A risk calculator to predict adult attention-deficit/hyperactivity disorder: generation and external validation in three birth cohorts and one clinical sample.

AIM: Few personalised medicine investigations have been conducted for mental health. We aimed to generate and validate a risk tool that predicts adult attention-deficit/hyperactivity disorder (ADHD). METHODS: Using logistic regression models, we generated a risk tool in a representative population cohort (ALSPAC - UK, 5113 participants, followed from birth to age 17) using childhood clinical and sociodemographic data with internal validation. Predictors included sex, socioeconomic status, single-parent family, ADHD symptoms, comorbid disruptive disorders, childhood maltreatment, ADHD symptoms, depressive symptoms, mother's depression and intelligence quotient. The outcome was defined as a categorical diagnosis of ADHD in young adulthood without requiring age at onset criteria. We also tested Machine Learning approaches for developing the risk models: Random Forest, Stochastic Gradient Boosting and Artificial Neural Network. The risk tool was externally validated in the E-Risk cohort (UK, 2040 participants, birth to age 18), the 1993 Pelotas Birth Cohort (Brazil, 3911 participants, birth to age 18) and the MTA clinical sample (USA, 476 children with ADHD and 241 controls followed for 16 years from a minimum of 8 and a maximum of 26 years old). RESULTS: The overall prevalence of adult ADHD ranged from 8.1 to 12% in the population-based samples, and was 28.6% in the clinical sample. The internal performance of the model in the generating sample was good, with an area under the curve (AUC) for predicting adult ADHD of 0.82 (95% confidence interval (CI) 0.79-0.83). Calibration plots showed good agreement between predicted and observed event frequencies from 0 to 60% probability. In the UK birth cohort test sample, the AUC was 0.75 (95% CI 0.71-0.78). In the Brazilian birth cohort test sample, the AUC was significantly lower -0.57 (95% CI 0.54-0.60). In the clinical trial test sample, the AUC was 0.76 (95% CI 0.73-0.80). The risk model did not predict adult anxiety or major depressive disorder. Machine Learning approaches did not outperform logistic regression models. An open-source and free risk calculator was generated for clinical use and is available online at https://ufrgs.br/prodah/adhd-calculator/. CONCLUSIONS: The risk tool based on childhood characteristics specifically predicts adult ADHD in European and North-American population-based and clinical samples with comparable discrimination to commonly used clinical tools in internal medicine and higher than most previous attempts for mental and neurological disorders. However, its use in middle-income settings requires caution.

No effect of oral or sample temperature on sensory assessment of fat content.

This work examined the possible influences of oral and sample temperature on the perception of fat content of a model food system. Melting or related phenomena may contribute to the greater sensation of fat content and the highly acceptable textural characteristics associated with certain fats. Thirty-one adults assessed the fat content of 0%, 12%, 24%, 36%, and 48% oil-in-water emulsions prepared with a commercial cocoa butter substitute having a melting range of 17-41 degrees C. Samples were evaluated at combinations of sample and mouth temperatures of 20 and 36 degrees C, with oral temperatures manipulated by repeated cold- and warm-water rinses prior to assessments. There were no significant differences amongst these treatments on perceived fat content of the samples, nor were subject characteristics of age or body composition related to judgments of fat content in these stimuli. Although previous studies had shown that degree of fat saturation is associated with enhanced perception of fat content (11), that does not appear to be related to the degree or occurrence of melting in the mouth over the ranges studies here.

Sensory assessment of fat content: effect of emulsion and subject characteristics.

This work examines some of the fundamental stimulus properties contributing to the perception and discrimination of fat content in foods. In an initial experiment, oil-in-water (O/W) emulsions were prepared with 0, 5, 10, ..., 40, 45, and 50% sunflower oil (SUN) and homogenized at pressures of 100 and 300 bar. These were rated for "fat content" and "smoothness" on 9-point category scales. There were significant effects of fat concentration and processing pressure. Higher pressures, associated with a decreased fat particle size/number ratio, generated a slightly enhanced perception of fat content. In a second experiment, O/W emulsions were prepared with 0, 12, 24, 36, and 48% fat at 100 and 300 bar, from two oils differing in fat saturation: SUN and Hycoa 5 (HY5, a highly saturated commercial cocoa butter substitute). The results indicate significant main and interactive effects of fat type and concentration, but no significant effect of processing pressure were seen in this experiment. HY5 emulsions had a substantially greater measured viscosity and were judged higher in fat content than those prepared from SUN, particularly at higher fat levels. Analyses indicate a significant independent contribution of fat concentration to perceived fat content, beyond increased viscosity. There were no consistent main or interactive effects of subject age or body composition on judgments of fat content.