是多种心血管疾病包括冠心病、心肌梗死、高血压、心律失常、病毒性心肌炎和遗传性心肌病等的终末阶段。一旦发生，几乎不可逆转。尽管近几十年来在心力衰竭的药物和设备管理方面取得了相当大的进展，但是流行病学调查表明，心衰患病率仍然很高，全世界心衰患者约为2250万，中国患者高达450万，每年大约有300万人因心力衰竭入院，而且预后差，患者5年病死率为50％，10年病死率超过90％。此外，心衰的患病率随年龄增长升高，60岁以下的患者低于2％，75岁以上的患者超过10％。因此，准确识别心衰高危患者，实现心衰的预警监控，探索新的治疗策略，对于心衰的防控具有重大的意义。
生物标志物是一种可量化、成本效益高且方便快捷的工具，用于识别预测心衰不良结局的潜在途径。在心衰中，生物标志物提供的预后价值超过临床检查或影像学检查的价值，例如利钠肽。心衰的异质性表明，评估反映不同病理生理途径的多种生物标志物可能更好地解释心衰。根据其所代表的主要病理生理途径，目前的生物标志物分为以下几类：心肌牵张/应激(即利钠肽)、心肌细胞损伤/死亡(即肌钙蛋白)、心肌纤维化(即半乳糖凝集素-3)、神经体液活化(即和肽素)、肾功能不全(即LCN2)等。然而，要阐明心衰复杂的病理生理学机制，以提高风险预测，还需要探索其它途径的生物标志物。
花生四烯酸(AA)存在于所有哺乳动物细胞中，是最丰富的多不饱和脂肪酸之一。AA衍生出一类具有不同结构和信号传导功能以及不同生物学作用的代谢物。尽管实验证据表明AA及其代谢物参与心衰的多种病理过程，包括脂质代谢、炎症反应、氧化应激和心肌细胞凋亡等，但关于AA代谢物在心衰患者中的潜在预后价值的临床数据仍然有限。既往研究显示，低二十碳五烯酸与AA的比值与住院心衰患者的较高死亡率有关。在另一项研究中，较低的二高-γ-亚麻酸与AA的比值可以预测急性失代偿性心衰患者的长期死亡率。全面了解AA代谢产物与心衰死亡率的关联可能有助于确定高死亡风险的患者亚组。此外，干扰AA产生或信号传导可能会有益于高危患者的治疗。
因此，探究AA代谢物在心衰患者中的预后价值，进一步发现新的治疗靶点，具有很高的临床意义和价值。
发明内容
本发明涉及一组血清学标志物在制备检测心衰患者预后情况的检测试剂盒中的应用，所述的血清学标志物为：花生四烯酸的代谢产物；优选的，所述的花生四烯酸代谢产物为：
14,15-DHET：14,15-dihydroxyeicosatrienoic acid，14,15-二羟基二十碳三烯酸；
14,15-EET：14,15-epoxyeicosatrienoic acid，14,15-环氧二十碳三烯酸；
PGD2：prostaglandin D2，前列腺素D2；
9-HETE：9-hydroxyeicosatetraenoic acid，9-羟基二十碳四烯酸。
所述的检测心衰患者预后情况为：将心衰患者区分为：1年内发生全因性死亡的高风险组，或1年内发生全因性死亡的低风险组。
具体的，当所述的血清学检测标记物的定量值：
14,15-DHET：高于0.44ng/mL；
和/或14,15-EET：小于0.37ng/mL；
和/或PGD2：高于0.26ng/mL；
和/或9-HETE：高于0.85ng/mL时；
所述患者为1年内发生全因性死亡高风险患者。
所述的检测试剂盒中包含对所述血清学标志物进行定量的试剂，所述的定量试剂包括但不限于：酶联免疫试剂、胶体金试剂、化学发光试剂、流式荧光定量试剂、质谱定量试剂；
优选的，所述的检测试剂盒为通过质谱检测定量所述血清学标志物的检测试剂盒。
本发明还涉及所述的检测试剂盒在进一步制备联用型心衰预后检测产品中的应用，所述的联用为：
(1)与BNP检测试剂盒联用；
或(2)与基于ADHERE模型的检测试剂盒联用；
或(3)与基于OPTIMIZE-HF模型的检测试剂盒联用；
或(4)与基于GWTG-HF模型的检测试剂盒联用。
本发明的有益效果在于，
1、本发明通过对大队列患者样本的长期监测，使用单因素和多因素COX回归分析筛选血清学代谢物组，确认了一组花生四烯酸代谢物可以做为心衰患者1年预后风险的评价指标；
2、通过统计学评分模型，确认了上述四个检测标记物的检测限，cutoff值等；
3、通过与现有预后模型联用的分析，证实上述四个检测标记物还能够有效的与现有模型联用，取得更好的预后检测结果；
4、样本选取为中国人群，在实验队列和验证队列中，都取得了一致的结果。
附图说明
图1、患者入组及随访实验流程图。
图2、使用单因素和多因素COX回归分析发现队列中的患者血清中20种AA代谢产物水平对其1年全因死亡发生率的预测价值评分图。
图3、利用弹性网络算法在发现队列中分析20种AA代谢产物对患者1年全因死亡发生率的预测价值评分图。
图4、以4个花生四烯酸代谢物(14,15-DHET、14,15-DHET/14,15-EET、PGD2、9-HETE)的数据或比值构成的评分模型，在验证队列中预测患者全因死亡事件的结果图。结果显示，以评分模型cutoff值为区分，模型计算结果≥cutoff值的患者，12个月内的全因死亡概率相比于模型计算结果＜cutoff值的患者，有极大的升高。
图5、不同群组(有结局事件/无结局事件)中的典型患者的血清样本质谱检测谱图，图5A、14,15-DHET检测结果；图5B、14,15-EET检测结果；图5C、PGD2检测结果；图5D、9-HETE检测结果。
具体实施方式
实施例1、入组患者筛选、血清样本处理及花生四烯酸代谢产物检测
选取连续入院的心衰患者(n＝805)开展临床注册研究(ClinicalTrials.gov Identifier:NCT04108182)，分为发现队列(n＝419)和验证队列(n＝386)，(所有参与者均提供了书面知情同意。本研究经北京安贞医院伦理委员会批准，伦理编号：KS2019017)检测患者入院基线血清AA代谢物的水平，每个患者进行为期 1年的随访。研究 结局事件为1年全因死亡。发现队列和验证队列分别有94名和90名患者出现结局事件。患者入组及随访实验流程图见图1。
入组患者的血清AA检测实验步骤：
1.样本制备
采用固相萃取(SPE)萃取血清。萃取前，用甲醇(1mL)和Milli-Q水(1mL)清洗Waters-Oasis HLB盒。样品加入同位素混合物(每种5ng)并装入盒中。用1mL 5％甲醇洗涤药盒。在高真空下从SPE盒中拔出水塞，并在高真空下进一步干燥20分钟。分析物用1mL甲醇洗脱到盒中。然后将洗脱液蒸发至干燥。
2.超高效液相色谱
色谱分离涉及使用UPLC BEH C18色谱柱(1.7μm，内径100×2.1mm)，该柱由乙烯桥联杂化颗粒(Waters，Milford，MA)组成。柱保持在25℃，注射体积设定为10μL。溶剂A为水，溶剂B为乙腈。流动相流速为0.6mL/min。色谱经过优化以在9min内分离出32种AA代谢物。梯度为0-1.5min，从30％到40％B；1.5-6.5min到60％B；6.5-7.6min到80％B，保持1min；8.6-8.8min降至30％B并保持0.2min。
3.质谱
AA代谢物的靶向制备使用5500QTRAP混合三四极杆线性离子阱质谱仪(AB Sciex，Foster City，CA)，配备涡轮离子喷雾电喷雾电离源。质谱仪使用Analyst 1.5.1软件进行操作。分析物在阴性模式下由MRM扫描检测。所有MRM实验使用的停留时间为25ms。离子源参数为CUR＝40psi，GS1＝30psi，GS2＝30psi，IS＝-4500V，CAD＝MEDIUM，TEMP＝500℃。
实施例2、心衰患者血清AA代谢物分析
1.单因素和多因素COX回归分析
根据随访结果，单因素和多因素COX回归分析发现队列AA代谢产物水平对心衰患者1年全因死亡率的预测价值。如图2结果显示，一些AA代谢物和代谢物比值对心衰患者1年全因死亡具有显著的预测价值。
2.用4个花生四烯酸代谢物或比值构成评分模型并验证
典型患者血清样本[全因死亡事件患者(有结局事件)和无事件患者(无结局事件)]的质谱检测图谱如图5所示，具体的：14,15-DHET检测结果见图5A、14,15-EET检测结果见图5B、PGD2检测结果见图5C、9-HETE检测结果见图5D。
4个标志物的化学名为：
14,15-DHET：14,15-dihydroxyeicosatrienoic acid，14,15-二羟基二十碳三烯酸；
14,15-EET：14,15-epoxyeicosatrienoic acid，14,15-环氧二十碳三烯酸；
PGD2：prostaglandin D2，前列腺素D2；
9-HETE：9-hydroxyeicosatetraenoic acid，9-羟基二十碳四烯酸。
在发现队列中利用弹性网络算法在发现队列中建立了由4个花生四烯酸代谢物或比值构成的评分模型-AA评分，结果如图3所示，并在验证队列中验证。
结果如图4所示(Cutoff值为约登指数最大值，约登指数＝灵敏度+特异度-1，图中Cutoff＝1.17)，使用约登指数最大值为分界点，K-M曲线分析显示此评分模型在发现和验证队列中均具有区分1年死亡和非死亡的潜力。14,15-DHET、14,15-EET、PGD2、9-HETE这四个代谢产物的质谱检测数据见下表1。
表1、两组患者队列样本中的4个AA代谢物检测浓度值分析
Figure PCTCN2022132026-appb-000001
表中，曼惠尼U检验用于比较LC-MS/MS测定的4种AA代谢物浓度，在发现队列(n＝419)和验证队列(n＝386)中，分别按死亡状态分组。P<0.05被认为是显著的。
表中，四个AA代谢物浓度表示形式为：中位数(四分位数)，单位是ng/mL。
3.AA评分模型与BNP和已有临床风险评分进行比较。
以发现组作为训练集，总结出本实施例的上述关键因子及计算公式，将该公式应用于验证组中，分析公式预测结果与临床随访结果之间的准确度，如表2.1、表2.2所示，AA评分可显著改善BNP和已有临床风险评分对心衰患者1年死亡的分类能力。
表2.1、以AA(花生四烯酸代谢物)作为心衰患者1年内全因死亡预测参数的评分表(发现队列)
Figure PCTCN2022132026-appb-000002
表2.2、以AA(花生四烯酸代谢物)作为心衰患者1年内全因死亡预测参数的评分表(验证队列)
Figure PCTCN2022132026-appb-000003
表2.1、2.2中，
ML-based AA score列是单独使用本发明建立的由四个花生四烯酸代谢物评分模型的评分；
BNP是单独使用心血管领域的常用血液标志物的检测结果的评分；
ADHERE、OPTIMIZE-HF、GWTG-HF三列是单独使用再先报道中建立的三个临床风险评分模型的检测结果的评分：具体的：
ADHERE＝Acute Decompensated Heart Failure National Registry；
OPTIMIZE-HF＝Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure；
GWTG-HF＝Get With the Guidelines Heart Failure；
+ML-based AA score列是联合使用本发明建立的由四个花生四烯酸代谢物评分模型与BNP、ADHERE、OPTIMIZE-HF、GWTG-HF模型联合使用的评分；
NRI＝net reclassification improvement；是评价评分对既往模型的改善情况分析。
△AUC，NRI，都是评价新建立的ML-based AA sore与既往三个临床风险评分联合与单独三个既往评分的比较，P值均<0.001，说明本发明建立的由四个花生四烯酸代谢物或比值构成的评分模型与既往评分联合，预后区分效能均优于单独临床评分。
最后需要说明的是，以上实施例仅用于帮助本领域技术人员理解本发明的实质，不用于限定本发明的保护范围。

Claims (5)

  1. 一组血清学标志物在制备检测心衰患者预后情况的检测试剂盒中的应用，所述的血清学标志物为：花生四烯酸的代谢产物；优选的，所述的花生四烯酸代谢产物为：
    14,15-DHET：14,15-dihydroxyeicosatrienoic acid，14,15-二羟基二十碳三烯酸；
    14,15-EET：14,15-epoxyeicosatrienoic acid，14,15-环氧二十碳三烯酸；
    PGD2：prostaglandin D2，前列腺素D2；
    9-HETE：9-hydroxyeicosatetraenoic acid，9-羟基二十碳四烯酸。
  2. 根据权利要求1所述的检测试剂盒，其特征在于，所述的检测心衰患者预后情况为：将心衰患者区分为：1年内发生全因性死亡的高风险组，或1年内发生全因性死亡的低风险组。
  3. 根据权利要求1或2所述的检测试剂盒，其特征在于，当所述的血清学检测标记物的定量值为：
    14,15-DHET：高于0.44ng/mL；
    和/或14,15-EET：小于0.37ng/mL；
    和/或PGD2：高于0.26ng/mL；
    和/或9-HETE：高于0.85ng/mL时；
    区分该患者为1年内发生全因性死亡的高风险组；反之为低风险组。
  4. 根据权利要求1-3任一所述的检测试剂盒，其特征在于，所述的检测试剂盒中包含：对所述血清学标志物进行定量的试剂，所述的定量试剂包括但不限于：酶联免疫试剂、胶体金试剂、化学发光试剂、流式荧光定量试剂、质谱定量试剂；优选的，所述的检测试剂盒为通过质谱检测定量所述血清学标志物的检测试剂盒。
  5. 权利要求1-4任一所述的检测试剂盒在制备联用型心衰预后检测产品中的应用，所述的联用为：
    (1)与BNP检测试剂盒联用；
    或(2)与基于ADHERE模型的检测试剂盒联用；
    或(3)与基于OPTIMIZE-HF模型的检测试剂盒联用；
    或(4)与基于GWTG-HF模型的检测试剂盒联用。
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