Chinese Journal of Tissue Engineering Research ›› 2025, Vol. 29 ›› Issue (30): 6574-6582.doi: 10.12307/2025.911
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Zhang Bochun1, Li Wei1, Li Guangzheng1, Ding Haoqin1, Li Gang2, Liang Xuezhen1, 2
Received:
2024-10-18
Accepted:
2024-11-26
Online:
2025-10-28
Published:
2025-03-29
Contact:
Liang Xuezhen, MD, Associate professor, Master’s supervisor, First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China; Department of Orthopaedic Microsurgery, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250014, Shandong Province, China
About author:
Zhang Bochun, MS, First College of Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, Shandong Province, China
Supported by:
CLC Number:
Zhang Bochun, Li Wei, Li Guangzheng, Ding Haoqin, Li Gang, Liang Xuezhen, . Association between neuroimaging changes and osteonecrosis: a large sample analysis from UK Biobank and FinnGen databases[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(30): 6574-6582.
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2.1 正向工具变量的筛选 此次研究对检测到的神经影像学特征数据进行了工具变量筛选,所得到的工具变量数据F值均 > 10,未发现F < 10的弱工具变量,证明因弱工具变量带来偏倚的可能性较小。 2.2 神经影像学变化对骨坏死的因果效应 在进行两样本孟德尔随机化分析时,采用Cochran’s Q检验来评估不同研究间的异质性,结果显示研究间没有显著的异质性(P > 0.05),因此选用固定效应模型来估算神经影像学变化与骨坏死之间的因果关系。结果显示共192种神经影像学特征与骨坏死存在可靠的潜在因果关系,其中97种神经影像学特征可以提高骨坏死的发病率(OR > 1,P < 0.05),而其余的95种则是骨坏死的保护因素,可以降低骨坏死的发生率(OR < 1,P < 0.05)。对所得到的数据进行整理分类,主要依据数据的首个单词进行划分,首个单词相同的数据被归为同一组,而数量较少的数据则被归入一组,最终共形成了5组数据:大脑皮质的解剖学划分(anatomical parcellation of the cerebral cortex,aparc)41种,白质-灰质强度对比[white-Grey(wg)intensity-contrast]16种,影像衍生表型(imaging- derived phenotypes,IDP)48种,静息态功能磁共振成像(resting-state functional magnetic resonance imaging,rfMRI)70种,剩余17种数据编为一组,对这5组数据进行逐一分析。 2.2.1 大脑皮质解剖学划分与骨坏死 两样本孟德尔随机化和逆方差加权分析结果显示,41种大脑皮质的解剖学划分神经影像学特征与骨坏死之间存在显著的因果关联(P均< 0.05),见图2。敏感性分析结果显示数据不存在异质性,但发现有两种数据存在水平多效性(aparc-pial lh area caudalmiddlefrontal,aparc-a2009s rh volume G-front-inf-Triangul),为确保结果的准确性,避免对结果产生影响,故除去这两组数据,共得到39组具有显著因果关系的数据,其中aparc-DKTatlas rh thickness medialorbitofrontal是最显著的潜在保护因素(逆方差加权分析结果OR=0.471,95%CI:0.317- 0.700,P=0.000 191 909),其他孟德尔随机化分析方法也得到了相似结果。潜在危险因素有:aparc-Desikan lh volume supramarginal、aparc-Desikan rh volume parahippocampal、aparc-Desikan rh volume superiortemporal等,潜在保护因素有:aparc-DKTatlas lh volume parsorbitalisaparc-DKTatlas rh volume inferiorparietal、aparc-a2009s rh volume G+S-cingul-Mid-Post等,见表1。 除了少数神经影像学数据的分析结果在不同方法间有所差异之外,大多数特征的分析结果在多种方法中显示出高度一致性。逆方差加权法相对简单高效,以每个遗传工具变量与暴露因素的关联效应大小和标准误为主要参数,通过对多个遗传工具变量估计值进行加权合并,得到总体因果效应的估计[30-31]。在没有异质性或水平多效性问题的情况下,以逆方差加权法为金标准,其余方法作为补充[32]。 2.2.2 白质-灰质强度对比与骨坏死 两样本孟德尔随机化和逆方差加权分析结果显示,16种白质-灰质强度对比神经影像学数据与骨坏死之间存在显著的因果关联(P均< 0.05),见图3。白质-灰质强度对比数据全部为潜在的保护因素,其中最显著的潜在保护因素是wg rh intensity-contrast"
parstriangularis(逆方差加权分析结果OR=0.753,95%CI:0.636-0.891,P= 0.001),其他孟德尔随机化分析结果与逆方差加权法相似。逆方差加权法法是孟德尔随机化的主要研究方法,其余4种方法也有很重要的统计意义,当各种统计方法之间数据存在差异时以逆方差加权法为准。 Cochran’s Q检验结果显示数据没有显著的异质性(P > 0.05),故选择固定效应模型来处理数据。利用MR-Egger回归和MR-presso方法对数据进行水平多效性检验,结果显示所有神经影像学特征数据中不存在显著的水平多效性问题(P均> 0.05),见表2,进一步确认了因果分析结果的稳定性。 2.2.3 影像衍生表型与骨坏死 两样本孟德尔随机化和逆方差加权分析结果显示,48种影像衍生表型神经影像学数据与骨坏死之间存在显著的因果关联(P均< 0.05)。敏感性分析结果显示数据不存在异质性,但发现有两种数据存在水平多效性(IDP dMRI TBSS MO Middle cerebellar peduncle,IDP T1 FAST ROIs R caudate)为确保结果的准确性,避免对结果产生影响,故除去这两组数据,共得到46种具有显著因果关系的数据,见图4、表3。IDP dMRI ProbtrackX FA str r是其中最显著的的保护性因素(逆方差加权分析结果OR=0.686,95%CI:0.559-0.843,P=0.000 3),其他孟德尔随机化分析结果与逆方差加权法相似。潜在的保护"
因素有:IDP dMRI ProbtrackX FA str r、IDP dMRI TBSS FA Fornix cres+Stria terminalis L、IDP dMRI TBSS ICVF Superior cerebellar peduncle R,潜在的危险因素有:IDP dMRI ProbtrackX L3 ifo l、IDP dMRI ProbtrackX L1 unc r,IDP dMRI TBSS L1 Retrolenticular part of internal capsule R。 2.2.4 静息态功能磁共振成像与骨坏死 共检测到70种静息态功能磁共振成像神经影像学数据,敏感性分析结果显示有两种神经影像学数据存在异质性[rfMRI connectivity (ICA100 edge 724),rfMRI connectivity (ICA100 edge 681)],两种数据存在水平多效性 [rfMRI connectivity (ICA100 edge 1247),rfMRI amplitudes (ICA100 node 47)],出于对结果稳定性和准确性的考虑,剔除这4组数据,从而得到了66组具有显著因果关系的数据,见图5、 表4。最显著的前三均为保护因素,分别是rfMRI connectivity (ICA100 edge 1046)、rfMRI connectivity (ICA100 edge 341)、rfMRI connectivity (ICA100 edge 1100)。 2.2.5 其他组别数据与骨坏死 最后因剩余的数据较少,所以归为一组进行分析。经过对数据进行筛选,发现共有17组数据与骨坏死存在显著的因果关联(P均< 0.05),见图6、表5。敏感性分析结果显示数据不存在异质性和水平多效性,其中最显著的潜在保护因素为HippSubfield lh volume Whole-hippocampus(逆方差加权分析结果OR=0.727,95%CI:0.581-0.909,P=0.005),最显著的潜在危险因素为aseg rh volume Pallidum(逆方差加权分析结果OR=1.359,95%CI:1.086-1.701,P=0.007)。 2.3 骨坏死对神经影像学的影响 基于上述神经影像学与骨坏死的因果关系结果,将骨坏死作为暴露因素进行反向孟德尔随机化分析,以深入了解骨坏死与神经影像学之间的因果关系。根据P < 5×10-5的临界值选择单核苷酸多态性,经过反向孟德尔随机化分析共得出147组数据,对数据进行敏感性分析,有5组数据存在异质性,有9组数据存在水平多效性,有2组数据(ThalamNuclei rh volume Whole-thalamus,rfMRI connectivity"
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