The study’s longitudinal design allowed tracking of depression rates in individuals subjected to dementia diagnosis over several years, thus capturing the temporal fluctuations of depressive symptoms in relation to the onset and progression of cognitive decline. Results indicated that depressive episodes significantly increase not only prior to a dementia diagnosis but continue to evolve with distinct patterns during and after diagnosis. Such findings provide crucial insights into the pathophysiology of dementia and pave the way for improved clinical intervention strategies.

Previous research has often treated depression as a secondary symptom or consequence of dementia, but this new evidence suggests a more bidirectional and intertwined relationship. Depression may act as both a prodrome and an integral feature of dementia’s neurodegenerative process. The temporal trajectory identified in this study emphasizes that depressive symptoms start rising years before any formal cognitive diagnosis, signaling potential utility in early dementia screening and risk prediction.

Specifically, the investigators employed advanced statistical modeling on a large population-based cohort data set, which included diverse demographic and clinical parameters. This allowed for controlled assessment of confounding factors such as age, sex, socioeconomic status, and comorbidities in identifying the precise timing and prevalence of clinically significant depression in relation to dementia diagnosis. Such methodological rigor strengthens the robustness of the evidence presented.

The study observed a pronounced peak in depression occurrence in the period immediately preceding dementia diagnosis. This surge may reflect the neurobiological changes that antecede the cognitive symptoms traditionally used to define dementia. Neuroinflammatory pathways, neurotransmitter dysregulation, and neurovascular alterations are posited mechanisms that could underlie this early rise in depressive symptoms, linking mood disruption with the emerging pathology of dementia.

During the dementia diagnosis period, depression rates showed a transient diminution, potentially attributable to increased medical attention, psychological adaptation, or initiation of therapeutic interventions. However, post-diagnosis, depression rates resurged, correlating with disease progression and increasing functional impairment. This biphasic pattern underscores the importance of ongoing mental health monitoring and adaptive care approaches throughout the dementia timeline.

Clinically, these findings underscore the necessity for integrated neuropsychiatric care models in which depression screening and management are embedded within dementia diagnostic and therapeutic frameworks. Early identification and treatment of depressive symptoms before the onset of overt dementia could mitigate disease burden and enhance quality of life for patients and caregivers alike.

The pathophysiological implications are profound, suggesting that depression and dementia may share overlapping molecular drivers, including alterations in brain-derived neurotrophic factor signaling, hypothalamic-pituitary-adrenal axis dysregulation, and neurodegenerative cascades affecting limbic structures critical for mood regulation and memory processing. Understanding these shared mechanisms could accelerate development of targeted pharmacological and behavioral interventions.

Moreover, the study’s population-based nature ensures the findings apply broadly beyond specialized clinical settings, highlighting the importance of public health strategies focused on mood disorders as potential harbingers of neurodegeneration. Screening programs at primary care levels could integrate cognitive assessments into routine depression evaluations for at-risk populations.

One of the novel methodological strengths was the use of comprehensive electronic health records data coupled with sophisticated trajectory analysis that delineated temporal patterns rather than cross-sectional snapshots. This approach allowed detection of subtle shifts in depression prevalence that correlate with dementia pathology stages, overcoming limitations of previous research that often relied on single-timepoint assessments.

The authors also discussed potential implications for dementia prevention strategies, suggesting that effective treatment of depression in mid-to-late life could conceivably delay or even mitigate onset of dementia symptoms. They propose randomized controlled trials to evaluate whether prevention or aggressive management of depression might alter dementia trajectories, a hypothesis that could reshape clinical paradigms.

Importantly, the research noted that while depression is a significant risk factor and marker, it is not deterministic for dementia; many individuals with depression do not develop cognitive impairment, emphasizing the need for multifactorial risk assessments integrating genetic, lifestyle, and comorbid medical conditions for precision prognostication.

The study also highlighted the psychosocial impacts tied to the overlapping conditions of depression and dementia. Patients experiencing both conditions often manifest diminished capacity for self-care, elevated caregiver burden, and increased morbidity and mortality, reinforcing the necessity of holistic care approaches and multidisciplinary support systems.

Future research directions articulated by the team include exploring biological biomarkers that correspond with the identified depressive trajectory patterns to enable more precise, individualized disease monitoring. Functional neuroimaging and cerebrospinal fluid analyses could illuminate the neuroanatomical and biochemical substrates linking mood and cognitive decline.

In conclusion, this pioneering population-based study advances our understanding of depression as a dynamic, evolving phenomenon intrinsically connected to the dementia process. By mapping depressive symptom trajectories before, during, and after diagnosis, Yang, Li, and colleagues provide an invaluable framework for clinicians and researchers aiming to unravel the intricate mind-brain relationships driving neurodegenerative diseases. These insights open promising avenues for early detection, preventative interventions, and comprehensive care models informed by a nuanced appreciation of the depression-dementia nexus.

Subject of Research: The temporal trajectory and relationship of depression occurrence in relation to dementia diagnosis and progression in a population-based cohort.

Article Title: Trajectory of depression occurrence before, during, and after dementia diagnosis: A population-based study.

Article References:
Yang, W., Li, W., Sakakibara, S. et al. Trajectory of depression occurrence before, during, and after dementia diagnosis: A population-based study. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03817-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41398-026-03817-w

Depression and anxiety significantly impair quality of life for many autistic adults, yet clinical responses to traditional psychological treatments have been inconsistent and poorly documented. This omission is critical because autistic individuals often present unique cognitive and emotional processing traits, meaning standard treatment modalities may require substantial adaptation to be truly effective. The new research addresses this gap by meticulously tracking symptom change over time in a large cohort of autistic adults receiving various evidence-based psychological interventions.

At the heart of this study lies a sophisticated longitudinal design, enabling researchers to observe patterns of symptom fluctuation throughout therapy. Rather than relying on static pre- and post-treatment snapshots, the team employed repeated measurements that capture the dynamic symptom oscillations of depression and anxiety. This approach provides a richer, more granular understanding of how these disorders interact with the therapy process, and crucially, whether improvements in one domain herald benefits in the other.

Key findings reveal a striking divergence in the therapeutic trajectories of depression and anxiety within the autistic population. While depressive symptoms tended to exhibit steady, incremental improvement over the course of therapy, anxiety symptoms often followed a more complicated path characterized by periods of both intensification and alleviation. This non-linear pattern suggests that anxiety may require distinct therapeutic strategies and careful monitoring to optimize outcomes.

Moreover, the temporal relationship between symptom changes emerged as a pivotal element; improvements in depressive symptoms were frequently predictive of subsequent reductions in anxiety. This lagged association underscores potential mechanistic links between these conditions, hinting at shared neurobiological or cognitive substrates that evolve sequentially in response to therapeutic interventions. Such insights destabilize the assumption that anxiety and depression universally respond synchronously to psychological therapies.

The researchers employed state-of-the-art statistical modeling techniques, notably growth curve analysis and cross-lagged panel models, to dissect these complex temporal interrelations. These methodologies transcend simplistic correlation measures by accounting for individual variability and bidirectional influences between symptom domains. This rigorous analytic framework strengthens confidence in the findings’ validity and their implications for clinical practice.

One of the study’s most compelling aspects is its focus on real-world therapeutic settings, encompassing a spectrum of psychological interventions such as cognitive-behavioral therapy (CBT), mindfulness-based approaches, and specialized autism-adapted programs. By capturing data from routine clinical environments rather than controlled trials alone, the results carry enhanced ecological validity and promise broader applicability to everyday mental health care.

These findings compel a reexamination of therapeutic goals and benchmarks for autistic adults with depression and anxiety. The prevailing one-size-fits-all treatments may inadequately address the asynchronous symptom dynamics uncovered here. Instead, clinicians might consider phased or integrative approaches that prioritize alleviating depression initially, with sequential or co-occurring strategies targeting anxiety as symptoms evolve.

Importantly, the research highlights the necessity for ongoing symptom monitoring throughout therapy rather than episodic assessments. Continuous tracking empowers clinicians to anticipate symptom fluctuations and intervene proactively. This strategy may mitigate anxiety exacerbations that, if unrecognized, could derail therapy adherence and outcomes.

Beyond therapeutic implications, the study raises foundational questions about the neurodevelopmental interactions between autism, depression, and anxiety. Understanding why these symptom trajectories diverge and influence one another over time may uncover novel biomarkers or neurocognitive targets, spurring innovation in psychopharmacology and psychotherapy.

The implications extend to policy and health system design as well. Personalized care plans, informed by symptom trajectory data, advocate for more flexible service models that accommodate fluctuating needs. Resource allocation can be optimized by anticipating therapy phases requiring intensified support, thereby improving cost-effectiveness and patient satisfaction.

This research also elevates the patient voice by emphasizing symptom variability inherent to autistic adults’ lived experience. Recognizing that mental health recovery is not a linear journey honors individual complexity and encourages collaborative therapeutic relationships grounded in empathy and responsiveness.

Future investigations are poised to build on these findings, exploring whether similar symptom patterns emerge in adolescent populations or those with differing autism phenotypes. Furthermore, integration of biological measures such as neuroimaging or genetic profiling could elucidate underlying mechanisms of symptom interplay, offering pathways to truly personalized mental health care.

In summary, this pioneering research redefines understanding of depression and anxiety symptom change within autistic adults during psychological therapy. Its meticulous longitudinal assessment, coupled with advanced analytic techniques, exposes intricate symptom interactions and non-linear trajectories demanding novel clinical strategies. The study not only enriches scientific knowledge but charts a course toward more nuanced, effective, and compassionate care for a population historically underserved in mental health treatment.

As the mental health field grapples with increasing demand and complexity, insights such as these underscore the vital importance of embracing heterogeneity and temporal dynamics in symptom management. Harnessing this knowledge holds the promise of transforming therapeutic outcomes and enhancing the well-being of autistic adults worldwide, marking a seminal advancement in neurodiverse mental health care.

Subject of Research: Symptom trajectories of depression and anxiety during psychological therapy in autistic adults

Article Title: Symptom change in depression and anxiety during psychological therapy for autistic adults

Article References:
Pender, R., El Baou, C., O’Nions, E. et al. Symptom change in depression and anxiety during psychological therapy for autistic adults. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-025-00567-4

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s44220-025-00567-4

The DESIE-study emerges at a pivotal moment, responding to a growing recognition that patients with significant physical ailments frequently suffer from concurrent depressive disorders, which exacerbate morbidity and complicate treatment outcomes. Depression in these patients is not merely a comorbid condition; it represents a bi-directional influence that can hinder physical recovery, lower quality of life, and inflate healthcare costs. The comprehensive study protocol outlines a prospective design that promises to capture longitudinal data, providing unprecedented insight into how depressive symptoms evolve in tandem with chronic physical disease progression.

One of the core innovations of the DESIE-study is its methodological rigor in assessing depression through both subjective self-reports and objective clinical evaluation methods. This dual approach addresses a longstanding challenge in psychosomatic research, where variability in diagnostic tools often clouds interpretability and clinical relevance. By integrating standardized psychiatric interviews with validated depression scales and biomarkers, the study aligns with current scientific imperatives to achieve precision psychiatry tailored to individual patient profiles.

The study protocol also highlights the importance of dissecting the psychosocial and biological mechanisms underpinning depression among this vulnerable patient population. The research team intends to investigate inflammatory markers, neuroendocrine dysfunctions, and psychosocial stressors as mediators that bridge somatic disease and mood disorders. Such comprehensive profiling could reveal novel pathways for intervention, moving beyond symptomatic treatment to targeted therapeutics that address the root etiologies of depression in chronic illness contexts.

Notably, the sample framework set forth in the DESIE-study embraces diversity both in terms of somatic illnesses included and demographic factors. The protocol accounts for a spectrum of severe diseases, such as cardiovascular disorders, oncological conditions, and autoimmune diseases, recognizing that each somatic pathology may interact uniquely with depressive states. Additionally, stratification by age, gender, and socioeconomic status enables an exploration of disparities in depression prevalence and treatment responsiveness, emphasizing equity in mental healthcare research.

The longitudinal nature of the DESIE-study is poised to capture the temporal dynamics of depression onset and persistence, a critical advancement over prior cross-sectional investigations that have limited causal inference capacity. The research design incorporates multiple follow-up assessments across frontline treatment phases and disease milestones, facilitating an understanding of risk periods and recovery trajectories. This temporal mapping has substantial implications for early detection and intervention strategies, which are vital for improving prognosis.

Intriguingly, the protocol proposes a multidisciplinary research team approach that synergizes expertise from psychiatry, psychology, immunology, and epidemiology. This collaborative model exemplifies modern research paradigms wherein complex health phenomena necessitate cross-disciplinary integration. It also sets a blueprint for similar future initiatives aiming to tackle psychosomatic disorders from a holistic standpoint.

Ethical considerations are meticulously addressed within the study protocol. Given the vulnerability of patients with severe somatic conditions, the authors detail stringent informed consent processes and emphasize safeguarding participant autonomy and confidentiality. Furthermore, they outline protocols for managing potential psychological distress that might arise during depression assessments, showcasing a responsible research ethos that prioritizes patient welfare.

The prospective DESIE-study also aims to contribute substantial data to support healthcare policy reforms. The researchers posit that highlighting the burden of depression in chronically ill patients will underline the necessity for integrated care models, where mental health services are embedded within somatic disease management frameworks. Such policy shifts could revolutionize treatment paradigms, promoting comprehensive care that aligns with patient-centered outcomes.

From a technological standpoint, the study leverages digital health tools, including electronic health records linkage and mobile-based symptom tracking, enhancing data accuracy and participant engagement. These innovations exemplify how contemporary research can harness digital integration to capture real-world evidence efficiently and effectively, minimizing participant burden while maximizing data richness.

While the DESIE-study protocol primarily focuses on depression, it implicitly acknowledges the broader spectrum of psychological distress that accompanies severe somatic disease. This awareness encourages an expansion of research horizons in the future to encompass anxiety disorders, post-traumatic stress, and factors such as cognitive impairment, thus deepening the understanding of mental health complexities in chronic disease populations.

Ultimately, the DESIE-study protocol represents a significant stride toward bridging the gap between somatic medicine and mental health care. Its prospective design, rigorous methodology, and multidisciplinary ethos set the stage for transformative insights that could alleviate the psychological burden borne by patients with severe physical illnesses, improving both life expectancy and quality of life. As the medical community eagerly awaits its findings, this study embodies a beacon of hope and a call to action for integrated biopsychosocial health research.

The anticipated impact of this study extends beyond academia, potentially reshaping clinical practice guidelines and therapeutic algorithms. Clinicians might soon have access to refined depression screening tools adapted for somatic disease contexts, allowing for timely psychiatric referral and intervention. This could result in a marked reduction of depressive symptomatology and its deleterious effects in profoundly vulnerable patient populations.

In conclusion, the upcoming DESIE-study stands as a testament to the evolving understanding that health is an indivisible synthesis of mind and body. By meticulously mapping depression within the trajectory of severe somatic diseases, it promises to deliver evidence critical for the next generation of mental health care innovations. The study holds the vision of transforming patient care from a fragmented model into one that fully integrates mental and physical health, thereby promoting holistic healing and enhanced well-being at the core of modern medicine.

Subject of Research: Depression in patients with severe somatic disease

Article Title: Depression in patients with severe somatic disease – study protocol of the prospective DESIE-study

Article References: Fischer, S., Freuer, D., Braadt, L. et al. Depression in patients with severe somatic disease – study protocol of the prospective DESIE-study. BMC Psychol 13, 1352 (2025). https://doi.org/10.1186/s40359-025-03810-w

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s40359-025-03810-w

The Body Roundness Index, a novel anthropometric metric devised to assess body shape and fat distribution, transcends traditional measures like Body Mass Index (BMI) by incorporating waist circumference and height parameters to provide a more accurate reflection of visceral adiposity. The researchers posited that BRI, given its enhanced predictive capacity for cardiometabolic risks, might also serve as a biomarker for psychological distress, particularly depressive symptomatology, which remains a pervasive yet often underrecognized health burden in aging populations.

Utilizing longitudinal data spanning multiple waves from CHARLS, the study meticulously tracked depressive symptoms employing standardized scales such as the Center for Epidemiologic Studies Depression Scale (CES-D). The extensive sample cohort included thousands of participants aged 45 and above, offering a rich demographic spectrum that encapsulates diverse socioeconomic backgrounds and regional disparities across China. This comprehensive dataset facilitated a robust temporal analysis of depressive symptom trajectories vis-à-vis BRI measurements.

One of the pivotal revelations from the study is the identification of distinct depressive symptom trajectories associated with varying BRI levels. Participants exhibiting elevated BRI were found to have a higher likelihood of persistent or worsening depressive symptoms over time, suggesting that body roundness may act as a significant predictor in the chronicity and intensification of depression among older adults. This finding underscores the intricate bi-directional interplay between physical and mental health domains.

The pathophysiological underpinnings of this association are multifaceted. Visceral adiposity, as captured by BRI, is known to contribute to systemic inflammation, dysregulated hypothalamic-pituitary-adrenal (HPA) axis activity, and insulin resistance—all of which have been implicated in the etiology of depressive disorders. Moreover, increased body roundness often correlates with diminished physical mobility and social isolation, compounding psychological vulnerability.

This study also contributes to the growing discourse on personalized medicine and preventative psychiatry by suggesting that anthropometric assessments like BRI could be integrated into routine screening processes for mental health risk stratification, particularly in resource-constrained settings. Early identification of individuals at risk for depression through non-invasive, easily obtainable physical health indices could potentially catalyze timely interventions.

Furthermore, the researchers highlight the sociocultural context of aging in China, where shifts towards Westernized lifestyles and urbanization have exacerbated metabolic health challenges. The increased prevalence of obesity and sedentary behavior, coupled with rapidly changing family structures and social support networks, may potentiate the observed associations between BRI and depressive symptom trajectories.

Importantly, the study employed advanced statistical modeling techniques, including latent class growth analysis, to categorize depressive symptom patterns over time, allowing for nuanced differentiation between transient, remitting, and persistent symptom profiles. This methodological rigor enhances the validity of the observed correlations and supports the dynamic conceptualization of depression as a heterogeneous and temporally variable condition.

The implications of these findings extend beyond epidemiological insights, advocating for integrated healthcare models that address both metabolic and mental health burdens concurrently. Public health policies that promote physical activity, nutritional education, and weight management could have collateral benefits in mitigating depression risk, particularly among middle-aged and elderly demographics.

Moreover, the study’s emphasis on the use of BRI as a more precise indicator than traditional BMI invites further exploration into the role of fat distribution rather than overall adiposity in mental health outcomes. This distinction could recalibrate clinical risk assessments and inform targeted therapeutic strategies that address specific metabolic phenotypes.

From a neuroscience perspective, the link between increased visceral fat and disrupted monoaminergic neurotransmission may provide a biological rationale for the observed depressive symptom trajectories. Chronic low-grade inflammation associated with adipose tissue can influence neuroinflammatory pathways, altering brain regions implicated in mood regulation such as the prefrontal cortex and hippocampus.

Ethnic and genetic factors unique to the Chinese population were also taken into account, acknowledging that variations in genetic polymorphisms related to fat metabolism and inflammatory responses might modulate susceptibility to depression, thereby enriching the contextual relevance of the findings.

In summary, this pioneering research firmly establishes the Body Roundness Index as not merely a marker of physical health, but as a potent predictor of mental health trajectories in aging populations. Its integration into routine health assessments could revolutionize the preventive framework for depression, underscoring the inseparable nexus between body and mind.

As mental health awareness gains momentum worldwide, especially in the context of an aging global demographic, studies like this exemplify the interdisciplinary approach necessary to unravel the complex biopsychosocial factors driving depression. The innovative use of BRI, combined with longitudinal data analytics, positions this research at the vanguard of psychiatric epidemiology and public health.

Future research directions include experimental interventions targeting visceral adiposity reduction and their subsequent effects on depressive symptom amelioration. Moreover, expanding similar analyses to different ethnic cohorts could validate the universality or specificity of the association between body roundness and depression.

By bridging the gap between somatic and psychological health through sophisticated measurement tools and longitudinal inquiry, this study paves the way for more holistic, predictive, and personalized health care paradigms that could substantially alleviate the global depression burden.

Subject of Research: The association between Body Roundness Index (BRI) and trajectories of depressive symptoms among middle-aged and older Chinese adults, analyzed through longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS).

Article Title: Association between body roundness index and trajectories of depressive symptoms among Chinese middle-aged and older adults: a nationwide cohort study from CHARLS.

Article References:
Zhao, D., Long, X. & Wang, J. Association between body roundness index and trajectories of depressive symptoms among Chinese middle-aged and older adults: a nationwide cohort study from CHARLS. BMC Psychol 13, 1274 (2025). https://doi.org/10.1186/s40359-025-03621-z

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s40359-025-03621-z

Childhood-onset depression, diagnosed before the age of 16, has long been associated with immediate psychological consequences. However, its long-term physiological ramifications, especially concerning chronic illness onset during elderly years, have remained understudied. This novel inquiry aimed to bridge that knowledge gap by exploring how early depressive episodes interact with the later emergence of chronic medical conditions.

The research employed rigorous epidemiological methods to calculate risk ratios (RR) alongside confident confidence intervals (CI), enabling precise quantification of the likelihood of new chronic disease diagnoses occurring after 65 years old in individuals with a documented history of childhood depression. Notably, the study looked at eight prevalent chronic conditions: hypertension, diabetes, cancer, chronic lung disease, cardiovascular problems, stroke, emotional or psychiatric disorders, and arthritis.

Findings revealed a statistically significant elevation in the risk for newly diagnosed chronic diseases among those with childhood-onset depression compared to their counterparts without such histories. Specifically, the overall risk ratio stood at 1.31 (95% CI 1.12–1.52), indicating a 31% increased likelihood of acquiring at least one chronic condition post-senescence. This revelation underscores critical cumulative health effects originating during formative years of mental health vulnerability.

Delving into specific illnesses, the study pinpointed chronic lung diseases as significantly more prevalent in the elderly subgroup with early depressive episodes. The risk ratio of 1.53 (95% CI 1.04–2.16) demonstrated a robust link, amplified by stringent Bonferroni corrections mitigating the probability of false-positive results. This association may hint at an intricate interplay between psychological stress in youth and sustained impacts on pulmonary function or vulnerability.

Equally striking were the findings regarding emotional and psychiatric disorders newly diagnosed in older age. Individuals with childhood depression exhibited more than double the risk (RR 2.17, 95% CI 1.34–3.31) of these late-onset psychiatric problems compared to those without early-life depression. This suggests that childhood mental health struggles may foster a persistent predisposition to psychiatric morbidity well into advanced age, possibly due to neurobiological or psychosocial mechanisms revealed over time.

The researchers emphasize that this study’s scope does not imply a direct causal relationship but instead highlights an important epidemiological association warranting further exploration. The chronic diseases examined were newly diagnosed post-65, disentangling the developmental origins from pre-existing conditions, which strengthens the inference about long-term risk patterns tied to childhood depression.

Importantly, this research advocates a paradigm shift in both clinical practice and public health policy. Early identification and intervention targeting childhood depression might not only alleviate immediate psychological distress but also attenuate downstream chronic disease burdens, thereby improving quality of life for aging populations. Integrating mental health care with preventive medicine could be key to mitigating this dual burden.

Moreover, these findings provide fertile ground for biological research aimed at decoding the mechanistic pathways connecting early depressive episodes to later physical health. Potential avenues include examining chronic inflammation, neuroendocrine dysregulation, and lifestyle factors influenced by mental health trajectories over the lifespan.

The study’s use of the Health and Retirement Longitudinal Study adds robustness, given its comprehensive demographic representation and longitudinal design, which captures nuanced changes in health status alongside extensive covariate adjustments. This methodology strengthens confidence in extrapolating these associations to broader elderly populations in diverse settings.

Future investigations are encouraged to dissect additional variables influencing these relationships, such as socioeconomic factors, genetic predispositions, and the role of resilience or protective interventions during childhood and beyond. Identifying modifiable risk factors could help sculpt targeted strategies for reducing chronic disease incidence among those previously diagnosed with childhood depression.

Overall, this research reinvigorates conversations around the lifelong impacts of childhood mental health and compels interdisciplinary collaboration involving psychiatry, gerontology, pulmonology, and primary care to address the complex needs of aging populations burdened by early psychological adversity.

By unraveling these critical links, the study paves the way for a more holistic understanding of human health that transcends isolated timeframes, underscoring the interconnectivity between emotional well-being in youth and physical health outcomes in later life.

Subject of Research:
The association between childhood-onset depression and the risk of newly diagnosed chronic diseases after age 65 years.

Article Title:
Childhood-onset depression and newly diagnosed chronic diseases after age 65: a large longitudinal cohort study.

Article References:
Li, Z., Liu, Z., Luo, Y. et al. Childhood-onset depression and newly diagnosed chronic diseases after age 65: a large longitudinal cohort study. BMC Psychiatry 25, 1025 (2025). https://doi.org/10.1186/s12888-025-07494-9

Image Credits: AI Generated

DOI:
https://doi.org/10.1186/s12888-025-07494-9

The intricate relationship between the immune system and psychiatric disorders has been a burgeoning area of research over the past decade, but much of the evidence has remained correlational and cross-sectional. This study, however, leverages a massive 10-year cohort with repeated measures of blood cell markers, delineating how dynamic shifts in these biomarkers precede or accompany the emergence of depressive symptoms. By meticulously stratifying participants by biological sex, the researchers elucidated starkly divergent trajectories, emphasizing the critical need to incorporate sex as a fundamental variable in mental health research.

At the heart of the investigation were specific blood cell types—including neutrophils, lymphocytes, monocytes, and eosinophils—each of which plays a crucial role in immune function and systemic inflammation. The authors leveraged sophisticated statistical modeling to track changes in these cellular populations over time and correlate these patterns with standardized clinical assessments of depression. Intriguingly, the study’s findings suggest that not only baseline levels but also the longitudinal fluctuations of these cells serve as predictive biomarkers of depression risk, highlighting a dynamic interplay between immune regulation and neuronal circuits implicated in mood.

The sex-stratified analyses revealed unique biomarker trajectories that differentiated males and females in terms of both risk profiles and temporal associations. For instance, in females, rising lymphocyte counts and stable neutrophil levels over the years appeared to be associated with an increased risk of depressive episodes. Conversely, males exhibited a different pattern, where increased neutrophil-to-lymphocyte ratios served as a more robust indicator of emerging depressive symptoms. These distinctions illuminate potential sex-specific immune mechanisms underlying depression, challenging the pervasive assumption that psychiatric pathophysiology is uniform across genders.

Dosage and timing emerged as subtle but powerful factors shaping these immunological trajectories. The study demonstrates that the risk of depression is not rooted simply in absolute immune cell counts but rather in their fluctuating patterns across several years. This dynamic perspective underscores how transient immune dysregulation—such as repeated inflammatory insults or chronic low-grade inflammation—might predispose individuals to depressive states. Such insights pave the way for targeted interventions aiming at modulating immune function before clinical symptoms fully develop.

This longitudinal evidence bolsters the immunopsychiatry framework, which posits that immune dysfunction is a core contributor to the etiology and maintenance of mood disorders. Up until now, the field has grappled with inconsistent findings due to reliance on single time-point measurements of inflammatory markers. The repeated-measures design employed here resolves much of that ambiguity by capturing the personal immune landscape’s ebb and flow, effectively linking immunological processes to mental health in a more causally informative manner.

The research team utilized cutting-edge high-throughput analytic methods to derive trajectory patterns from tens of thousands of blood samples. They applied machine learning algorithms to detect subtleties in biomarker changes predictive of future depression onset. This represents a critical leap forward, as previous studies have often been constrained by limited sample sizes or lack of longitudinal depth. Also, the granularity afforded by machine learning enabled the discovery of nonlinear associations and interaction effects between cell types, deepening our mechanistic understanding.

Another striking element of the study is its potential clinical applicability. The identified biomarkers could serve as readily accessible blood-based tests for early detection of individuals at heightened risk for depression. This has profound implications for preventative psychiatry, where timely interventions could forestall or mitigate the severity of depressive episodes. Moreover, by pinpointing sex-specific molecular signatures, the findings may guide personalized treatment paradigms that optimize immune modulation strategies—such as anti-inflammatory agents or lifestyle interventions tailored to biological sex.

Beyond prediction and prevention, the findings implicate novel therapeutic targets for drug development. If immune cell trajectories causally influence depressive symptoms, targeting these pathways might complement existing antidepressants, which primarily focus on neurotransmitter systems. Anti-inflammatory approaches, immune-modulating biologics, or even precision nutrition aimed at restoring immune homeostasis could emerge as adjunct therapies informed by this research, marking a paradigmatic shift in treating depression as an immune-related disorder.

The study also raises compelling questions about the origins of these differential trajectories. The authors speculate that genetic, epigenetic, and environmental factors—including stress exposure, microbiome composition, and hormonal fluctuations—likely interact to shape individual immune profiles over time. Unraveling these complex interactions will require interdisciplinary research integrating immunology, neurobiology, endocrinology, and environmental sciences to fully decode depression’s multifactorial origins.

Importantly, the rigor and scale of this analysis afford a robust foundation for future studies to explore related mood and anxiety disorders, extending the immunological trajectory framework beyond depression. The interplay of immune biomarkers with cognitive decline, psychosis, or bipolar disorder may reveal shared or distinct pathways, refining diagnostic categories and enhancing treatment precision across psychiatric illnesses.

The sex-specific approach adopted by the researchers sets a new gold standard in mental health research, urging the scientific community to consistently account for biological sex differences rather than treat gender as a mere covariate. Given the well-documented disparities in depression prevalence and symptomatology between males and females, such integrative immune profiling promises to unravel the biological basis for these disparities and close the gap in mental health outcomes.

This study also invites a re-examination of public health strategies, emphasizing the importance of longitudinal biomarker monitoring in at-risk populations. By integrating routine immune marker assessments into primary care and mental health screenings, clinicians may gain a powerful tool for early intervention. Moreover, the accessibility of blood sampling implies feasibility even in large-scale epidemiological surveillance, broadening the reach of personalized mental health care.

While the findings are promising, the authors acknowledge limitations, such as potential confounding factors related to lifestyle behaviors, infections, and medication use that could influence immune cell counts. They advocate for further research utilizing randomized controlled trials to determine whether modifying blood cell trajectories can causally reduce depression risk—an essential step before clinical translation.

In summary, this landmark decade-long investigation anchors a new paradigm in understanding depression, framing it as a temporally dynamic immune-mediated disorder with distinct biological signatures between sexes. By illuminating how blood cell biomarker trajectories foretell and accompany depressive risk, the research opens exhilarating avenues for predictive diagnostics, tailored therapeutics, and precision psychiatry. As mental health burdens continue to escalate globally, integrating immunology into psychiatric care could herald a future where depression is preempted and personalized like never before.

Subject of Research: The longitudinal relationship between blood cell biomarker trajectories and depression risk, with a focus on sex-specific differences.

Article Title: Blood cell biomarker trajectories and depression risk in a sex-stratified 10-year longitudinal cohort analysis.

Article References:
Wang, L., Lin, Y., Fu, T. et al. Blood cell biomarker trajectories and depression risk in a sex-stratified 10-year longitudinal cohort analysis. Nat. Mental Health (2025). https://doi.org/10.1038/s44220-025-00517-0

Image Credits: AI Generated

Metabolic syndrome, characterized by a constellation of conditions including hypertension, insulin resistance, obesity, and dyslipidemia, markedly increases an individual’s vulnerability to cardiovascular diseases and diabetes. Beyond these well-documented risks, individuals with MetS are also disproportionately affected by depression, a mental health condition that profoundly diminishes quality of life and complicates clinical management. Detecting depression early in this high-risk population is paramount for effective intervention, yet remains a formidable challenge due to the multifactorial nature of depression’s etiology and presentation.

This pioneering investigation employed data spanning four years, from baseline records in 2011 to follow-up data in 2015, capturing a rich longitudinal portrait of over five thousand patients diagnosed with MetS within CHARLS. The researchers meticulously curated the dataset, excluding variables suffering from more than 20% missing values to ensure robust analytical integrity. Ultimately, 38 diverse features were considered, encompassing demographic details, lifestyle habits, comorbidities, physiological health indicators, and detailed blood biochemistry profiles.

To distill the most salient predictors of depression from this expansive feature set, the research team applied the Least Absolute Shrinkage and Selection Operator (LASSO) method. This powerful statistical technique shrinks the coefficients of less informative variables towards zero, thereby enabling the identification of 11 key contributors most strongly associated with depression among participants. These factors collectively informed the construction of predictive models designed to assess depression risk with enhanced accuracy.

Six distinct machine learning models were developed and rigorously evaluated to determine the most effective predictive framework. These included both classical statistical approaches such as logistic regression (LR), as well as advanced algorithms like Extreme Gradient Boosting (XGBoost). The results revealed intriguing parity between LR and XGBoost in predictive performance within the test set, both achieving an Area Under the Curve (AUC) of 0.749, a metric indicating solid discriminatory ability between depressed and non-depressed individuals.

Further validation using the 2015 CHARLS wave reinforced these findings, with the optimized XGBoost model maintaining strong predictive capacity (AUC of 0.737). Such temporal validation affirms the model’s generalizability over time, a critical attribute for real-world clinical applicability. The researchers also integrated interpretability tools such as SHapley Additive exPlanations (SHAP) to visualize and elucidate the influence of individual predictors within the model, thereby enhancing transparency and facilitating clinical trust in machine learning outputs.

Perhaps most compelling is the introduction of a nomogram distilled from these analytic insights, serving as an intuitive graphic calculator for clinicians. This tool allows healthcare professionals to input patient-specific data and promptly estimate personalized depression risk, enabling earlier and more targeted psychosocial interventions. Given the high prevalence of depression among the MetS cohort—reported at 48.6% in the study—such resources could significantly shift therapeutic trajectories and improve patient outcomes.

The implications of these findings ripple far beyond academic curiosity; they gesture toward a future where integrated, data-driven approaches become standard practice in managing complex comorbidities encompassing both physical and mental health dimensions. By illuminating the links between physiological disruptions inherent in MetS and psychological distress, the study provides critical leverage points for early prevention, continuous monitoring, and tailored treatment.

Moreover, the convergence of logistic regression and machine learning models in performance underscores the continuing value of classical statistical methods while celebrating the enhancements brought by artificial intelligence. This duality suggests a balanced path forward, where interpretability and predictive power coexist in harmony to better serve patient needs and inform clinical decision-making.

To operationalize these advancements, collaboration between data scientists, clinicians, and community health workers will be crucial. Training programs emphasizing the deployment of nomograms and SHAP visualizations can equip frontline personnel with the capabilities to identify at-risk individuals proactively, potentially alleviating the heavy mental health burden often borne silently by those with chronic illnesses.

In conclusion, this landmark study not only provides a robust framework for predicting depression risk in middle-aged and elderly patients with metabolic syndrome but also exemplifies the potent synergy achievable between epidemiological data, statistical rigor, and machine learning innovation. As the global population ages and the prevalence of metabolic disorders escalates, such research heralds a new dawn in holistic, anticipatory healthcare aimed at preserving both body and mind.

Subject of Research: Prediction of depression risk in middle-aged and elderly patients with metabolic syndrome using nomograms and interpretable machine learning models based on longitudinal data from CHARLS.

Article Title: Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS.

Article References: Chen, J., Lin, Y., Hu, R. et al. Prediction model for depression risk in middle-aged and elderly patients with metabolic syndrome: a nomogram and interpretable machine learning approach based on CHARLS. BMC Psychiatry 25, 987 (2025). https://doi.org/10.1186/s12888-025-07434-7

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s12888-025-07434-7

Pain and depression have long been recognized as interrelated conditions, each capable of exacerbating the other. However, the timing of these developments has remained elusive until now. The UCL team harnessed data from the English Longitudinal Study of Ageing (ELSA), encompassing over two decades of health information from a nationally representative sample of English adults aged 50 and above. Their analysis compared 3,668 individuals regularly experiencing moderate to severe pain with an equivalent number of matched controls who reported minimal or no pain, thereby enabling robust longitudinal comparisons.

In individuals who developed pain, depressive symptoms were observed to escalate sharply up to eight years before the reported onset of pain, reaching a peak precisely at the time pain began. Notably, these elevated symptoms persisted in the years following pain manifestation, implying a sustained mental health burden. Conversely, those in the non-pain group exhibited relatively stable and less severe depressive symptoms over the same period. This divergence highlights the potential of depressive symptoms as early markers or even contributors to future pain experiences.

Alongside depression, loneliness emerged as a significant co-factor in the trajectory leading to pain. The study documented a parallel increase in feelings of loneliness in the years preceding and following pain onset among those suffering pain, whereas loneliness levels remained comparatively low and consistent in the control group. Importantly, distinctions were drawn between loneliness—a subjective perception of inadequate social connection—and social isolation, which reflects an objective lack of social contact. The research revealed no meaningful differences in social isolation, emphasizing that the quality rather than the quantity of social interactions might influence both mental health and pain development.

The mechanisms linking depressive symptoms and loneliness to subsequent pain are multifaceted and biologically plausible. Chronic psychological stress associated with depression and loneliness is known to induce systemic inflammation, a recognized contributor to nociceptive processes and pain sensitization. Stress-related dysregulation of the autonomic nervous system can also alter pain perception dynamically by modulating the ‘fight or flight’ response and immune function. Such physiological pathways suggest that mental health is not merely a comorbid factor but an active participant in the pathogenesis of pain.

Interestingly, the study identified socioeconomic disparities in the relationship between depression and pain. Participants with lower educational attainment and wealth reported more pronounced increases in depressive symptoms prior to and after pain onset. The researchers attribute this to the limited access to resources that facilitate effective mental health care and pain management in these populations. These findings underscore an urgent need for public health strategies that prioritize vulnerable groups with inadequate socioeconomic support through targeted, accessible mental health and community interventions.

Despite the richness of the dataset, the researchers acknowledge certain limitations. The cohort was predominantly white, reflecting England’s demographic composition within the studied age range, calling for further research to examine whether these findings generalize across more ethnically diverse populations and younger age groups. Moreover, while the survey did not explicitly differentiate chronic pain from acute pain, supplementary analyses focusing on participants reporting persistent pain across multiple surveys suggested that the results are applicable to chronic pain syndromes.

The study’s methodological rigor is enhanced by controlling for numerous potential confounders, including but not limited to sex, age, birth cohort, education, wealth, comorbid long-term health conditions, levels of physical activity, alcohol use, and smoking habits. This analytical approach strengthens the argument that depressive symptoms and loneliness independently anticipate the development of significant pain, rather than merely co-occurring with it due to overlapping risk factors.

From a clinical perspective, these discoveries hold substantial implications for pain management paradigms. Traditionally, pain treatment has focused heavily on biological factors, targeting physical causes and symptoms. However, this evidence advocates for integrated models incorporating psychological assessments and interventions aimed at mitigating depression and enhancing social connectedness well before pain manifests. Such proactive mental health measures could potentially delay, reduce, or even prevent the onset of debilitating pain conditions.

Further investigation into the neurobiological underpinnings may shed light on specific pathways through which depressive symptoms modulate pain sensitivity. For instance, stress-induced inflammation drives glial cell activation within the central nervous system, altering pain processing circuits. Similarly, alterations in neurotransmitter systems involved in mood regulation may impact endogenous pain inhibition mechanisms, providing a fertile ground for therapeutic innovation.

Finally, the study calls attention to the societal and healthcare implications of its findings. With aging populations worldwide facing increasing burdens of both depression and chronic pain, health systems must evolve to address these interconnected epidemics. Policymakers and practitioners are urged to implement multidisciplinary approaches that integrate mental health care, social support, and traditional pain therapies to improve patients’ overall quality of life and reduce healthcare costs associated with chronic pain.

In conclusion, this UCL study compellingly demonstrates that worsening depressive symptoms and loneliness are precursors to significant pain in later life, urging a paradigm shift in our approach to pain prevention and management. Addressing mental health proactively presents a promising avenue not only for alleviating psychological distress but also for mitigating future physical suffering among older adults.

Subject of Research: The temporal relationship between depressive symptoms, loneliness, and onset of moderate to severe pain in middle-aged and older adults.

Article Title: Not explicitly stated in the excerpt.

News Publication Date: 20-May-2025

Keywords: Pain, Chronic pain, Back pain, Depression, Mental health, Psychological stress

Psychological abuse in childhood remains an elusive yet profoundly damaging form of maltreatment, often overshadowed by its more visible counterparts such as physical or sexual abuse. The current investigation adds critical evidence demonstrating how verbal aggression, threats, and intrusive behaviors in early life uniquely contribute to mental health trajectories during adolescence—a period marked by significant neurodevelopmental and emotional changes. The implications suggest that psychological maltreatment is not a transient risk factor but a potent determinant of depressive symptoms that persist and fluctuate during critical developmental windows.

The researchers recruited a robust cohort of 1,300 middle school students from two geographically and culturally distinct provinces in China, ensuring a diverse sample. Over a single semester, these adolescents completed three rounds of standardized questionnaires designed to measure depressive symptoms and experiences of psychological abuse. By using latent growth modeling, a sophisticated statistical approach capable of capturing individual differences in symptom progression, the study has charted the subtle dynamics of depression trajectories rather than relying on static snapshots.

Results reveal a compelling trend: depressive symptoms among adolescents generally decreased linearly across the semester. However, beneath this average decline lay striking individual variability in both the initial severity of symptoms and the pace at which these symptoms ameliorated or intensified. This heterogeneity underscores why some adolescents recover more swiftly from depression while others experience prolonged or escalating distress, highlighting a need for personalized interventions.

Crucially, childhood psychological abuse emerged as a significant predictor of both the baseline level and the rate of change in depressive symptoms. This dual influence implies that early psychological maltreatment leaves a lasting imprint that not only seeds initial depressive manifestations but also shapes their subsequent course. The findings dismantle any simplistic notion that adolescent depression might be solely the product of current environmental stressors or genetic predispositions.

Moreover, the investigation dissected psychological abuse into three core dimensions: verbal aggression, threats, and intrusiveness, each of which independently and significantly affected depressive symptom trajectories. This multidimensional perspective invites clinicians and policymakers to appreciate the varied facets of psychological abuse, recognizing that interventions must address the complex interplay among different forms of maltreatment to be truly effective.

The study also explored the interrelationship between dynamics of psychological abuse and depressive symptom change, finding a significant correlation. This suggests a feedback loop wherein ongoing psychological stress exacerbates depressive symptoms, and worsening mental health further sensitizes adolescents to abusive environments. Understanding this bidirectional mechanism opens avenues for interruption points in therapeutic settings.

Notably, the use of latent growth modeling represents a methodological advancement in the study of adolescent depression. Traditional cross-sectional or linear regression analyses often gloss over individual trajectories, but latent variable growth models capture the ebb and flow of symptoms over time, offering a more nuanced and predictive understanding. This approach could revolutionize how longitudinal mental health research is conducted, emphasizing temporal dynamics.

Intersecting neuroscientific evidence supports these findings. Early psychological abuse may induce lasting alterations in brain regions involved in mood regulation, stress response, and cognitive control, such as the prefrontal cortex and amygdala. These neural changes could mediate the link between childhood abuse and depressive symptom trajectories, although further neuroimaging studies are warranted to elucidate these pathways.

Importantly, the data suggest a critical window for intervention during early secondary school years. Tailored prevention and early intervention programs targeting psychological abuse could play a pivotal role in altering the depressive symptom trajectory, potentially averting chronic mental health disorders. This proactive stance aligns with global mental health initiatives emphasizing early detection and treatment.

The study’s findings extend beyond the Chinese context, given the universal prevalence of psychological abuse and adolescent depression. The multi-dimensional insights into abuse types and symptom progression have broad applicability and call for integrating psychological abuse screening into adolescent mental health assessments worldwide.

In conclusion, this research elevates the discourse on childhood maltreatment by revealing the intricate and lasting consequences of psychological abuse on adolescent depression. Beyond academic merit, these findings advocate for systemic changes in educational, clinical, and social frameworks to recognize and address the silent but severe impact of childhood psychological maltreatment.

As mental health professionals, educators, and policymakers digest these insights, the call to action is clear: effective, evidence-based interventions targeting psychological abuse must be prioritized to safeguard the mental well-being of future generations. The intricate trajectories uncovered in this study offer a scientific roadmap for such transformative efforts, holding the promise of healthier adolescence and adulthood.

Subject of Research: Impact of childhood psychological abuse on adolescent depressive symptoms trajectories

Article Title: Impact of childhood psychological abuse on the trajectory of adolescent depressive symptoms: a latent growth modeling approach

Article References:
Yuan, F., Feng, Y., Wu, J. et al. Impact of childhood psychological abuse on the trajectory of adolescent depressive symptoms: a latent growth modeling approach. BMC Psychiatry 25, 421 (2025). https://doi.org/10.1186/s12888-025-06884-3

Image Credits: AI Generated

DOI: https://doi.org/10.1186/s12888-025-06884-3

The behavioral explanation of health inequalities has long posited that socioeconomic disparities manifest partly through differences in health behaviors, particularly in formative years. By analyzing a robust Norwegian cohort of 1,109 individuals over ten time points spanning ages 13 through 40, the study meticulously examined whether adolescent SES, represented by parental income and education, influenced specific health-related behaviors: breakfast regularity, leisure time physical activity (LTPA), sleep difficulties, alcohol consumption, and smoking habits. Further, it probed the extent to which these behaviors correlate with adult depressed mood and if such associations might be moderated by SES indicators.

The methodological rigor involved employing linear regression models to dissect distinct relationships: the impact of parental SES on adolescent health behaviors and the consequent effect of these behaviors on adult depressive symptoms. The researchers aimed not only to elucidate direct pathways but also to understand how these health behaviors contribute to the covariance between adult SES and depressed mood — a statistical reflection of social inequality in mental health.

Contrary to conventional wisdom, findings revealed only modest socioeconomic differentials in adolescent health behaviors. Specifically, higher household income was associated with greater engagement in leisure time physical activity, and higher parental education corresponded to more consistent breakfast consumption. However, other prevalent behaviors like difficulties falling asleep, alcohol use, and smoking did not significantly vary by SES, indicating a more complex landscape than anticipated.

Most strikingly, none of the adolescent health behaviors examined exhibited a direct or moderated association with adult depressed mood. This pivotal result implies that these behaviors do not independently mediate the link between adolescent SES and adult mental health, challenging the strength of the behavioral pathway within the APM framework. These findings question the emphasis placed on modifying adolescent health behaviors as a primary strategy to mitigate long-term socioeconomic disparities in depression.

Delving deeper, the study underscores adolescent depressed mood itself as the most potent predictor of adult depressed mood, eclipsing the influence of health behaviors. This suggests that interventions targeting depressive symptoms during adolescence might yield more substantial dividends in reducing adult mental health inequalities than those solely focused on lifestyle factors.

The implications of this research are multifaceted. From a public health perspective, it urges a recalibration of priorities away from generic health behavior modifications toward more nuanced psychological support and early identification of depression during adolescence. It highlights the necessity of addressing mental health directly to disrupt the persistence of socioeconomic disparities in adult mood disorders.

Moreover, this study contributes importantly to the field of psychiatric epidemiology by employing longitudinal data with repeated measures, thereby enhancing the reliability of findings and avoiding the pitfalls of cross-sectional designs. The longitudinal framework captures developmental trajectories and temporal sequences essential for understanding causality in complex biopsychosocial phenomena.

Yet, the minimal socioeconomic variance detected in adolescent health behaviors questions the generalizability of the behavioral explanation of health inequalities across different sociocultural contexts. It opens avenues for further investigation into alternative mechanisms whereby SES impacts adult mental health, such as psychosocial stressors, environmental exposures, access to resources, and genetic predispositions.

This nuanced picture underscores the need for interdisciplinary approaches combining psychology, sociology, and public health to unravel the multifactorial nature of depression and its social determinants. As depression remains a leading cause of global disability, these insights bear significant relevance for designing targeted, effective prevention and intervention strategies.

Finally, the study’s emphasis on adolescence as a critical period for mental health trajectories reinforces the value of longitudinal research designs in capturing dynamic developmental processes. Future research may benefit from integrating biological markers and more granular socioeconomic indicators to further elucidate pathways linking early-life conditions to adult psychiatric outcomes.

In sum, while adolescent health behaviors correlate modestly with parental SES, their lack of predictive power for adult depressed mood refines our understanding of the behavioral explanation of health inequalities. This landmark longitudinal analysis redirects focus toward adolescent depressive states as crucial leverage points for disrupting intergenerational cycles of mental health disparities.

Subject of Research: The role of adolescent health behaviors in mediating the effect of socioeconomic status on adult depressed mood.

Article Title: Socioeconomic differences in adolescent health behaviors and their effect on inequalities in adult depressed mood: findings from a 27-year longitudinal study.

Article References:
Jørgensen, M., Wold, B., Smith, O.R. et al. Socioeconomic differences in adolescent health behaviors and their effect on inequalities in adult depressed mood: findings from a 27-year longitudinal study. BMC Psychiatry 25, 364 (2025). https://doi.org/10.1186/s12888-025-06679-6

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12888-025-06679-6