A Deep Learning Neural Network to Classify Obesity Risk in Portuguese Adolescents Based on Physical Fitness Levels and Body Mass Index Percentiles: Insights for National Health Policies.

The increasing prevalence of overweight and obesity among adults is a risk factor for many chronic diseases and death. In addition, obesity among children and adolescents has reached unprecedented levels and studies show that obese children and adolescents are more likely to become obese adults. Therefore, both the prevention and treatment of obesity in adolescents are critical. This study aimed to develop an artificial intelligence (AI) neural network (NNET) model that identifies the risk of obesity in Portuguese adolescents based on their body mass index (BMI) percentiles and levels of physical fitness. Using datasets from the FITescola® project, 654 adolescents aged between 10-19 years old, male: 334 (51%), female: n = 320 (49%), age 13.8 ± 2 years old, were selected to participate in a cross-sectional observational study. Physical fitness variables, age, and sex were used to identify the risk of obesity. The NNET had good accuracy (75%) and performance validation through the Receiver Operating Characteristic using the Area Under the Curve (ROC AUC = 64%) in identifying the risk of obesity in Portuguese adolescents based on the BMI percentiles. Correlations of moderate effect size were perceived for aerobic fitness (AF), upper limbs strength (ULS), and sprint time (ST), showing that some physical fitness variables contributed to the obesity risk of the adolescents. Our NNET presented a good accuracy (75%) and was validated with the K-Folds Cross-Validation (K-Folds CV) with good accuracy (71%) and ROC AUC (66%). According to the NNET, there was an increased risk of obesity linked to low physical fitness in Portuguese teenagers.

HLA-A, -B, -C, -DQA1, -DQB1, -DRB1, -E, -F and -G genotyping of 130 individuals from Terceira Island, Azores, Portugal.

One hundred and thirty unrelated Azorean individuals were randomly selected to study the frequencies of high-resolution HLA alleles and haplotypes in the Azorean (Terceira) population. HLA-A, -B, -Cw, -DRB1, -DQA1 and -DQB1 high-resolution genotyping was performed by polymerase chain reaction using commercial kits. HLA-E, -F and -G alleles, were genotyped by sequence-based typing. All loci were in HWE, showing no locus-level deviations. The genotype data is available in the Allele Frequencies Net Database under the population name "Azores Terceira Island" and the identifier (AFND112579).