{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:10:19Z","timestamp":1788952219630,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"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. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Static gestational weight gain (GWG) guidelines may be inadequate for women of advanced maternal age (AMA), a population with distinct physiological characteristics including diminished metabolic flexibility and elevated baseline risks. This study seeks to delineate dynamic GWG trajectories in this cohort to facilitate more precise risk stratification during pregnancy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective cohort study analyzed routinely collected antenatal weight measurements from 3,818 AMA women. Distinct GWG trajectory patterns across the second and third trimesters were identified using latent class mixed models, from which velocity dynamics were derived as first derivatives and assessed for associations with key perinatal outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Five GWG trajectory patterns were identified: low-gain with terminal acceleration (LGT-Accel, 10.6%), steady-gain with mid-gestation deceleration (SGM-Decel, 29.7%, reference), modest-gain with terminal surge (MGT-Surge, 21.6%), high-gain with trajectory attenuation (HGT-Atten, 26.6%), and maximal-gain with steepest slope (MGS-Slope, 11.6%). All followed a consistent four-phase kinetic profile but differed in cumulative gain and velocity transitions. Compared to the SGM-Decel group, the LGT-Accel and MGT-Surge groups increased risks of low birthweight and small for gestational age, whereas the HGT-Atten and MGS-Slope groups were associated with macrosomia and large for gestational age, with the MGS-Slope group also linked to low birthweight risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study reveals that GWG in AMA women follows heterogeneous dynamic trajectories that are not captured by static, one-size-fits-all guidelines. Moving beyond total gain to monitor phase-specific velocity dynamics offers a promising paradigm for enhancing real-time risk assessment and enabling personalized antenatal care in this vulnerable population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1782650","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:16:38Z","timestamp":1788948998000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling dynamic velocity-based gestational weight gain trajectories in advanced maternal age: association with perinatal outcomes"],"prefix":"10.3389","volume":"13","author":[{"given":"Guobin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Statistical Science, School of Mathematics, Sun Yat-Sen University","place":["Guangzhou, 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