Evidence map›Paper›PMID 39825224›Full record

ArticleBMC pregnancy and childbirth2025

Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population.

Anna Booman, Kimberly K Vesco, Rachel Springer, Dang Dinh, Shuling Liu, Kristin Lyon-Scott, Miguel Marino, Jean O'Malley, Amy Palma, Teresa Schmidt and 4 more

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Anna BoomanOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA. booman@ohsu.edu.
Kimberly K VescoKaiser Permanente Center for Health Research, Portland, OR, USA.
Rachel SpringerDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA.
Dang DinhDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA.
Shuling LiuDepartment of Family Medicine, Oregon Health & Science University, Portland, OR, USA.
Kristin Lyon-ScottOCHIN, Inc., Portland, OR, USA.
Miguel MarinoOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
Jean O'MalleyOCHIN, Inc., Portland, OR, USA.
Amy PalmaOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
Teresa SchmidtOCHIN, Inc., Portland, OR, USA.
Jonathan M SnowdenOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
Kalera StrattonOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
Sarah-Truclinh TranOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.
Janne Boone-HeinonenOregon Health & Science University-Portland State University School of Public Health, Portland, OR, USA.

Funding

PROMISE: PReventing Obesity through healthy Maternal gestational weight gain In the Safety nEtR01DK118484 · NIDDK · OREGON HEALTH & SCIENCE UNIVERSITY · PI BOONE-HEINONEN, JANNE, SNOWDEN, JONATHAN M · 2020 to 2024
$3.1M
NIDDK NIH HHS R01 DK118484
6 · The paper itself

Abstract

backgroundUnderstanding the risks and effects of gestational weight gain (GWG) is a prominent area of perinatal research but approaches for quantifying GWG are evolving and remain underdeveloped, especially in clinical settings for underserved demographic subgroups. To fill this gap, we demonstrated and compared six GWG metrics across pre-pregnancy BMI classifications: total GWG, trimester-specific linear rate of GWG, adherence to total and trimester-specific recommendations, area under the curve, and GWG for gestational age z-scores.

methodsWe used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups.

resultsTotal GWG was lower in individuals with higher pre-pregnancy BMI; yet more temporally resolved analyses revealed differences in trimester-specific weight change. Differences included common first trimester weight loss in people with pre-pregnancy class II or III obesity and substantial first trimester weight gain in people with pre-pregnancy underweight, with the greatest pre-pregnancy BMI-related variation in GWG occurring in the second trimester. These differences are reflected to varying degrees in the AUC and GWG z-score metrics.

conclusionsOur findings inform development of GWG guidelines within BMI categories, especially in obesity subclasses and underweight, and selection, refinement, and application of GWG metrics in future research. GWG metrics differ to varying degrees across BMI categories in a population consisting of several underserved subgroups: pregnant people of color, with larger body sizes, or with lower incomes. Stronger evidence on safe levels of first trimester weight loss and obesity class-specific recommendations is needed.

Indexed as

Electronic Health RecordsGestational Weight GainAdultBody Mass IndexFemaleGestational AgeHumansObesityPregnancyPregnancy ComplicationsPregnancy TrimestersSafety-net ProvidersThinnessYoung AdultBiasElectronic health recordsGestational weight gainPregnancyUnderstudied populations

Identifiers

PMID39825224
PMCPMC11740392

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.