JCEM:使用生物标志物参考曲线和相应的Z评分对儿童内分泌激素进行临床分类

2022-03-26 从医路漫漫 MedSci原创

在对包括性腺功能减退/亢进、性发育差异(DSDs)、肿瘤和自身免疫性疾病(损害内分泌功能)在内的多种疾病进行临床研究时,用于评估儿科患者血液检查结果的生殖激素参考是必不可少的。

背景:在对包括性腺功能减退/亢进、性发育差异(DSDs)、肿瘤和自身免疫性疾病(损害内分泌功能)在内的多种疾病进行临床研究时,用于评估儿科患者血液检查结果的生殖激素参考是必不可少的。这种病理可能与异常的身体发育和青春期时间的改变有关。从人口层面来看,自20世纪70年代以来,女性青春期开始时间每十年减少3个月,并且似乎仍在下降。随着青春期提前的长期趋势,特别是在女孩中观察到的,儿科内分泌学中应用的相关参考文献应定期更新。此外,儿童期肥胖和青春期提前之间的联系保证了定量基准和进一步的关注

   在儿童期和青春期,激素和生化标记物的循环水平经常因性别和年龄而有很大差异。通常,肾上腺皮质的觉醒(肾上腺发育)先于阴毛(耻骨发育)和性腺功能(性腺发育)。生化参考区间是评估样本和确保正确诊断和治疗的基本工具。在儿科内分泌学中,这需要根据主要协变量(即年龄、性别和青春期阶段)进行适当的调整或分层。成熟且广泛应用的非参数方法对年龄组进行任意划分,以表格形式定义一系列中心95%置信区间(Cis )。然而,当将儿科患者分配到这样的预定年龄分区时,相应的参考区间将不会考虑这样的事实,即大多数这样的分区内的生物化学观察结果可能呈现年龄相关的偏斜,符合非高斯分布。此外,临床实验室标准协会C28-A3c标准坚持认为,临床有效的参考区间最好来自至少120次观察。在这方面,伦理限制使得招募健康儿童队列以建立足够强大和全面的儿科参考资料成为众所周知的挑战。值得注意的是,加拿大儿科参考区间实验室倡议(CALIPER)和北欧参考区间项目(NORIP)之前已经通过非参数划分和分位数回归建立了综合范围

目的:建立临床使用的性别特异性生物标志物参考曲线,以及激素、青春期表型和体重指数(身体质量指数)之间的基准关联。

设计背景和参与者:来自2139名健康挪威儿童和青少年的横断面人群样本数据。

主要测量结果:青春期状态,乳腺腺体组织(女孩)和睾丸体积(男孩)的超声测量,身体质量指数和17个临床生物标记物的实验室测量。

结果:青春期激素和相关生物标记物的参考曲线被建模以调整年龄和性别。生物标记水平和人体测量值的Z评分等价物被汇编在每个性别的综合β系数矩阵中。不考虑年龄因素,身体质量指数与女性乳腺腺体体积(β = 0.5,p<0.001)和瘦素(β= 0.6,p<0.001)呈正相关,与血清性激素结合球蛋白(SHBG)水平呈负相关(β = -0.4,p<0.001)。通过青春期表型和身体质量指数体重等级分层的队列亚组之间的生物标志物z得分分布显著不同。

表1 男性和女性青春期表型的基线特征BGS2队列中的参与者在检查时根据不同的青春期表型进行分层,所得样本量和基线特征以中位数(p2.5至p97.5)表示。数据集中最早和最晚出现的青春期,通过达到4毫升睾丸体积(男孩)或坦纳阶段B2(女孩)来定义,被设置为该分层分析的相应年龄界限。

表2 通过将机器学习应用于来自294名体重不足(身体质量指数z得分≤ -1.0)和“超重”(身体质量指数z得分≥ 1.0)儿童的生物标记谱生物标记z得分,对身体质量指数体重等级进行分类。分析中包括“随机森林”决策树分类模型,在预测身体质量指数体重等级之前,使用当前混淆矩阵中显示的剩余25%未显示数据中的75%数据进行训练。ML模型的分类性能超过了身体质量指数体重等级的任何单个生物标志物。对ML模型的分类一致性进行了令人满意的评估:Cohen的kappa值为0.89 (95% CI: 0.74至0.89)。

图1 连续生物标记参考曲线使用LMS算法将来自BGS2群组的血清样品中量化的生物标记水平建模为参考曲线。男性(左栏面板a、c、e、g、I、k)和女性(右栏面板b、d、f、h、j、l)参考分别建模。

图2青春期发育的标准化β系数矩阵、人体测量和激素概况年龄调整的z值(来自人体测量LMS生长图和当前生物标记LMS参考曲线)相关联,以获得描述所有变量之间关系的标准化β系数。举例来说,在身体质量指数,无论年龄大小,每增加1个标准差分值,瘦素循环水平就会增加0.6个标准差分值。顶部(a)男性矩阵中包含睾丸体积-年龄z分数,底部(b)女性矩阵中包含相应的女性腺体组织体积-年龄z分数。标准化β系数计算为两两z得分之间的线性回归(Pearson r ),并根据指示的热图标度进行着色。

图4。在154名体重不足(身体质量指数-SDS ≤ -1.0)和140名“超重”(身体质量指数-SDS ≥ 1.0)的男孩和女孩中,通过主成分分析(PCA)将生物标记物水平和体重类别维度减少之间的关联应用于17个生物标记物和青春期状态,即睾丸体积或腺组织体积。单个变量对数据集方差的方向贡献显示在与“体重不足”(红点)和“体重超重”(蓝点)身体质量指数体重等级聚类相关的双标图中。1.5标准差置信椭圆根据数据集方差定义每个权重类聚类。

结论:生物标志物参考曲线和相应的Z评分为儿科内分泌学的临床实施提供了直观的框架,有利于机器分类学习和协变量精确医学在儿科患者中的应用。

原文出处: Madsen A,  Almås B,  Bruserud IS, et al.Reference curves for pediatric endocrinology: leveraging biomarker z-scores for clinical classifications.J Clin Endocrinol Metab 2022 Mar 17

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    2022-05-08 smallant2015
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    2022-08-04 achengzhao
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    2022-04-12 gwc392
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