ArticleInternational journal of obesity (2005)2025
U.S. weight trends: a longitudinal analysis of an NIH-partnered dataset.
Article in International journal of obesity (2005), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.The Journal of surgical research · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundObesity is a major public health challenge in the U.S. Existing datasets utilized for calculating obesity prevalence, such as the National Health and Nutrition Examination Survey (NHANES) and Behavioral Risk Factor Surveillance System (BRFSS), have limitations. Our objective was to analyze weight trends in the U.S. using a nationally representative dataset that incorporates longitudinal electronic health record data.
methodsUsing the National Institutes of Health All of Us Research Program (AoU) dataset, we identified patients aged 18-70 years old who had at least two height and weight measurements within a 5-year period from 2008 to 2021. Baseline and most recent BMI values were used to calculate total body weight (%TBW) changes. %TBW change predictors were determined using multivariable linear regression.
resultsWe included 30,862 patients (mean age 48.9 [ ± 12.6] years; 60.5% female). At the 5-year follow-up, the prevalences of obesity and severe obesity were 37.4% and 20.7%, respectively. The frequency of patients with normal weight or overweight BMI who gained ≥5% TBW at follow-up was 37.8% and 33.1%, respectively. Nearly 24% of the cohort lost ≥ 5% TBW, and 6.5% with severe obesity lost weight to achieve a BMI < 30 kg/m
conclusionsThis evaluation of an NIH-partnered dataset suggests that patients are continuing to gain weight in the U.S. AoU represents a unique tool for obesity prediction, prevention, and treatment given its longitudinal nature and unique behavioral and genetic data.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.