This study addresses the limitation of traditional suicide risk models that focus on isolated factors by examining how sleep disturbances, depressive symptoms, interpersonal vulnerabilities, and impulsivity co-occur within individuals. Using latent profile analysis in a large sample of university students, the study identifies five distinct multidimensional risk profiles, highlighting the heterogeneity of suicidal risk. These findings support the need for more tailored, profile-based screening and intervention strategies in university mental health settings.
The study addressed the problem by using a multidimensional, data-driven approach rather than examining suicide risk factors in isolation. Drawing on survey data from 971 university students, the authors applied latent profile analysis to identify naturally occurring subgroups based on sleep, depressive symptoms, suicidal ideation, interpersonal distress, and impulsivity. This allowed them to uncover distinct patterns of risk that may be missed by traditional one-factor screening approaches.
The study used the ANSWERS dataset (Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide) from the National Sleep Research Resource (NSRR). The population consisted of 971 U.S. university students who participated in a cross-sectional survey conducted from June 2020 to June 2021; participants were 18 to 52 years old (mean age 20.10 years), and 73.2% were female. The dataset included self-reported demographic information and questionnaire-based measures of sleep, depressive symptoms, suicidal ideation, interpersonal needs, and impulsivity.
Latent profile analysis identified five distinct suicide-risk profiles in university students, showing that risk is not uniform but clusters into clinically meaningful patterns. These included a severely distressed profile with the highest levels of depressive symptoms, suicidal ideation, sleep disturbance, and interpersonal burden; an interpersonally burdened profile with mild affective symptoms; a moderately symptomatic profile; a psychologically resilient profile with minimal symptoms; and a high impulsivity/emotional dysregulation profile. All indicators differed significantly across profiles, supporting the clinical distinctiveness of these subgroups and suggesting that suicide prevention in university settings may benefit from more tailored, profile-based approaches.
The findings highlight that suicide risk in university students is heterogeneous and best understood through multidimensional profiles rather than single risk factors. Identifying distinct subgroups suggests that screening and prevention strategies should be personalized, targeting specific combinations of vulnerabilities such as sleep disturbance, interpersonal distress, and impulsivity. These results support a shift toward transdiagnostic, profile-based approaches in campus mental health, with potential to improve early identification and intervention.
Suicide risk in university students is often assessed by focusing on single factors such as depression or the presence of suicidal thoughts. However, this approach may overlook how different domains of vulnerability interact within individuals. In this study, we used data from the ANSWERS dataset available through the National Sleep Research Resource (NSRR) to explore suicide risk from a multidimensional perspective, integrating sleep, mood, interpersonal functioning, and impulsivity. Analyzing data from 971 university students, we applied latent profile analysis to identify distinct patterns of co-occurring risk factors. Rather than a single continuum of severity, five different profiles emerged, each characterized by a unique combination of symptoms. While one group showed high levels of distress across all domains, others presented more specific patterns, such as elevated interpersonal burden or high impulsivity despite relatively low suicidal ideation. These findings highlight that suicide risk is not uniform and that some individuals may be at risk even in the absence of overt suicidal thoughts. In particular, students with high impulsivity or interpersonal distress may remain undetected by traditional screening approaches focused primarily on depression or ideation. Overall, our results suggest that adopting a multidimensional, profile-based approach could improve the identification of at-risk individuals and support more targeted prevention strategies in university settings.
Baldini, V., Varallo, G., Pisanò, G., Gnazzo, M., De Ronchi, D., Tubbs, A., Brand, S., Plazzi, G., & Fiorillo, A. (2026). Latent Profiles of Suicide Risk in University Students: A Multidimensional Model Integrating sleep, mood, interpersonal, and Behavioral Factors. Psychiatric Quarterly. https://doi.org/10.1007/s11126-026-10256-9
Guest Blogger: Dr. Valentina Baldini, Department of Biomedical and Neuromotor Sciences, University of Bologna, Italy