Cognitive and brain reserve refer to individual differences that allow some people to better withstand brain pathology than others. Although early life stress has been recognized as a risk factor for ...low reserve in late life, no research yet has studied this across midlife.
To examine the associations of life stress with brain and cognitive reserve in midlife.
We included 1,232 middle-aged women who participated in the ORACLE Study between 2002-2006). Life stress was calculated as the shared variance of four cumulative stress domains, created from items measured between pregnancy and 10 years after childbirth. Brain reserve was defined as healthy-appearing brain volume measured with MRI; cognitive reserve as better cognitive functioning than expected based on age, education, and brain MRI measures, using structural equation modelling.
More life stress was associated with lower brain (standardized adjusted difference: -0.18 95% CI 0.25,-0.12) and cognitive reserve (-0.19 -0.28,-0.10). Although, effect sizes were typically smaller, cumulative stress domains were also associated with brain reserve (life events: -0.10 -0.16,-0.04; contextual stress: -0.13 -0.19,-0.07; parenting-related stress: -0.13-0.19,-0.07; interpersonal stress: -0.10 -0.16,-0.04) and cognitive reserve (life events: -0.18 -0.25,-0.11; contextual stress: -0.15 -0.10,-0.02; parenting-related stress: -0.10 -0.18,-0.03; interpersonal stress not significant).
Women who experience more life stress in midlife were found to have lower reserve. Effects were primarily driven by shared variance across cumulative stress domains, suggesting that focusing on single domains may underestimate effects. The effect of life stress on lower reserve may make women with stress more prone to neurodegenerative disease later in life than women without stress.
Exposure to specific stressors has been found to associate with higher adiposity in adulthood. However, the potential overlapping effects of stress domains have been overlooked, as well as the role ...of parenting‐related stressors that mothers are widely exposed to in mid‐adulthood. Therefore, we assessed the association of overlapping effects of stress domains, including parenting‐related stress, with subsequent adiposity in mothers. In 3957 mothers from the population‐based Generation R Study, life stress was assessed during the first 10 years of child‐rearing and measured as a reflective latent variable of stress domains. Structural equation modelling was used to assess the association of life stress and its individual domains with body mass index (BMI) and waist circumference after 14 years of follow‐up. Greater life stress over the course of 10 years was associated with a higher BMI (standardized adjusted difference: 0.57 kg/m2 95% CI: 0.41–0.72) and a larger waist circumference (1.15 cm 0.72–1.57). When examining individual stress domains, we found that life events was independently associated with a higher BMI (0.16 kg/m2) and contextual stress was independently associated with a higher BMI (0.43 kg/m2) and larger waist circumference (1.04 cm). Parenting stress and interpersonal stress were not independently associated with adiposity at follow‐up. The overlap of multiple domains of stress in mothers is associated with a higher risk of adiposity. This effect was stronger than for individual life stress domains, reiterating the need to consider overlapping effects of different life stress domains.
Harmonizing and aggregating data across studies enable pooled analyses that support external validation and enhance replicability and generalizability. However, the multidimensional nature of sleep ...poses challenges for data harmonization and aggregation. Here we describe and implement our process for harmonizing self-reported sleep data.
We established a multi-phase framework to harmonize self-reported sleep data: (1) compile items; (2) group items into domains; (3) harmonize items; and (4) evaluate harmonizability. We applied this process to produce a pooled multi-cohort sample of five United States cohorts plus a separate yet fully harmonized sample from Rotterdam, Netherlands. Sleep and sociodemographic data are described and compared to demonstrate the utility of harmonization and aggregation.
We collected 190 unique self-reported sleep items and grouped them into 15 conceptual domains. Using these domains as guiderails, we developed 14 harmonized items measuring aspects of Satisfaction, Alertness/Sleepiness, Timing, Efficiency, Duration, Insomnia, and Sleep Apnea. External raters determined that 13 of these 14 items had moderate-to-high harmonizability. Alertness/Sleepiness items had lower harmonizability, while continuous, quantitative items (e.g., timing, total sleep time, efficiency) had higher harmonizability. Descriptive statistics identified features that are more consistent (e.g., wake-up time, duration) and more heterogeneous (e.g., time in bed, bedtime) across samples.
Our process can guide researchers and cohort stewards towards effective sleep harmonization and provides a foundation for further methodological development in this expanding field. Broader national and international initiatives promoting common data elements across cohorts are needed to enhance future harmonization and aggregation efforts.
