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孕中期血压与血糖水平在影响胎儿生长及新生儿结局方面的相互依存关系:对风险效益评估及联合管理的启示

The interdependence of mid-trimester blood pressure and glucose levels in shaping fetal growth and neonatal outcomes: implications for risk-benefit assessment and co-management.

作者信息

Lv Lijuan, Yang Jingbo, Li Linjie, Huang Chuanyi, Shi Huihua, Fang Yiwen, Zuo Lushu, Liu Ting, Duan Hongli, Wen Jiying, Yang Qing, Henry Amanda, Han Cha, Yin Aihua, Zhou Xin

机构信息

Medical Genetic Center, Department of Obstetrics, Guangdong Women and Children Hospital, Xinnan Avenue, Panyu District, Guangzhou, 511442, China.

Department of Cardiology, Tianjin Medical University General Hospital, 154, Anshan Road, Heping District, Tianjin, 300052, China.

出版信息

BMC Med. 2025 Mar 14;23(1):161. doi: 10.1186/s12916-025-03990-7.

DOI:10.1186/s12916-025-03990-7
PMID:40087732
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11909891/
Abstract

BACKGROUND

Maternal hypertension and hyperglycemia are closely related but have distinct impacts on fetal growth and are managed independently. How the interdependence of blood pressure (BP) and glucose levels quantitatively influences risk patterns for abnormal fetal growth and neonatal complications remains unexplored.

METHODS

Maternal BP and fasting plasma glucose (FPG) levels were measured between 20 and 28 weeks of gestation in a cohort including 56,881 singleton pregnancies. Linear and quantile regression analyses were used to evaluate the relationship between BP and FPG. We examined the dose-response relationships between BP and FPG with small-for-gestational age (SGA) and large-for-gestational age (LGA) by using restricted cubic spline (RCS) curves. Additionally, multivariable fractional polynomial interaction (MFPI) analysis was conducted to assess the effects of higher versus lower BP levels across the full range of FPG levels. Heatmaps were created to visualize the contributions of BP and FPG by categorizing them into ordered groups.

RESULTS

Quantile regression revealed consistent positive correlations between mean arterial pressure (MAP) and FPG, with a steeper increase in MAP coefficients above the 0.5 quantile of FPG. MAP had a non-linear positive association with SGA risk, while FPG showed a non-linear negative association. Heatmaps revealed the highest SGA risk with high BP (MAP ≥ 85 mmHg)/low glucose (< 85 mg/dL) combinations and the lowest risk with low BP (MAP < 85 mmHg)/high glucose (≥ 85 mg/dL), with equivalent risk at both high BP/high glucose and low BP/low glucose. In hypertensive patients, SGA risk worsened continuously as glucose levels decreased. LGA risk was not influenced by BP levels. Neonatal complications decreased by approximately 47% as MAP declined from the highest to lowest category, and by about 17% with decreasing glucose levels.

CONCLUSIONS

Based on a large pregnancy cohort in China, this study revealed an interdependent association between maternal BP and glucose levels and their combined impact on the risk of SGA. It provided quantitative evidence of how this interdependence shapes the transition of risk patterns for SGA, neonatal complications, and LGA. These findings underscore the need for an integrated approach to co-managing BP and glucose levels during pregnancy.

摘要

背景

孕产妇高血压和高血糖密切相关,但对胎儿生长有不同影响且分别进行管理。血压(BP)和血糖水平的相互依存关系如何定量影响胎儿生长异常和新生儿并发症的风险模式仍未得到探索。

方法

在一个包含56,881例单胎妊娠的队列中,于妊娠20至28周测量孕产妇血压和空腹血糖(FPG)水平。采用线性和分位数回归分析评估血压与空腹血糖之间的关系。我们使用受限立方样条(RCS)曲线研究血压与空腹血糖与小于胎龄儿(SGA)和大于胎龄儿(LGA)之间的剂量反应关系。此外,进行多变量分数多项式相互作用(MFPI)分析以评估在整个空腹血糖水平范围内较高与较低血压水平的影响。通过将血压和空腹血糖分类为有序组来创建热图,以直观显示它们的贡献。

结果

分位数回归显示平均动脉压(MAP)与空腹血糖之间存在一致的正相关,在空腹血糖的0.5分位数以上,MAP系数的增加更为陡峭。MAP与SGA风险呈非线性正相关关系,而空腹血糖则呈非线性负相关关系。热图显示,高BP(MAP≥85 mmHg)/低血糖(<85 mg/dL)组合的SGA风险最高,低BP(MAP<85 mmHg)/高血糖(≥85 mg/dL)组合的风险最低,高BP/高血糖和低BP/低血糖组合的风险相当。在高血压患者中,随着血糖水平降低,SGA风险持续恶化。LGA风险不受血压水平影响。随着MAP从最高类别降至最低类别,新生儿并发症减少约47%,随着血糖水平降低减少约17%。

结论

基于中国的一个大型妊娠队列,本研究揭示了孕产妇血压和血糖水平之间的相互依存关系及其对SGA风险的综合影响。它提供了关于这种相互依存关系如何塑造SGA、新生儿并发症和LGA风险模式转变的定量证据。这些发现强调了在孕期共同管理血压和血糖水平时采用综合方法的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/00544f530545/12916_2025_3990_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/c5fb4534c6cf/12916_2025_3990_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/f98a24047690/12916_2025_3990_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/9d83acb04568/12916_2025_3990_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/00544f530545/12916_2025_3990_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/c5fb4534c6cf/12916_2025_3990_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/f98a24047690/12916_2025_3990_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/9d83acb04568/12916_2025_3990_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6a6/11909891/00544f530545/12916_2025_3990_Fig4_HTML.jpg

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