Major depressive disorder (MDD) is a prevalent and disabling condition. Approximately 30-50% of patients do not respond to first-line medication or psychotherapy. Therefore, several studies have investigated the predictive potential of pretreatment severity rating or neuroimaging features to guide clinical approaches that can speed optimal treatment selection.

To evaluate the performance of 1) severity ratings (scores of Hamilton Depression/Anxiety Scale, illness duration, and sleep quality, etc.) and demographic characteristic and 2) brain magnetic resonance imaging (MRI) features in predicting treatment outcomes for MDD. Second, to assess performance variations among varied modalities and interventions in MRI studies.

We searched studies in PubMed, Embase, Web of Science, and Science Direct databases before March 22, 2023. We extracted a confusion matrix for prediction in each study. Separate meta-analyses were performed for clinical and MRI studies. The logarithm of diagnostic odds ratio [log(DOR)], sensitivity, and specificity were conducted using Reitsma’s random effect model. The area under curve (AUC) of summary receiver operating characteristic (SROC) curve was calculated.

Subgroup analyses were conducted in MRI studies based on modalities: resting-state functional MRI (rsfMRI), task-based fMRI (tbfMRI), and structural MRI (sMRI), and interventions: antidepressant (including selective serotonin reuptake inhibitors [SSRI]) and electroconvulsive therapy (ECT). Meta-regression was conducted 1) between clinical and MRI studies and 2) among modality or intervention subgroups in MRI studies.

We included ten studies used clinical features covering 6494 patients, yielded a log(DOR) of 1.42, AUC of 0.71, sensitivity of 0.61, and specificity of 0.74. In terms of MRI, 44 studies with 2623 patients were included, revealing an overall log(DOR) of 2.53. The AUC, sensitivity, and specificity were 0.89, 0.78, and 0.75.

Studies using MRI features had a higher sensitivity (0.89 vs. 0.61) in predicting treatment outcomes than clinical features (P < 0.001). RsfMRI had higher specificity (0.79 vs. 0.69) than tbfMRI subgroup (P = 0.01). No significant differences were found between sMRI and other modalities, nor between antidepressants (SSRIs and others) and ECT. Antidepressant studies primarily identified predictive imaging features in limbic and default mode networks, while ECT mainly focused on limbic network.

Our findings suggest a robust promise for pretreatment brain MRI features in predicting treatment outcomes in MDD, offering higher accuracy than clinical studies. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. For MRI studies, overlapping but distinct network level measures predicted outcomes for antidepressants and ECT.

None Declared

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder. It is considered that dysregulation of cytokine levels is related to the pathophysiological mechanism of OCD. However, the results of previous studies on cytokine levels in OCD are inconsistent.

To perform a meta-analysis assessing cytokine levels in peripheral blood of OCD patients.

We searched in PubMed, Web of Science, and Embase from inception to March 31, 2023 for eligible studies. We conducted multivariate meta-analysis in combined proinflammatory cytokines (interleukin-6 [IL-6], IL-1β, IL-2, tumor necrosis factor-α [TNF-α], and interferon-γ [IFN-γ]) and combined anti-inflammatory cytokines (IL-10 and IL-4) respectively, and calculated the same meta-analysis in each cytokine. We also performed sensitivity analysis and publication bias tests, as well as subgroup analysis (i.e. different age groups, varied cytokine measurement methods, medication treated or naïve, and presence of psychiatric comorbidities) and meta-regression analysis (variables including patients’ sex ratio, age, age at symptom onset, illness duration, scores of Y-BOCS, family history of psychiatric disorders, and BMI).

17 original studies (13, 13, 10, 5, 4, 3, 2 studies for IL-6, TNF-α, IL-1β, IL-10, IL-2, IL-4, and IFN-γ, respectively), 573 patients (mean age, 25.2; 50.3% female) and 498 healthy controls (HC; mean age, 25.3; 51.4% female) were included. The results showed that the levels of combined pro- or anti-inflammatory cytokines and each signle cytokine were not significantly different between OCD patients and HC (all P>0.05), with significant heterogeneities in all analyses (I2 from 79.1% to 91.7%). We did not find between-group differences in cytokine levels in all subgroup analyses. Meta-regression analysis suggested that age at onset (P=0.0003) and family history (P=0.0062) might be the source of heterogeneity in TNF-α level. Sensitivity analysis confirmed that all results were stable, except for IL-4 where different cytokine measurement methods may be the contributing factor. Egger test did not find publication bias.

Our study showed no difference in cytokine levels between OCD patients and HC, but age at onset and family history may affect TNF-α level. Confounding factors such as age at onset, family history, and cytokine measurement methods should be controlled in future studies to further explore the immune mechanism of OCD.

None Declared

Adolescence is a period marked by highest vulnerability to the onset of depression, with profound implications for adult health. Neuroimaging studies have revealed considerable atrophy in brain structure in these patients with depression. Of particular importance are regions responsible for cognitive control, reward, and self-referential processing. However, the causal structural networks underpinning brain region atrophies in adolescents with depression remain unclear.

This study aimed to investigate the temporal course and causal relationships of gray matter atrophy within the brains of adolescents with depression.

We analyzed T1-weighted structural images using voxel-based morphometry in first-episode adolescent patients with depression (n=80, 22 males; age = 15.57±1.78) and age, gender matched healthy controls (n=82, 25 males; age = 16.11±2.76) to identify the disease stage-specific gray matter abnormalities. Then, with granger causality analysis, we arranged the patients’ illness duration chronologically to construct the causal structural covariance networks that investigated the causal relationships of those atypical structures.

Compared to controls, smaller volumes in ventral medial prefrontal cortex (vmPFC), dorsal anterior cingulate cortex (dACC), middle cingulate cortex (MCC) and insula areas were identified in patients with less than 1 year illness duration, and further progressed to the subgenual ACC, regions of default, frontoparietal networks in longer duration. Causal network results revealed that dACC, vmPFC, MCC and insula were prominent nodes projecting exerted positive causal effects to regions of the default mode and frontoparietal networks. The dACC, vmPFC and insula also had positive projections to the reward network, which included mainly the thalamus, caudate and putamen, while MCC also exerted a positive causal effect on the insula and thalamus.

These findings revealed the progression of structural atrophy in adolescent patients with depression and demonstrated the causal relationships between regions involving cognitive control, reward and self-referential processes.

None Declared

There’s large heterogeneity present in major depressive disorder (MDD) and controversial evidence on alterations of brain functional connectivity (FC), making it hard to elucidate the neurobiological basis of MDD. Subtyping is one promising solution to characterize this heterogeneity.

To identify neurophysiological subtypes of MDD based on FC derived from resting-state functional magnetic resonance imaging using large multisite data and investigate the differences in genetic mechanisms and neurotransmitter basis of FC alterations, and the differences of FC-related cognition between each subtype.

Consensus clustering of FC patterns was applied to a population of 829 MDD patients from REST-Meta-MDD database after data cleaning and image quality control. Gene transcriptomic data derived from Allen Human Brain Atlas and neurotransmitter receptor/transporter density data acquired by using neuromap toolbox were used to characterize the molecular mechanism underlying each FC-based subtype by identifying the gene set and neurotransmitters/transporters showing high spatial similarity with the profiles of FC alterations between each subtype and 770 healthy controls. The FC-related cognition in each subtype was also selected by lasso regression.

Two stable neurophysiological MDD subtypes were found and labeled as hypoconnectivity (n=527) and hyperconnectivity (n=299) characterized by the FC differences in each subtype relative to controls, respectively. The two subtypes did not differ in age, sex, and scores of Hamilton Depression/Anxiety Scale.

The genes related to FC alterations were enriched in ion transmembrane transport, synaptic transmission/organization, axon development, and regulation of neurotransmitter level for both subtypes, but specifically enriched in glial cell differentiation for hypoconnectivity subtype, while enriched in regulation of presynaptic membrane and regulation of neuron differentiation for hyperconnectivity subtype.

FC alterations were associated with the density of 5-HT2a receptor in both subtypes. For hyperconnectivity subtype, FC alterations were also correlated with the density of norepinephrine transporter, glutamate receptor, GABA receptor, 5-HT1b receptor, and cannabinoid receptor.

Both subtypes showed correlations between FC and categorization, motor inhibition, and localization. The FC in hypoconnectivity subtype correlated with response inhibition, selective attention, face recognition, sleep, empathy, expertise, uncertainty, and anticipation, while that was related to inference, speech perception, and reward anticipation in hyperconnectivity subtype.

Our findings suggested the presence of two neuroimaging subtypes of MDD characterized by hypo or hyper-connectivity. The two subtypes had both shared and distinct genetic mechanisms, neurotransmitter receptor/transporter profiles, and cognition types.

None Declared

Patients with Fontan failure are high-risk candidates for heart transplantation and other advanced therapies. Understanding the outcomes following initial heart failure consultation can help define appropriate timing of referral for advanced heart failure care.

This is a survey study of heart failure providers seeing any Fontan patient for initial heart failure care. Part 1 of the survey captured data on clinical characteristics at the time of heart failure consultation, and Part 2, completed 30 days later, captured outcomes (death, transplant evaluation outcome, and other interventions). Patients were classified as “too late” (death or declined for transplant due to being too sick) and/or “care escalation” (ventricular assist device implanted, inotrope initiated, and/or listed for transplant), within 30 days. “Late referral” was defined as those referred too late and/or had care escalation.

Between 7/2020 and 7/2022, 77 Fontan patients (52% inpatient) had an initial heart failure consultation. Ten per cent were referred too late (6 were too sick for heart transplantation with one subsequent death, and two others died without heart transplantation evaluation, within 30 days), and 36% had care escalation (21 listed ± 5 ventricular assist device implanted ± 6 inotrope initiated). Overall, 42% were late referrals. Heart failure consultation < 1 year after Fontan surgery was strongly associated with late referral (OR 6.2, 95% CI 1.8–21.5, p=0.004).

Over 40% of Fontan patients seen for an initial heart failure consultation were late referrals, with 10% dying or being declined for transplant within a month of consultation. Earlier referral, particularly for those with heart failure soon after Fontan surgery, should be encouraged.

There is compelling evidence for gradient effects of household income on school readiness. Potential mechanisms are described, yet the growth curve trajectory of maternal mental health in a child's early life has not been thoroughly investigated. We aimed to examine the relationships between household incomes, maternal mental health trajectories from antenatal to the postnatal period, and school readiness.

Prospective data from 505 mother–child dyads in a birth cohort in Singapore were used, including household income, repeated measures of maternal mental health from pregnancy to 2-years postpartum, and a range of child behavioural, socio-emotional and cognitive outcomes from 2 to 6 years of age. Antenatal mental health and its trajectory were tested as mediators in the latent growth curve models.

Household income was a robust predictor of antenatal maternal mental health and all child outcomes. Between children from the bottom and top household income quartiles, four dimensions of school readiness skills differed by a range of 0.52 (95% Cl: 0.23, 0.67) to 1.21 s.d. (95% CI: 1.02, 1.40). Thirty-eight percent of pregnant mothers in this cohort were found to have perinatal depressive and anxiety symptoms in the subclinical and clinical ranges. Poorer school readiness skills were found in children of these mothers when compared to those of mothers with little or no symptoms. After adjustment of unmeasured confounding on the indirect effect, antenatal maternal mental health provided a robust mediating path between household income and multiple school readiness outcomes (χ2 126.05, df 63, p < 0.001; RMSEA = 0.031, CFI = 0.980, SRMR = 0.034).

Pregnant mothers with mental health symptoms, particularly those from economically-challenged households, are potential targets for intervention to level the playing field of their children.

Coronavirus disease 2019 (COVID-19) pandemic is a major public health concern all over the world. Little is known about the impact of COVID-19 pandemic on mental health in the general population. This study aimed to assess the mental health problems and associated factors among a large sample of college students during the COVID-19 outbreak in China.

This cross-sectional and nation-wide survey of college students was conducted in China from 3 to 10 February 2020. A self-administered questionnaire was used to assess psychosocial factors, COVID-19 epidemic related factors and mental health problems. Acute stress, depressive and anxiety symptoms were measured by the Chinese versions of the impact of event scale-6, Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, respectively. Univariate and hierarchical logistic regression analyses were performed to examine factors associated with mental health problems.

Among 821 218 students who participated in the survey, 746 217 (90.9%) were included for the analysis. In total, 414 604 (55.6%) of the students were female. About 45% of the participants had mental health problems. The prevalence rates of probable acute stress, depressive and anxiety symptoms were 34.9%, 21.1% and 11.0%, respectively. COVID-19 epidemic factors that were associated with increased risk of mental health problems were having relatives or friends being infected (adjusted odds ratio = 1.72–2.33). Students with exposure to media coverage of the COVID-19 ≥3 h/day were 2.13 times more likely than students with media exposure <1 h/day to have acute stress symptoms. Individuals with low perceived social support were 4.84–5.98 times more likely than individuals with high perceived social support to have anxiety and depressive symptoms. In addition, senior year and prior mental health problems were also significantly associated with anxiety or/and depressive symptoms.

In this large-scale survey of college students in China, acute stress, anxiety and depressive symptoms are prevalent during the COVID-19 pandemic. Multiple epidemic and psychosocial factors, such as family members being infected, massive media exposure, low social support, senior year and prior mental health problems were associated with increased risk of mental health problems. Psychosocial support and mental health services should be provided to those students at risk.

We aimed to investigate the heterogeneity of seasonal suicide patterns among multiple geographically, demographically and socioeconomically diverse populations.

Weekly time-series data of suicide counts for 354 communities in 12 countries during 1986–2016 were analysed. Two-stage analysis was performed. In the first stage, a generalised linear model, including cyclic splines, was used to estimate seasonal patterns of suicide for each community. In the second stage, the community-specific seasonal patterns were combined for each country using meta-regression. In addition, the community-specific seasonal patterns were regressed onto community-level socioeconomic, demographic and environmental indicators using meta-regression.

We observed seasonal patterns in suicide, with the counts peaking in spring and declining to a trough in winter in most of the countries. However, the shape of seasonal patterns varied among countries from bimodal to unimodal seasonality. The amplitude of seasonal patterns (i.e. the peak/trough relative risk) also varied from 1.47 (95% confidence interval [CI]: 1.33–1.62) to 1.05 (95% CI: 1.01–1.1) among 12 countries. The subgroup difference in the seasonal pattern also varied over countries. In some countries, larger amplitude was shown for females and for the elderly population (≥65 years of age) than for males and for younger people, respectively. The subperiod difference also varied; some countries showed increasing seasonality while others showed a decrease or little change. Finally, the amplitude was larger for communities with colder climates, higher proportions of elderly people and lower unemployment rates (p-values < 0.05).

Despite the common features of a spring peak and a winter trough, seasonal suicide patterns were largely heterogeneous in shape, amplitude, subgroup differences and temporal changes among different populations, as influenced by climate, demographic and socioeconomic conditions. Our findings may help elucidate the underlying mechanisms of seasonal suicide patterns and aid in improving the design of population-specific suicide prevention programmes based on these patterns.

Patients with chronic kidney disease (CKD) have more cognitive impairments. However, the etiologies are not fully clear. Plasma homocysteine levels and vascular burden rise in CKD; meanwhile, high homocysteine levels and vascular factors are known risk factors of dementia in non-CKD patients. Thus, we aimed to investigate the association between homocysteine, vascular burden and cognitive impairment in CKD and to see if the effect of elevated homocysteine on cognitive impairment mediated by vascular factor.

146 patients with CKD and 69 normal comparisons were recruited. Cognitive function was evaluated by comprehensive neuropsychological tests assessing processing speed, executive function, language, visuospatial function, memory, and attention domains. Vascular burden was assessed by Framinghan cardiovascular risk scale (FCRS) which indicates risk of atherosclerotic diseases including stroke.

In controlled analysis, patients with CKD had lower scores in all cognitive domains, and had higher homocysteine levels (18.5±6.4 vs. 9.8±2.9, p< 0.0001) and FCRS(17.0±4.7 vs. 14.0±4.7, p< 0.0001). Among patients with CKD, higher homocysteine levels (p=0.026) were associated with lower score on digit symbol task which is related to processing speed and executive function with controlling for age, sex, education and stage of CKD. The association persisted (p=0.047) after controlling for vascular risks.

Patients with CKD had extensive cognitive impairments. Elevated homocysteine levels may be an risk factor, which is independent of vascular burden, of cognitive impairment on processing speed and executive function. Further studies to investigate if normalization of homocysteine can improve cognitive function will be suggested.