Suicidal ideation is prevalent among university students and is associated with a complex interplay of psychological, interpersonal, and behavioral factors. While prior research has examined individual predictors such as sleep disturbances, depressive symptoms, and impulsivity, less is known about how these factors co-occur in clinically distinct profiles. This study aimed to identify latent profiles of suicide risk using a multidimensional model.We conducted a secondary data analysis using the Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide (ANSWERS) dataset, which includes self-reported data from 971 U.S. university students aged 18 to 52 years (M = 20.10, SD = 2.41). Seven continuous variables were included as indicators: sleep quality (PSQI), insomnia severity (ISI), depressive symptoms (CES-D), suicidal ideation severity (C-SSRS), thwarted belongingness and perceived burdensomeness (INQ), and total impulsivity (UPPS-P). Latent Profile Analysis (LPA) was employed to identify subgroups, and model fit was assessed using the AIC, BIC, and entropy.Latent Profile Analysis identified five distinct profiles based on indicators of sleep, affect, interpersonal behavior, and impulsivity. These included a severely distressed profile characterized by elevated depressive symptoms, suicidal ideation, sleep disturbances, and interpersonal burden; an interpersonally burdened profile with mild affective symptoms; a moderately symptomatic profile; a psychologically resilient profile with minimal symptoms across domains; and a high impulsivity profile accompanied by emotional dysregulation.This study identified five clinically distinct profiles of suicide risk in a large sample of university students. These results may inform the development of tailored screening and intervention strategies in campus-based mental health settings.
Baldini et al. (Tue,) studied this question.