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.
Highlights
The development of obesity is most likely due to a combination of biological and environmental factors some of which might still be unidentified
The treatment options have been described in detail [7] and included medical treatment with very low energy diet (VLED) for 12–20 weeks followed by an energy restricted diet up to 12 months or surgical treatment (Roux-en-Y gastric bypass (RYGB) or sleeve Gastrectomy (SG))
In this study of the baseline characteristics of the BASUN population we found that variables associated with socioeconomic status, age, sex, lifestyle and habits are the strongest predictors for body mass index (BMI) levels
Summary
The development of obesity is most likely due to a combination of biological and environmental factors some of which might still be unidentified. Obesity is a complex but treatable disease with major individual and societal consequences. The Global Burden of Disease Study has recently established that obesity is a major global health challenge, demanding population-wide but country-specific initiatives to mitigate the burden of a wide range of diseases [2]. As formulated by WHO, the fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended against a background environmental and societal factors [1]. It is quite possible that different mosaics of biological and environmental factors in different individuals, contribute to the development of the disease
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