Beijing Anding Hospital

Bipolar disorder is a severe mental disorder affecting millions worldwide, necessitating comprehensive policies and interventions. To provide assessment of global inequalities in the burden of bipolar disorder and their projected trajectories to 2050. Global Burden of Disease 2021 data from 204 countries and territories were analyzed, stratified by age, gender, and Socio-demographic Index (SDI) quintiles. Age-standardized prevalence (ASPR), incidence (ASIR), and years lived with disability (ASR YLD) per 100,000 population were calculated. Inequalities were assessed using the slope index of inequality (SII) and concentration index (CI), and ARIMA models were applied to project trends to 2050. From 1990 to 2021, global incidence of BDincreased, while prevalence and years lived with disability (YLDs) remained relatively stable (ASPR: 453.7 [95% UI: 381.6-540.8] to 454.6 [95% UI: 377.9-545.8]). Females consistently had higher prevalence than males (474.2 vs. 435.0 per 100,000 in 2021). High-SDI regions reported the highest rates, with Australasia reaching 1110.8 (95% UI: 940.3-1305.9). The SII for incidence rose slightly (10.87-11.38), while the CI declined (0.096-0.012), indicating increasing absolute but decreasing relative inequalities. Projections suggest a rising global burden, with female prevalence remaining higher and incidence rates converging between genders (global ASIR: 33.8 per 100,000). Global inequalities in bipolar disorder persist, disproportionately affecting females and high-SDI regions. Projected trends indicate an increasing burden with a narrowing gender gap in incidence, emphasizing the need for targeted interventions and further research on long-term impacts, including the effects of COVID-19.

Current treatment algorithms for major depressive disorder (MDD) lack dynamic prediction capabilities, leading to delayed therapeutic adjustments. This study sought to develop escitalopram-specific decision tree models to identify critical treatment adjustment time points and optimize personalized treatment strategies for MDD. Using longitudinal data from two multicenter studies in China (2015-2020), we analyzed 800 patients with MDD receiving escitalopram monotherapy. Decision tree models incorporated baseline characteristics (age, BMI, disease duration, depressive symptoms) and dynamic treatment parameters (dose, 2-/4-week improvement) to predict full response (>50% symptom reduction) or non-full response (≤50% reduction) at weeks 2 and 4, and remission status (QIDS-SR16≤5 vs. >5) at week 8. Model performance was assessed by accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). The week 2 model (n=800) identified BMI, age, disease duration, course and baseline symptom severity as primary predictors (accuracy=61.88%, NPV=84.04%). By week 4 (n=650), early response status (week 2) merged as a key predictor (accuracy=69.23%, NPV=71.62%). The week 8 model (n=456) demonstrated enhanced predictive power, driven by life quality score, week 2/4 response status, and week 4 dosage (accuracy=78.02%, PPV=81.48%, NPV=72.97%). Logistic regression confirmed week 4 response status as a significant predictor of week 8 outcome (p<0.005). Week 4 emerges as a key decision point for escitalopram-treated MDD patients, where integration of baseline profiles, early response patterns, and dose parameters allows timely intervention. Our decision tree framework offers a methodological approach for dynamic decision points that warrant prospective validation and extension to other antidepressants.

While general procrastination is common, psychopathological procrastination, a debilitating phenotype often indicative of subclinical psychiatric conditions, remains poorly understood in terms of its neurobiological underpinning. Challenging its traditional conceptualization as a mere behavioral deficit, we investigated the neurogenetic architecture of psychopathological procrastination. Leveraging a prospective adolescent twin cohort (N = 71 twin pairs) with neuroanatomical imaging (baseline) and psychopathological procrastination phenotyping in young adulthood (8-year follow-up), we first established moderate heritability for psychopathological procrastination (h² = 0.47, 95% CI: 0.14 - 0.71). Employing normative modeling of brain morphology, we found that adolescent neurodevelopmental deviations, specifically within the nucleus accumbens (NAcc), predicted adult psychopathological procrastination. Crucially, these predictive adolescent NAcc deviations exhibited a strong shared genetic basis with adult psychopathological procrastination (rg = 0.89, 95% CI: 0.89 - 1.00). Beyond regional effects, psychopathological-procrastination-specific whole-brain deviation patterns were identified, which showed neurobiological enrichment with cortical functional gradient and key dopaminergic (DAT/D1) and serotonergic (5-HT receptors) neurotransmitter systems. Both cross-sectional and longitudinal transcriptomic integration of these neuroimaging signatures with human brain gene expression data pinpointed significant associations with molecular transport, neuroimmune responses, and neuroinflammation, further implicating dysregulation within serotonergic and dopaminergic pathways. Collectively, our findings delineate a multisystem neurogenetic architecture of psychopathological procrastination, providing supportive evidence that recontextualizes this debilitating phenotype from a simple behavioral issue to a condition with neurodevelopmental antecedents, potentially suggesting its conceptualization as a subclinical brain disorder.

Chronic fatigue syndrome (CFS) is a neurological disorder characterized by persistent fatigue. Previous studies have shown structural and functional brain alterations in CFS patients. However, how chronic fatigue affects white matter remains unclear. We recruited 100 CFS patients and 100 healthy controls (HCs) and collected their diffusion weighted imaging (DWI) data along with fatigue severity scores. Group comparisons were conducted using 87 white matter tract templates derived from 1068 healthy individuals to identify impaired tracts. Fiber bundle quantitative analysis was then applied to localize segmental disruptions. Furthermore, a machine learning model was developed to evaluate the importance of the identified tracts in diagnosing CFS, and treatment efficacy analysis was performed to examine the causal relationship between recovery of fiber bundle structure and improvement in fatigue symptoms. Compared with HCs, CFS patients exhibited reduced fractional anisotropy (FA) in the left cingulum parolfactory tract and the left cingulum frontal-parahippocampal tract. The quantitative analysis indicated that disruptions in these two tracts were localized to the middle-posterior portion of the cingulum bundle. Moreover, FA values of the two tracts yielded 85 % accuracy in distinguishing CFS patients, and causal analysis demonstrated that recovery of these segmentally disrupted tracts was associated with improvements in fatigue symptoms. This large-sample study provides comprehensive insights into microstructural impairments induced by chronic fatigue and highlights the critical role of the cingulum bundle in the pathological mechanisms of CFS, thereby advancing our understanding of CFS.