Lifestyle, habits, age, sex and socioeconomic status strongest predictors for BMI levels

Variables associated with lifestyle, habits, age, sex and socioeconomic status are the strongest predictors for BMI levels, according to researchers from the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. They also reported that self-reported anxiety and depression through questionnaires also have strong predictive value, stronger than self-reported diagnosis or pharmaceutical treatment of these disorders. The findings were published in the paper, ‘The BAriatic surgery SUbstitution and nutrition (BASUN) population: a data-driven exploration of predictors for obesity’, published in BMC Endocrine Disorders.

For the BASUN study, the researchers used a machine learning technique to examine the relative importance of more than 100 clinical variables as predictors for BMI, specifically, factors strongly linked to severe obesity. BASUN is an ongoing prospective cohort study following 971 individuals accepted for treatment of obesity, medical or surgical (Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG), in clinical practice in Region Västra Götaland, Sweden for ten years. An important aim of BASUN is to compare effects and complications of surgical and medical treatment of obesity, but the overall goal is to improve the care of individuals with obesity as well as reduce adverse outcomes of treatment.


The questionnaires covered three main areas: gastrointestinal symptoms and eating habits, physical activity and quality of life, and psychological health. The variables were divided manually into 15 clinically similar domains (socioeconomic status, age/sex, lifestyle and habits, metabolic disease, cardiovascular disease, potential anxiety/depression, biomarkers for cardiovascular disease and diabetes, other biomarkers, medication for cardiovascular disease or diabetes, psychiatric disease, gastrointestinal disease, endocrine conditions, musculoskeletal disease, previous surgery and other conditions). The predictive values of the different domains were assessed, as well as the predictive value of each individual variable.


Of the 971 study individuals, 382 had medical treatment, 388 had RYGB and 201 had SG. The investigators found that there were differences with regard to marital status and education, as well as nicotine usage. The groups were similar with regard to previous diabetes but there were slight differences in other reported metabolic disease (hyperlipidaemia, hypertension and sleep apnoea), as well as levels of HbA1c, glucose and low-density lipoprotein.


Information on previous psychiatric illness was self-reported in questions on known diagnosis and pharmaceutical treatment as well as specific questionnaires. Self-reported previous depression or anxiety and treatment for these disorders differed between the groups as well as the results from the questionnaires focusing on depression and anxiety. There was also a difference in reported usage of antipsychotics between the groups. Factors that might influence the choice of bariatric surgery, such as haemoglobin levels, known deficiencies of vitamins and minerals, eating habits or previous malignancies were not different between the treatment groups - however there was a difference in known gastrointestinal-, pulmonary- and cardiovascular disease.


The researchers reported that the strongest predictive domains observed were socioeconomic status, age/sex, other biomarkers (haemoglobin, calcium, TSH, T4, liver transaminases, creatinine), lifestyle and habits, biomarkers for cardiovascular disease and diabetes (HbA1c, glucose, TG, HDL, LDL, urinary albumin), potential anxiety and depression, metabolic disease, medication for cardiovascular disease or diabetes and other conditions. The six remaining domains that had little or no predictive value.


“Comparing different types of machine learning methods on the BASUN data and dividing the population by class of obesity might be of value. Prospective studies using machine learning techniques including individuals that are overweight and not yet obese, might also add valuable information on predictive factors for obesity. Planned analyses of follow-up data from BASUN will be used to find predictive variables for successful obesity treatment,” they concluded. “We propose that future studies should examine the value of wider characterisation of patients treated for obesity.”


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