Stress and Sleep Duration in Immune and Neuroendocrine Patterning. An Analytical Triangulation in ELSA
Hamilton, O. S.; Steptoe, A.
Show abstract
Background. Proinflammatory and neuroendocrine mediators are implicated in disease aetiopathogenesis. Stress increases concentrations of immune-neuroendocrine biomarkers through a complex network of brain-body signalling pathways. Suboptimal sleep further modulates these processes by altering major effector systems that sensitise the brain to stress. Given the ubiquitous, impactful nature of material deprivation, we tested for a synergistic association of financial stress and suboptimal sleep with these molecular processes. Methods. With data drawn from the English Longitudinal Study of Ageing (ELSA), associations were tested on 4,940 participants ([~]66{+/-}9.4 years) across four-years (2008-2012). Through analytical triangulation, we tested whether financial stress (>60% insufficient resources) and suboptimal sleep ([≤]5/[≥]9 hours) were independently and interactively associated with immune-neuroendocrine profiles, derived from a latent profile analysis (LPA) of C-reactive protein, fibrinogen, white blood cell counts, hair cortisol, and insulin-like growth factor-1. Results. A three-class LPA model offered the greatest parsimony. After adjustment for genetic predisposition, sociodemographics, lifestyle, and health, financial stress was associated with short-sleep cross-sectionally (RRR=1.45; 95%CI=1.18-1.79; p<0.001) and longitudinally (RRR=1.31; 95%CI=1.02-1.68; p=0.035), and it increased risk of belonging to the high-risk biomarker profile by 42% (95%CI=1.12-1.80; p=0.004). Suboptimal sleep was not related to future risk of high-risk profile membership, nor did it moderate financial stress-biomarker profile associations. Discussion. Results advance psychoneuroimmunological knowledge by revealing how immune-neuroendocrine markers cluster in older cohorts and respond to financial stress over time. Financial stress associations with short-sleep are supported. The null role of suboptimal sleep, as exposure and mediator, in profile membership, provides valuable insight into the dynamic role of sleep in immune-neuroendocrine processes.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Immune-Neuroendocrine Patterning and Response to Stress. A latent profile analysis in the English Longitudinal Study of Ageing 93%
- Biopsychosocial Correlates of Resting and Stress-Reactive Salivary GDF15: Preliminary Findings 93%
- Poor sleep quality, insomnia, and short sleep duration before infection predict long-term symptoms after COVID-19 92%
Similar papers in this journal
- Associations Between Daily Outdoor Temperature and Subjective Real-time Ratings of Emotional States and Sleep in Mood Disorder Subtypes 90%
- Unstable sleep and rest-activity rhythms in adolescents at-risk for bipolar disorder: links to mood symptoms and the effect of sleep stabilization 89%
- Age-related changes in physiology in individuals with bipolar disorder 89%
Similar papers in this journal
- The effect of sleep continuity disruption on multimodal emotion processing and regulation: a laboratory-based, randomized, controlled experiment in good sleepers 94%
- The effects of daylight saving time clock changes on accelerometer-measured sleep duration in the UK Biobank 93%
- Insomnia symptoms and risk of bloodstream infections: prospective data from the prospective population-based HUNT Study, Norway 93%
Similar papers in this journal
- Hormone-sleep interactions predict cerebellar connectivity and behavior in aging females 93%
- Effects of acute psychological stress on blood cell-free mitochondrial DNA (cf-mtDNA): A crossover experimental study 91%
- Longitudinal Associations between Hair Cortisol, PTSD Symptoms, and Sleep Disturbances in a Sample of Firefighters with Duty-related Trauma Exposure 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.