The team’s central tool was genomic structural equation modeling, a statistical framework that allows researchers to work with latent factors, meaning unobserved variables that summarize the genetic overlap among groups of related conditions. Instead of asking whether, say, rheumatoid arthritis is genetically correlated with depression in isolation, the investigators first organized the immune-mediated diseases into latent factors such as an overall autoimmune liability, and organized psychiatric disorders into latent factors such as an internalizing dimension that captures shared genetic risk across depression, anxiety, and related conditions. This approach mirrors how clinical reality actually presents: patients rarely carry a single diagnosis, and genetic risk similarly clusters across diagnostic boundaries. By modeling these clusters, the study could ask a more powerful question than pairwise correlations allow.

That question was whether the genetic liability for specific immune markers, circulating in measurable quantities in the blood, statistically mediates the relationship between the immune disease factors and the psychiatric disorder factors. Mediation, in this context, means that part of the genetic correlation between two clusters of illness flows through a third variable. The fourteen markers examined included some of the most intensively studied molecules in psychoneuroimmunology, among them C-reactive protein, universally measured in clinical settings as a marker of systemic inflammation, and interleukin-6, a signaling molecule of the immune system that stimulates the liver to produce C-reactive protein in the first place. Other markers spanned additional branches of immune signaling, giving the analysis coverage of multiple biological pathways rather than a single inflammatory axis.

The headline finding is a striking correction to a widely repeated narrative. C-reactive protein has been described extensively in the literature as psychiatrically relevant, with elevated levels repeatedly linked to depression and other conditions in large cohort studies. But when the researchers accounted for the overlapping genetic signal with interleukin-6, the associations between psychiatric traits and C-reactive protein were highly attenuated, essentially shrinking toward zero. In other words, the psychiatric relevance long attributed to C-reactive protein appears to be better explained by its biological precursor. Because interleukin-6 sits upstream, driving the production of C-reactive protein, this reframing matters enormously for how researchers interpret inflammation measurements in psychiatry. A blood test result that looks like a C-reactive protein story may in fact be an interleukin-6 story wearing a different label.

Across the full set of analyses, the genetic liability of six immune markers significantly mediated eleven distinct psychiatric-immune disease genetic relationships. The magnitude of some of these mediated effects was remarkable. Interleukin-6 alone mediated roughly sixty percent of the genetic link between the latent internalizing factor and Crohn’s disease, an inflammatory bowel condition. That means a majority of the shared genetic architecture between a cluster of mood and anxiety related disorders and a chronic gut inflammation disease appears to travel through variation in circulating levels of a single immune signaling molecule. For a field that has often spoken in generalities about inflammation and mental health, this is the kind of quantitative specificity that can redirect research programs.

Equally informative was the pattern of the mediation effects. The identified markers did not behave as narrow, one-to-one bridges between a single immune disease and a single psychiatric diagnosis. Instead, many of the significant markers showed mediating effects across multiple disorder clusters, and many individual immune-psychiatric relationships were mediated by more than one marker. This suggests a network-like architecture in which a small set of circulating immune molecules serves as a shared conduit connecting diverse forms of physical and mental illness. At the same time, the study’s diagnostic relevance comes from what it excluded: only a subset of the fourteen examined markers emerged as significant mediators. The immune system produces hundreds of measurable proteins, and this analysis indicates that the psychiatric-immune connection is not diffuse noise across all of them but concentrated in a specific, identifiable handful.

The technical machinery behind these conclusions deserves attention, because genetic mediation studies are easy to get wrong. Genomic structural equation modeling combines genome-wide association study summary statistics, which estimate the genetic contribution to each trait from hundreds of thousands of genetic variants across large populations, into models that can test whether correlations between traits are consistent with an underlying causal pathway. Because the method works with summary statistics rather than individual patient data, it can integrate enormous samples from separate consortia studying immune diseases, psychiatric disorders, and blood biomarkers. The mediation tests then ask whether the genetic correlation between, for example, the autoimmune factor and the internalizing factor diminishes once the genetic variance shared with interleukin-6 levels is statistically accounted for. A substantial reduction indicates that the marker carries a meaningful share of the relationship.

What makes this mechanistic framing biologically plausible is the established physiology of the molecules involved. Interleukin-6 is a cytokine released by immune cells during inflammation, and it crosses into and signals within the central nervous system, where it can influence neurotransmitter metabolism, neural circuit function, and behavior. C-reactive protein, produced by the liver under interleukin-6 stimulation, is generally viewed as a downstream readout of inflammatory activity rather than an active driver. The study’s statistical results align neatly with this biology: the upstream signaling molecule, not the downstream readout, carries the genetic relationship to psychiatric liability. This convergence between statistical mediation and known molecular pathways strengthens the case that the findings reflect genuine biology rather than statistical artifact.

The clinical implications are considerable, though the authors’ findings are genetic and mediational, not proof of causation in individual patients. If a substantial fraction of the genetic overlap between immune diseases and psychiatric disorders flows through a small set of immune markers, then those markers become prime candidates for intervention studies, biomarker development, and patient stratification. A patient with Crohn’s disease and elevated interleukin-6 might represent a distinct subgroup whose psychiatric risk is mechanistically tied to their inflammatory disease, potentially warranting closer monitoring or treatments that target the shared pathway. Conversely, the finding that C-reactive protein is not itself a significant mediator cautions against continuing to treat it as the default inflammatory biomarker in psychiatric research, a practice the authors’ results suggest may have been tracking the wrong molecule.

More broadly, the study exemplifies a shift in how genetics is being used to dissect the architecture of comorbidity. Rather than cataloging correlations and stopping there, methods like genomic structural equation modeling allow researchers to propose and test specific mechanistic hypotheses about how genetic liabilities travel between organ systems. The immune system and the brain have long been studied by separate communities with separate journals, separate conferences, and separate vocabularies. Findings like these, which pinpoint interleukin-6 and five other markers as the molecular bridges linking eleven immune diseases to thirteen psychiatric disorders, provide a common quantitative language for both. The work does not close the book on the immune-psychiatric connection, but it does something arguably more valuable: it tells researchers exactly which pages to read next, and which molecules to watch when the story of body and mind continues to unfold.

Subject of Research: Genetic mediation of relationships between immune-mediated diseases and psychiatric disorders by circulating immune markers

Article Title: Immune markers mediate genetic relationships between immune diseases and psychiatric disorders

Article References: Breunig, S., Kelly, K. M., Schaffer, L. S., Lawrence, J. M., Lee, Y. H., Karlson, E. W., & Grotzinger, A. D. (2026). Immune markers mediate genetic relationships between immune diseases and psychiatric disorders. PLOS Genetics, 22(9), e1012328. https://doi.org/10.1371/journal.pgen.1012328

Image Credits: AI Generated

DOI: 10.1371/journal.pgen.1012328

Keywords: genomic structural equation modeling, interleukin-6, C-reactive protein, immune-mediated diseases, psychiatric disorders, genetic correlation, mediation analysis, Crohn's disease, internalizing disorders, inflammation, GWAS, psychoneuroimmunology

The study, led by Jamie L. Flannery and Jessica R. Lunsford-Avery, enrolled eighty-five adolescents between the ages of eleven and seventeen, with roughly half female, drawn from the community rather than from sleep clinics. Each participant wore a single-channel EEG device for seven consecutive nights while sleeping at home. Unlike conventional polysomnography, the gold-standard sleep test that requires an overnight stay in a laboratory wired with electrodes across the scalp, a single-channel device records brain activity from one electrode location and relies on automated algorithms to stage sleep. The researchers paired this physiological data with physical health measurements, including body mass index and blood pressure, subjective sleep questionnaires, adolescent-reported behavioral health measures, and parent-reported histories of psychiatric diagnoses.

The technical appeal of the approach lies in what it can see that questionnaires cannot. Adolescents are notoriously poor at estimating their own sleep, and parents often cannot observe what happens after lights go out. Single-channel EEG, by contrast, captures the architecture of sleep: how long teenagers spend in rapid eye movement (REM) sleep, the stage associated with emotional processing and memory consolidation; how much stage 3 non-REM sleep, or N3, they obtain, the deep slow-wave sleep linked to physical restoration; how efficiently they sleep once asleep; and how long it takes them to fall asleep. The device also allowed the team to compute the overnight decline in delta power, the slowing of brain waves across the night that is considered a marker of sleep pressure dissipation.

The physical health results were striking. The odds of being overweight or obese decreased by about 2.72 percent for every additional minute of REM sleep, and dropped by a full 100 percent per one-percentage-point increase in the proportion of time spent in REM. In other words, adolescents whose sleep contained more REM tended to weigh less relative to their height. Blood pressure told a different story: the odds of hypertension decreased by 4.78 percent for each additional minute spent in N3, the deepest stage of sleep. These associations align with a growing body of evidence that REM sleep plays a role in metabolic regulation, while slow-wave sleep is tied to cardiovascular health, and they suggest that wearable EEG can detect these relationships outside the artificial environment of a sleep laboratory.

The behavioral health findings were equally detailed. In multivariate analyses, reduced N3 sleep was associated with parent-reported ADHD symptoms, hinting that the deepest, most restorative stage of sleep may be diminished in youth showing attention problems. Meanwhile, shorter total sleep time, lower sleep efficiency, and longer sleep onset latency were each linked to higher adolescent-reported ADHD symptoms and conduct problems. When the researchers looked at formal diagnoses, the odds of having an ADHD diagnosis rose by 1.92 percent for every additional minute it took to fall asleep and were dramatically higher, by 89.4 percent, for each 0.01-unit slowing in the overnight decline of delta power, but fell by 7.78 percent for each one-percentage-point increase in sleep efficiency. A sluggish dissipation of sleep pressure across the night, detectable only through EEG, emerged as a particularly intriguing correlate of ADHD.

Internalizing disorders, which include anxiety and depression, showed their own distinct sleep signature. The odds of an internalizing diagnosis increased by 12.4 percent for each percentage point of higher sleep efficiency and by 3.18 percent for each additional minute of wake after sleep onset, but decreased by 9.62 percent for every extra minute of REM sleep. That seemingly paradoxical combination, more fragmented sleep with higher measured efficiency and less REM, illustrates why the researchers argue that physiological sleep data adds information beyond what teenagers or their parents can report. Two adolescents may describe their sleep identically on a questionnaire while their brains are doing very different things across the night.

Adolescence is a developmental window in which sleep is uniquely vulnerable. Biological shifts push circadian rhythms later, making it harder for teens to fall asleep at a clock-appropriate hour, while early school start times, homework loads, and evening screen use compress the opportunity for sleep. Researchers have described this convergence as a perfect storm. At the same time, sleep disturbances during these years heighten the risk of obesity, hypertension, and emerging psychiatric illness, conditions whose trajectories often harden into adulthood. Despite this, sleep physiology is rarely assessed in pediatric care, where screening typically relies on brief conversations or, at best, wrist-worn activity trackers that estimate sleep from movement rather than measuring brain activity directly.

This is the gap the Duke team hopes wearable EEG can close. Because the devices are inexpensive, comfortable, and usable without technician supervision, they can collect multiple consecutive nights of data in the environment where sleep actually happens, an approach known as ecologically valid assessment. Multi-night home monitoring also captures night-to-night variability that a single laboratory night cannot, improving the reliability of sleep estimates. Earlier work by the same group demonstrated that adolescents find the devices feasible and acceptable to wear, a critical prerequisite for any tool intended for routine pediatric use. The new study extends that feasibility work by showing that the data these devices produce correlates meaningfully with real health outcomes in a general community sample, not just in clinical populations.

The authors are careful about interpretation. These are cross-sectional associations in a modestly sized sample, so the data cannot establish whether altered sleep causes obesity, hypertension, or psychiatric conditions, whether poor health disrupts sleep, or whether shared underlying mechanisms drive both. Some findings, such as the link between higher sleep efficiency and internalizing diagnoses, will require replication and deeper investigation before they can guide clinical decisions. The researchers also note that single-channel EEG, while validated against polysomnography for sleep staging, provides a coarser view of brain activity than full laboratory recordings. Still, the pattern of results is consistent with mechanistic literature linking REM sleep to metabolic and emotional regulation and slow-wave sleep to cardiovascular recovery, lending biological plausibility to the observed associations.

The practical vision emerging from this research is one of precision prevention. If a pediatrician could review a week of home EEG data during a routine visit and see that a twelve-year-old has chronically short REM sleep, prolonged sleep onset, and an abnormally slow decline in delta power, that child might be flagged for sleep-focused intervention years before obesity, hypertension, or a psychiatric diagnosis takes hold. Sleep interventions, from behavioral therapy to adjustments in school schedules and evening routines, are comparatively low-risk and well understood. By capturing physiological features invisible to self-report, wearable sleep EEG could complement routine care, identify youth who would benefit from targeted sleep treatment, and support developmentally informed approaches to protecting adolescent health. What was once confined to a hospital sleep lab for a single artificial night may soon become a quiet, nightly conversation between a teenager’s brain and their doctor.

Subject of Research: Wearable single-channel sleep EEG correlates of physical and behavioral health in adolescents

Article Title: Leveraging convenient wearable technology to assess adolescent sleep: physical and behavioral health correlates of single-channel sleep electroencephalogram in a community sample of youth

Article References: Flannery, J. L., Engelhard, M. M., Kansagra, S., Kollins, S. H., Krystal, A., & Lunsford-Avery, J. R. (2026). Leveraging convenient wearable technology to assess adolescent sleep: physical and behavioral health correlates of single-channel sleep electroencephalogram in a community sample of youth. Journal of Clinical Sleep Medicine, 22(1), Article 113. https://doi.org/10.1007/s44470-026-00118-3

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00118-3

Keywords: adolescent sleep, wearable EEG, REM sleep, N3 sleep, obesity, hypertension, ADHD, internalizing disorders, sleep efficiency, pediatric health, sleep medicine, home sleep monitoring

The concept at the center of the framework is known as stress generation. Unlike stressors that occur independently of a person’s actions, stress-generating events are at least partly shaped by an individual’s behaviour, relationships or decisions. Examples might include escalating an argument, withdrawing from social contact, failing to meet an obligation because of low motivation, or repeatedly seeking reassurance in ways that strain relationships. The researchers emphasize that this process is not deliberate and should not be interpreted as blaming people for their suffering. Instead, stress generation describes a dynamic interaction between symptoms and the social environment, in which psychological difficulties can alter behaviour and those behavioural changes can produce new sources of stress.

The proposed model begins with changes in affective functioning, the systems involved in emotional experience and regulation. Internalizing disorders are commonly associated with heightened negative affect, reduced positive affect, emotional reactivity and difficulty returning to an emotional baseline after distress. A person may become more sensitive to rejection, more easily overwhelmed by ordinary setbacks or less able to experience reward from social and daily activities. These affective alterations can influence how situations are interpreted and how quickly a person responds to them. A minor disagreement, for example, may feel unusually threatening, while a neutral message from a friend may be experienced as evidence of disapproval. Over time, such emotional shifts can shape patterns of action that affect relationships, work and other areas of life.

The next stage involves everyday behaviour. When people are experiencing persistent sadness, fear, irritability or emotional exhaustion, they may avoid demanding situations, reduce communication, postpone responsibilities or respond more intensely during interpersonal conflict. These behaviours can provide short-term relief. Avoiding a difficult conversation may temporarily reduce anxiety, and withdrawing from social contact may protect someone from immediate embarrassment or perceived rejection. However, the same strategies can create longer-term consequences, including missed deadlines, financial complications, social isolation or resentment from family members and colleagues. In this way, behaviour that is understandable in the moment can unintentionally increase the probability of future stress.

The model gives particular importance to cognitive vulnerability, which can determine whether an emotional or behavioural reaction remains contained or develops into a larger chain of events. Cognitive vulnerability includes persistent patterns such as negative interpretations, rumination, hopeless expectations and beliefs that stressful outcomes are inevitable. Rumination can keep attention fixed on a conflict long after it has ended, while threat-focused thinking can encourage defensive or avoidant responses. When these cognitive processes interact with strong negative emotion, a person may repeatedly revisit the same event, interpret ambiguous actions in the most damaging way and respond as though a feared outcome has already occurred. That response may then provoke real interpersonal difficulties, providing apparent confirmation of the original belief.

This sequence can create a feedback loop between the individual and the surrounding social environment. An emotionally distressed person may withdraw, communicate less clearly or react defensively. Other people may respond with frustration, criticism or reduced support. The individual can then perceive those reactions as further evidence of rejection or hostility, increasing emotional distress and making additional problematic behaviour more likely. The process is not necessarily confined to one relationship or one type of stressor. Repeated cycles may spread across friendships, family interactions, romantic relationships, academic settings and workplaces. As stress accumulates, the resulting burden may worsen symptoms, creating the conditions for another round of stress generation.

The researchers describe this process as transdiagnostic, meaning that it may operate across multiple internalizing conditions rather than belonging to a single diagnosis. Depression, generalized anxiety, social anxiety and related forms of psychopathology often differ in their specific symptoms, but they can share underlying features such as negative affect, avoidance, interpersonal sensitivity and repetitive negative thinking. These common factors may help explain why stress generation is linked to impairment across diagnostic categories. Instead of treating each disorder as an isolated cause of stress, the framework maps how shared emotional, behavioural and cognitive mechanisms can produce similar patterns of escalating difficulty in different people.

A major implication of the model is that stress generation should not be viewed as a single event with a single cause. It is better understood as a process unfolding over time. Affective disruption may increase the likelihood of a particular behaviour, the behaviour may alter the social environment, and the resulting consequences may activate cognitive vulnerabilities that intensify emotional symptoms. The timing and sequence of these steps are important. A brief episode of avoidance may have little effect in one context but lead to substantial stress when it occurs repeatedly, affects an important responsibility or is interpreted by others as indifference. This process-based perspective may help researchers identify which links in the chain are most influential for different individuals.

The framework also points to several possible intervention targets. Treatment could focus directly on emotional regulation, helping people reduce reactivity and recover more effectively after distress. Behavioural approaches could address avoidance, withdrawal, disrupted routines and communication patterns that increase the likelihood of conflict or missed obligations. Cognitive interventions could target rumination, catastrophic interpretations and rigid expectations about rejection or failure. Because the model describes a reciprocal relationship between individuals and their environments, interventions might also include interpersonal strategies designed to repair damaged relationships, increase constructive communication and strengthen access to social support. Interrupting even one part of the cycle could potentially prevent later stressors from developing.

The Perspective does not present stress generation as an unavoidable consequence of internalizing psychopathology, nor does it suggest that individuals are responsible for every stressful event they experience. Rather, it offers a mechanistic account of how symptoms and circumstances can influence one another, sometimes producing self-reinforcing patterns that prolong illness and increase impairment. By bringing affective, behavioural and cognitive processes into a single framework, the model may help explain why some people recover after a stressful episode while others enter a cycle of accumulating difficulties. It also shifts attention toward prevention: identifying early changes in emotion, behaviour or thinking could make it possible to intervene before ordinary problems escalate into major life stress. For a field increasingly focused on personalized and transdiagnostic treatment, understanding how stress is generated may become a crucial step toward breaking the feedback loops that keep internalizing disorders alive.

Subject of Research: Stress generation processes in internalizing disorders

Article Title: A process model of stress generation in internalizing disorders

Article References: Starr, L.R., Dozois, D.J.A., Rnic, K. et al. A process model of stress generation in internalizing disorders. Nature Reviews Psychology (2026). https://doi.org/10.1038/s44159-026-00607-5

Image Credits: AI Generated

DOI: 10.1038/s44159-026-00607-5

Keywords: stress generation, internalizing disorders, depression, anxiety, affective functioning, cognitive vulnerability, behavioural processes, rumination, interpersonal stress, mental health interventions