Many patients experience psychological distress in the preoperative phase, whilst screening based on cut-off points of assessment scales showed limited value in predicting clinical postoperative adverse outcomes.
To identify preoperative psychological distress and investigate their associations with adverse surgery-related outcomes, we included 16,662 patients from the China Surgery and Anesthesia Cohort (CSAC). We applied dimensionality reduction and unsupervised machine learning algorithms to classify participants into distinct psychological patterns. We then assessed the associations of machine learning-identified psychological patterns and traditional cut-off based psychological symptoms, with various adverse surgery-related outcomes, using logistic and linear regression models while adjusting for other relevant covariates.
We successfully established clustering algorithms for 16,298 participants, demonstrating strong consistency in pattern features. Six distinct psychological patterns among participants were identified, including one group with normal psychological functioning and five groups with varying levels of psychological distress. All identified psychological distress patterns were significantly associated with most surgery-related adverse outcomes, both in short-term (e.g., any within-hospital postoperative complication, odds ratios [ORs] = 1.24–1.30) and long-term (e.g., cognitive impairment at 12 months postsurgery, 1.29–2.35). In contrast, traditional cut-off-based methods identified only 266 patients with significant psychological symptoms, which showed no association with some key short-term outcomes (e.g., length of hospital stay and postoperative complication), though they remained linked to most long-term outcomes.
Our findings demonstrate the effectiveness of machine learning in accurately identifying patients with preoperative psychological distress who may require clinical attention, highlighting the potential of these techniques to guide targeted preoperative interventions and ultimately improve surgical outcomes.
With rapid population aging in China, understanding the relationship between depression symptoms and cognitive function is crucial for improving the mental health of older adults. This study investigates these dynamics using data from the China Health and Retirement Longitudinal Study (CHARLS).
We analyzed data from the 2015, 2018, and 2020 waves of CHARLS, including 5203 participants aged 60 and above. Depression symptoms were measured using the Centre for Epidemiological Studies Depression-10 (CESD-10) scale, while cognitive function was assessed via the Mini-Mental State Examination (MMSE) scale. Cross-sectional network analysis was utilized for constructing the contemporaneous network, and cross-lagged panel network (CLPN) analysis was subsequently employed for longitudinal analysis.
In all three cross-sectional networks, “Hope” was identified as a key bridge symptom connecting the depression symptom community and the cognitive function community, while “Depressed mood” was found to be the central symptom of the entire network. In temporal networks, higher drawing ability at wave 1 was associated with greater “Hope” at wave 2, whereas higher “Fear” at wave 1 was related to lower recall ability at wave 2. Moreover, lower memory ability at wave 2 was associated with lower “Bothered” at wave 3.
This study uncovered the dynamic interplay between specific depression symptoms and cognitive functions among Chinese older adults, thereby providing further validation for the scar theory and the cognitive vulnerability model. Additionally, it provides a critical theoretical foundation for developing intervention strategies targeting mental health and cognitive function in the aging population, as well as a scientific basis for related policy formulation. Future research should integrate quantitative and qualitative data for stronger causal validation.
Autistic traits (ATs) and social anxiety (SA) are closely associated; however, few studies have investigated the potential mediating mechanism of this relationship using longitudinal data. This study examined: (1) the developmental trajectories of ATs, rumination, and SA among college students; (2) whether the baseline levels of ATs predicted the developmental trajectories of SA; and (3) whether the trajectories of rumination mediated this longitudinal association.
This study enrolled 397 college students to complete Broad Autism Phenotype Questionnaire, Discriminative Response Scale, and SA Disorder Dimension three times over the course of a year. Three unconditional latent growth models (LGMs) were first used to explore the trajectories of ATs, rumination, and SA, respectively. Then, a conditional LGM was used to examine the direct longitudinal association between ATs and SA. Finally, a structural equation model was further used to examine the longitudinal mediating role of rumination between ATs and SA.
For college students, ATs remained relatively stable, whereas rumination and SA declined across the study period. Furthermore, ATs positively predicted the intercept of SA (β = 0.66, p < 0.001), and negatively predicted the slope of SA (β = −0.29, p < 0.001). More importantly, higher baseline levels of rumination mediated ATs on the baseline value of SA (0.14, 95% confidence interval [CI] [0.08 0.22]), and slower rates of decline of rumination mediated ATs on the change in SA (−0.15, 95% CI [−0.46 −0.01]).
These results indicate that college is a critical period for the abatement of rumination and SA. Furthermore, rumination might be one of the mechanisms underlying the link between ATs and SA. Interventions to prevent the negative impact of ATs might help to decrease the risk of rumination and SA in the college students.
This cross-sectional study, utilizing National Health and Nutritional Examination Surveys (NHANESs) data from 2005 to 2018, examines the association between age at first live birth and depression among women aged 12 years or older.
Data encompassed 10,399 participants, with 1260 exhibiting depressive symptoms. The 9-item Patient Health Questionnaire (PHQ-9) assessed depression. Age at first live birth was categorized as <18, 18–25, and >25.
Women with depressive symptoms were more likely to be single, have lower incomes and education levels, be smokers, and exhibit higher body mass indexes (BMIs) or sleep disorders. Younger age at first live birth correlated with higher depression prevalence. Univariate analysis shows decreased depression chances for women with first live births at 18–25 (47% decrease) or >25 (76% decrease), with an 11% reduction for every year increase in age at first birth. Multivariate analyses confirm a significant association between age at first live birth and depression, even after adjusting for various factors.
This study underscores the association between age at first live birth and depression, highlighting the need for considering reproductive history in mental health assessments. The findings emphasize the multifaceted nature of this relationship, demonstrating the impact of sociodemographic and lifestyle factors on mental health outcomes among women.
Background: This study investigated the relationships between anxiety, depression symptoms, and C-reactive protein (CRP) across insomnia severity levels using network analysis and examined the structural differences within these networks.
Methods: Gaussian graphical model network analysis with Least Absolute Shrinkage and Selection Operator (LASSO) regularization was conducted on UK Biobank data (N = 143,027). Depression and anxiety symptoms were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively. CRP was quantified using immunoturbidimetric-high-sensitivity analysis. Participants were categorized by insomnia frequency (never/rarely, sometimes, and usually). The strength symptoms and expected influence identified core symptoms, while bridge expected influence (bridge EI) determined bridge symptoms. Network comparison tests (NCTs) were performed pairwise across the three groups to assess differences in global strength and edge weights.
Results: Across all networks, “Depressed mood” demonstrated the highest strength centrality, while “Irritability” exhibited the highest bridge EI. “Depressed mood” had the highest expected influence centrality in the never/rarely insomnia group and “Uncontrollable worry” in other groups. NCTs revealed significant differences in global strength (S = 0.178, p < 0.01) and edge weights (M = 0.062, p < 0.01) between the never/rarely and usually insomnia groups, with stronger connections between depressive symptoms (energy/appetite) and CRP in the usually insomnia group (p < 0.001).
Conclusions: The central roles of depressed mood, uncontrollable worry, and irritability in the anxiety–depression–CRP network across all insomnia severity groups suggest that these symptoms represent potential targets for future intervention research. Notably, network structure differed across insomnia severity; the strengthened associations between depressive symptoms and CRP in the usually insomnia group suggest that insomnia severity may be an important factor to consider in understanding the relationships between affective and inflammatory processes.
There is a growing interest in understanding insight or illness awareness in anxiety; however, most assessment instruments were designed for psychosis. The unique features of anxiety highlight the need for tailored measures to accurately evaluate insight. The aim of this study was to develop and validate the Schedule for the Assessment of Insight in Anxiety (SAI-A), a clinician-rated scale for assessing insight in anxiety disorders. We interviewed 46 participants diagnosed with anxiety disorders, conducted SAI-A interviews, and administered self-report measures. Using correlation and principal component analysis (PCA), we identified and assessed scale components, ensuring their reliability and consistency. The SAI-A demonstrated acceptable psychometric properties, including convergent validity with an established self-report measure (r = −0.39, p = 0.008) and internal consistency (Cronbach’s α = 0.70). It showed moderate to strong agreement, interrater reliability (weighted kappa = 0.53, intraclass correlation coefficient [ICC] = 0.67), and test–retest reliability (ICC = 0.65). Two distinct insight components emerged: awareness of disorder and need for treatment. Higher overall SAI-A scores correlated with symptom severity and impairment (r = 0.56, r = 0.51, p < 0.001, respectively) and medication usage. The SAI-A is a valid and reliable assessment tool, providing a comprehensive framework for understanding and addressing insight in the context of anxiety disorders.
The advancement of artificial intelligence (AI) tools utilizing electroencephalography (EEG) for diagnosing major depressive disorder (MDD) has shown significant progress. However, the practical implementation of these tools is often impeded by the large amount of EEG data required for training AI models and the lack of explanations for the MDD diagnoses. This study aims to develop an interpretable deep-learning-based computer-aided diagnostic system for diagnosing male MDD patients using explainable AI (XAI) algorithms. The CAD system was designed to facilitate the diagnostic process by using a reduced number of EEG channels and data length while enhancing understanding of the neurophysiological characteristics of male MDD. Resting-state EEG data were collected from 40 male MDD patients (20–63 years) and 41 gender-matched healthy controls (HCs, 19–61 years). A shallow convolutional neural network (CNN; Shallow ConvNet) model was utilized to distinguish between MDD patients and HCs. Relevance scores were extracted by the layer-wise relevance propagation (LRP) method, integrated with the Shallow ConvNet, to interpret the outcomes of the deep-learning-based CAD system. Additionally, changes in diagnostic performance were assessed by progressively reducing the number of channels using an LRP-based channel selection method, as well as EEG data length. Our XAI-based CAD system showed a high diagnostic performance of 100% when using the whole 62 channels with 180-s EEG data. A relatively high diagnostic performance over 90% was retained with only five channels with 60-s EEG data. Neurophysiologically meaningful brain areas, such as fronto-central, centro-parietal, and occipital areas, also revealed significant differences in relevance scores extracted by the LRP-method between the two groups. This study successfully developed a high performance and practical XAI-based CAD system for male MDD patients. Our developed CAD system not only achieves high diagnostic accuracy but also provides meaningful neurophysiological biomarkers for male MDD patients.
Objectives: The present study aimed to examine the bidirectional relationship between internet use and depressive symptoms among middle-aged and older adults. Moreover, it explored whether proactive health behavior mediates the association between internet use and depressive symptoms.
Methods: We used the latest three-wave data (2015, 2018, and 2020) from the China Health and Retirement Longitudinal Study (CHARLS), which included 11,332 participants aged 45 years and older. The bidirectional relationship between internet use and depressive symptoms was examined using a cross-lagged model. The mediating role of proactive health behavior was also investigated using a cross-lagged mediation model.
Results: Cross-lagged models indicated reciprocal effects between depressive symptoms and internet use. Internet use had a greater impact on subsequent depressive symptoms than vice versa. Mediation analyses further revealed that proactive health behavior significantly mediated the path from internet use to depressive symptoms. Furthermore, subgroup analyses showed these effects were not significantly heterogeneous in subgroups by age and chronic disease status.
Conclusions: This study sheds light on the direction of the association between internet use and depressive symptoms. Internet use could reduce depressive symptoms among middle-aged and older adults by enhancing proactive health behavior.
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