Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression

Juan Liu, Zhuang Liu, Yan-ge Wei, Yanbo Zhang, F. Womer, Duan Jia, Shengnan Wei, Feng Wu, Ling-tao Kong, Xiaowei Jiang, Luheng Zhang, Yanqing Tang, Xizhe Zhang, Fei Wang
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Abstract

Objective: Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder. Methods: A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel. Results: Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel. Conclusion: Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.
从扩散张量成像和脂质谱的多层次信息中获得的内表型的组合面板作为抑郁症的预测因子
目的:重性抑郁症的临床异质性可能反映了该病的病因和影响因素的范围,如遗传风险。基于生物标志物(如白质完整性和脂质相关代谢物)识别更精细的亚群可以促进重性抑郁症的精准医疗。方法:共有148名参与者(15名遗传高危参与者,57名首发重度抑郁症患者和76名健康对照)接受了弥散张量成像和血脂分析。在遗传高风险参与者和首发重度抑郁症患者中发现了白质完整性和脂质代谢物的改变。然后,通过因素分析和逻辑回归,利用遗传高风险和首发重性抑郁症之间的共同改变来开发基于遗传的重性抑郁症的影像学x代谢物诊断面板。采用五重交叉验证试验对诊断组进行评估。结果:在遗传高危参与者和首发重性抑郁症患者中均发现辐射冠、上纵束和胼胝体白质完整性改变和不饱和脂肪酸代谢失调。由白质完整性和不饱和脂肪酸代谢测量组成的成像x代谢物诊断面板,在接受者工作特征曲线下的面积达到0.86,诊断性能明显高于单独使用任何一种测量。交叉验证证实了诊断面板具有足够的可靠性和准确性。结论:结合胼胝体、上纵束和辐射冠白质完整性和不饱和脂肪酸谱,可提高重性抑郁症基因内表型的鉴定,推进精准医疗策略。
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