DNA methylation biomarkers prospectively predict both antenatal and postpartum depression.

DNA methylation biomarkers prospectively predict both antenatal and postpartum depression.
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DOI:
10.1016/j.psychres.2019.112711
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发表时间:
2020-03
影响因子:
11.3
通讯作者:
Kaminsky ZA
Kaminsky ZA
中科院分区:
医学2区
文献类型:
--
作者:
Payne JL;Osborne LM;Cox O;Kelly J;Meilman S;Jones I;Grenier W;Clark K;Ross E;McGinn R;Wadhwa PD;Entringer S;Dunlop AL;Knight AK;Smith AK;Buss C;Kaminsky ZA

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我们试图复制和扩展以前的工作,证明产前TTC9B和HP1BP3基因甲基化可以前瞻性地预测产后抑郁症(PPD),准确率约为80%。在埃默里的一项早产研究中,Illumina甲基EPIC微阵列得出的第一个但不是第三个三个月的生物标志物模型预测了第三个三个月的爱丁堡产后抑郁量表(EPDS)评分≥13,AUC=0.8(95%CI:0.63-0.8)。在加州大学欧文分校的妊娠队列和独立的约翰·霍普金斯大学妊娠队列的第三个妊娠队列中,使用亚硫酸盐焦测序对所有三个月的孕妇进行了亚硫酸氢盐焦测序衍生生物标记物甲基化。支持向量机模型结合了妊娠晚期EPDS评分、TTC9B和HP1BP3甲基化状态,预测了加州大学欧文分校和约翰霍普金斯大学队列中晚期妊娠血中产后4周至6周的EPDS≥13(AUC=0.78,95%CI:0.64-0.78),两者均独立于先前的精神病诊断。约翰霍普金斯大学队列的一个子集中的技术重复预测显示出很强的交叉实验相关性。这项研究证实了PPD预测模型有可能发展成为一种临床工具,能够识别可能从临床干预中受益的具有PPD未来风险的孕妇。
We sought to replicate and expand upon previous work demonstrating antenatal TTC9B and HP1BP3 gene DNA methylation is prospectively predictive of postpartum depression (PPD) with ~80% accuracy. In a preterm birth study from Emory, Illumina MethylEPIC microarray derived 1st but not 3rd trimester biomarker models predicted 3rd trimester Edinburgh Postnatal Depression Scale (EPDS) scores ≥ 13 with an AUC=0.8 (95% CI: 0.63–0.8). Bisulfite pyrosequencing derived biomarker methylation was generated using bisulfite pyrosequencing across all trimesters in a pregnancy cohort at UC Irvine and in 3rd trimester from an independent Johns Hopkins pregnancy cohort. A support vector machine model incorporating 3rd trimester EPDS scores, TTC9B, and HP1BP3 methylation status predicted 4 week to 6 week postpartum EPDS ≥ 13 from 3rd trimester blood in the UC Irvine cohort (AUC=0.78, 95% CI: 0.64–0.78) and from the Johns Hopkins cohort (AUC=0.84, 95% CI: 0.72–0.97), both independent of previous psychiatric diagnosis. Technical replicate predictions in a subset of the Johns Hopkins cohort exhibited strong cross experiment correlation. This study confirms the PPD prediction model has the potential to be developed into a clinical tool enabling the identification of pregnant women at future risk of PPD who may benefit from clinical intervention.
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