Eating Behavior Traits, Weight Loss Attempts, and Vertebral Dimensions Among the General Northern Finnish Population

Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra-a proof-of-concept study

Sex estimation is a key element in the analysis of unknown skeletal remains. The vertebrae display clear sex discrepancy and have proven accurate in conventional morphometric sex estimation. This proof-of-concept study aimed to investigate the possibility to develop a deep learning algorithm for sex estimation even from a single peripheral quantitative computed tomography (pQCT) slice of the fourth lumbar vertebra (L4). The study utilized a total of 117 vertebrae from the Terry Anatomical Collection. There were 58 male and 59 female cadavers, all of the white ethnicity, with the average age at death 49 years and a range of 24 to 77 years. A coronal pQCT scan was taken from the midway of the L4 corpus. Sex estimation was performed in a total of 19 neural network architectures implemented in the AIDeveloper software. Of the explored architectures, a LeNet5-based algorithm reached the highest accuracy of 86.4% in the test set. Sex-specific classification rates were 90.9% among males and 81.8% among females. This preliminary finding advances the field by encouraging and directing future research on artificial intelligence-based methods in sex estimation from individual skeletal traits such as the vertebrae. Combining quickly obtained imaging data with automated deep learning algorithms may establish a valuable pipeline for forensic anthropology and provide aid when combined with traditional methods.

Low eating self-efficacy is associated with unfavorable eating behavior tendencies among individuals with overweight and obesity

Success in long-term weight management depends partly on psychological and behavioral aspects. Understanding the links between psychological factors and eating behavior tendencies is needed to develop more effective weight management methods. This population-based cross-sectional study examined whether eating self-efficacy (ESE) is associated with cognitive restraint (CR), uncontrolled eating (UE), emotional eating (EE), and binge eating (BE). The hypothesis was that individuals with low ESE have more unfavorable eating behavior tendencies than individuals with high ESE. Participants were classified as low ESE and high ESE by the Weight-Related Self-Efficacy questionnaire (WEL) median cut-off point. Eating behavior tendencies were assessed with Three Factor Eating Questionnaire R-18 and Binge Eating Scale, and additionally, by the number of difficulties in weight management. The difficulties were low CR, high UE, high EE, and moderate or severe BE. Five hundred and thirty-two volunteers with overweight and obesity were included in the study. Participants with low ESE had lower CR (p < 0.03) and higher UE, EE, and BE (p < 0.001) than participants with high ESE. Thirty-nine percent of men with low ESE had at least two difficulties in successful weight control while this percentage was only 8% in men with high ESE. In women, the corresponding figures were 56% and 10%. The risk of low ESE was increased by high UE [OR 5.37 (95% CI 1.99-14.51)], high EE [OR 6.05 (95% CI 2.07-17.66)], or moderate or severe BE [OR 12.31 (95% CI 1.52-99.84)] in men, and by low CR [OR 5.19 (95% CI 2.22-12.18)], high UE [OR 7.20 (95% CI 2.41-19.22)], or high EE [OR 23.66 (95% CI 4.79-116.77)] in women. Low ESE was associated with unfavorable eating behavior tendencies and multiple concomitant difficulties in successful weight loss promotion. These eating behavior tendencies should be considered when counseling patients with overweight and obesity.

Baseline anthropometric indices predict change in vertebral size in early adulthood - A 10-year follow-up MRI study

The vertebral cross-sectional area (CSA) has an independent effect on vertebral strength. Recent evidence has shown that vertebral dimensions significantly increase in the third decade of life, and that lifestyle factors such as body size and composition are clearly associated with vertebral CSA. This study aimed to test the hypothesis that general anthropometric traits (stature, total body mass, lean body mass, fat mass, body mass index, waist circumference), each objectively measured at baseline, predict the change in vertebral CSA over the subsequent decade. A representative sample of young Northern Finnish adults was used (n = 371) with repeated magnetic resonance imaging (MRI) scans from ~20 and ~30 years (baseline and follow-up, respectively). Vertebral CSA was measured from the MRI scans with high reliability and low measurement error. The statistical analysis was performed using linear regression models adjusted for sex and exact length of MRI interval. According to the regression models, in descending order of effect size, lean body mass (standardized beta coefficient 0.243 [95% confidence interval 0.065-0.420]), total body mass (0.158 [0.043-0.273]), body mass index (0.125 [0.026-0.224]), waist circumference (0.119 [0.010-0.228]), and fat mass (0.104 [0.004-0.205]) were positively and significantly associated with CSA gain over the follow-up, whereas stature (0.079 [-0.066-0.224]) was not associated with CSA change. The results of this study suggest that anthropometric indices may be used for estimating subsequent change in vertebral size. In particular, greater lean body mass seems to be beneficial for vertebral size and thus potentially also for vertebral strength. Future studies should aim to replicate these associations in a dataset with longitudinal anthropometric trajectories and identify the potential common factors that influence both anthropometric traits and vertebral CSA gain.