Classic theories of stress and health are largely based on assumptions regarding how different psychosocial stressors influence biological processes that, in turn, affect human health and behavior. Although theoretically rich, this work has yielded little consensus and led to numerous conceptual, measurement, and reproducibility issues. Social Safety Theory aims to address these issues by using the primary goal and regulatory logic of the human brain and immune system as the basis for specifying the social-environmental situations to which these systems should respond most strongly to maximize reproductive success and survival. This analysis gave rise to the integrated, multi-level formulation described herein, which transforms thinking about stress biology and provides a biologically based, evolutionary account for how and why experiences of social safety and social threat are strongly related to health, well-being, aging, and longevity. In doing so, the theory advances a testable framework for investigating the biopsychosocial roots of health disparities as well as how health-relevant biopsychosocial processes crystalize over time and how perceptions of the social environment interact with childhood microbial environment, birth cohort, culture, air pollution, genetics, sleep, diet, personality, and self-harm to affect health. The theory also highlights several interventions for reducing social threat and promoting resilience.

Purpose of Review: Although females are at relatively greater risk for a variety of disorders, including depression, the biological mechanisms underlying this striking health disparity remain unclear. To address this issue, we highlight sex differences in stress susceptibility as a key mechanism potentially driving this effect and describe the interacting inflammatory, hormonal, epigenomic, and social-environmental mechanisms involved. Recent Findings: Using the Social Signal Transduction Theory of Depression as a theoretical framework, women’s elevated risk for depression may stem from a tight link between life stress, inflammation, and depression in women. Further, research finds hormonal contraceptive use alters cortisol and inflammatory reactivity to acute stress in ways that may increase depression risk in females. Finally, beyond established epigenetic mechanisms, mothers may transfer risk for depression to their female offspring through stressful family environments, which influence stress generation and stress-related gene expression. Summary: Together, these findings provide initial, biologically plausible clues that may help explain the relatively greater risk for depression in females vs. males. Looking forward, much more research is needed to address the longstanding underrepresentation of females in biomedical research on the biology of stress and depression.

Despite great interest in how dynamic fluctuations in psychological states such as mood, social safety, energy, present-focused attention, and burnout impact stress, well-being, and health, most studies examining these constructs use retrospective assessments with relatively long time-lags. Here, we discuss how ecological momentary assessments (EMAs) address methodological issues associated with retrospective reports to help reveal dynamic associations between psychological states at small timescales that are often missed in stress and health research. In addition to helping researchers characterize daily and within-day fluctuations and temporal dynamics between different health-relevant processes, EMAs can elucidate mechanisms through which interventions reduce stress and enhance well-being. EMAs can also be used to identify changes that precede critical health events, which can in turn be used to deliver ecological momentary interventions, or just-in-time interventions, to help prevent such events from occurring. To enable this work, we provide examples of scales and single-item questions used in EMA studies, recommend study designs and statistical approaches that capitalize on EMA data, and discuss limitations of EMA methods. In doing so, we aim to demonstrate how, when used carefully, EMA methods are well poised to greatly advance our understanding of how intrapersonal dynamics affect stress levels, well-being, and human health.

Despite breakthroughs in the scientific understanding of how stress affects psychological and biological systems in ways that, over time, lead to disease and disability, non-communicable disorders ...

Psychoneuroimmunology and immunopsychiatry are quickly approaching a critical point where the clinical translatability of their evidence base will be tested. To maximize chances for translational success, we believe researchers must adopt causal inference techniques that augment the causal relevance of estimates given theorized causal structures. To illustrate the utility of incorporating causal inference perspectives into psychoneuroimmunology, we applied directed acyclic graphs and a combination of empirical and simulated data to demonstrate the consequences of controlling for adiposity when testing the association between inflammation and depression under the plausible causal structure of increases in adipose tissue leading to greater inflammation that in turn promotes depression. Effect size estimates were pulled from a dataset combining the Midlife in the United States 2 (MIDUS-2) and MIDUS Refresher datasets. Data were extracted and used to simulate data reflecting an adiposity → inflammation → depression causal structure. Next, a Monte Carlo simulation study with 1,000 iterations and three sample size scenarios (Ns = 100, 250, and 500) was conducted testing whether controlling for adiposity when estimating the relation between inflammation and depression influenced the precision of this estimate. Across all simulation scenarios, controlling for adiposity reduced precision of the inflammation → depression estimate, suggesting that researchers primarily interested in quantifying inflammation → depression associations should not control for adiposity. This work thus underscores the importance of incorporating causal inference approaches into psychoneuroimmunological research.