# Feasibility of Multiplex Cytokine Profiling in Preterm Labor: Towards Biomarker Discovery

## TL;DR

This study explores using urine samples to detect cytokine patterns that could predict preterm labor, offering a non-invasive alternative to current methods.

## Contribution

The study introduces a non-invasive urine-based cytokine profiling method for predicting preterm labor and identifies key cytokines involved.

## Key findings

- Macrophage-derived cytokines like MIP-1α, MIP-1β, IL-15, and IL-22 are central in distinguishing preterm from term deliveries.
- Urinary IL-5 and IL-31 levels correlate positively with pregnancy duration, while IL-1β and TNFα show inverse associations.
- The IL-1 pathway is highlighted as important in preterm labor pathophysiology.

## Abstract

Preterm birth, defined as giving birth before 37 weeks of pregnancy, affects about 10% of pregnancies around the world and is a major cause of illness and death in newborns. However, it remains difficult to identify which women are most at risk. Many current methods require taking samples of blood or fluid from around the baby, which are invasive and not ideal for routine use. In this study, we explored a new non-invasive method based on analyzing small urine samples from pregnant women. We measured inflammation-related molecules called cytokines, which play a key role in triggering labor. Using advanced statistical methods, we found specific patterns of cytokines in urine that were linked to how long the pregnancy lasted, and we identified a group of cytokines that may be especially important in early labor. These findings open the door to future studies and suggest that urine tests could eventually help monitor immune activity during pregnancy and support efforts to prevent preterm birth.

Preterm delivery affects approximately 10% of pregnancies worldwide and remains a major clinical challenge due to the lack of reliable early predictive tools. Existing strategies are often invasive, relying on blood or amniotic fluid samples and requiring complex processing. In this study, we describe a novel non-invasive approach based on the multiplex detection of inflammatory cytokines in small urine volumes from pregnant women. To account for clinical and temporal variability, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to adjust for gestational age at sampling and obstetric factors. Correlation network analyses revealed cytokine interactions that distinguished preterm from term deliveries, with macrophage-derived cytokines—MIP-1α, MIP-1β, IL-15, and IL-22—emerging as central nodes. These findings highlight the involvement of the IL-1 pathway in the pathophysiology of preterm labor. Furthermore, urinary IL-5 and IL-31 levels correlated positively with pregnancy duration, whereas IL-1β and IL-1Ra in urine and TNFα in amniotic fluid showed inverse associations. Altogether, this non-invasive methodology provides insight into immune dynamics during pregnancy and offers a foundation for future studies focused on biomarker discovery and mechanistic understanding of preterm birth.