Evidence map›Paper›PMID 41680500›Full record

ArticleCommunications medicine2026

Protein networks are influenced by maternal BMI and differentiate preterm birth types.

Paola A Lopez Zapana, Chelsea A DeBolt, Luka Karginov, Valerie Riis, Liqhwa Ncube, Andrea G Edlow, Michal A Elovitz, Douglas A Lauffenburger

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Paola A Lopez Zapana *Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA.
Chelsea A DeBolt *Icahn School of Medicine at Mount Sinai, Obstetrics, Gynecology & Reproductive Science, New York, NY, USA.ORCID http://orcid.org/0000-0002-9532-4109
Luka KarginovMassachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA.
Valerie RiisIcahn School of Medicine at Mount Sinai, Obstetrics, Gynecology & Reproductive Science, New York, NY, USA.
Liqhwa NcubeIcahn School of Medicine at Mount Sinai, Obstetrics, Gynecology & Reproductive Science, New York, NY, USA.
Andrea G EdlowMassachusetts General Hospital (MGH), Vincent Center for Reproductive Biology, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2915-5949
Michal A ElovitzNuttall Women's Health, New York, NY, USA.
Douglas A LauffenburgerMassachusetts Institute of Technology, Department of Biological Engineering, Cambridge, MA, USA. lauffen@mit.edu.ORCID http://orcid.org/0000-0002-0050-989X

Funding

Research Project 2 The pregnancy AdaptOMEU19AI167899 · NIAID · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI DOUGLAS A LAUFFENBURGER · 2022 to 2026
$14.7M
NIAID NIH HHS U19 AI167899U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER) U19AI167899
6 · The paper itself

Abstract

backgroundPreterm birth remains a leading cause of neonatal morbidity and mortality. It is classified as spontaneous, characterized by the unexpected onset of labor, or medically indicated, resulting from obstetric intervention due to pregnancy complications. The mechanisms underlying each subtype are incompletely understood, and obesity further modulates preterm birth risk through unclear biological pathways. This study aims to identify second trimester maternal plasma proteomic signatures distinguishing spontaneous and medically-indicated preterm birth and to determine how body mass index modifies these profiles.

methodsIn 100 pregnant individuals (30 spontaneous preterm birth, 30 medically-indicated preterm birth, 40 uncomplicated term deliveries), second trimester plasma was profiled using 7 K SomaScan v4.1 aptamer-based proteomic assay. Multivariate modeling and pathway analyses identified protein signatures distinguishing preterm birth subtypes, and computational network modeling with in silico perturbation analysis defined protein intermediates linking body mass index and preterm birth subtypes.

resultsHere we show distinct proteomic signatures among spontaneous preterm birth, medically-indicated preterm birth, and term deliveries. Supervised modeling achieves clear separation and identifies key discriminatory proteins including SIGLEC6, DHFR, UBASH3A, and PHB2. Early pregnancy body mass index substantially contributes to proteomic variance and modifies preterm birth associated expression of inflammatory (PROK2, IL36A), vascular (F11R) and oxidative stress (GLX1) proteins. Network perturbation identifies FABP4, CRP, UBE2G2, and LRP8 as critical intermediates linking body mass index and preterm birth.

conclusionsDistinct proteomic profiles characterize spontaneous and medically-indicated preterm birth. Body mass index emerges as a key modifier of these molecular signatures, offering insight into the obesity-associated pathways underlying preterm birth.

Identifiers

PMID41680500
PMCPMC12902018

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.