Natural language indicators of differential gene regulation in the human immune system

The innate story code

Code biology reveals a great many codes beyond the genetic code as integral to biological functioning. Recent scholars have linked the growing field of code biology to analytical psychology, confirming that the encoded information inherited by the human organism is indeed massive and capable of great sophistication. In this discussion, I will expand on this project by showing how developments in embodied cognition reveal a code that links the world of universal emotional responses to common experiences to the world of embodied visuospatial narratives--i.e., the "archetypes" of analytical psychology. Viewed in this manner, archetypes become spontaneous symbolic narratives that symbolize universal emotional responses to typical human environments. Such symbolic narratives aim toward adaptation, and use a universal code that maps such situations to visuospatial narratives, with the adaptor being the human body itself.

Social Genomic Determinants of Health: Understanding the Molecular Pathways by Which Neighborhood Disadvantage Affects Cancer Outcomes

PURPOSE

Neighborhoods represent complex environments with unique social, cultural, physical, and economic attributes that have major impacts on disparities in health, disease, and survival. Neighborhood disadvantage is associated with shorter breast cancer recurrence-free survival (RFS) independent of individual-level (race, ethnicity, socioeconomic status, insurance, tumor characteristics) and health system-level determinants of health (receipt of guideline-concordant treatment). This persistent disparity in RFS suggests unaccounted mechanisms such as more aggressive tumor biology among women living in disadvantaged neighborhoods compared with advantaged neighborhoods. The objective of this article was to provide a clear framework and biological mechanistic explanation for how neighborhood disadvantage affects cancer survival.

METHODS

Development of a translational epidemiological framework that takes a translational disparities approach to study cancer outcome disparities through the lens of social genomics and social epigenomics.

RESULTS

The social genomic determinants of health, defined as the physiological gene regulatory pathways (ie, neural/endocrine control of gene expression and epigenetic processes) through which contextual factors, particularly one's neighborhood, can affect activity of the cancer genome and the surrounding tumor microenvironment to alter disease progression and treatment outcomes.

CONCLUSION

We propose a novel, multilevel determinants of health model that takes a translational epidemiological approach to evaluate the interplay between political, health system, social, psychosocial, individual, and social genomic determinants of health to understand social disparities in oncologic outcomes. In doing so, we provide a concrete biological pathway through which the effects of social processes and social epidemiology come to affect the basic biology of cancer and ultimately clinical outcomes and survival.

When I am sixty-four… evaluating language markers of well-being in healthy aging narratives

Natural language use is a promising candidate for the development of innovative measures of well-being to complement self-report measures. The type of words individuals use can reveal important psychological processes that underlie well-being across the lifespan. In this preregistered, cross-sectional study, we propose a conceptual model of language markers of well-being and use written narratives about healthy aging (N = 701) and computerized text analysis (LIWC) to empirically validate the model. As hypothesized, we identified a model with three groups of language markers (reflecting affective, evaluative, and social processes). Initial validation with established self-report scales (N = 30 subscales) showed that these language markers reliably predict core components of well-being and underlying processes. Our results support the concurrent validity of the conceptual language model and allude to the added benefits of language-based measures, which are thought to reflect less conscious processes of well-being. Future research is needed to continue validating language markers of well-being across the lifespan in a theoretically informed and contextualized way, which will lay the foundation for inferring people's well-being from their natural language use.

Exploring the Association Between Textual Parameters and Psychological and Cognitive Factors

Background

Textual data analysis has become a popular method for examining complex human behavior in various fields, including psychology, psychiatry, sociology, computer science, data mining, forensic sciences, and communication studies. However, identifying the most relevant textual parameters for analyzing complex behavior is still a challenge.

Goal of Study

This paper aims to explore potential textual parameters that could be useful in analyzing behavior through complex textual data. Furthermore, we have examined the randomly generated text based on different textual parameters.

Methods

To achieve this goal, we conducted a comprehensive review of the literature on textual data analysis and identified several potential topics that could be relevant, such as sentiment analysis, discourse analysis, lexical analysis, and syntactic analysis. We discuss the theoretical background and practical implications of each parameter and provide examples of how they have been used in previous research. Furthermore, we highlight the importance of considering the context in which these parameters are applied and the need for interdisciplinary collaboration to gain a deeper understanding of complex behavior through textual data analysis. Furthermore, we have provided Python code in the Supplementary Materials to facilitate a comprehensive analysis of such behaviors. In addition, to generate the text for analysis, we utilized ChatGPT 3.5 Turbo by requesting it to generate a random text of 1000 words divided into five paragraphs. Afterwards, we applied the provided Python code to analyze the randomly generated text.

Conclusion

Overall, this paper provides a foundation for researchers to identify relevant textual parameters to analyze complex human behavior in their respective fields such as linguistics, sociology, psychiatry, and psychology.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

Background

Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.

Methods

We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide.

Results

Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance.

Conclusion

Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.

Investigating the Relationship between Resident Physician Implicit Bias and Language Use during a Clinical Encounter with Hispanic Patients

Prior research suggests that the implicit biases of physicians are negatively associated with quality of medical care and patient satisfaction among minority patients. However, relatively little is known about how physicians express these subtle forms of bias in patient interactions. This study examined the implicit and explicit anti-Hispanic biases of 53 resident physicians and the relationship between anti-Hispanic bias and language use during outpatient medical appointments with 291 Hispanic patients. Physician implicit bias was positively associated with use of interrogatives and work-related words and negatively associated with the use of prepositions and relativity-related words (e.g., words related to time and the future). These findings contribute to the growing body of evidence suggesting that, in addition to nonverbal and paraverbal behaviors, providers may communicate implicit bias to patients through the words they use during a clinical visit.

Only Words Count; the Rest Is Mere Chattering: A Cross-Disciplinary Approach to the Verbal Expression of Emotional Experience

The analysis of sequences of words and prosody, meter, and rhythm provided in an interview addressing the capacity to identify and describe emotions represents a powerful tool to reveal emotional processing. The ability to express and identify emotions was analyzed by means of the Toronto Structured Interview for Alexithymia (TSIA), and TSIA transcripts were analyzed by Natural Language Processing to shed light on verbal features. The brain correlates of the capacity to translate emotional experience into words were determined through cortical thickness measures. A machine learning methodology proved that individuals with deficits in identifying and describing emotions (n = 7) produced language distortions, frequently used the present tense of auxiliary verbs, and few possessive determiners, as well as scarcely connected the speech, in comparison to individuals without deficits (n = 7). Interestingly, they showed high cortical thickness at left temporal pole and low at isthmus of the right cingulate cortex. Overall, we identified the neuro-linguistic pattern of the expression of emotional experience.

Would the Real Loneliness Please Stand Up? The Validity of Loneliness Scores and the Reliability of Single-Item Scores

Several measures that assess loneliness have been developed for adults. Across three studies, we investigated psychometric features of scores of different versions of the Rasch-Type Loneliness Scale, the University of California Los Angeles Loneliness Scale, and three single-item measures. In Study 1 (N = 697 self-ratings, N = 282 informant-ratings of 160 targets) and Study 2 (N = 1,216 individuals from 608 couples), we investigated convergent validity, self-informant agreement, and nomological nets of the item scores using correlates related to demographic aspects, personality, satisfaction, and network characteristics. In Study 3 (N = 411), we estimated a reliability of rxx>.70 for scores of three single-item measures of loneliness. Overall, scores of all measures and their nomological nets were highly correlated within and across studies, indicating that the scores of the included measures are all reliable and valid. Recommendations for choosing a loneliness measure are discussed.

Flourishing in Healthcare Trainees: Psychological Well-Being and the Conserved Transcriptional Response to Adversity

While much attention has been paid to healthcare provider and trainee burnout, less is known about provider well-being (i.e., flourishing) or about the effects of well-being on immune function. This study examined the demographic and psycho-social correlates of well-being among healthcare trainees (resident physicians and physician assistant (PA) trainees) and evaluated the association of well-being with the "conserved transcriptional response to adversity" (CTRA) characterized by up-regulated expression of pro-inflammatory genes and down-regulated expression of innate antiviral genes. Participants (n = 58) completed self-reported assessments of sleep disturbance, loneliness, depressive symptoms, anxiety, stress, and well-being (flourishing). Blood sample RNA profiles were analyzed by RNA sequencing to assess the CTRA. Slightly over half (n = 32; 55.2%) of healthcare trainees were categorized as flourishing. Flourishing was less prevalent among primary caregivers, and more prevalent among trainees who exercised more frequently and those with fewest days sick. Loneliness (AOR = 0.75; 95% CI = 0.61, 0.91; p = 0.003) and stress (AOR = 0.65; 95% CI = 0.45, 0.94; p = 0.02) were associated with decreased odds of flourishing when controlling for other variables. Flourishing was associated with down-regulated CTRA gene expression, whereas loneliness was associated with up-regulated CTRA gene expression (both p < 0.05). Assessing these relationships in a larger, multi-site study is of critical importance to inform policy, curricula, and interventions to bolster sustainable trainee well-being.

Correlation of natural language assessment results with health-related quality of life in adult glioma patients

OBJECTIVE

Impairments of speech are common in patients with glioma and negatively impact health-related quality of life (HRQoL). The benchmark for clinical assessments is task-based measures, which are not always feasible to administer and may miss essential components of HRQoL. In this study, the authors tested the hypothesis that variations in natural language (NL) correlate with HRQoL in a pattern distinct from task-based measures of language performance.

METHODS

NL use was assessed using audio samples collected unobtrusively from 18 patients with newly diagnosed low- and high-grade glioma. NL measures were calculated using manual segmentation and correlated with Quality of Life in Neurological Disorders (Neuro-QoL) outcomes. Spearman's rank-order correlation was used to determine relationships between Neuro-QoL scores and NL measures.

RESULTS

The distribution of NL measures across the entire patient cohort included a mean ± SD total time speaking of 11.5 ± 2.20 seconds, total number of words of 27.2 ± 4.44, number of function words of 10.9 ± 1.68, number of content words of 16.3 ± 2.91, and speech rate of 2.61 ± 0.20 words/second. Speech rate was negatively correlated with functional domains (rho = -0.62 and p = 0.007 for satisfaction with social roles; rho = -0.74 and p < 0.001 for participation in social roles) but positively correlated with impairment domains (rho = 0.58 and p = 0.009 for fatigue) of Neuro-QoL.

CONCLUSIONS

Assessment of NL at the time of diagnosis may be a useful measure in the context of treatment planning and monitoring outcomes for adult patients with glioma.

The Type I interferon antiviral gene program is impaired by lockdown and preserved by caregiving

Previous research has linked perceived social isolation (loneliness) to reduced antiviral immunity, but the immunologic effects of the objective social isolation imposed by pandemic "shelter in place" (SIP) policies is unknown. We assessed the immunologic impact of SIP by relocating 21 adult male rhesus macaques from 2,000-m² field cage communities of 70 to 132 other macaques to 2 wk of individual housing in indoor shelters. SIP was associated with 30% to 50% reductions in all circulating immune cell populations (lymphocytes, monocytes, and granulocytes), down-regulation of Type I interferon (IFN) antiviral gene expression, and a relative up-regulation of CD16^- classical monocytes. These effects emerged within the first 48 h of SIP, persisted for at least 2 wk, and abated within 4 wk of return to social housing. A subsequent round of SIP in the presence of a novel juvenile macaque showed comparable reductions in circulating immune cell populations but reversal of Type I IFN reductions and classical monocyte increases observed during individual SIP. Analyses of lymph node tissues showed parallel up-regulation of Type I IFN genes and enhanced control of viral gene expression during juvenile-partnered SIP compared to isolated SIP. These results identify a significant adverse effect of SIP social isolation on antiviral immune regulation in both circulating immune cells and lymphoid tissues, and they suggest a potential behavioral strategy for ameliorating gene regulatory impacts (but not immune cell declines) by promoting prosocial engagement during SIP.

Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., "the," "a,"). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.

Insight Into the Hispanic Paradox: The Language Hypothesis

Hispanics have a lower burden of heart disease than would be predicted from their risk factors. Explanations for this phenomenon, the Hispanic paradox, focus on specific characteristics of the culture that affect stress appraisal and accumulation, including social connections. Features of culture evolve in the context of language, which influences the way emotions are appraised and expressed. The Spanish language, a unifying component defining Hispanic cultures, has unique features that may promote emotional expression, expand the emotional concepts implicated in the construction of emotion, and influence the appraisal of stress. Under chronic stress conditions, sustained responses can become maladaptive, leading to disease. Features of the Spanish language allow its speakers a wide range of emotion schemas by virtue of its emotion lexicon, the ability to easily minimize or exaggerate expressions, and ease in considering hypothetical situations with the use of the subjunctive. The hypothesis here proposes that the Spanish language is directly and indirectly (via culture) responsible for mitigating some of the effects of acute stress responses in Hispanics and, therefore, limits stress accumulation and is partly responsible for the Hispanic paradox.

Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing

Introduction: Social isolation and loneliness (SI/L) are growing problems with serious health implications for older adults, especially in light of the COVID-19 pandemic. We examined transcripts from semi-structured interviews with 97 older adults (mean age 83 years) to identify linguistic features of SI/L. Methods: Natural Language Processing (NLP) methods were used to identify relevant interview segments (responses to specific questions), extract the type and number of social contacts and linguistic features such as sentiment, parts-of-speech, and syntactic complexity. We examined: (1) associations of NLP-derived assessments of social relationships and linguistic features with validated self-report assessments of social support and loneliness; and (2) important linguistic features for detecting individuals with higher level of SI/L by using machine learning (ML) models. Results: NLP-derived assessments of social relationships were associated with self-reported assessments of social support and loneliness, though these associations were stronger in women than in men. Usage of first-person plural pronouns was negatively associated with loneliness in women and positively associated with emotional support in men. ML analysis using leave-one-out methodology showed good performance (F1 = 0.73, AUC = 0.75, specificity = 0.76, and sensitivity = 0.69) of the binary classification models in detecting individuals with higher level of SI/L. Comparable performance were also observed when classifying social and emotional support measures. Using ML models, we identified several linguistic features (including use of first-person plural pronouns, sentiment, sentence complexity, and sentence similarity) that most strongly predicted scores on scales for loneliness and social support. Discussion: Linguistic data can provide unique insights into SI/L among older adults beyond scale-based assessments, though there are consistent gender differences. Future research studies that incorporate diverse linguistic features as well as other behavioral data-streams may be better able to capture the complexity of social functioning in older adults and identification of target subpopulations for future interventions. Given the novelty, use of NLP should include prospective consideration of bias, fairness, accountability, and related ethical and social implications.

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

Challenges and Opportunities for Grounding Cognition

According to the grounded perspective, cognition emerges from the interaction of classic cognitive processes with the modalities, the body, and the environment. Rather than being an autonomous impenetrable module, cognition incorporates these other domains intrinsically into its operation. The Situated Action Cycle offers one way of understanding how the modalities, the body, and the environment become integrated to ground cognition. Seven challenges and opportunities are raised for this perspective: (1) How does cognition emerge from the Situated Action Cycle and in turn support it? (2) How can we move beyond simply equating embodiment with action, additionally establishing how embodiment arises in the autonomic, neuroendocrine, immune, cardiovascular, respiratory, digestive, and integumentary systems? (3) How can we better understand the mechanisms underlying multimodal simulation, its functions across the Situated Action Cycle, and its integration with other representational systems? (4) How can we develop and assess theoretical accounts of symbolic processing from the grounded perspective (perhaps using the construct of simulators)? (5) How can we move beyond the simplistic distinction between concrete and abstract concepts, instead addressing how concepts about the external and internal worlds pattern to support the Situated Action Cycle? (6) How do individual differences emerge from different populations of situational memories as the Situated Action Cycle manifests itself differently across individuals? (7) How can constructs from grounded cognition provide insight into the replication and generalization crises, perhaps from a quantum perspective on mechanisms (as exemplified by simulators).

Stress and Health: A Review of Psychobiological Processes

The cumulative science linking stress to negative health outcomes is vast. Stress can affect health directly, through autonomic and neuroendocrine responses, but also indirectly, through changes in health behaviors. In this review, we present a brief overview of (a) why we should be interested in stress in the context of health; (b) the stress response and allostatic load; (c) some of the key biological mechanisms through which stress impacts health, such as by influencing hypothalamic-pituitary-adrenal axis regulation and cortisol dynamics, the autonomic nervous system, and gene expression; and (d) evidence of the clinical relevance of stress, exemplified through the risk of infectious diseases. The studies reviewed in this article confirm that stress has an impact on multiple biological systems. Future work ought to consider further the importance of early-life adversity and continue to explore how different biological systems interact in the context of stress and health processes.

Population-based RNA profiling in Add Health finds social disparities in inflammatory and antiviral gene regulation to emerge by young adulthood

Health in later life varies significantly by individual demographic characteristics such as age, sex, and race/ethnicity, as well as by social factors including socioeconomic status and geographic region. This study examined whether sociodemographic variations in the immune and inflammatory molecular underpinnings of chronic disease might emerge decades earlier in young adulthood. Using data from 1,069 young adults from the National Longitudinal Study of Adolescent to Adult Health (Add Health)-the largest nationally representative and ethnically diverse sample with peripheral blood transcriptome profiles-we analyzed variation in the expression of genes involved in inflammation and type I interferon (IFN) response as a function of individual demographic factors, sociodemographic conditions, and biobehavioral factors (smoking, drinking, and body mass index). Differential gene expression was most pronounced by sex, race/ethnicity, and body mass index (BMI), but transcriptome correlates were identified for every demographic dimension analyzed. Inflammation-related gene expression showed the most pronounced variation as a function of biobehavioral factors (BMI and smoking) whereas type I IFN-related transcripts varied most strongly as a function of individual demographic characteristics (sex and race/ethnicity). Bioinformatic analyses of transcription factor and immune-cell activation based on transcriptome-wide empirical differences identified additional effects of family poverty and geographic region. These results identify pervasive sociodemographic differences in immune-cell gene regulation that emerge by young adulthood and may help explain social disparities in the development of chronic illness and premature mortality at older ages.

Population-based RNA profiling in Add Health finds social disparities in inflammatory and antiviral gene regulation to emerge by young adulthood

Health in later life varies significantly by individual demographic characteristics such as age, sex, and race/ethnicity, as well as by social factors including socioeconomic status and geographic region. This study examined whether sociodemographic variations in the immune and inflammatory molecular underpinnings of chronic disease might emerge decades earlier in young adulthood. Using data from 1,069 young adults from the National Longitudinal Study of Adolescent to Adult Health (Add Health)-the largest nationally representative and ethnically diverse sample with peripheral blood transcriptome profiles-we analyzed variation in the expression of genes involved in inflammation and type I interferon (IFN) response as a function of individual demographic factors, sociodemographic conditions, and biobehavioral factors (smoking, drinking, and body mass index). Differential gene expression was most pronounced by sex, race/ethnicity, and body mass index (BMI), but transcriptome correlates were identified for every demographic dimension analyzed. Inflammation-related gene expression showed the most pronounced variation as a function of biobehavioral factors (BMI and smoking) whereas type I IFN-related transcripts varied most strongly as a function of individual demographic characteristics (sex and race/ethnicity). Bioinformatic analyses of transcription factor and immune-cell activation based on transcriptome-wide empirical differences identified additional effects of family poverty and geographic region. These results identify pervasive sociodemographic differences in immune-cell gene regulation that emerge by young adulthood and may help explain social disparities in the development of chronic illness and premature mortality at older ages.

The Conserved Transcriptional Response to Adversity

Gene expression profiling studies of people exposed to chronic threat have identified a Conserved Transcriptional Response to Adversity (CTRA) in circulating immune cells. This physiological pattern is characterized by up-regulated expression of genes involved in inflammation and down-regulated expression of genes involved in Type I interferon responses. The CTRA is mediated by beta-adrenergic signaling pathways that transduce sympathetic nervous system activity into changes in transcription factor activity and hematopoietic output of myeloid lineage immune cells (monocytes, neutrophils, and dendritic cells). Recent research has begun to identify the CNS processes that regulate peripheral CTRA activity, define its implications for disease, and explore the role of positive psychosocial factors in buffering such effects. The CTRA provides a genomic framework for understanding PNI relationships and connecting macro-level psychosocial processes to the micro-level biology of health and disease.

School-Aged Children's Cognitive Interdependence as a Prospective Link Between Their Depressive Symptoms and Physiological Stress Reactivity

Associations between children's depressive symptoms and physiological stress reactivity have been identified across many investigations. Similarly, a large body of literature explores the cognitive correlates of depressive symptomatology in childhood. To date, few studies conducted with children have integrated these approaches. In the present study, we examine a well-documented correlate of depression in adults; low cognitive interdependence (as measured via pronoun use, or we-ratio), in a child population. We explore the relation of low cognitive interdependence to children's concurrent depressive symptoms as well as their concurrent and later stress reactivity. At Time 1, we assessed school-aged children's (N = 60) depressive symptoms and children's we-ratio from an interview about their school experiences. Two weeks later (Time 2), children provided salivary cortisol samples before and after a stressor task. At Time 3 (1.5 years later), children provided cortisol samples before and after completing a different stressor task. Children's depressive symptoms were concurrently associated with lower we-ratio, which in turn was prospectively, but not concurrently, associated with higher cortisol reactivity, acting as an indirect effect between depression and later reactivity. These findings suggest that low levels of cognitive interdependence may be one mechanism by which children's depressive symptoms forecast heightened reactivity to stress.

More than a feeling: A unified view of stress measurement for population science

Stress can influence health throughout the lifespan, yet there is little agreement about what types and aspects of stress matter most for human health and disease. This is in part because "stress" is not a monolithic concept but rather, an emergent process that involves interactions between individual and environmental factors, historical and current events, allostatic states, and psychological and physiological reactivity. Many of these processes alone have been labeled as "stress." Stress science would be further advanced if researchers adopted a common conceptual model that incorporates epidemiological, affective, and psychophysiological perspectives, with more precise language for describing stress measures. We articulate an integrative working model, highlighting how stressor exposures across the life course influence habitual responding and stress reactivity, and how health behaviors interact with stress. We offer a Stress Typology articulating timescales for stress measurement - acute, event-based, daily, and chronic - and more precise language for dimensions of stress measurement.