Characterisation of pulmonary function trajectories: results from a Brazilian cohort.

Background: Pulmonary function (PF) trajectories are determined by different exposures throughout the life course. The aim of this study was to investigate characteristics related to PF trajectories from 15 to 22 years in a Brazilian cohort. Methods: A birth cohort study (1993 Pelotas Birth Cohort) was conducted with spirometry at 15, 18 and 22 years. PF trajectories were built based on z-score of forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and their ratio using a group-based trajectory model. Associations with exposures reported from perinatal to 22 years were described. Results: Three trajectories, low (LT), average (AT) and high (HT) were identified in 2917 individuals. Wealthiest individuals belonged to the HT of FEV1 (p=0.023). Lower maternal pregestational body mass index (BMI) (22.4±0.2; p<0.001 and 22.1±0.14; p<0.001) and lower birth weight (3164.8±25.4; p=0.029 and 3132.3±19.4; p=0.005) were related to the LT of FEV1 and FVC. Mother's smoking exposure during pregnancy (37.7%; p=0.002), active smoking at ages 18 and 22 years (20.1% and 25.8%; p<0.001) and family history of asthma (44.8%; p<0.001) were related to the LT of FEV1/FVC. Wheezing, asthma and hospitalisations due to respiratory diseases in childhood were related to the LT of both FEV1 and FEV1/FVC. Higher BMIs were related to the HT of FEV1 and FVC at all ages. Conclusions: PF trajectories were mainly related to income, pregestational BMI, birth weight, hospitalisation due to respiratory diseases in childhood, participant's BMI, report of wheezing, medical diagnosis and family history of asthma, gestational exposure to tobacco and current smoking status in adolescence and young adult age

Could we find any signal of the stratosphere-ionosphere coupling in Antarctica?

An investigation searching for a possible coupling between the lower ionosphere and the middle atmosphere in Antarctica is here performed on the basis of stratospheric vertical temperature profiles and ionospheric absorption data observed at the Antarctic Italian Base of Terra Nova Bay (74.69S, 164.12E) during local summer time. The result obtained by applying a multi-regression analysis and a Superimposed Epoch Analysis (SEA) shows a statistically significant ionosphere-stratosphere interaction. In particular, by selecting stratospheric temperature maxima occurring at different heights as the referring epoch for the SEA approach, the ionospheric absorption is found to show a positive and/or negative trend (several days) around it. The tendency for an increasing/decreasing absorption is obtained for temperature maxima occurring below/above the stratospheric level of about 17-19 km, respectively

A risk calculator to predict adult Attention-deficit/Hyperactivity disorder::Generation and external validation in three birth cohorts and one clinical sample.

AimFew 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).MethodsUsing 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).ResultsThe 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/.ConclusionsThe 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

Low Maternal Capital Predicts Life History Trade-Offs in Daughters: Why Adverse Outcomes Cluster in Individuals

Background: Some individuals appear prone to multiple adverse outcomes, including poor health, school dropout, risky behavior and early reproduction. This clustering remains poorly understood. Drawing on evolutionary life history theory, we hypothesized that maternal investment in early life would predict the developmental trajectory and adult phenotype of female offspring. Specifically, we predicted that daughters receiving low investment would prioritize the life history functions of “reproduction” and “defense” over “growth” and “maintenance,” increasing the risk of several adverse outcomes. // Methods: We investigated 2,091 mother-daughter dyads from a birth cohort in Pelotas, Brazil. We combined data on maternal height, body mass index, income, and education into a composite index of “maternal capital.” Daughter outcomes included reproductive status at 18 years, growth, adult anthropometry, body composition, cardio-metabolic risk, educational attainment, work status, and risky behavior. We tested whether daughters' early reproduction (<18 years) and exposure to low maternal capital were associated with adverse outcomes, and whether this accounted for the clustering of adverse outcomes within individuals. // Results: Daughters reproducing early were shorter, more centrally adipose, had less education and demonstrated more risky behavior compared to those not reproducing. Low maternal capital was associated with greater likelihood of the daughter reproducing early, smoking and having committed violent crime. High maternal capital was positively associated with the daughter's birth weight and adult size, and the likelihood of being in school. Associations of maternal capital with cardio-metabolic risk were inconsistent. Daughters reproducing early comprised 14.8% of the population, but accounted for 18% of obesity; 20% of violent crime, low birth weight and short stature; 32% of current smoking; and 52% of school dropout. Exposure to low maternal capital contributed similarly to the clustering of adverse outcomes among daughters. Outcomes were worst among daughters characterized by both low maternal capital and early reproduction. // Conclusion: Consistent with life history theory, daughters exposed to low maternal capital demonstrate “future discounting” in behavior and physiology, prioritizing early reproduction over growth, education, and health. Trade-offs associated with low maternal capital and early reproduction contribute to clustering of adverse outcomes. Our approach provides new insight into inter-generational cycles of disadvantage