CN112348166A - 基于血清肌酐和胱抑素c预测肌力下降筛查肌少症的方法 - Google Patents
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Abstract
本发明公开了一种基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,属于肌少症检测领域。本发明包括:获取血清肌酐和胱抑素C;将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物;构建并训练血清肌酐和胱抑素C的比值与握力的关系模型;基于所述关系模型分别确定不同性别用户的血清肌酐和胱抑素C的比值的合适诊断临界值;根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值;根据所述预测值预测肌力下降。本发明能够确定血清肌酐和胱抑素C的比值与握力的关系,通过计算出的血清肌酐和胱抑素C的比值识别出人群中握力降低的情况,从而为进一步检测肌少症提供精准的预测,极大地缩小需要筛查肌少症的人群规模。
Description
技术领域
本发明涉及肌少症检测领域,尤其涉及一种基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法。
背景技术
肌少症是一种增龄相关的骨骼肌疾病,表现为骨骼肌萎缩和功能下降。该疾病在世界范围内流行,尤其是在老年人中,已成为重要的公共卫生问题。肌少症诊断使用最广泛的标准是欧洲老年肌少症工作组(EWGSOP)的标准。EWGSOP的最新建议(EWGSOP2)表明,在诊断肌少症时首先需要测量肌肉力量(通常使用握力计测量握力),若肌力存在异常,再进一步使用双能X射线(DXA)、计算机断层扫描(CT)、生物电阻抗(BIA)和磁共振成像(MRI)评估肌肉质量,然后综合诊断肌少症。
根据EWGSOP2,握力降低是肌少症诊断的先决条件。此外,在不同人群中,握力降低本身可能导致一系列不良健康结局,如抑郁、生活质量差和死亡。因此,筛查老年人握力是否降低具有重要意义。但是,测量握力时需要使用不同类型的测力计,如Jamar测力计,这些测力在临床实践中,尤其是在基层医疗中,通常无法获得。因此,基于常规血液检查结果的血清生物标志物将有助于预测或识别握力降低,然后可进一步筛查肌少症,预测更广泛人群中不良健康结局的风险。
众所周知,血清肌酐(Cr)是肾功能和肌肉质量的生物标志物,因为其前体(磷酸肌酸)90%在肌肉中产生,并且依赖肾脏分泌。血清胱抑素C(CysC)是一种小分子蛋白,由所有有核细胞分泌,临床实践中常用于评估肾功能。为了减少肾功能对血清Cr浓度的影响,Kashani等人最近开发了一种新的生物标志物,用于评估肌肉质量,并将其命名为“肌少症指数”(血清Cr[mg/dL]/血清CysC[mg/L]×100)。但是,研究发现,肌少症指数不能准确识别社区老年人的骨骼肌质量是否降低或是否患有肌少症。
发明内容
本发明的目的是提供一种能够准确筛查人群中肌力下降的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,从而为是否进一步评估肌少症提供便捷而准确的依据。
本发明解决其技术问题,采用的技术方案是:基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,包括如下步骤:
获取血清肌酐和胱抑素C;
将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物;
构建并训练血清肌酐和胱抑素C的比值与握力的关系模型;
基于所述关系模型分别确定不同性别用户的血清肌酐和胱抑素C的比值的合适诊断临界值;
根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值;
根据所述预测值检测肌少症。
进一步的是,从血样库中存储的血样中获取获取血清肌酐和胱抑素C。
进一步的是,所述血清肌酐和胱抑素C的比值用于反映不同人群的骨骼肌质量,基于反映出的不同人群的骨骼肌质量,将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物。
进一步的是,所述关系模型为深度神经网络模型。
进一步的是,通过所述深度神经网络模型,确定在大于等于40岁的人群中,血清肌酐和胱抑素C的比值与握力呈正相关关系。
进一步的是,在大于等于40岁的人群中,男性的诊断临界值为8.9,女性的诊断临界值为8.0。
进一步的是,根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值,具体是指:使用血清肌酐和胱抑素C的比值预测不同定义下握力降低的敏感性和特异性。
本发明的有益效果是,通过上述基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,能够确定血清肌酐和胱抑素C的比值与握力的关系,通过计算出的血清肌酐和胱抑素C的比值识别出人群中握力降低的情况,从而为进一步检测肌少症提供精准的预测,极大地缩小需要筛查肌少症的人群规模,节约人力和成本,适用于大规模成年人群的肌少症筛查。
附图说明
图1为本发明基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法的流程图。
具体实施方式
下面结合附图及实施例,详细描述本发明的技术方案。
本发明提出的一种基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其流程图见图1,其中,该方法包括如下步骤:
获取血清肌酐和胱抑素C;
将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物;
构建并训练血清肌酐和胱抑素C的比值与握力的关系模型;
基于所述关系模型分别确定不同性别用户的血清肌酐和胱抑素C的比值的合适诊断临界值;
根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值;
根据所述预测值检测肌少症。
上述方法中,为了能够及时有效地获取到血清肌酐和胱抑素C,可以从血样库中存储的血样中获取获取血清肌酐和胱抑素C。
并且,血清肌酐和胱抑素C的比值用于反映不同人群的骨骼肌质量,基于反映出的不同人群的骨骼肌质量,本发明将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物。
由于采样范围较大,为了研究血清肌酐和胱抑素C的比值与握力的关系,关系模型可以优选为深度神经网络模型,并且,本申请中所用到的深度神经网络模型为一般常用的神经网络模型即可,无需独立构建并训练。
实际应用中,可以通过深度神经网络模型,确定在大于等于40岁的人群中,血清肌酐和胱抑素C的比值与握力呈正相关关系。
并且,在大于等于40岁的人群中,通过反复的实验,最终,确定男性的诊断临界值约为8.9,女性的诊断临界值约为8.0。
具体应用过程中,本发明中提到的根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值,具体是指:使用血清肌酐和胱抑素C的比值预测不同定义下握力降低的敏感性和特异性。
实施例
本实施例以人群为基础,在不同人群中,40岁(31-40岁)人群的握力最高;因此,我们邀请年龄≥40岁的社区成年人参加本研究。排除标准为肾功能受损(估计肾小球滤过率<60mL/分钟/1.73m2)、手部和手臂有任何可能影响握力测量的损伤或疾病以及任何类型的癌症。由经过采集受试者的临床信息和血样,并进行人体测量和握力测试。
在进行握力测量时,使用数字测力计测量握力,该仪器的握力可调,测量值在0.1-100.0kg之间,增量为0.1kg。测量方案采用《中国国民体质测定标准》提出的建议。第一,向受试者分发测力计使用说明;第二,要求受试者两脚分开与肩同宽站立,肘部完全伸展;第三,要求受试者握住测力计保持在正中位置,食指屈曲90°,测力计不触碰到身体;第四,要求受试者用全力连续挤压握把≥3s。测量期间不允许口头鼓励或视觉反馈,受试者每只手进行三次交替试验,每次测量之间休息≥30s,握力结果选取六次测量的最大值。
并且,临床和实验室参数的测量,通过面对面访谈收集以下临床特征:年龄、性别、吸烟状况、饮酒状况、受教育程度以及高血压和糖尿病史。使用自动装置测量身高(精确至0.5cm)和体重(精确至0.1kg)。体重指数(BMI)为体重/身高2(kg/m2)。禁食≥8h后,在早晨从每例受试者的肘前静脉采集血样。使用Cobas c702化学自动分析仪检测血清中尿酸、Cr、CysC、肌酸激酶(CK)和白蛋白的浓度。使用Modular Analytics Cobas 6000分析仪,通过比浊免疫法检测血清中C反应蛋白(CRP)的浓度。使用Sysmex XE-500分析仪检测血红蛋白浓度。Cr/CysC比值计算:Cr/CysC比值=血清Cr(mg/dL)/血清CysC(mg/L)×10。
在统计分析时,连续数据用平均值和标准差(SD)或中位数和四分位距(IQR)表示,分类数据用数量和百分比表示,正态分布变量采用单因素方差分析法评价组间差异,非正态分布变量采用曼-惠特尼U检验,分类变量采用卡方检验,握力分析根据性别分层,因为既往研究显示了男性和女性之间有显著差异。
视情况采用皮尔逊相关系数或斯皮尔曼相关系数评估握力与Cr/CysC比值、年龄、CK浓度等血清生物标志物的关系,同时进行多元线性回归分析,探索握力与这些生物标志物的线性关系,通过深度神经网络模型,探索与握力降低相关的生物标志物,根据EWG-SOP2标准(男性<27kg,女性<16kg)和亚洲肌少症工作组标准(AWGS,男性<26kg,女性<18kg)识别。
然后,根据ROC曲线下面积(AUC)和95%置信区间(CI),采用受试者工作特征(ROC)曲线分析评价Cr/CysC比值识别握力降低的效用。计算约登指数(敏感性+特异性-1),以确定识别男性和女性握力降低的最佳临界点。本实施例中,还计算了敏感性、特异性、阳性似然比和阴性似然比。使用IBM SPSS软件和MedCalc统计软件进行所有统计分析。所有统计检验均为双侧检验,基于P值<0.05确定统计学显著性。
表1为按性别列示的受试者特征,其中,*数据用中位数(四分位距)表示,数据用平均值(标准差)表示,为检测男女差异,正态分布的连续变量采用单因素ANOVA;非正态分布的连续变量采用曼-惠特尼U检验;分类变量采用卡方检验,Cr/CysC比值=血清肌酐(mg/dL)/血清胱抑素C(mg/L)×10。
表1
通过表1可知,这里招募了2539例成年人,但一部分受试者因以下原因被排除:估计肾小球滤过率<60mL/分钟/1.73m2(n=103)、患有任何类型的癌症(n=65)、手部和手臂有损伤或疾病(n=32)以及握力数据缺失(n=29)。因此,有2339例受试者(中位年龄:55岁,范围40-89岁)符合分析条件,包括1098例男性和1241例女性。
并且,在评价影响握力的因素时,表1显示了握力和潜在协变量之间的简单相关性。在男性和女性中,握力与年龄(男性:r=-0.329,p<0.001;女性:r=-0.341,p<0.001)、CRP浓度(男性:r=-0.043,p=0.020;女性:r=-0.072,p=0.004)、CysC浓度(男性:r=-0.207,p<0.001;女性:r=-0.276,p<0.001)呈负相关,而握力与Cr/CysC比值(男性:r=0.376,p<0.001;女性:r=0.407,p<0.001)、血红蛋白浓度(男性:r=0.304,p<0.001;女性:r=0.157,p<0.001)、白蛋白浓度(男性:r=0.414,p<0.001;女性:r=0.341,p<0.001)呈正相关。在男性中,BMI(r=0.134,p<0.001)和CK浓度(r=0.008,p=0.006)与握力呈正相关,但在女性中并非如此。男性中尿酸浓度与握力呈正相关(r=0.083,p=0.006),而女性中尿酸浓度与握力呈负相关(r=-0.118,p=0.001)。
数据表明男性(r=0.376,p<0.001)和女性(r=0.407,p<0.001)握力与Cr/CysC比值之间的正线性相关,男性(r=-0.467,p<0.001)和女性(r=-0.587,p<0.001)中Cr/CysC比值与年龄呈负相关。同样,男性(r=-0.537,p<0.001)和女性(r=-0.485,p<0.001)握力均与年龄呈负相关。
在这种情况下,握力降低是肌少症的关键因素,也是各种不良健康结局的预测因素。因此,在研究和临床实践中识别握力降低患者是有价值的。据我们所知,本研究首次评估了Cr/CysC比值在识别大规模人群握力降低中的预测价值。在临床实践中常规检测血清Cr和CysC浓度,该检测成本较低,可重现性较好。因此,在研究和临床环境中,Cr/CysC比值可作为筛查握力降低的标志物。
实际应用中,因为握力测量被认为是评估肌少症的第一步,所以本研究结果也表明,Cr/CysC比值可能有助于筛查肌少症。既往的几项研究最近已经解决了这个问题,尽管结果仍然存在争议。例如,Kusunoki等人开展的横断面研究显示,在日本社区老年人中,Cr/CysC比值与肌少症相关。此外,Osaka等人认为,基于285例日本2型糖尿病患者的数据,Cr/CysC比值是肌少症筛查的有效生物标志物。但是,Singhal等人报告称,在印度一家三级医院的100例老年门诊患者中,Cr/CysC比值与肌少症无关。我们的研究团队之前也发现,“肌少症指数”(即Cr/CysC比值×10)不能准确识别中国社区老年人中的肌少症。因此,需要进一步开展基于大规模人群的研究,以明确Cr/CysC比值在肌少症筛查中的潜在作用。
在年龄≥40岁且肾功能正常的成年人样本中,Cr/CysC比值与握力呈正线性相关。在不同的临床环境(包括健康体检)中常规检测Cr和CysC浓度。在中国,65岁以上的社区老年人每年有机会接受一次的免费体检(包括Cr和CysC检测)。因此,通过Cr/CysC比值有助于筛查该人群是否存在肌力降低,而不会增加任何耗时的步骤、医疗器械或经济负担。本实例中,我们将男性和女性的Cr/CysC比值临界值分别设定为<8.9和<8.0,因为其在识别肾功能正常的中国社区居民握力降低时具有可接受的敏感性和中高特异性。
最近,亚洲肌少症共识的更新版本(AWGS 2019)提出了“可能的肌少症”(PossibleSarcopenia)的概念,其定义即握力或体能降低,因此,本实施例提出的Cr/CysC比值及其诊断切点,也有助于在中国成年人群中筛查“可能的肌少症”。尽管如此,这些临界值可能并不适用于所有种族人群,还需要进一步开展大规模研究来验证其在其他种族人群中的效用。
综上所述,本实施例能够将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物,并且可以通过构建并训练血清肌酐和胱抑素C的比值与握力的关系模型,分别确定不同性别用户的血清肌酐和胱抑素C的比值的合适诊断临界值,然后根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值,该方法为在大规模人群中筛查肌力降低人群(以便进一步评估肌少症)提供了便捷可靠的手段。
Claims (7)
1.基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,包括如下步骤:
获取血清肌酐和胱抑素C;
将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物;
构建并训练血清肌酐和胱抑素C的比值与握力的关系模型;
基于所述关系模型分别确定不同性别用户的血清肌酐和胱抑素C的比值的合适诊断临界值;
根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值;
根据所述预测值检测肌少症。
2.根据权利要求1所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,从血样库中存储的血样中获取获取血清肌酐和胱抑素C。
3.根据权利要求1所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,所述血清肌酐和胱抑素C的比值用于反映不同人群的骨骼肌质量,基于反映出的不同人群的骨骼肌质量,将血清肌酐和胱抑素C的比值作为筛查握力降低的标志物。
4.根据权利要求1所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,所述关系模型为深度神经网络模型。
5.根据权利要求4所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,通过所述深度神经网络模型,确定在大于等于40岁的人群中,血清肌酐和胱抑素C的比值与握力呈正相关关系。
6.根据权利要求1所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,在大于等于40岁的人群中,男性的诊断临界值为8.9,女性的诊断临界值为8.0。
7.根据权利要求1所述的基于血清肌酐和胱抑素C预测肌力下降筛查肌少症的方法,其特征在于,根据不同性别用户的合适诊断临界值确定相应性别的握力降低中的预测值,具体是指:使用血清肌酐和胱抑素C的比值预测不同定义下握力降低的敏感性和特异性。
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