The study enrolled 542 adolescents and young adults with mental disorders who underwent in-hospital treatment between October 2022 and December 2023. For each participant, the researchers gathered a broad set of information: clinical characteristics, scores on the Brøset Violence Checklist, a structured instrument for assessing short-term risk of aggression, and serum hormone levels measured in blood samples. The goal was to determine which of these many variables, alone or in combination, were most strongly associated with a baseline history of suicide attempt, meaning an attempt that had occurred before or at the time of admission.

To build a model that would generalize well rather than simply fit the data at hand, the team randomly divided the cohort into a training set of 379 patients and a validation set of 163 patients, in a seven-to-three ratio. All statistical model building was performed on the training set, while the validation set was reserved to test how well the resulting tool performed on patients it had never seen. This internal validation strategy is considered a cornerstone of responsible predictive modeling in clinical research, because a model that only performs well on the data used to build it is of little practical value at the bedside.

Before fitting the final model, the researchers applied least absolute shrinkage and selection operator regression, commonly known as LASSO, a technique that shrinks the coefficients of weak predictors toward zero and effectively filters out redundant or uninformative variables. LASSO regression is particularly useful in medical datasets where many candidate predictors are correlated with one another, as it reduces overfitting and produces a more parsimonious set of features. The variables that survived this screening step were then entered into a multivariate logistic regression model, the standard statistical framework for estimating the independent contribution of each factor to a binary outcome, in this case the presence or absence of a suicide-attempt history.

Five variables ultimately earned a place in the final nomogram: a history of non-suicidal self-injury, serum prolactin levels, plasma total cortisol levels, the number of previous hospitalizations, and educational level. Each of these factors carries its own clinical logic. Non-suicidal self-injury, the deliberate harming of one’s own body without suicidal intent, has long been recognized as a powerful signal of underlying emotional distress and is one of the most consistent correlates of later suicidal behavior in young people. Repeated hospitalizations suggest a chronic or unstable course of illness, while lower educational attainment may reflect the cumulative disruption that mental disorders impose on development and social functioning.

The inclusion of two hormones adds a biological dimension that sets this study apart from many purely psychosocial risk models. Prolactin, best known for its role in lactation, is also influenced by stress and by several psychiatric medications, and altered prolactin levels have been linked in previous research to self-directed violence and emotional dysregulation. Cortisol, the body’s primary stress hormone, reflects the activity of the hypothalamic-pituitary-adrenal axis, the neuroendocrine system that orchestrates the physiological response to stress. Dysregulation of this axis has repeatedly been implicated in mood disorders and suicidal behavior, and measuring plasma total cortisol offers an objective, laboratory-based window into a patient’s stress biology that cannot be captured by interview alone.

A nomogram translates the coefficients of a logistic regression model into a visual chart on which a clinician can plot the value of each predictor, read off assigned points, sum them, and convert the total into an estimated probability. In this case, the estimated probability refers to the likelihood that a given hospitalized adolescent or young adult has a baseline history of suicide attempt. The appeal of the format lies in its simplicity: no calculator or software is required, and the relative weight of each factor is immediately visible, which can help clinicians understand why a particular patient scores as high or low risk.

The performance metrics reported by the team suggest a moderately strong tool. The area under the receiver operating characteristic curve, or AUC, a measure of how well a model distinguishes between those with and without the outcome, reached 0.771 in the training set, with a 95 percent confidence interval of 0.715 to 0.827, and 0.788 in the validation set, with a confidence interval of 0.711 to 0.865. Values in this range indicate discrimination that is useful but not definitive, which the authors explicitly acknowledge. Calibration, meaning the agreement between predicted probabilities and observed outcomes, was strong: the Hosmer-Lemeshow goodness-of-fit test yielded a chi-square of 12.672 with a p-value of 0.123 in the training set and a chi-square of 8.919 with a p-value of 0.349 in the validation set, both indicating satisfactory model fit. Decision curve analysis, a method for quantifying the clinical net benefit of a model across a range of decision thresholds, identified effective thresholds from 19 percent to 73 percent in the training set and from 10 percent to 74 percent in the validation set, supporting the tool’s utility as a risk-stratification reference.

The authors are careful to position the nomogram as an auxiliary quantitative reference rather than a standalone screening instrument. Suicide risk assessment in young people is a complex clinical judgment that must integrate interview findings, collateral information, mental state examination and safety planning, and no statistical model can replace that process. What this tool offers is a structured way to visualize how multiple dimensions of a patient’s profile, from self-injury history to endocrine markers, converge to shape risk, potentially flagging high-risk patients who might otherwise receive insufficient attention during a busy admission. The researchers suggest it may help inform targeted suicide-prevention interventions within inpatient psychiatric settings.

Because the study is cross-sectional, it captures associations at a single point in time and cannot establish that the five predictors cause suicide attempts, nor can it predict future attempts prospectively. The cohort was drawn from a single hospital network in China, so external validation in other countries, cultures and healthcare systems will be needed before broader adoption. Nonetheless, the work represents a meaningful step toward integrating biological and psychosocial data into accessible bedside tools for one of medicine’s most urgent challenges. As the authors note, characterizing the risk profiles of hospitalized adolescents and young adults with mental disorders is essential for targeted suicide prevention, and a validated, visual, multidimensional model gives clinicians one more way to see that risk clearly.

Subject of Research: A nomogram integrating psychosocial and hormonal correlates of baseline suicide-attempt history in hospitalized adolescents and young adults with mental disorders

Article Title: Development and internal validation of a nomogram for visualising multidimensional correlates of baseline suicide‑attempt history among hospitalized adolescents and young adults with mental disorders

Article References: Ren, Y., Liu, Q., Zhu, Y., Wu, J., Chen, H., Pu, L., Dong, Z., Zhu, H., & Zhang, X. (2026). Development and internal validation of a nomogram for visualising multidimensional correlates of baseline suicide‑attempt history among hospitalized adolescents and young adults with mental disorders. BMC Psychiatry. https://doi.org/10.1186/s12888-026-08613-w

Image Credits: AI Generated

DOI: 10.1186/s12888-026-08613-w

Keywords: nomogram, suicide-attempt history, adolescents and young adults, mental disorders, non-suicidal self-injury, prolactin, cortisol, LASSO regression, logistic regression, risk prediction, BMC Psychiatry, inpatient psychiatry

The adolescent and young adult phases are pivotal in shaping lifelong mental health trajectories, yet they are also periods characterized by heightened vulnerability to psychiatric conditions. By systematically analyzing data encompassing over thirty years, the study offers granular insights into the distribution and intensity of mental health burdens in various geographic and socio-economic contexts. This high-resolution perspective illuminates areas of heightened risk, facilitating more targeted public health interventions.

Central to the findings is the notion that mental disorders during adolescence and young adulthood contribute significantly to the global disease burden, a factor often underestimated in public health dialogues dominated by infectious and non-communicable physical diseases. The study employs disability-adjusted life years (DALYs) as its metric, which captures the dual impact of premature mortality and years lived with disability, thereby painting a holistic picture of mental health’s toll.

Notably, the study identifies anxiety disorders, depressive disorders, and substance use disorders as the primary contributors to the mental health burden among young people worldwide. The epidemiological trends depicted reveal nuanced differences: while depressive disorders show a marked increase in some regions, substance use disorders remain persistently high in others. These disparities highlight the complex interactions between culture, socioeconomic factors, and access to care.

In-depth regional analysis reveals that high-income countries record some of the highest rates of anxiety and mood disorders among youth, but middle- and low-income countries are experiencing rapidly rising trends. This stresses that the mental health crisis is no longer confined to affluent societies, calling for global resource mobilization and inclusion of mental health in broader health frameworks, especially in developing regions.

Drawing from the vast dataset, the authors emphasize that early onset of mental disorders, often occurring in adolescence or young adulthood, portends a chronic course that can impair educational attainment, occupational productivity, and social integration, amplifying economic and social costs on a massive scale. Interventions aimed at early detection, streamlined access to care, and societal destigmatization are crucial components for mitigating this trajectory.

Another important dimension the study explores is gender differences in mental disorder burdens. It highlights that females tend to report higher incidences of anxiety and depressive disorders, whereas males disproportionately experience substance use disorders. Such distinctions necessitate gender-sensitive mental health strategies to effectively address the disparate needs and risks faced by young men and women.

The implications of these findings for mental health services are profound. Many regions face considerable gaps in mental health infrastructure, a shortage of trained professionals, and limited integration of mental health care into primary health services. Scaling up mental health services to meet the growing demand among youth populations must therefore be a public health priority, supported by policy reforms and increased funding.

Given the rise of digital technology use worldwide, the study also prompts consideration of innovative approaches, such as telepsychiatry and digital therapeutics, that could bridge care gaps and extend mental health services into underserved and remote areas. Harnessing such technologies offers scalable solutions compatible with contemporary youth behaviors and preferences.

The analysis further examines how comorbidities and social determinants influence the burden of mental disorders in adolescents and young adults. Socioeconomic deprivation, exposure to violence, and social exclusion markedly exacerbate mental health risks, underscoring the importance of multisectoral approaches that link social policy, education, and health services in youth mental health promotion.

Political contexts and public health emergencies also factor into the mental wellbeing of young populations. The enduring impact of conflicts, large-scale displacement, and more recently, the COVID-19 pandemic, have intensified mental health challenges on a global scale. The study’s temporal scope, covering 1990–2021, encompasses many such disruptive events, contributing to the up-to-date relevance of its insights.

Importantly, the research leverages a unified global health framework, providing consistent and comparable data across countries and regions. This quantitative rigor enables policymakers and stakeholders to identify priority areas for intervention, monitor trends, and allocate resources more effectively in an international context often hindered by fragmented mental health data.

The authors highlight that addressing the mental health landscape is not solely a medical challenge but a societal one, necessitating the active involvement of families, schools, communities, and governments. Holistic strategies incorporating prevention, early intervention, and stigma reduction are essential to alleviate the escalating burden and to foster resilience among young populations globally.

Mental health is now recognized as a pivotal element of the United Nations’ Sustainable Development Goals, reflecting its integral role in health, education, and economic development. This study’s detailed mapping of mental disorder burdens offers vital evidence to drive global commitments towards achieving these goals, particularly by reducing health inequities and enhancing adolescent and young adult wellbeing.

With this extensive and systematic analysis, the study paves the way for a new era of mental health prioritization that is data-driven and globally coordinated. It challenges stakeholders worldwide to acknowledge the silent, often invisible, epidemic of youth mental disorders, pushing for comprehensive action that bridges research, policy, and practice.

In conclusion, the call to action emerging from this research is clear: tackling the global burden of adolescent and young adult mental disorders demands urgent, sustained, and multifaceted efforts. Only through collaborative international initiatives, innovative service delivery models, and societal commitment can the tide of mental health challenges be stemmed, securing a healthier and more hopeful future for the world’s youth.

Subject of Research: Global, regional, and national burden of mental disorders among adolescents and young adults from 1990 to 2021, analyzed through the Global Burden of Disease Study framework.

Article Title: Global, regional, and national burden of mental disorders among adolescents and young adults, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021.

Article References:
Wang, Z., Dou, Y., Yang, X. et al. Global, regional, and national burden of mental disorders among adolescents and young adults, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Transl Psychiatry 15, 397 (2025). https://doi.org/10.1038/s41398-025-03623-w

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41398-025-03623-w