CN111679018B - 用于诊断认知障碍的生物标记物及其应用 - Google Patents
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Abstract
本发明提供了生物标志物SM(d18:1/24:1(15Z))在制备诊断认知障碍试剂或试剂盒中的应用,该通过检测生物标志物SM(d18:1/24:1(15Z))以及TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z)),PC(P‑16:0/22:4(7Z,10Z,13Z,16Z))或PC(P‑18:0/18:4(6Z,9Z,12Z,15Z))可以用来判断患者是否存在认知障碍。
Description
技术领域
本发明属于生物检测技术领域,具体涉及用于诊断认知障碍的生物标记物及其应用。
背景技术
众所周知,认知障碍是一种进行性神经退行性疾病,认知障碍如果不能及早的被发现、关注和干预,就容易发展成为痴呆,例如阿尔兹海默症(AD)。
AD患者的整个大脑中细胞外β-淀粉样蛋白沉积的老年斑,和细胞内过磷酸化tau蛋白神经细胞死亡,是构成阿尔兹海默症的主要原因,这是一种导致渐进性记忆障碍、认知缺陷、个人性格的变化等的疾病。在全世界有超过500万名以上患有阿尔茨海默病,而处于认知障碍的患者数量更多。
研究表明,认知障碍者已超过千万,但是,目前为止,尽管有许多对发病机制的研究,但是依然没有发现很好的诊断标志物。
认知障碍( mild cognitive impairment,MCI)是正常认知与AD之间的中间状态。研究显示,轻度认知障碍(MCI)在65岁以上人群中患病率为10%至20%,向AD转化的累积概率为33%。因此,对于AD以及AD前期的MCI的早期诊断和及时干预,将把防治AD疾病的关口前移,可望有效延缓AD疾病的进展,降低家庭的负担,对于整个社会和医学发展都有意义。目前,临床上诊断轻度认知障碍( mild cognitive impairment,MCD)和阿尔茨海默病(A1zheimer disease,AD)的主要方法有量表检测、影像学检测、脑脊液生物标志物检测。量表检测,需要提问和回答,消耗非常大的医学资源,耗时耗力;影像学检测需用到磁共振成像(MRI)、正电子发射型计算机断层显像(PET)等昂贵设备无法大量普及;脑脊液取样具有创伤性,取样难度大,患者及家属多不愿配合。目前尚无种准确性高,特异性强的MCI和AD外周血生物标志物。
代谢组学是一种新兴的组学技术,在生物学研究中发挥着越来越重要的作用,因为它能够揭示机体细胞代谢的独特化学指纹特征。代谢组学作为一种无偏的小分子代谢物研究方法,为发现更多的认知障碍的生物标志物提供了希望。越来越多的证据表明神经系统疾病,伴随着胆汁酸,脂肪酸和氨基酸的紊乱。并且这些结果证明代谢紊乱可能预示着认知障碍的发生,但具体哪种物质能明确检测出作为辨别是认知障碍,还不清楚。
发明内容
为了能有效预测和诊断认知障碍,本发明提供了用于诊断认知障碍的生物标记物。
为实现上述目的,本发明采用以下的技术方案为:
用于诊断认知障碍的生物标记物,该生物标记物为鞘磷脂(sphingomyelin,SM)SM(d18:1/24:1(15Z))。如上所述的诊断认知障碍的生物标志物在制备检测试剂中的应用。
如上所述的应用,优选地,所述生物标志物还包括甘油三酯(Triglyceride,TG)TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z)),磷脂酰胆碱(Phosphatidyl cholines,PC)PC(P-16:0/22:4(7Z,10Z,13Z,16Z))或PC(P-18:0/18:4(6Z,9Z,12Z,15Z))。
如上所述的应用,优选地,所述SM(d18:1/24:1(15Z))与TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z)) 的含量单位为mg/L时,根据计算公式响应变量TC= 79.43-2.199×10-4×BM3+1.206×10-4×BM2,计算TC值(TC值即公式中的响应变量值),公式中BM3为SM(d18:1/24:1(15Z))的含量,BM2为TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))的含量,根据TC值预测认知障碍:若TC≥0.5,则判定为认知障碍;若TC<0.5,则为正常。
如上所述的应用,优选地,所述SM(d18:1/24:1(15Z))与PC(P-16:0/22:4(7Z,10Z,13Z,16Z)) 的含量单位为mg/L时,根据计算公式TC= 604.8 – 5.028×10-4×BM3- 1.909×10-3×BM4,计算TC值,公式中BM3为SM(d18:1/24:1(15Z)),BM4为PC(P-16:0/22:4(7Z,10Z,13Z,16Z)),根据TC值预测认知障碍:若TC≥0.421,则判定为认知障碍;若TC<0.421,则为正常。
如上所述的应用,优选地,所述SM(d18:1/24:1(15Z))与PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) 的含量单位为mg/L时,根据计算公式:TC=48.98-4.266×10-5×BM3-1.897×10-4×BM5,计算TC值,公式中BM3为SM(d18:1/24:1(15Z)),BM5为PC(P-18:0/18:4(6Z,9Z,12Z,15Z)),根据TC值预测认知障碍:若TC≥0.749,则判定为认知障碍;若TC<0.749,则为正常。
本发明的有益效果在于:
本发明提供的一种诊断认知障碍的生物标记物,该该生物标记物为SM(d18:1/24:1(15Z)),结合TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))、PC(P-16:0/22:4(7Z,10Z,13Z,16Z))或PC(P-18:0/18:4(6Z,9Z,12Z,15Z)),检测其含量,根据TC值预测认知障碍,有助于诊断是否存在认知障碍的倾向,可用于提前预防。
附图说明
图1为正负离子模式下VIP>1的样本;
图2为正负离子模式下(O)PLS-DA的得分图;
图3 为正负离子模式下S-plot图;
图4为基于逻辑回归模型的ROC曲线(变量为BM3+BM2);
图5 为为基于逻辑回归模型的ROC曲线(变量为BM3+BM4);
图6 为为基于逻辑回归模型的ROC曲线(变量为BM3+BM5)。
具体实施方式
以下实施例用于进一步说明本发明,但不应理解为对本发明的限制。在不背离本发明精神和实质的前提下,对本发明所作的修饰或者替换,均属于本发明的范畴。
若未特别指明,实施例中所用的技术手段为本领域技术人员所熟知的常规手段。
实施例1
一、患者人群标准
1. 模型建立样本群80 人(内部人群,即指建立预测模型时所用样本群体)
对照人群:男女比例为1:1,年龄范围:45岁以上,MMSE量表分数>26分,并且 Moca量表分数>27分,MRI核磁检测显示无异常。
患者人群:男女比例为1:1,年龄范围,45岁以上,MMSE量表分数<26分,并且 Moca量表分数<27分,MRI核磁检测部分显示异常。
2. 模型验证样本群体80人(外部人群,即指验证模型时所用的样本(非内部人群))取样标准同上。
二、实验仪器及试剂
样本采集:选取经临床评价为正常和认知障碍的患者血清进行试验。
实验仪器:1.冷冻离心机:型号D3024R,Scilogex公司,美国;2.漩涡振荡器:型号MX-S,Scilogex公司,美国;3.高分辨质谱仪:ESI-QTOF/MS;型号:Xevo G2-S Q-TOF;厂家:Waters, Manchester, UK;4.超高效液相色谱:UPLC;型号:ACQUITY UPLC I-Class系统;厂家:Waters, Manchester, UK;5.数据采集软件:MassLynx4.1; 厂家:Waters;6.分析鉴定软件:Progenesis QI; 厂家:Waters。
实验试剂:异丙醇、乙腈,甲酸,甲酸氨,亮氨酸脑啡肽,甲酸钠;厂家均为Fisher。
三、实验方法
1.样品前处理
收集的血清样本在冰上解冻,200μL的血浆用600μL的预冷异丙醇萃取,涡流1min,室温孵育10min,然后将萃取混合物在-20℃下储存过夜,4000r离心20min后,将上清液转移到新的离心管,用异丙醇/乙腈/水(按体积比为2:1:1)稀释至1:10。样品在LC-MS分析前保存在-80℃。此外,还将每个萃取混合物的10μL组合在一起制备混合血浆样品。
2. 脂质组学的超高效液相色谱-质谱联用方法
样品用ACQUITY UPLC(Waters,美国)连接到带有ESI的Xevo-G2XS高分辨飞行时间(QTOF)质谱仪(Waters)进行分析。采用CQUITY UPLC BEH C18色谱柱(2.1×10 0 mm,1.7μm,Waters),流动相为10 mM甲酸铵-0.1%甲酸-乙腈(A,乙腈:水为60:40,v/v)和10 mM甲酸铵-0.1%甲酸-异丙醇-乙腈(B,异丙醇:乙腈为90:10,v/v)。在大规模研究之前,进行了包括10分钟、15分钟和20分钟洗脱期的中试实验,以评估流动相组成和流速对脂质保留时间的潜在影响。在正离子模式(PIM)中,丰富的脂质前体离子和碎片以相同的顺序分离,具有相似的峰形和离子强度。此外,具有10分钟洗脱期的混合质控(QC)样品也表现出与测试样品相似的前体和碎片的基峰强度。流动相流速为0.4mL/min。该柱最初用40%B洗脱,然后在2分钟内线性梯度到43%B,然后在0.1min内将B的百分比增加到50%。在接下来的3.9分钟内,梯度进一步增加到54%B,然后B的量0.1分钟内增加到70%。在梯度的最后部分,B的量在1.9分钟内增加到99%。最后,溶液B在0.1分钟内返回到40%,并且在下一次进样之前将色谱柱平衡1.9分钟。每次进样量为5μL,用Xevo-G2XS型QTOF质谱仪检测正负两种模式下的脂质,采集范围为 m/z50~1200年,采集时间为0.2s/次。离子源温度为120℃,去溶温度为600℃,气体流量为1000L/h,以氮气为流动气体。毛细管电压为2.0kV(+)/锥体电压为1.5kV(-),锥体电压为30V。甲酸钠和亮氨酸脑啡肽作校正液(waters质谱仪器自带)。样品被随机排序。每10个样本注入一个质控(QC)样本并进行分析,以调查数据的重复性。
四、结果分析:
1.利用多元统计学寻找血清差异物质
对于内部人群采用正交偏最小二乘判别分析(OPLS-DA)结合了正交信号矫正(OSC)和 PLS-DA 方法,通过去除不相关的差异来筛选差异变量。如图1为正负离子模式下VIP>1的代谢物,VIP值为OPLSDA第一主成分的变量重要性投影,通常以VIP>1为代谢组学常用评判标准,作为差异代谢物筛选的标准之一,其中,A正离子模式,B为负离子模式;图2为正负离子模式下(O)PLS-DA的得分图,其中,C为正离子模式下(O)PLS-DA的得分图,D为负离子模式下(O)PLS-DA的得分图,认知障碍组(DIS表示)和空白对照组(CK表示)两个分组中的第一主成分和第二主成分通过降维的方式所得的得分图,横坐标表示组间差异,纵坐标表示组内差异,且两组结果分离较好,说明此方案可以使用。图3为正负离子模式下S-plot图,E为正离子模式下S-plot图,F为负离子模式下S-plot图,横坐标表示主成分与代谢物的协相关系数,纵坐标表示主成分与代谢物的相关系数,同时满足p<0.05,VIP>1的条件下,负离子模式有59个差异物,正离子模式有117个差异物。
2.约登指数分析
为了进一步缩小范围,将VIP阈值提高到3,同时体现正常和患者之间的倍数差异在0.8倍以下,或者增加1.2倍以上,最终得到以下10个化合物,具体见表1。
然后对他们进行youden 约登指数计算,用来反映单个指标对整体的诊断和预测效果,结果如下表1:
表1 认知障碍相关脂质的约登指数分析
化合物名称 | AUC值 | 特异性 | 敏感度 |
TG(16:0/18:1(9Z)/18:2(9Z,12Z)) | 0.861 | 0.833 | 0.833 |
PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:0) | 0.889 | 0.833 | 0.833 |
SM(d18:1/24:1(15Z)) | 0.917 | 0.833 | 1 |
TG(16:0/18:1(9Z)/18:3(6Z,9Z,12Z)) | 0.917 | 1 | 0.833 |
PC(O-16:0/20:4(8Z,11Z,14Z,17Z)) | 0.861 | 0.833 | 0.833 |
SM(d18:2/24:1) | 0.944 | 0.833 | 1 |
SM(d16:1/20:0) | 0.889 | 1 | 0.667 |
PC(P-16:0/22:4(7Z,10Z,13Z,16Z)) | 0.917 | 0.833 | 1 |
PE(18:0/17:0) | 0.889 | 0.667 | 1 |
PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) | 0.917 | 0.833 | 1 |
表1列出来单个代谢物预测认知障碍的曲线下面积(AUC)、敏感度和特异性,相关参数显示以上20种脂质中,SM(d18:2/24:1)的预测能力最好(AUC=0.944)。而24:1,就是表示含有神经酸链。
3. 内部人群七折交叉验证结果
根据上述结果,挑选YOUDEN AUC值大于0.9的变量化合物,进行下一步的分析。
表2
编号 | m/z | 化合物名称 | AUC值 |
BM1 | 855.6603 | SM(d18:2/24:1) | 0.944 |
BM2 | 896.770388 | TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z)) | 0.917 |
BM3 | 813.6863083 | SM(d18:1/24:1(15Z)) | 0.917 |
BM4 | 838.5974778 | PC(P-16:0/22:4(7Z,10Z,13Z,16Z)) | 0.917 |
BM5 | 810.5649318 | PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) | 0.917 |
将内部人群随机分为7份,选择1份为验证集,其他为训练集,如此反复七次,考察最佳的变量组合。将其次的结果,包括AUC,敏感度,特异性都取平均值,并进行统计学显著性计算,结果如下表3。
表3
组合 | 逻辑回归 AUC值 | 敏感度 | 特异性 |
BM3+BM2 | 1 | 1 | 1 |
BM3+BM4 | 1 | 1 | 1 |
BM3+BM5 | 0.972 | 1 | 1 |
组合之间,AUC值并没有显著性p<0.05差异。
基于上述建立逻辑回归模型A、B、C如下:
"模型A" 变量为上述BM3+BM2,计算公式为:TC= 79.43-2.199×10-4×BM3+1.206×10-4×BM2,计算TC值,公式中BM3为SM(d18:1/24:1(15Z))的含量,BM2为TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))的含量,根据TC值预测认知障碍:若TC≥0.5,则判定为认知障碍;若TC<0.5,则为正常;
"模型B" 变量为上述BM3+BM4,计算公式为:TC= 604.8 – 5.028×10-4×BM3-1.909×10-3×BM4,计算TC值,公式中BM3为SM(d18:1/24:1(15Z))的含量,BM4为PC(P-16:0/22:4(7Z,10Z,13Z,16Z))的含量,根据TC值预测认知障碍:若TC≥0.421,则判定为认知障碍;若TC<0.421,则为正常。
"模型C" 变量为上述BM3+BM5,变量为TG(13:0/17:1(9Z)/20:2(11Z,14Z))+ SM(d18:1/24:1(15Z)),计算公式为:48.98-4.266×10-5×BM3-1.897×10-4×BM5,计算TC值,公式中BM3为SM(d18:1/24:1(15Z))的含量,BM5为PC(P-18:0/18:4(6Z,9Z,12Z,15Z))的含量,根据TC值预测认知障碍:若TC≥0.749,则判定为认知障碍;若TC<0.749,则为正常。
4. 外部数据集,逻辑回归模型验证
通过外部人群的数据集验证上述结果的准确性,并绘制相应的ROC曲线图。结果如下:
"模型A" 变量为上述BM3+BM2,结果如图4,Sensitivity(敏感性)=1,Specificity(特异性)=1,Accuracy(准确度) =1。
"模型B" 变量为上述BM3+BM4,结果如图5,Sensitivity(敏感性)=1,Specificity(特异性)=0.833,Accuracy(准确度)=1。
"模型C" 变量为上述BM3+BM5,,结果如图6,Sensitivity(敏感性)=0.833,Specificity(特异性)=1,Accuracy(准确度)=1。
数据显示:SM(d18:1/24:1(15Z)),或结合TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z)),PC(P-16:0/22:4(7Z,10Z,13Z,16Z)),PC(P-18:0/18:4(6Z,9Z,12Z,15Z))都表现出非常高的诊断能力,都能进行临床试剂盒的应用。
通过对样本信息的对比分析可知:以上5种生物标记物,与正常组相比,BM1、BM3、BM4和BM5在认知障碍组均呈下降趋势,BM2则相反。
Claims (1)
1.生物标志物SM(d18:1/24:1(15Z))在制备诊断认知障碍试剂或试剂盒中的应用,其是将SM(d18:1/24:1(15Z))和TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))、PC(P-16:0/22:4(7Z,10Z,13Z,16Z))或PC(P-18:0/18:4(6Z,9Z,12Z,15Z))用于制备诊断认知障碍试剂或试剂盒,
其中,所述SM(d18:1/24:1(15Z))与TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))的含量单位为mg/L时,根据计算公式TC= 79.43-2.199×10-4×BM3+1.206×10-4×BM2,计算TC值,公式中BM3为SM(d18:1/24:1(15Z))的含量,BM2为TG(16:0/18:0/18:4(6Z,9Z,12Z,15Z))的含量,根据TC值预测认知障碍:若TC≥0.5,则判定为认知障碍;若TC<0.5,则为正常;
其中,所述SM(d18:1/24:1(15Z))与PC(P-16:0/22:4(7Z,10Z,13Z,16Z)) 的含量单位为mg/L时,根据计算公式TC= 604.8 – 5.028×10-4×BM3- 1.909×10-3×BM4,计算TC值,公式中BM3为SM(d18:1/24:1(15Z)) 的含量,BM4为PC(P-16:0/22:4(7Z,10Z,13Z,16Z)) 的含量,根据TC值预测认知障碍:若TC≥0.421,则判定为认知障碍;若TC<0.421,则为正常;
其中,所述SM(d18:1/24:1(15Z))与PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) 的含量单位为mg/L时,根据计算公式:TC=48.98-4.266×10-5×BM3-1.897×10-4×BM5,计算TC值,公式中BM3为SM(d18:1/24:1(15Z)) 的含量,BM5为PC(P-18:0/18:4(6Z,9Z,12Z,15Z)) 的含量,根据TC值预测认知障碍:若TC≥0.749,则判定为认知障碍;若TC<0.749,则为正常。
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