{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:54:04Z","timestamp":1785423244416,"version":"3.56.0"},"reference-count":50,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Preterm birth (PTB) remains a major clinical and public health challenge worldwide. Gestational diabetes mellitus (GDM), complicating 14%\u201325% of pregnancies, elevates PTB risk via metabolic dysregulation. Although various early-pregnancy exposures are associated with PTB, their differential contributions in GDM-affected and unaffected pregnancies remain inadequately explored. This study aimed to identify distinct first-trimester modifiable risk factors for PTB in these two populations and to quantify the accurately preventable burden using an advanced estimation approach that accounts for interdependencies among risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In this prospective cohort study conducted in Central China (2019\u20132024), 2,825 pregnant women were stratified into GDM (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009554) and non-GDM (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092,271) groups. Assessed early-pregnancy exposures included advanced maternal age, smoking, depressive symptoms, physical inactivity, insufficient sleep, and pre-pregnancy overweight or obesity. Multivariable logistic regression and principal component analysis-adjusted population attributable fractions (PAFs) were employed to estimate the preventable PTB proportion, adjusting for overlap among risk factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Six modifiable risk factors were identified for GDM pregnancies, with a combined PAF of 73.7% and an adjusted combined PAF of 50.5%. For non-GDM pregnancies, four factors yielded a combined PAF of 44.2% and an adjusted combined PAF of 21.5%. Shared significant factors included smoking (PAF 27.4%, adjusted PAF 11.7% in GDM vs. PAF 22.7%, adjusted PAF 9.1% in non-GDM), depressive symptoms (22.7%, 11.6% vs. 15.0%, 6.0%), and overweight or obesity (18.1%, 7.7% vs. 11.9%, 4.8%). Risk factors specific to GDM pregnancies were advanced maternal age (11.6%, 4.9%), physical inactivity (19.3%, 8.2%), and insufficient sleep (14.9%, 6.4%). Low education was uniquely associated with PTB in non-GDM pregnancies (3.7%, 1.5%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study delineates distinct early-pregnancy modifiable risk profiles for PTB in GDM and non-GDM populations, supporting the development of targeted preventive strategies. Subsequent studies are warranted to validate these findings across diverse populations and to assess the effectiveness of tailored first-trimester interventions based on this risk stratification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1725116","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:45:36Z","timestamp":1771915536000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Distinct modifiable risk factors and preventable burdens of preterm birth: a risk-stratified analysis of pregnancies with and without gestational diabetes mellitus"],"prefix":"10.3389","volume":"14","author":[{"given":"Yuhang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University","place":["Changsha, Hunan, 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