Nutrients, Vol. 17, Pages 2651: Predicting Metabolic and Cardiovascular Healthy from Nutritional Patterns and Psychological State Among Overweight and Obese Young Adults: A Neural Network Approach
Nutrients doi: 10.3390/nu17162651
Authors:
Geovanny Genaro Reivan Ortiz
Laura Maraver-Capdevila
Roser Granero
Background and objectives: Overweight and obesity are global public health problems, as they increase the risk of chronic diseases, reduce quality of life, and generate a significant economic and healthcare burden. This study evaluates the capacity of nutritional patterns and psychological status to predict the presence of cardiometabolic risk among overweight and obese young adults, from a neural network approach. Method: The study included N = 188 overweight or obese students, who provided measures on their dietary intake, physical and psychological state, and sociodemographic profile. Neural networks were used to predict their metabolic status, classified into two categories based on anthropometric, biochemical, and cardiometabolic risk factors: metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO). Results: The predictive models demonstrated differences in specificity and sensitivity capacity depending on the criteria employed for the classification of MUO/MHO and gender. Among the female subsample, MUO was predicted by poor diet (low consumption of mineral and vitamins, and high consumption of fats and sodium) and high levels of depression and stress, while among the male subsample high body mass index (BMI), depression, and anxiety were the key factors. Protective factors associated to MHO were lower BMI, lower psychopathology distress and more balanced diets. Predictive models based on the HOMA-IR criterion yielded very high specificity and low sensibility (high capacity to identify MHO but low accuracy to identify MUO). The models based on the IDF criterion achieved excellent discriminative capacity for men (specificity and sensitivity around 92.5%), while the model for women obtained excellent sensitivity and low specificity. Conclusions: The results provide empirical support for personalized prevention and treatment programs, accounting for individual differences with the aim of promoting healthy habits among young adults, especially during university education.
Background and objectives: Overweight and obesity are global public health problems, as they increase the risk of chronic diseases, reduce quality of life, and generate a significant economic and healthcare burden. This study evaluates the capacity of nutritional patterns and psychological status to predict the presence of cardiometabolic risk among overweight and obese young adults, from a neural network approach. Method: The study included N = 188 overweight or obese students, who provided measures on their dietary intake, physical and psychological state, and sociodemographic profile. Neural networks were used to predict their metabolic status, classified into two categories based on anthropometric, biochemical, and cardiometabolic risk factors: metabolically unhealthy obesity (MUO) versus metabolically healthy obesity (MHO). Results: The predictive models demonstrated differences in specificity and sensitivity capacity depending on the criteria employed for the classification of MUO/MHO and gender. Among the female subsample, MUO was predicted by poor diet (low consumption of mineral and vitamins, and high consumption of fats and sodium) and high levels of depression and stress, while among the male subsample high body mass index (BMI), depression, and anxiety were the key factors. Protective factors associated to MHO were lower BMI, lower psychopathology distress and more balanced diets. Predictive models based on the HOMA-IR criterion yielded very high specificity and low sensibility (high capacity to identify MHO but low accuracy to identify MUO). The models based on the IDF criterion achieved excellent discriminative capacity for men (specificity and sensitivity around 92.5%), while the model for women obtained excellent sensitivity and low specificity. Conclusions: The results provide empirical support for personalized prevention and treatment programs, accounting for individual differences with the aim of promoting healthy habits among young adults, especially during university education. Read More