ArticleBMC endocrine disorders2021
The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity.
Article in BMC endocrine disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03152617 (BASUN - Risks for Malnutrition, Metabolic Bone Disease and Impaired Oral Health After Obesity Treatment - Interaction With Gastrointestinal Microbiota, Psychiatric Morbidity and Socioeconomics), which is not on this map. Cited by 2 papers.
What it found
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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.
BASUN - Risks for Malnutrition, Metabolic Bone Disease and Impaired Oral Health After Obesity Treatment - Interaction With Gastrointestinal Microbiota, Psychiatric Morbidity and Socioeconomics
Who cites it
2 citing papers in PubMed, 22 citations in OpenAlex.
- Behavioral Factors Associated With ≥ 15% Versus < 5% Weight Loss in Adults With Severe Obesity: A Retrospective Clinical Study.Obesity science & practice · 2026Article
- Predicting pregnancy at the first year following metabolic-bariatric surgery: development and validation of machine learning models.Surgical endoscopy · 2025Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe development of obesity is most likely due to a combination of biological and environmental factors some of which might still be unidentified. We used a machine learning technique to examine the relative importance of more than 100 clinical variables as predictors for BMI.
methodsBASUN is a prospective non-randomized cohort study of 971 individuals that received medical or surgical treatment (treatment choice was based on patient's preferences and clinical criteria, not randomization) for obesity in the Västra Götaland county in Sweden between 2015 and 2017 with planned follow-up for 10 years. This study includes demographic data, BMI, blood tests, and questionnaires before obesity treatment that cover three main areas: gastrointestinal symptoms and eating habits, physical activity and quality of life, and psychological health. We used random forest, with conditional variable importance, to study the relative importance of roughly 100 predictors of BMI, covering 15 domains. We quantified the predictive value of each individual predictor, as well as each domain.
resultsThe participants received medical (n = 382) or surgical treatment for obesity (Roux-en-Y gastric bypass, n = 388; sleeve gastrectomy, n = 201). There were minor differences between these groups before treatment with regard to anthropometrics, laboratory measures and results from questionnaires. The 10 individual variables with the strongest predictive value, in order of decreasing strength, were country of birth, marital status, sex, calcium levels, age, levels of TSH and HbA1c, AUDIT score, BE tendencies according to QEWPR, and TG levels. The strongest domains predicting BMI were: Socioeconomic status, Demographics, Biomarkers (notably TSH), Lifestyle/habits, Biomarkers for cardiovascular disease and diabetes, and Potential anxiety and depression.
conclusionsLifestyle, habits, age, sex and socioeconomic status are some of the strongest predictors for BMI levels. Potential anxiety and / or depression and other characteristics captured using questionnaires have strong predictive value. These results confirm previously suggested associations and advocate prospective studies to examine the value of better characterization of patients eligible for obesity treatment, and consequently to evaluate the treatment effects in groups of patients.
trial registrationMarch 03, 2015; NCT03152617 .
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