The multiomic landscape of epidemiological factors contributing to preterm birth in low- and middle-income countries

Preterm birth (PTB) is the leading cause of death in children under five, yet comprehensive studies are hindered by its multiple complex etiologies. Epidemiological associations between PTB and maternal characteristics have been previously described. This work employed multiomic profiling and multivariate modeling to investigate the biological signatures of these characteristics. Maternal covariates were collected during pregnancy from 13,841 pregnant women across five sites. Plasma samples from 231 participants were analyzed to generate proteomic, metabolomic, and lipidomic datasets. Machine learning models showed robust performance for the prediction of PTB (AUROC=0.70), time-to-delivery (r=0.65), maternal age (r=0.59), gravidity (r=0.56), and BMI (r=0.81). Time-to-delivery biological correlates included fetal-associated proteins (e.g., ALPP, AFP, PGF) and immune proteins (e.g., PD-L1, CCL28, LIFR). Maternal age negatively correlated collagen COL9A1; gravidity with endothelial NOS and inflammatory chemokine CXCL13; and BMI with leptin and structural protein FABP4. These results provide an integrated view of epidemiological factors associated with PTB and identify biological signatures of clinical covariates impacting this disease. 

Methods
The study population comprised pregnant women selected from 5 biorepository-supported cohorts in Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania. The study was approved by the Stanford University Institutional Review Board, and ethical exemptions were sought and obtained independently from the respective country by each birth cohort supported by the Alliance for Maternal and Newborn Health Improvement (AMANHI) and the Global Alliance to Prevent Prematurity and Stillbirth (GAPPS) biorepositories. Written informed participant consent was obtained from each participant in the original cohorts and extends to the present study. No compensation or incentives were provided for participating in this study. We followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) reporting guideline. This study analyzed plasma collected from May 2014 to August 2018.

Gestational age at the time of sampling was determined by ultrasonographic assessment. From all AMANHI and GAPPS cohorts, trained phlebotomists or nursing staff collected blood samples for centrifugation and aliquoting of serum, plasma, and buffy coat for storage in -80°C and future analyses. Collection and processing of all sample types were performed according to harmonized operating procedures at all study cohorts. Lipidomics and metabolomics features were generated using untargeted liquid-chromatography mass spectrometry, while proteomic features were generated using a highly multiplexed immunoassay (Olink Proteomics Inc.).

早产（Preterm Birth, PTB）是五岁以下儿童死亡的首要诱因，但其复杂多样的病因阻碍了全面研究的开展。此前已有研究描述了早产与孕产妇特征间的流行病学关联。本研究采用多组学谱分析与多变量建模，探究此类特征的生物学特征。研究从5个临床中心的13841名孕产妇孕期收集了协变量信息，对231名受试者的血浆样本进行分析，得到蛋白质组（proteomic）、代谢组（metabolomic）与脂质组（lipidomic）数据集。机器学习模型在预测早产（受试者工作特征曲线下面积（AUROC）=0.70）、分娩时间（相关系数r=0.65）、孕产妇年龄（r=0.59）、孕次（gravidity，r=0.56）及体重指数（BMI，r=0.81）方面表现优异。与分娩时间相关的生物学特征包括胎儿相关蛋白（如ALPP、AFP、PGF）与免疫蛋白（如PD-L1、CCL28、LIFR）；孕产妇年龄与胶原蛋白COL9A1呈负相关；孕次与内皮型一氧化氮合酶（endothelial NOS）及炎症趋化因子CXCL13相关；体重指数则与瘦素（leptin）及结构蛋白FABP4相关。本研究结果全面呈现了与早产相关的流行病学因素，并明确了影响该疾病的临床协变量的生物学特征。

研究方法
本研究队列纳入了来自孟加拉国马塔布拉、赞比亚卢萨卡、孟加拉国锡尔赫特、巴基斯坦卡拉奇以及坦桑尼亚彭巴的5个生物样本库支持的队列中的孕产妇。本研究已通过斯坦福大学机构审查委员会审批；由母婴健康改善联盟（Alliance for Maternal and Newborn Health Improvement, AMANHI）与全球预防早产及死胎联盟（Global Alliance to Prevent Prematurity and Stillbirth, GAPPS）生物样本库支持的各分娩队列，已分别向所在国家申请并获得伦理豁免。原始队列的所有受试者均已签署书面知情同意书，本研究可沿用该同意文件。本研究未向受试者提供任何报酬或参与奖励。本研究遵循《个体预后或诊断多变量预测模型透明报告规范》（Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, TRIPOD）。本研究分析的血浆样本采集于2014年5月至2018年8月期间。

采样时的孕周通过超声检查确定。所有AMANHI与GAPPS队列的血液样本均由经过培训的采血师或护理人员采集，经离心后分装血清、血浆及血沉棕黄层，于-80℃冷冻保存以备后续分析。所有研究队列的各类样本采集与处理流程均遵循统一操作规范。脂质组与代谢组特征通过非靶向液相色谱-质谱联用技术获取，蛋白质组特征则采用高多重免疫分析法（Olink Proteomics Inc.）获取。