Cohort profile: Colombian Cohort for the Early Prediction of Preterm Birth (COLPRET): early prediction of preterm birth based on personal medical history, clinical characteristics, vaginal microbiome, biophysical characteristics of the cervix and maternal serum biochemical markers.

ABSTRACT

PURPOSE: Preterm birth (PTB) is a public health issue. Interventions to prolong the length of gestation have not achieved the expected results, as the selection of population at risk of PTB is still a challenge. Cervical length (CL) is the most accepted biomarker, however in the best scenario the CL identifies half of the patients. It is unlikely that a single measure identifies all pregnant women who will deliver before 37 weeks of gestation, considering the multiple pathways theory. We planned this cohort to study the link between the vaginal microbiome, the proteome, metabolome candidates, characteristics of the cervix and the PTB. PARTICIPANTS: Pregnant women in the first trimester of a singleton pregnancy are invited to participate in the study. We are collecting biological samples, including vaginal fluid and blood from every patient, also performing ultrasound measurement that includes Consistency Cervical Index (CCI) and CL. The main outcome is the delivery of a neonate before 37 weeks of gestation. FINDINGS TO DATE We have recruited 244 pregnant women. They all have measurements of the CL and CCI. A vaginal sample for microbiome analysis has been collected in the 244 patients. Most of them agreed to blood collection, 216 (89%). By August 2021, 100 participants had already delivered. Eleven participants (11 %) had a spontaneous PTB. FUTURE PLANS A reference value chart for the first trimester CCI will be created. We will gather information regarding the feasibility, reproducibility and limitations of CCI. Proteomic and metabolomic analyses will be done to identify the best candidates, and we will validate their use as predictors. Finally, we plan to integrate clinical data, ultrasound measurements and biological profiles into an algorithm to obtain a multidimensional biomarker to identify the individual risk for PTB.