Multimorbidity, especially physical–mental multimorbidity, is an emerging global health challenge. However, the characteristics and patterns of physical–mental multimorbidity based on the diagnosis of mental disorders in Chinese adults remain unclear.

A cross-sectional study was conducted from November 2004 to April 2005 among 13,358 adults (ages 18–65years) residing in Liaoning Province, China, to evaluate the occurrence of physical–mental multimorbidity. Mental disorders were assessed using the Composite International Diagnostic Interview (version 1.0) with reference to the Diagnostic and Statistical Manual of Mental Disorders (3rd Edition Revised), while physical diseases were self-reported. Physical–mental multimorbidity was assessed based on a list of 16 physical and mental morbidities with prevalence ≥1% and was defined as the presence of one mental disorder and one physical disease. The chi-square test was used to calculate differences in the prevalence and comorbidity of different diseases between the sexes. A matrix heat map was generated of the absolute number of comorbidities for each disease. To identify complex associations and potential disease clustering patterns, a network analysis was performed, constructing a network to explore the relationships within and between various mental disorders and physical diseases.

Physical–mental multimorbidity was confirmed in 3.7% (498) of the participants, with a higher prevalence among women (4.2%, 282) than men (3.3%, 216). The top three diseases with the highest comorbidity rate and average number of comorbidities were dysphoric mood (86.3%; 2.86), social anxiety disorder (77.8%; 2.78) and major depressive disorder (77.1%; 2.53). A physical–mental multimorbidity network was visually divided into mental and physical domains. Additionally, four distinct multimorbidity patterns were identified: ‘Affective-addiction’, ‘Anxiety’, ‘Cardiometabolic’ and ‘Gastro-musculoskeletal-respiratory’, with the digestive-respiratory-musculoskeletal pattern being the most common among the total sample. The affective-addiction pattern was more prevalent in men and rural populations. The cardiometabolic pattern was more common in urban populations.

The physical–mental multimorbidity network structure and the four patterns identified in this study align with previous research, though we observed notable differences in the proportion of these patterns. These variations highlight the importance of tailored interventions that address specific multimorbidity patterns while maintaining broader applicability to diverse populations.

Depression affects twice as many women as men. Risk factors for depression certainly impact this difference, but their strong interconnectedness challenges the assessment of standalone contributions. Network models allow the identification of systematic independent relationships between individual symptoms and risk factors. This study aimed to evaluate whether the extended networks of depressive symptoms, cognitive functions, and leisure activities in like-sex twins differ depending on gender or zygosity.

Twins, including 2,040 women (918 monozygotic and 1,122 dizygotic) and 1,712 men (730 monozygotic and 982 dizygotic), were selected from the Danish Twin Registry for having, along with their like-sex co-twin, completed measures of depressive symptoms, cognition, and leisure activities (physical, intellectual, and social). Network models were estimated and compared at three levels: co-twins to each other within groups defined by gender and zygosity; monozygotic to dizygotic twins within the same gender, and between genders.

No significant differences were observed when co-twins were compared to each other, regardless of the pair’s zygosity or gender, nor when monozygotic twins were compared to dizygotic twins within gender. However, the gendered networks differed significantly in global strength, structure, and partial correlations between specific depressive symptoms and risk factors, all indicating stronger connectedness in women relative to men.

Environmental factors appear to best explain between-gender network differences. Women’s networks showed significantly stronger associations both among depressive symptoms and between depressive symptoms and risk factors (i.e., decreased cognition and leisure activities). Longitudinal research is needed to determine the causality and directionality of these relationships.

Although cognitive remediation (CR) improves cognition and functioning, the key features that promote or inhibit its effectiveness, especially between cognitive domains, remain unknown. Discovering these key features will help to develop CR for more impact.

To identify interrelations between cognition, symptoms, and functioning, using a novel network analysis approach and how CR affects these recovery outcomes.

A secondary analysis of randomized controlled trial data (N = 165) of CR in early psychosis. Regularized partial correlation networks were estimated, including symptoms, cognition, and functioning, for pre-, post-treatment, and change over time. Pre- and post-CR networks were compared on global strength, structure, edge invariance, and centrality invariance.

Cognition, negative, and positive symptoms were separable constructs, with symptoms showing independent relationships with cognition. Negative symptoms were central to the CR networks and most strongly associated with change in functioning. Verbal and visual learning improvement showed independent relationships to improved social functioning and negative symptoms. Only visual learning improvement was positively associated with personal goal achievement. Pre- and post-CR networks did not differ in structure (M = 0.20, p = 0.45) but differed in global strength, reflecting greater overall connectivity in the post-CR network (S = 0.91, p = 0.03).

Negative symptoms influenced network changes following therapy, and their reduction was linked to improvement in verbal and visual learning following CR. Independent relationships between visual and verbal learning and functioning suggest that they may be key intervention targets to enhance social and occupational functioning.

Diagnosis in psychiatry faces familiar challenges. Validity and utility remain elusive, and confusion regarding the fluid and arbitrary border between mental health and illness is increasing. The mainstream strategy has been conservative and iterative, retaining current nosology until something better emerges. However, this has led to stagnation. New conceptual frameworks are urgently required to catalyze a genuine paradigm shift.

We outline candidate strategies that could pave the way for such a paradigm shift. These include the Research Domain Criteria (RDoC), the Hierarchical Taxonomy of Psychopathology (HiTOP), and Clinical Staging, which all promote a blend of dimensional and categorical approaches.

These alternative still heuristic transdiagnostic models provide varying levels of clinical and research utility. RDoC was intended to provide a framework to reorient research beyond the constraints of DSM. HiTOP began as a nosology derived from statistical methods and is now pursuing clinical utility. Clinical Staging aims to both expand the scope and refine the utility of diagnosis by the inclusion of the dimension of timing. None is yet fit for purpose. Yet they are relatively complementary, and it may be possible for them to operate as an ecosystem. Time will tell whether they have the capacity singly or jointly to deliver a paradigm shift.

Several heuristic models have been developed that separately or synergistically build infrastructure to enable new transdiagnostic research to define the structure, development, and mechanisms of mental disorders, to guide treatment and better meet the needs of patients, policymakers, and society.

Few studies have examined the long-term outcomes of first-episode psychosis (FEP) among patients beyond symptomatic and functional remission. This study aimed to broaden the scope of outcome indicators by examining the relationships between 12 outcomes of FEP patients at 20.9 years after their initial diagnosis.

At follow-up, 220 out of 550 original patients underwent a new assessment. Twelve outcomes were assessed via semistructured interviews and complementary scales: symptom severity, functional impairment, personal recovery, social disadvantage, physical health, number of suicide attempts, number of episodes, current drug use, dose-years of antipsychotics (DYAps), cognitive impairment, motor abnormalities, and DSM-5 final diagnosis. The relationships between these outcome measures were investigated using Spearman’s correlation analysis and exploratory factor analysis, while the specific connections between outcomes were ascertained using network analysis.

The outcomes were significantly correlated; specifically, symptom severity, functioning, and personal recovery showed the strongest correlations. Exploratory factor analysis of the 12 outcomes revealed two factors, with 11 of the 12 outcomes loading on the first factor. Network analysis revealed that symptom severity, functioning, social disadvantage, diagnosis, cognitive impairment, DYAps, and number of episodes were the most interconnected outcomes.

Network analysis provided new insights into the heterogeneity between outcomes among patients with FEP. By considering outcomes beyond symptom severity, the rich net of interconnections elucidated herein can facilitate the development of interventions that target potentially modifiable outcomes and generalize their impact on the most interconnected outcomes.

The school–vacation cycle may have impacts on the psychological states of adolescents. However, little evidence illustrates how transition from school to vacation impacts students’ psychological states (e.g. depression and anxiety).

To explore the changing patterns of depression and anxiety symptoms among adolescent students within a school–vacation transition and to provide insights for prevention or intervention targets.

Social demographic data and depression and anxiety symptoms were measured from 1380 adolescent students during the school year (age: 13.8 ± 0.88) and 1100 students during the summer vacation (age: 14.2 ± 0.93) in China. Multilevel mixed-effect models were used to examine the changes in depression and anxiety levels and the associated influencing factors. Network analysis was used to explore the symptom network structures of depression and anxiety during school and vacation.

Depression and anxiety symptoms significantly decreased during the vacation compared to the school period. Being female, higher age and with lower mother's educational level were identified as longitudinal risk factors. Interaction effects were found between group (school versus vacation) and the father's educational level as well as grade. Network analyses demonstrated that the anxiety symptoms, including ‘Nervous’, ‘Control worry’ and ‘Relax’ were the most central symptoms at both times. Psychomotor disturbance, including ‘Restless’, ‘Nervous’ and ‘Motor’, bridged depression and anxiety symptoms. The central and bridge symptoms showed variation across the school vacation.

The school–vacation transition had an impact on students’ depression and anxiety symptoms. Prevention and intervention strategies for adolescents’ depression and anxiety during school and vacation periods should be differentially developed.

One of the most relevant risk factors for suicide is the presence of previous attempts. The symptomatic profile of people who reattempt suicide deserves attention. Network analysis is a promising tool to study this field.

To analyze the symptomatic network of patients who have attempted suicide recently and compare networks of people with several attempts and people with just one at baseline.

1043 adult participants from the Spanish cohort “SURVIVE” were part of this study. Participants were classified into two groups: single attempt group (n = 390) and reattempt group (n = 653). Different network analyses were carried out to study the relationships between suicidal ideation, behavior, psychiatric symptoms, diagnoses, childhood trauma, and impulsivity. A general network and one for each subgroup were estimated.

People with several suicide attempts at baseline scored significantly higher across all clinical scales. The symptomatic networks were equivalent in both groups of patients (p > .05). Although there were no overall differences between the networks, some nodes were more relevant according to group belonging.

People with a history of previous attempts have greater psychiatric symptom severity but the relationships between risk factors show the same structure when compared with the single attempt group. All risk factors deserve attention regardless of the number of attempts, but assessments can be adjusted to better monitor the occurrence of reattempts.

Among those with common mental health disorders (e.g. mood, anxiety, and stress disorders), comorbidity of substance and other addictive disorders is prevalent. To simplify the seemingly complex relationships underlying such comorbidity, methods that include multiple measures to distill which specific addictions are uniquely associated with specific mental health disorders rather than due to the co-occurrence of other related addictions or mental health disorders can be used.

In a general population sample of Jewish adults in Israel (N = 4002), network analysis methods were used to create partial correlation networks of continuous measures of problematic substance (non-medical use of alcohol, tobacco, cannabis, and prescription sedatives, stimulants, and opioid painkillers) and behavioral (gambling, electronic gaming, sexual behavior, pornography, internet, social media, and smartphone) addictions and common mental health problems (depression, anxiety, and post-traumatic stress disorder [PTSD]), adjusted for all variables in the model.

Strongest associations were observed within these clusters: (1) PTSD, anxiety, and depression; (2) problematic substance use and gambling; (3) technology-based addictive behaviors; and (4) problematic sexual behavior and pornography. In terms of comorbidity, the strongest unique associations were observed for PTSD and problematic technology-based behaviors (social media, smartphone), and sedatives and stimulants use; depression and problematic technology-based behaviors (gaming, internet) and sedatives and cannabis use; and anxiety and problematic smartphone use.

Network analysis isolated unique relationships underlying the observed comorbidity between common mental health problems and addictions, such as associations between mental health problems and technology-based behaviors, which is informative for more focused interventions.