Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.

introductionObesity, defined as a body mass index ≥30 kg/m

methodsThe All of Us dataset was used to identify adults between 18 and 70 ys old with weight measurements 2 y apart between 2008 and 2022. Patients with a history of cancer, bariatric surgery, or pregnancy were excluded. Demographics, vital signs, laboratory results, comorbidities, and survey data (Alcohol Use Disorder Identification Test, Patient-Reported Outcomes Measurement Information System physical and mental health scores) were included as model parameters. Elastic net and XGBoost machine learning models were developed with and without survey data to predict ≥10% total body weight gain within 2 y. The data were split into a training sample (60%) and a testing sample (40%), and parameters were tuned using 10-fold cross-validation. Performance was compared using area under the receiver operating characteristic curves (AUCs).

resultsOur cohort consisted of 34,715 patients (mean [SD] age 50.9 [13.4] y; 45.7% White; 55.3% female). Over a 2-y span, 10.4% of the cohort gained ≥10% total body weight. AUCs were 0.677 [95% DeLong confidence interval 0.665-0.688] for elastic net and 0.706 [0.695-0.717] for XGBoost. Incorporation of survey data did not improve predictability, with AUCs of 0.681 [0.669-0.692] and 0.705 [0.694-0.716], respectively.

conclusionsOur machine learning weight gain prediction models had modest performance that was not improved by survey data. The addition of other All of Us variables, including genomic data, may be informative in future studies.