Testosterone Trajectories and Reference Ranges in a Large Longitudinal Sample of Male Adolescents

Pubertal dynamics plays an important role in physical and psychological development of children and adolescents. We aim to provide reference ranges of plasma testosterone in a large longitudinal sample. Furthermore, we describe a measure of testosterone trajectories during adolescence that can be used in future investigations of development.We carried out longitudinal measurements of plasma testosterone in 2,216 samples obtained from 513 males (9 to 17 years of age) from the Avon Longitudinal Study of Parents and Children. We used integration of a model fitted to each participant's testosterone trajectory to calculate a measure of average exposure to testosterone over adolescence. We pooled these data with corresponding values reported in the literature to provide a reference range of testosterone levels in males between the ages of 6 and 19 years.The average values of total testosterone in the ALSPAC sample range from 0.82 nmol/L (Standard Deviation [SD]: 0.09) at 9 years of age to 16.5 (SD: 2.65) nmol/L at 17 years of age; these values are congruent with other reports in the literature. The average exposure to testosterone is associated with different features of testosterone trajectories such as Peak Testosterone Change, Age at Peak Testosterone Change, and Testosterone at 17 years of age as well as the timing of the growth spurt during puberty.The average exposure to testosterone is a useful measure for future investigations using testosterone trajectories to examine pubertal dynamics

Work/ family demands and cardiometabolic risk and sleep duration in extended care employees: multilevel findings from the Work, family and Health Network

The study investigates the associations of work-family conflict and other work and family conditions with objectively-measured outcomes cardiometabolic risk and sleep duration in a study of employees in nursing homes. Multilevel analyses are used to assess cross-sectional associations between employee and job characteristics and health in analyses of 1,524 employees in 30 extended care facilities in a single company. We examine work and family conditions in relation to two major study health outcomes: 1) a validated, Framingham cardiometabolic risk score based on measured blood pressure, cholesterol, glycosylated hemoglobin (HbA1c), body mass index (BMI), and self-reported tobacco consumption, and 2) wrist actigraphy-based measures of sleep duration. In fully-adjusted multi-level models, Work-To-Family conflict, but not Family-to-Work conflict was positively associated with cardiometabolic risk. Having a lower-level occupation (nursing assistants vs. nurses) was also associated with increased cardiometabolic risk, while being married and having younger children at home was protective. A significant age by Work-To-Family conflict interaction revealed that higher Work-To-Family conflict was more strongly associated with increased cardiometabolic risk in younger employees. With regard to sleep duration, high Family-To-Work Conflict was significantly associated with shorter sleep duration. In addition, working long hours and having younger children at home were both independently associated with shorter sleep duration. High Work-To-Family Conflict was associated with longer sleep duration. These results indicate that different dimensions of work-family conflict (i.e., Work-To-Family Conflict and Family-To-Work Conflict) may both pose threats to cardiometabolic risk and sleep duration for employees. This study contributes to the research on work- family conflict suggesting that Work-To-Family and Family-To-Work conflict are associated with specific outcomes. Translating theory and our findings to preventive interventions entails recognition of the dimensionality of work and family dynamics and the need to target specific work and family conditions

Nicotine exposure results in food consumption differences between adolescent and adult female mice

Background : Individuals with disordered eating have the highest mortality rate of any psychiatric disorder and females make up the majority of the eating disordered population . Tobacco smokers have lower BMIs than do their non-smoking counterparts, and adolescent and adult females report using tobacco to lose or maintain body weight. Multiple biobehavioural factors contribute to this nicotine-body weight relationship, rodent studies suggest that reduced food intake following nicotine exposure may be a primary factor. Objective : To examine the effects of nicotine on body weight changes in response to different food types. Methods : We used an oral nicotine administration paradigm to investigate body weight changes in the presence of standard chow, high sweet and high fat foods in adolescent (N=63) and adult (N=60) female C57BL/6J mice. Mice were exposed to nicotine (200 µg/ml) or water along with one of three food types for 7 days. Results : Adult mice weighed more but ate less food than did adolescents (p < 0.05). Mice exposed to high fat food weighed the most, but ate the least (p < 0.05). While there were no main effects of nicotine on body weight in either age group, nicotine-exposed adults consumed less food than did water-exposed adults (p < 0.05), this effect was not seen in adolescents. Among the nicotine-exposed mice, adolescents consumed more food than did adults (p < 0.05). Conclusions : These findings suggest, in females, the appetite suppression qualities of nicotine differ based on age, with nicotine exposure actually increasing food consumption in adolescents. Nicotine's effects on food intake do not result in body weight changes in either age group

Using Cognitive Modeling to Study Behavior Moderators: Pre-Task Appraisal and Anxiety

this paper do not necessarily reflect the position or the policies of the U.S. Government, and no official endorsement should be inferre

Sleep duration and the cortisol awakening response in dementia caregivers utilizing adult day services

Sleep complaints are common among caregivers and are associated with detriments in mental and physical health. Cortisol, a biomarker of the stress process, may link sleep with subsequent health changes in caregivers. The current study examines whether sleep duration is directly associated with the cortisol awakening response (CAR), or if it is moderated by Adult Day Services (ADS) use, an intervention found previously to influence daily CAR by reducing stressor exposure. Methods Associations were examined in caregivers (N=158) of individuals with dementia (IWD) on days when IWDs attended ADS and days when IWDs did not attend ADS. Data were gathered over 8 consecutive days. Caregivers were primarily female (87.3%) with a mean age of 61.59. A multi-level growth curve model tested the association of an interaction of today\u27s ADS use and last night\u27s sleep duration with today\u27s CAR as the outcome. Results The interaction between ADS use and within-person sleep duration was significant such that when an individual sleeps longer than their average but does not use ADS, they have a smaller or blunted CAR. On the other hand when an individual sleeps longer than their average and uses ADS, they have a higher but nonsignificant CAR. Sleeping shorter than usual was associated with a dynamic rise regardless of ADS use. Conclusions Findings indicate that ADS use moderates the association between sleep duration and CAR such that longer than average sleep is associated with blunted, dysregulated cortisol patterns only on non-ADS days

Modeling the range of performance on the serial subtraction task

We present a model of serial subtraction, a task where subjects repeatedly subtract a 1- or 2-digit number from a 4-digit number. The model performs 4 min. blocks of these subtractions like subjects do. The current model replicates part of the pace and % correct for group data. Because performance on this task varies widely between subjects, we explore what it means to match the data distribution. We find that our model represents individual subjects better than group means. We can start to model a distribution of performance and illustrate some of what this approach will entail