Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population
Abstract Background Understanding 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 de...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-01-01
|
| Series: | BMC Pregnancy and Childbirth |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12884-025-07139-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832594357222375424 |
|---|---|
| author | Anna Booman Kimberly K. Vesco Rachel Springer Dang Dinh Shuling Liu Kristin Lyon-Scott Miguel Marino Jean O’Malley Amy Palma Teresa Schmidt Jonathan M. Snowden Kalera Stratton Sarah-Truclinh Tran Janne Boone-Heinonen |
| author_facet | Anna Booman Kimberly K. Vesco Rachel Springer Dang Dinh Shuling Liu Kristin Lyon-Scott Miguel Marino Jean O’Malley Amy Palma Teresa Schmidt Jonathan M. Snowden Kalera Stratton Sarah-Truclinh Tran Janne Boone-Heinonen |
| author_sort | Anna Booman |
| collection | DOAJ |
| description | Abstract Background Understanding 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. Methods We used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups. Results Total 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. Conclusions Our 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. |
| format | Article |
| id | doaj-art-ad5723de6a7440c89f94634ad3dbc057 |
| institution | Kabale University |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-ad5723de6a7440c89f94634ad3dbc0572025-01-19T12:42:41ZengBMCBMC Pregnancy and Childbirth1471-23932025-01-0125111410.1186/s12884-025-07139-5Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net populationAnna Booman0Kimberly K. Vesco1Rachel Springer2Dang Dinh3Shuling Liu4Kristin Lyon-Scott5Miguel Marino6Jean O’Malley7Amy Palma8Teresa Schmidt9Jonathan M. Snowden10Kalera Stratton11Sarah-Truclinh Tran12Janne Boone-Heinonen13Oregon Health & Science University-Portland State University School of Public HealthKaiser Permanente Center for Health ResearchDepartment of Family Medicine, Oregon Health & Science UniversityDepartment of Family Medicine, Oregon Health & Science UniversityDepartment of Family Medicine, Oregon Health & Science UniversityOCHIN, Inc.Oregon Health & Science University-Portland State University School of Public HealthOCHIN, Inc.Oregon Health & Science University-Portland State University School of Public HealthOCHIN, Inc.Oregon Health & Science University-Portland State University School of Public HealthOregon Health & Science University-Portland State University School of Public HealthOregon Health & Science University-Portland State University School of Public HealthOregon Health & Science University-Portland State University School of Public HealthAbstract Background Understanding 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. Methods We used clinical data on 44,801 pregnant people from community-based health care organizations with extensive longitudinal measures and substantial representation of understudied subgroups. Results Total 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. Conclusions Our 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.https://doi.org/10.1186/s12884-025-07139-5Gestational weight gainPregnancyUnderstudied populationsBiasElectronic health records |
| spellingShingle | Anna Booman Kimberly K. Vesco Rachel Springer Dang Dinh Shuling Liu Kristin Lyon-Scott Miguel Marino Jean O’Malley Amy Palma Teresa Schmidt Jonathan M. Snowden Kalera Stratton Sarah-Truclinh Tran Janne Boone-Heinonen Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population BMC Pregnancy and Childbirth Gestational weight gain Pregnancy Understudied populations Bias Electronic health records |
| title | Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population |
| title_full | Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population |
| title_fullStr | Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population |
| title_full_unstemmed | Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population |
| title_short | Methods for modeling gestational weight gain: empirical application using electronic health record data from a safety net population |
| title_sort | methods for modeling gestational weight gain empirical application using electronic health record data from a safety net population |
| topic | Gestational weight gain Pregnancy Understudied populations Bias Electronic health records |
| url | https://doi.org/10.1186/s12884-025-07139-5 |
| work_keys_str_mv | AT annabooman methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT kimberlykvesco methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT rachelspringer methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT dangdinh methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT shulingliu methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT kristinlyonscott methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT miguelmarino methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT jeanomalley methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT amypalma methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT teresaschmidt methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT jonathanmsnowden methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT kalerastratton methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT sarahtruclinhtran methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation AT jannebooneheinonen methodsformodelinggestationalweightgainempiricalapplicationusingelectronichealthrecorddatafromasafetynetpopulation |