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BACKGROUND: Craniotomy to remove brain tumors is an intricate procedure with multiple postoperative symptoms. However, there has been limited research on the symptom networks of these patients. To this end, this study aims to explore these symptom networks, revealing their interplay to inform better symptom control, hasten the discovery of postoperative issues, and tailor Enhanced Recovery After Surgery (ERAS) protocols, all to enhance recovery and enhance patient care. METHODS: From September 2023 to March 2024, 211 patients with primary brain tumors who underwent craniotomy at Shanghai Tongji Hospital were recruited. Their symptoms were assessed using the MDASI-BT (M.D. Anderson Symptom Inventory Brain Tumor Module) one day post-craniotomy. The symptom network of 22 symptoms was visualized using R, with central and bridge symptoms identified. RESULTS: Sadness (rs=2.482) and difficulty in understanding (rs=1.138) have the highest strength of all symptoms, indicating they are the central symptoms. Sadness (rb=2.155) and loss of appetite (rb=1.828) have the highest value of betweenness, indicating they are the bridge symptoms. Strong correlations were found between difficulty in understanding and difficulty in speaking (r = 0.701), distress and sadness (r = 0.666), fatigue and lethargy (r = 0.632), and nausea and vomiting (r = 0.601). Subgroup analysis revealed that noninvasive tumor patients exhibited similar symptom networks to the overall cohort, whereas invasive tumor patients showed weak symptom connections, resulting in no discernible network. CONCLUSION: This study underscores the importance of understanding symptom networks in brain tumor patients post-craniotomy, highlighting key symptom interrelationships. These insights can guide more effective symptom management, early complication detection, and optimization of ERAS protocols, ultimately enhancing recovery and patient care.

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BACKGROUND: Arm symptoms commonly endure in post-breast cancer period and persist into long-term survivorship. However, a knowledge gap existed regarding the interactions among these symptoms. This study aimed to construct symptom networks and visualize the interrelationships among arm symptoms in breast cancer survivors (BCS) both with and without lymphedema (LE). PATIENTS AND METHODS: We conducted a secondary analysis of 3 cross-sectional studies. All participants underwent arm circumference measurements and symptom assessment. We analyzed 17 symptoms with a prevalence >15%, identifying clusters and covariates through exploratory factor and linear regression analysis. Contemporaneous networks were constructed with centrality indices calculated. Network comparison tests were performed. RESULTS: 1116 cases without missing data were analyzed, revealing a 29.84% prevalence of LE. Axillary lymph node dissection [ALND] (vs sentinel lymph node biopsy [SLNB]), longer post-surgery duration, and radiotherapy significantly impacted overall symptom severity (P < .001). "Lymphatic Stasis," "Nerve Injury," and "Movement Limitation" symptom clusters were identified. Core symptoms varied: tightness for total sample network, firmness for non-LE network, and tightness for LE network. LE survivors reported more prevalent and severe arm symptoms with stronger network connections than non-LE group (P = .010). No significant differences were observed among different subgroups of covariates (P > .05). Network structures were significantly different between ALND and SLNB groups. CONCLUSION: Our study revealed arm symptoms pattern and interrelationships in BCS. Targeting core symptoms in assessment and intervention might be efficient for arm symptoms management. Future research is warranted to construct dynamic symptom networks in longitudinal data and investigate causal relationships among symptoms.

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BACKGROUND AND AIMS: Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets. DESIGN, SETTING AND PARTICIPANTS: Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part. MEASUREMENTS: Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks. FINDINGS: Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09). CONCLUSIONS: Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

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BACKGROUND: Chronic tic disorders (CTD) are multifaceted disorders characterized by multiple motor and/or vocal tics. They are often associated with complex tics including echophenomena, paliphenomena, and coprophenomena as well as psychiatric comorbidities such as attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). OBJECTIVES: Our goal was to uncover the inter-relational structure of CTD and comorbid symptoms in children and adults and to understand changes in symptom structure across development. METHODS: We used network and graph analyses to uncover the structure of association of symptoms in childhood/adolescence (n = 529) and adulthood (n = 503) and how this structure might change from childhood to adulthood, pinpointing core symptoms as a main target for interventions. RESULTS: The analysis yielded core symptom networks in young and adult patients with CTD including complex tics and tic-related phenomena as well as touching people and objects. Core symptoms in childhood also included ADHD symptoms, whereas core symptoms in adults included symptoms of OCD instead. Interestingly, self-injurious behavior did not play a core role in the young CTD network, but became one of the central symptoms in adults with CDT. In addition, we found strong connections between complex motor and vocal tics as well as echolalia and echopraxia. CONCLUSIONS: Next to other complex tics, echophenomena, paliphenomena, and coprophenomena can be regarded core symptoms of CTD. ADHD symptoms are closely related to CTD in childhood, whereas symptoms of OCD and self-injurious behavior are closely associated with CTD in adults. Our results suggest that a differentiation between motor and vocal tics is somewhat arbitrary.

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PURPOSE: Breast cancer patients experience symptoms and side effects from multimodal treatments, which often include menopausal symptoms resulting from cytotoxic chemotherapy or estrogen suppression therapy. This study aimed to explore the symptom network and clusters and its relationship to quality of life (QoL) in breast cancer patients who receive multimodal cancer treatment and experience treatment-related menopausal symptoms. METHODS: A correlational study was conducted. Breast cancer patients receiving multimodal cancer treatment and experiencing treatment-related menopausal symptoms were included while they were receiving radiation therapy (N = 250). Symptoms, functions and QoL were assessed using the EORTC QLQ-C30 and BR45. Network analysis, principal component analysis, exploratory factor analysis, and multiple linear regression analysis were conducted. RESULTS: Fatigue was the most central symptom in the symptom-only network as well as in the network consisting of symptoms and QoL. Fatigue, systemic therapy side effects, appetite loss, and cognitive symptoms demonstrated significant associations with QoL. The cancer and treatment related symptom cluster consisted of fatigue, cognitive symptoms, emotional symptoms and systemic therapy side effects. Breast cancer therapy-specific symptoms, such as arm symptoms, skin mucosis symptoms, and breast symptoms, formed a cluster with pain. CONCLUSION: Fatigue was the most central symptom in breast cancer patients receiving multimodal cancer treatment and experiencing menopausal symptoms. Evaluation of fatigue and providing interventions to manage fatigue would contribute to improvement of QoL of breast cancer patients receiving multimodal cancer treatments. Future network analysis and symptom cluster studies should specify the population of interest and the treatment phase using comprehensive symptom evaluation tools.

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Ephedra herb (EH), an important medicine prescribed in herbal formulas by Traditional Chinese Medicine practitioners, has been widely used in the treatment of viral pneumonia in China. However, the molecular basis of EH in viral pneumonia remains unclear. In this study, a ternary correlation multi-symptom network strategy was established based on in vivo chemical profile identification and metabolomics to explore the molecular basis of EH against viral pneumonia. Results showed that 143 compounds of EH and 70 prototype components were identified in vivo. EH could reduce alveolar-capillary barrier disruption in rats with viral pneumonia and significantly downregulate the expression of inflammatory factors and bronchoalveolar lavage fluid. Plasma metabolomics revealed that EH may be involved in the regulation of arachidonic acid, tryptophan, tyrosine, nicotinate, and nicotinamide metabolism. The multi-symptom network showed that 12 compounds have an integral function in the treatment of viral pneumonia by intervening in many pathways related to viruses, immunity and inflammation, and lung injury. Further verification demonstrated that sinapic acid and frambinone can regulate the expression of related genes. It has been shown to be a promising representative of the pharmacological constituents of ephedra.

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BACKGROUND AND OBJECTIVES: Depression and anxiety often co-occur and have worse impacts on the elderly when experienced simultaneously. Although physical exercise may alleviate depression and anxiety, how it affects the specific symptoms is not fully understood. METHODS: A total of 8884 participants was selected from the 2018 CLHLS database. The 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Generalized Anxiety Disorder Scale-7 (GAD-7) were used to assess depression and anxiety, respectively. Participants were divided into the exercise and the nonexercise groups using propensity score matching to minimize the influence of confounding variables. Depression-anxiety symptom networks were constructed, and network indexes were computed for each group, based on various packages of R. By computing network connectivity, invulnerability simulation was used to investigate the role of physical exercise in network robustness. RESULTS: Both groups had D3 (sad mood), A4 (trouble relaxing) and A2 (uncontrollably worry) as central symptoms. In the exercise group, A1 (nervousness), A3 (too much worry) and D1 (bothered by little things) were the strongest bridge nodes. In the nonexercise group, A1 (nervousness), D1 (bothered by little things) and A4 (trouble relaxing) played this role. Participation in physical exercise decreased the centrality of D9 (cannot get doing) but increased the centrality of A3 (too much worry). Furthermore, the exercise group had higher network invulnerability than the nonexercise group under random attack conditions. CONCLUSIONS: Physical exercise affected core symptoms of depression-anxiety and the interactions of symptoms. Targeting central or bridge nodes may be an effective intervention for alleviating the comorbidity. Increased network invulnerability manifested the positive effects of physical exercise.

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BACKGROUND: Robust evidence suggests that individuals exposed to childhood trauma are more vulnerable to suffering from later depression. However, the pathway connecting the experience of childhood trauma and depression remains unclear. PARTICIPANTS AND SETTINGS: A total of 3663 participants from six colleges in China completed the Childhood Trauma Questionnaire-Short Form, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, and Multidimensional Existential Meaning Scale. Among all participants, 3115 (Mage = 19.20, SDage = 1.38, males = 1384) participants met the selective standard of suffering from childhood trauma and were divided into the traumatized depressed group (the DT group) (n = 1432, Mage = 19.26, males = 700) and traumatized non-depressed group (the UDT group) (n = 1683, Mage = 19.15, males = 684). METHODS: In the present study, we examined the comorbidity of anxiety and the facets of meaning in the life network model. We then calculated the bridge symptoms and compared the networks of the DT group and the UDT group. RESULTS: The results of the t-test showed that the DT group scored significantly higher on all symptoms of anxiety and significantly lower on all dimensions of meaning in life compared to the UDT group. Meanwhile, the strongest bridge exists between "Mattering" and "Restlessness" in the symptom network of the DT group, while there is no bridge in the symptom network of the UDT group. The result of NCT indicates that the global strength and the EI value of "Mattering" are significantly higher in the symptom network of the DT group than in the UDT group. CONCLUSION: Intervention targeting improving the self-esteem of individuals suffering from childhood trauma may help to alleviate their depression and anxiety symptoms.

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INTRODUCTION: Due to aging, older adults with cancer (OAC) may be confronted with a complex interplay of multiple age-related issues; coupled with receiving cancer treatment, OAC may experience multiple concurrent symptoms that require the identification of the core symptom for effective management. Constructing symptom networks will help in the identification of core symptoms and help achieve personalized and precise interventions. Currently, few studies have used symptom networks to identify core symptoms in OAC. Our objectives were to construct symptom networks of OAC, explore the core symptoms, and compare the differences in symptom networks among various subgroups. MATERIALS AND METHODS: Secondary analysis was performed using data from 485 OAC collected in 2021 from a cross-sectional survey named the Shanghai CANcer Survivor (SCANS) Report. The MD Anderson Symptom Inventory (MDASI) was used to assess the incidence and severity of cancer-related symptoms. We used the R package to construct symptom networks and identify the centrality indices. The network comparison test was used to compare network differences among the subgroups. RESULTS: The most common and severe symptoms reported were fatigue, disturbed sleep, and difficulty remembering. The network density was 0.718. Vomiting (rs = 1.81, rb = 2.13), fatigue (rs = 1.54, rb = 1.93), and sadness (rs = 0.81, rb = 0.69) showed the highest strength values, which suggested that these symptoms were more likely to co-occur with other symptoms. The network comparison tests showed significant differences in symptom network density between the subgroups categorized as survival "< 5 years" and survival "≥ 5 years" (p = 0.002), as well as between the those with comorbidities and those without comorbidities (p = 0.037). DISCUSSION: Our study identified symptom networks in 485 OAC. Vomiting, fatigue, and sadness were important symptoms in the symptom networks of OAC. The symptom networks differed among populations with different survival durations and comorbidities. Our network analysis provides a reference for future targeted symptom management and interventions in OAC. In the future, conducting dynamic research on symptom networks will be crucial to explore interaction mechanisms and change trends between symptoms.

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AIMS: The main aim of this study was to assess the prevalence of suicidal ideation and previous suicide attempts among Iranian patients diagnosed with Type-1 diabetes (T1D) and Type-2 diabetes (T2D). Additionally, the study sought to estimate the network structure of depressive symptoms and cognitive functions. METHODS: 1073 patients participated in the current study. We used Patient Health Questionnaire-9 (PHQ-9), Ask Suicide-Screening Questionnaire, diabetes-related factors, and a battery of cognitive functions tasks to estimate network structures. Also, suicidal ideations and suicide attempts prevalence have been estimated. Statistical analyses were performed using R-studio software, including mixed-graphical models (MGMs) for undirected effects and Directed Acyclic Graphs (DAGs) for directed effects. RESULTS: The prevalence of suicidal ideation was 29.97% in T1D and 26.81% in T2D (p < 0.05). The history of suicide attempts was higher in T1D (10.78%) compared to T2D (8.36%) (p < 0.01). In the MRF networks for T1D, suicidal ideation was directly linked to 'feeling guilt (PHQ.6)', 'Suicide (PHQ.9)', HbA1c, and FBS, while the Inhibition node was directly related to suicidal ideation. The DAGs suggested connections between 'depression', HbA1c, and 'inhibition' with suicidal ideation, along with a link between the current family history of suicide attempts and the patient's history of suicide attempts. For T2D, the MRF networks indicated direct links between suicidal ideation and 'anhedonia (PHQ.1)', 'suicide (PHQ.9)', age, being female, and BMI, with inhibition also being directly related to suicidal ideation. The DAGs revealed connections between 'depression', age, and 'inhibition' with suicidal ideation, as well as links between being female or single/divorced and the patient's history of suicide attempts. CONCLUSION: The findings suggest that suicide ideation is highly prevalent in patients with diabetes, and these symptoms should be carefully monitored in these patients.

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Background: A shift in research interest from separate care problem to care problem clusters among caregivers of people living with dementia may contribute to a better understanding of dementia care. However, the care problems network among caregivers of people living with dementia are still unknown. This study aimed to identify care problem clusters and core care problems, and explore demographic variables associated with these care problem clusters among caregivers of people living with dementia. Methods: Participants were recruited through memory clinics and WeChat groups. The principal component analysis was applied to identify care problem clusters. The network analysis was conducted to describe the relationships among care problems and clusters. Multiple linear models were used to explore the associated factors for the occurrence of the overall care problems and top three central care problem clusters. Results: A total of 1,012 carer-patient pairs were included in the analysis. Nine care problem clusters were identified. In the entire care problem network, "deterioration in activities of daily living" was the most core care problem cluster across the three centrality indices, followed by "verbal and nonverbal aggression" and "loss of activities of daily living." Variables including marital status, years of dementia diagnosis, number of dementia medication type, and caregiver's educational attainment were associated with the prevalence of these three care problem clusters. Conclusion: Our study suggests that there is a need to evaluate care problem clusters for the improvement of care problem management among people living with dementia. It is particularly important to include assessment and treatment of core care problem as an essential component of the dementia care.

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BACKGROUND: Posttraumatic stress symptoms of healthcare workers have become a significant public concern in the healthcare system that have long COVID-19. It is less known how the pandemic impacts the HCWs' PTSS longitudinally and long-term risk factors for it. METHODS: Four consecutive surveys were conducted among healthcare workers in China from 2019 to 2023 COVID-19 outbreaks. Multilevel mixed-effect models were used to examine longitudinal changes and risk factors. Network analysis was utilized to explore network centrality changes in PTSS symptoms. RESULTS: HCWs' PTSS symptoms were increased over time during the COVID-19 pandemic. Being female, being nurse, working in the emergency department, working longer hours, less frequently going back home and having COVID-19 infection are risk factors of PTSS for HCWs; unmarried is the protective factor. Significant interaction between symptom changes and profession exists. PTSS networks showed that Avoidance of thoughts, Emotional-cue activity, Exaggerated startle response and Hypervigilance were the central symptoms during four waves. The global strength of the PTSS network grows over time, and nodal strength of Avoidance of thoughts, Loss of interest and Negative beliefs increased by COVID-19. CONCLUSION: The pandemic's impacts on healthcare workers vary by professions. PTSS symptoms exacerbate, reinforce each other, and persists with recurring waves.

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BACKGROUND: This study aimed to elucidate the characteristics of symptom network of childhood trauma (CT) and sleep disorder (SD) in Chinese adolescents, with the influence of depressive symptoms taken into account. METHOD: A total of 1301 adolescent students were included, and their CT, SD and depressive symptoms were measured using the Pittsburgh sleep quality index (PSQI), the Childhood Trauma Questionnaire-Short Form (CTQ-SF), and The Patient Health Questionnaire-9 (PHQ-9), respectively. Central symptoms and bridge symptoms were identified based on centrality indices and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. RESULTS: In CT and SD symptom network, emotional abuse and sleep quality symptoms had the highest centrality values, and two bridge symptoms, i.e., emotional abuse and sleep disturbance symptoms, were also identified. In symptom network for CT, SD, and depressive symptoms, sleeping difficulty symptoms, daily dysfunction symptoms, and emotional abuse appeared to be potential bridge symptoms. In symptom network of CT, SD, and depressive symptoms (excluding the symptom of sleeping difficulty), daily dysfunction symptoms, emotional abuse, and sleep disturbance symptoms appeared to be bridge symptoms. CONCLUSIONS: In this study, emotional abuse and poor sleep quality were found to be central symptoms in the CT-SD network structure among Chinese adolescent students, with daytime dysfunction as the bridge symptom in the CT-SD-depression network structure. Systemic multi-level interventions targeting the central symptoms and bridge symptoms may be effective in alleviating the co-occurrence of CT, SD and depression in this population.

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BACKGROUND: Symptom networks can provide empirical evidence for the development of personalized and precise symptom management strategies. However, few studies have established networks of symptoms experienced by older patients on maintenance hemodialysis. Our goal was to examine the type of symptom clusters of older maintenance hemodialysis patients during dialysis and construct a symptom network to understand the symptom characteristics of this population. METHODS: The modified Dialysis Symptom Index was used for a cross-sectional survey. Network analysis was used to analyze the symptom network and node characteristics, and factor analysis was used to examine symptom clusters. RESULTS: A total of 167 participants were included in this study. The participants included 111 men and 56 women with a mean age of 70.05 ± 7.40. The symptom burdens with the highest scores were dry skin, dry mouth, itching, and trouble staying asleep. Five symptom clusters were obtained from exploratory factor analysis, of which the clusters with the most severe symptom burdens were the gastrointestinal discomfort symptom cluster, sleep disorder symptom cluster, skin discomfort symptom cluster, and mood symptom cluster. Based on centrality markers, it could be seen that feeling nervous and trouble staying asleep had the highest strength, and feeling nervous and feeling irritable had the highest closeness and betweenness. CONCLUSIONS: Hemodialysis patients have a severe symptom burden and multiple symptom clusters. Dry skin, itching, and dry mouth are sentinel symptoms in the network model; feeling nervous and trouble staying asleep are core symptoms of patients; feeling nervous and feeling irritable are bridge symptoms in this symptom network model. Clinical staff can formulate precise and efficient symptom management protocols for patients by using the synergistic effects of symptoms in the symptom clusters based on sentinel symptoms, core symptoms, and bridge symptoms.

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Background: Endocrine therapy-related symptoms are associated with early discontinuation and quality of life among breast cancer survivors. Although previous studies have examined these symptoms and clinical covariates, little is known about the interactions among different symptoms and correlates. This study aimed to explore the complex relationship of endocrine therapy-related symptoms and to identify the core symptoms among breast cancer patients. Methods: This is a secondary data analysis conducted based on a multicenter cross-sectional study of 613 breast cancer patients in China. All participants completed the 19-item Chinese version of the Functional Assessment of Cancer Therapy-Endocrine Subscale (FACT-ES). Multivariate linear regression analysis was performed to identify significant factors. A contemporaneous network with 15 frequently occurring symptoms was constructed after controlling for age, payment, use of aromatase inhibitors, and history of surgery. Network comparison tests were used to assess differences in network structure across demographic and treatment characteristics. Results: All 613 participants were female, with an average age of 49 years (SD = 9.4). The average duration of endocrine therapy was 3.6 years (SD = 2.3) and the average symptom score was 18.99 (SD = 11.43). Irritability (n = 512, 83.52%) and mood swings (n = 498, 81.24%) were the most prevalent symptoms. Lost interest in sex (mean = 1.95, SD = 1.39) and joint pain (mean = 1.57, SD = 1.18) were the most severe symptoms. The edges in the clusters of emotional symptoms ("irritability-mood swings"), vasomotor symptoms ("hot flashes-cold sweats-night sweats"), vaginal symptoms ("vaginal discharge-vaginal itching"), sexual symptoms ("pain or discomfort with intercourse-lost interest in sex-vaginal dryness"), and neurological symptoms ("headaches-dizziness") were the thickest in the network. There were no significant differences in network structure (P = 0.088), and global strength (P = 0.330) across treatment types (selective estrogen receptor modulators vs. aromatase inhibitors). Based on an evaluation of the centrality indices, irritability and mood swings appeared to be structurally important nodes after adjusting for the clinical covariates and after performing subgroup comparisons. Conclusion: Endocrine therapy-related symptoms are frequently reported issues among breast cancer patients. Our findings demonstrated that developing targeted interventions focused on emotional symptoms may relieve the overall symptom burden for breast cancer patients during endocrine therapy.

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BACKGROUND AND AIMS: Among the 11 current diagnostic criteria, craving is a potential central marker for understanding and for treatment of Substance Use Disorders (SUD). Our objective was to explore craving centrality across SUD based on the study of symptom interactions in cross-sectional network analyses of DSM-5 SUD diagnostic criteria. We hypothesized the centrality of "Craving" in SUD across substance types. DESIGN: Participants from the ADDICTAQUI clinical cohort with regular use (2 times per week threshold for a substance) and at least one DSM-5 SUD. SETTING: Outpatient substance use treatment services in Bordeaux, France. PARTICIPANTS: The sample of 1359 participants, had a mean age of 39 years old and 67% were males. The prevalence of SUD over the time course of the study was: 93% for alcohol, 98% for opioids, 94% for cocaine, 94% for cannabis and 91% tobacco. MEASUREMENTS: Construction of a Symptom Network Model conducted on the DSM-5 SUD criteria evaluated over the past 12 months for Alcohol-, Cocaine-, Tobacco-, Opioid- and Cannabis Use disorder. FINDINGS: The only symptom that consistently remained in terms of centrality was "Craving" [3.96 - 6.17] (z-scores), indicating that it exhibits a high degree of connections in the entire symptom network regardless of the substance. CONCLUSION: Identifying craving as central in SUD symptoms network confirms the role of craving as a marker of addiction. This constitutes a major avenue in the understanding of the mechanisms of addiction, with implications to ameliorate diagnostic validity and clarify treatment targets.

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Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person's day-to-day activity. In addition, MDD affects one's linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person's condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient-psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.

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INTRODUCTION: as late-life depression is associated with poor somatic health, we aimed to investigate the role of depression severity and symptom phenotypes in the progression of somatic multimorbidity. METHODS: we analysed data from 3,042 dementia-free individuals (60+) participating in the population-based Swedish National Study on Aging and Care in Kungsholmen. Using the baseline clinical assessment of 21 depressive symptoms from the Comprehensive Psychopathological Rating Scale, we: (i) diagnosed major, minor (in accordance with DSM-IV-TR) and subsyndromal depression; (ii) extracted symptom phenotypes by applying exploratory network graph analysis. Somatic multimorbidity was measured as the number of co-occurring chronic diseases over a 15-year follow-up. Linear mixed models were used to explore somatic multimorbidity trajectories in relation to baseline depression diagnoses and symptom phenotypes, while accounting for sociodemographic and behavioural factors. RESULTS: in multi-adjusted models, relative to individuals without depression, those with major (ß per year: 0.33, 95% confidence interval [CI]: 0.06-0.61) and subsyndromal depression (ß per year: 0.21, 95%CI: 0.12-0.30) experienced an accelerated rate of somatic multimorbidity accumulation, whereas those with minor depression did not. We identified affective, anxiety, cognitive, and psychomotor symptom phenotypes from the network analysis. When modelled separately, an increase in symptom score for each phenotype was associated with faster multimorbidity accumulation, although only the cognitive phenotype retained its association in a mutually adjusted model (ß per year: 0.07, 95%CI: 0.03-0.10). CONCLUSIONS: late-life major and subsyndromal depression are associated with accelerated somatic multimorbidity. Depressive symptoms characterised by a cognitive phenotype are linked to somatic health change in old age.

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OBJECTIVES: To develop symptom networks and examine the longitudinal relationships of depressive symptoms among middle-aged and older adults in China. METHOD: This study used three-wave data from the China Health and Retirement Longitudinal Study (2013 (T1), 2015 (T2), and 2018 (T3)). Depressive symptoms were measured by the 10-item Center for Epidemiologic Studies Depression Scale (CES-D). A multilevel vector autoregression model (VAR) was used to identify ten depressive symptoms dynamically interacting with each other over time. RESULTS: A total of 3,558 participants were included in the final analysis. The strongest direct effects were 'D10: felt fearful' -> 'D6: felt everything I did was an effort' (ß = 0.14). 'D10: felt fearful' reported the largest value of out-predictability (r = 0.064) and out-strength (r = 0.635). 'D3: felt depressed' reported the largest value of in-predictability (r = 0.077) and in-strength (r = 0.545). Substantial heterogeneity in the network may stem from an individual's sex and place of residence. CONCLUSIONS: 'Felt fearful' was the strongest predictor compared to the other nine depressive symptoms based on node centrality. Our study suggests that, after understanding the causes of fear, strategies to reduce fear should be incorporated into multimodal interventions for middle-aged and older adults with depressive symptoms.

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