ArticleJAMA network open2026
Maternal History of Weight Loss and Prospective Gestational Weight Gain.
Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Higher gestational weight gain is associated with adverse outcomes, including macrosomia. Weight cycling (cycles of weight loss followed by weight regain) is associated with increased risk of future weight gain, but little is known about how history of weight cycling is associated with pregnancy weight gain. Objective: To assess whether an association exists between weight cycling history and gestational weight gain in a US pregnancy cohort. Design, Setting, and Participants: This cohort study used data collected from participants recruited from prenatal clinics in New Hampshire for The New Hampshire Birth Cohort Study, an ongoing prospective cohort study initiated in 2009. Eligibility for the study included being between 18 and 45 years of age and experiencing a singleton pregnancy. The present analysis took place between April 2024 and September 2025 and reflects participants enrolled prior to June 2021. Exposure: History of weight cycling, defined as the number of times an individual self-reported losing 20 pounds (9.1 kg) or more in adulthood (excluding after pregnancy). Main Outcomes and Measures: The primary outcome was total gestational weight gain. Results: Of 2628 enrolled participants, 1188 with complete exposure, outcome, and covariate data were included in the analytic sample. Among them, the mean (SD) age was 31.5 (4.7) years, and the mean (SD) prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) was 25.6 (5.6). Approximately half the participants (n = 561 [47.2%]) reported any history of weight cycling, and the mean (SD) gestational weight gain was 16.9 (6.5) kg. In linear models adjusting for highest lifetime BMI, age, educational attainment, and smoking history, individuals reporting 1, 2, and 3 or more weight cycles gained a mean of 1.7 (95% CI, 0.8-2.6) kg, 3.2 (95% CI, 2.0-4.5) kg, and 6.2 (95% CI, 4.7-7.7) kg more, respectively, over their entire pregnancy than individuals who reported no history of weight cycling (P < .001 for trend). Conclusions and Relevance: This cohort study of pregnant individuals found that a history of weight cycling was positively associated with gestational weight gain, independent of BMI. These findings may have important clinical implications for identifying individuals at high risk of excessive gestational weight gain. Additional research is needed to assess potential implications for maternal and child health.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.