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母亲脆弱性对压力生物标志物和孕早期生长的影响:鹿特丹围孕期队列(Predict 研究)。

The impact of maternal vulnerability on stress biomarkers and first-trimester growth: the Rotterdam Periconceptional Cohort (Predict Study).

机构信息

Department of Obstetrics and Gynecology, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.

Department of Clinical Chemistry, Erasmus MC, University Medical Center, Rotterdam, the Netherlands.

出版信息

Hum Reprod. 2024 Nov 1;39(11):2423-2433. doi: 10.1093/humrep/deae211.


DOI:10.1093/humrep/deae211
PMID:39298717
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11532602/
Abstract

STUDY QUESTION: Is the degree of maternal vulnerability positively associated with stress biomarkers (stress hormones, C-reactive protein, tryptophan metabolites, and one-carbon metabolites), and does long-term exposure to stress hormones reduce first-trimester growth? SUMMARY ANSWER: The maternal vulnerability risk score is positively associated with concentrations of hair cortisol and cortisone and negatively with tryptophan, while higher hair cortisol concentrations are associated with reduced first-trimester growth without mediation of tryptophan. WHAT IS KNOWN ALREADY: A high degree of maternal vulnerability during the periconception period is associated with impaired first-trimester growth and pregnancy complications, with consequences for long-term health of the child and future life course. However, due to the challenges of early identification of vulnerable women, the uptake of periconception care is low in this target group. STUDY DESIGN, SIZE, DURATION: Between June 2022 and June 2023, this study was conducted in a sub-cohort of 160 pregnant women participating in the Rotterdam Periconceptional Cohort (Predict Study), an ongoing prospective tertiary hospital-based cohort. PARTICIPANTS/MATERIALS, SETTING, METHODS: One hundred and thirty-two women with ongoing pregnancies and available stress biomarker data were included in the analysis. Data on periconceptional social, lifestyle, and medical risk factors were collected via self-administered questionnaires, and these factors were used for the development of a composite maternal vulnerability risk score. Stress biomarkers, including stress hormones (hair cortisol and cortisone) and inflammatory and oxidative stress biomarkers (C-reactive protein, total homocysteine, and tryptophan metabolites) were determined in the first trimester of pregnancy. First-trimester growth was assessed by crown-rump length (CRL) and embryonic volume (EV) measurements at 7, 9, and 11 weeks gestation by making use of an artificial intelligence algorithm and virtual reality techniques using 3D ultrasound data sets. The associations between the maternal vulnerability risk score and stress biomarkers were identified using linear regression models, and between stress hormones and CRL- and EV-trajectories using mixed models. A mediation analysis was performed to assess the contribution of tryptophan. All associations were adjusted for potential confounders, which were identified using a data-driven approach. Several sensitivity analyses were performed to check the robustness of the findings. MAIN RESULTS AND THE ROLE OF CHANCE: The maternal vulnerability risk score was positively associated with concentrations of hair cortisol and cortisone (pg/mg) (β = 0.366, 95% CI = 0.010-0.722; β = 0.897, 95% CI = 0.102-1.691, respectively), and negatively with tryptophan concentrations (µmol/L) (β = -1.637, 95% CI = -2.693 to -0.582). No associations revealed for C-reactive protein and total homocysteine. Higher hair cortisol concentrations were associated with reduced EV-trajectories (3√EV: β = -0.010, 95% CI = -0.017 to -0.002), while no associations were found with CRL-trajectories. Mediation by tryptophan was not shown. LIMITATIONS, REASONS FOR CAUTION: Residual confounding cannot be ruled out, and the external validity may be limited due to the study's single-center observational design in a tertiary hospital. WIDER IMPLICATIONS OF THE FINDINGS: There is mounting evidence that a high degree of maternal vulnerability negatively affects maternal and perinatal health, and that of the future life course. The results of our study emphasize the need to identify highly vulnerable women as early as possible, at least before conception. Our findings suggest that the chronic stress response and alterations of the maternal tryptophan metabolism are involved in maternal vulnerability, affecting first-trimester growth, with potential impact on the long-term health of the offspring. STUDY FUNDING/COMPETING INTEREST(S): This study was funded by the Departments of Obstetrics and Gynecology and Clinical Chemistry of the Erasmus MC, University Medical Center, Rotterdam, the Netherlands, and the Junior Award granted by the De Snoo-van 't Hoogerhuijs Foundation in March 2022. There are no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

摘要

研究问题:产妇易感性程度是否与应激生物标志物(应激激素、皮质醇和可的松、C 反应蛋白、色氨酸代谢物和一碳代谢物)呈正相关,长期暴露于应激激素是否会降低第一孕期的生长?

总结答案:产妇易感性风险评分与头发皮质醇和皮质酮的浓度呈正相关,与色氨酸呈负相关,而较高的头发皮质醇浓度与第一孕期生长减少相关,且不受色氨酸的影响。

已知情况:受孕期间产妇易感性程度较高与第一孕期生长受损和妊娠并发症有关,这对儿童的长期健康和未来的生命过程都有影响。然而,由于早期识别脆弱女性的挑战,该目标群体中接受受孕前保健的比例较低。

研究设计、规模、持续时间:在 2022 年 6 月至 2023 年 6 月期间,这项研究在参加鹿特丹受孕前队列(预测研究)的 160 名孕妇的子队列中进行,这是一项正在进行的三级医院为基础的前瞻性队列研究。

参与者/材料、设置、方法:在分析中纳入了 132 名有持续妊娠且有应激生物标志物数据的女性。通过自我管理问卷收集受孕前的社会、生活方式和医疗风险因素的数据,这些因素用于开发一个复合产妇易感性风险评分。在妊娠早期,通过使用人工智能算法和虚拟现实技术,利用 3D 超声数据集,测量 7、9 和 11 周妊娠时的头臀长(CRL)和胚胎体积(EV),来评估第一孕期的生长。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。使用混合模型评估应激激素与 CRL 和 EV 轨迹之间的关系。

研究资金/利益冲突:这项研究由鹿特丹伊拉斯谟医学中心妇产科和临床化学系以及荷兰德 Snoo-van 't Hoogerhuijs 基金会于 2022 年 3 月授予的初级奖资助。没有利益冲突。

试验注册编号:无。

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[2]
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[4]
Periconceptional maternal social, lifestyle and medical risk factors impair embryonic growth: The Rotterdam Periconceptional Cohort.

Reprod Biomed Online. 2022-6

[5]
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[6]
Cohort Profile Update: the Rotterdam Periconceptional Cohort and embryonic and fetal measurements using 3D ultrasound and virtual reality techniques.

Int J Epidemiol. 2021-11-10

[7]
Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data.

BMJ. 2020-11-11

[8]
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The importance of mediation in reproductive health studies.

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