The menopausal transition represents a pivotal period in female aging, marked by profound endocrine, metabolic and cellular shifts. Increasing evidence indicates that menopausal symptoms - vasomotor instability, sleep disturbances, fatigue and cognitive complaints - are more than consequences of estrogen withdrawal, and may serve as a potential clinical indicator of biological aging. Experimental and clinical data suggest that declining estrogen signaling contributes to mitochondrial dysfunction, inflammation and telomere attrition, processes that are closely linked to cellular senescence and tissue deterioration. In addition to estrogen decline, the menopausal transition involves broader endocrine changes. Rising follicle stimulating hormone (FSH) levels, alteration in androgen balance and cortisol dysregulation of the hypothalamic-pituitary-adrenal axis may influence metabolic regulation, musculoskeletal health, stress physiology and body composition. Through these mechanisms, menopausal hormonal changes may contribute to increased cardiometabolic, musculoskeletal and neurocognitive vulnerability in midlife women. Clinical observations increasingly show that severe menopausal symptoms are associated with adverse cardiometabolic profiles, vascular dysfunction and markers of accelerated biological aging. Sleep disturbances and fatigue may further exacerbate metabolic dysregulation and systemic vulnerability, while cognitive complaints may reflect neuroinflammatory and vascular processes associated with aging. By restoring estrogen signaling, menopausal hormone therapy alleviates menopausal symptoms and may influence biological pathways involved in aging. Whether these effects translate into a modification of the aging trajectory remains unclear.

Obesity and mental health disorders are among the greatest public health challenges of the 21st century. Interestingly, an altered microbiome profile has been associated with both conditions. The aim of this randomized, double-blind, placebo-controlled clinical trial was to evaluate the effects of dietary supplementation with a specific probiotic strain (Lactiplantibacillus plantarum KABP051) on body composition and gut microbiome balance, together with measures of mood state, in a population of healthy overweight subjects. Sixty healthy, moderately stressed, nondepressed and overweight or obese volunteers were supplemented for 12 weeks with probiotic (L. plantarum KABP051; 1 billion colony forming units/day) or placebo (microcrystalline cellulose). The KABP051 group experienced significantly greater improvements compared with placebo on body composition measurements, including a reduction in body weight and waist circumference, which decreased in 1.97 ± 0.77 (mean ± SE) kg and 2.15 ± 0.81 (mean ± SE) cm versus placebo at the end of the intervention (both P < .05, mixed model for repeated measures [MMRM] and post-hoc analysis). Microbiome composition improved in KABP051 group, with significant increase in the relative abundance of Lactiplantibacillus spp. versus placebo. Body fat percentage, profile of mood states fatigue, and confusion sub-scores showed a global trend toward improvement compared with placebo, with the change at 12 weeks being significant in the three measurements in post-hoc analysis (P = .015, P = .014, and P = .016, respectively). No serious adverse events were registered during the intervention period. These results suggest that a specific strain of probiotic bacteria (L. plantarum KABP051) may have both metabolic and psychobiotic effects and may be beneficial for enhancing weight loss and body composition, improving energy (less fatigue) and mood levels while embarking on a healthy lifestyle regimen. ClinicalTrials.gov identifier: NCT06808061.

The integration of machine learning (ML) approaches with immune biomarker research may facilitate the identification of candidate markers for achieving personalized medicine approaches in severe mental illnesses (SMI). This systematic review synthesizes the available evidence on ML algorithms applied to immune biomarkers in major depressive (MDD), bipolar (BD) and schizophrenic spectrum disorders (SZ), examining their performance across different clinical uses including diagnostic, prediction, monitoring, prognostic categories, in accordance with the Food and Drug Administration - Biomarker, EndpointS, and other Tools (FDA BEST) framework. We performed a PRISMA-compliant systematic search of PubMed, Web of Science, Scopus and PsycINFO databases until 14 July 2025, including 43 eligible studies with a total sample of 11,556 participants, 8339 with SMI (3228 MDD, 2614 BD and 2497 SZs) and 3217 healthy controls. We systematically described population, ML input data (including blood collection conditions, pre-processing steps, sample type, laboratory assay, missing data, and multimodality), and algorithms (supervised versus unsupervised models, feature selection, validation strategy, outcomes, and performance metrics). Overall, ML models showed moderate to high but heterogeneous performance. Diagnostic applications were the most common (AUC = 0.650-0.990), though predictive, monitoring, and prognostic uses were underrepresented and more variable. Across disorders, pro-inflammatory markers (IL-6, IL-8, TNF-α, IFN-γ, CRP) and IL-10 emerged most consistently, and data-driven approaches suggested shared immune subtypes beyond categorical diagnoses. However, substantial methodological and biological heterogeneity was observed, including inconsistent handling of missing data, limited external validation, and variable feature selection. Immunology-specific sources of variability (such as fasting status, circadian rhythms, and measurement batch effects) were rarely addressed, and the long-term stability of immune-based ML signatures remains largely unexplored. These gaps currently limit clinical translation and underscore the need for standardized protocols and more rigorous ML pipelines.

Childhood trauma (CT) is a major risk factor for major depressive disorder (MDD), potentially via altered stress system development. Previous studies have shown stress-related changes in brain network function in clinical populations, but evidence in MDD with CT remains scarce. This study examined changes in functional brain networks in adults with MDD and CT during acute and delayed phases following stress.

Resting-state functional magnetic resonance imaging (fMRI) was acquired during acute (15 min) and delayed (135 min) stress phases following the Trier Social Stress Test in 66 adults with MDD and CT and 33 controls. Voxel-wise eigenvector centrality (EC) mapping quantified the network importance of eight functional brain networks. Subjective stress and affect were measured using visual analog scales.

Both groups showed significant subjective stress responses, with greater increases in tension in the MDD + CT group; affective reactivity did not differ. No significant changes in EC were observed between acute and delayed phases in any network, nor were there main effects of group or group × time interactions. Sensitivity analyses in severe MDD and multiple CT subtypes confirmed these null findings.

Network centrality did not differentiate individuals with MDD and CT from controls following stress, while subjective tension responses were higher in the MDD + CT group. These results suggest that global resting-state network centrality may not be a sensitive indicator of stress vulnerability following CT. Future multimodal studies incorporating task-based paradigms and biological markers are warranted to elucidate the neural and behavioral pathways linking MDD, CT, and stress reactivity.

Childhood maltreatment (CM) is a risk factor for adult psychiatric and substance use disorders (SUD). Retrospectively assessed CM has been linked to increased proinflammatory cytokines, including IL-6. Induced by inflammation, the neurotoxic branch of the kynurenine pathway has been implicated in psychiatric disorders and SUD. This study explored proinflammatory responses and kynurenine metabolites following acute stress in participants with, and without, prospectively recorded CM, with or without, lifetime SUD.

The study included 89 participants, divided into 4 groups based on the presence or absence of prospectively assessed CM and lifetime SUD: CM + SUD, n = 24, CM only, n = 20, SUD only, n = 22, and healthy controls (HC), n = 23. Participants underwent an acute stress task. Blood was collected at five time-points measuring IL-6 and kynurenine metabolites. Linear mixed models assessed the effects of CM, SUD, and time on IL-6 and kynurenine metabolite levels.

Participants with prospectively recorded CM had higher baseline IL-6 compared to those without CM (mean difference = 0.37, 95% CI = 0.09-0.57, p = 0.008). Stress increased IL-6 in all participants (p < 0.001), with no significant group differences. We found no association between CM exposure and KYNA or QUIN concentrations. Participants with SUD, irrespective of CM-status, had a lower KYNA/QUIN ratio (mean difference: 0.02, 95% CI: 0.00-0.04, p = 0.047).

Our findings of low-grade proinflammatory activity support the hypothesis that CM contributes to long-term immune system alterations, but these findings do not support the role of the kynurenine pathway in this process. However, increased neurotoxicity through kynurenine metabolism was associated to SUD-diagnosis.

For decades, disorders of gut-brain interaction (DGBI) have been ensnared in an epistemological bottleneck, clinically managed as diagnoses of exclusion despite the absence of structural pathology on conventional endoscopy. Traditional bottom-up models of visceral hypersensitivity fail to explain the profound subjective-objective symptom mismatches observed in clinical practice. This Perspective proposes a radical paradigm shift: leveraging the Predictive Processing (PP) framework to reconceptualize DGBI as a hierarchical computational dysfunction termed "interoceptive overfitting". We postulate that rigid, high-precision threat priors force the salience network (dACC and aIns) to misallocate pathologically high precision weighting to baseline physiological noise, such as healthy 3-cycles-per-minute (cpm) gastric slow waves. This top-down failure synthesizes illusory pain and triggers genuine autonomic disruption via active inference, creating a self-fulfilling loop of GI micro-sabotage. We present a clinical roadmap utilizing high-resolution body surface gastric mapping (BSGM) and Ecological Momentary Assessment (EMA) to identify "Probabilistic Mismatch Points" within a multimodal diagnostic matrix that accounts for non-rhythmic peripheral modulators. To resolve therapeutic stagnation, we propose closed-loop digital therapeutics (DTx) designed to recalibrate the brain's predictive engine through validation-correction loops, targeted extinction learning, and dual-stream telemetry. This computational framework provides a rigorously scientific blueprint to resolve therapeutic stagnation in DGBI.

Mounting evidence suggests that artificial intelligence can support the self-management of chronic diseases, including skin conditions, insulin management, and blood pressure control. This study aimed to investigate the potential use of artificial intelligence (AI) in chronic condition management among patients in Saudi Arabia, where the prevalence of such diseases is increasing. Specifically, we assessed AI perception, self-efficacy, and cognitive symptom management; examined their associations with demographic variables, and evaluated the influence of AI perception and self-efficacy on cognitive symptom management.

This study employed a cross-sectional, descriptive-correlational design. Data were collected at a single time point to characterize the sample and explore relationships among variables. A convenience sample of 163 patients with chronic conditions was recruited. A structured questionnaire was used to assess AI perception, self-efficacy, cognitive symptom management, and demographic characteristics. Data were collected between December 2024 and March 2025 and were analyzed using descriptive statistics, Pearson's correlation coefficient, one-way analysis of variance, and multiple regression analysis, as appropriate.

The findings revealed that sex significantly influenced AI awareness, indicating a need for targeted outreach, particularly for women who demonstrated lower levels of AI awareness. Additionally, self-efficacy was a significant predictor of better cognitive symptom management (p < 0.01), as participants with higher self-efficacy reported significantly better management of cognitive symptoms and greater engagement in health-promoting behaviors compared to those with lower self-efficacy.

Our results highlight that self-efficacy is a key factor in managing cognitive symptoms associated with chronic conditions and underscore the importance of targeted interventions to enhance inclusivity and strengthen individuals' confidence in managing their health. These findings can also inform the development of healthcare programs aimed at empowering patient self-management through AI-based tools.

Schizophrenia (SCZ) is a severe neurodevelopmental mental disorder with age-dependent onset, and healthy siblings of SCZ patients are a pivotal cohort for exploring genetic susceptibility. This study aimed to characterize age-differentiated gray matter volume (GMV) patterns in high-risk and non-high-risk age siblings of SCZ patients via structural magnetic resonance imaging (sMRI), and clarify the regulatory role of age in neurostructural correlates of genetic susceptibility. A total of 31 SCZ patients, 62 healthy siblings (divided into age-sensitive window siblings [ASW-SIB, 18-35 years, n=31] and post-age-sensitive window siblings [PASW-SIB, 36-45 years, n=31]), and 31 healthy controls (HCs) were enrolled. Patients with schizophrenia (SCZ, n=31) and healthy controls (HCs, n=31) were age-matched to the sibling cohort (overall mean age: 30.55 ± 7.84 years) but not further stratified by age, as the core aim was to compare age-specific sibling subgroups with non-stratified reference groups, however, post-hoc pairwise comparisons (Bonferroni-corrected) showed that PASW-SIB were significantly older than both SCZ (p < 0.001) and HCs (p < 0.001). sMRI data were processed using voxel-based morphometry (VBM8), and inter-group GMV comparisons were performed with one-way ANOVA and two-sample t-tests. Results showed no significant differences in demographic characteristics among the three groups (all p>0.05). One-way ANOVA revealed significant main effects of group on GMV in brain regions including the caudate nucleus, pallidum, insula, parahippocampal gyrus, and precuneus (F = 1.28-1.96, all p<0.01). Pairwise comparisons indicated that compared with HCs, PASW-SIB exhibited significantly increased GMV in the caudate nucleus, pallidum, and insula (all p<0.05, FDR-corrected), while ASW-SIB only showed reduced GMV in the parahippocampal gyrus and precuneus (all p<0.05, FDR-corrected). In contrast, SCZ patients exhibited reduced GMV in multiple regions (inferior temporal gyrus, superior frontal gyrus, postcentral gyrus, and insula) compared to HCs (all p < 0.05, FDR-corrected). No significant correlations were found between GMV and clinical symptoms (PANSS scores) or disease duration in SCZ patients (all p>0.05). These findings suggest age-associated GMV differences in healthy siblings of SCZ patients: PASW-SIB show widespread GMV alterations in the basal ganglia and insula, while ASW-SIB exhibit localized GMV differences in the default mode network. The age-specific neurostructural patterns are consistent with potential risk-related brain phenotypes for SCZ, which may provide imaging targets for future studies of early risk stratification and intervention in high-risk populations.

Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms are frequently reported among incarcerated populations and have been associated with impulsivity, emotional dysregulation, and adverse mental health outcomes. Despite growing international evidence, no previous studies have examined screening-positive ADHD symptoms among prisoners in Paraguay or evaluated Spanish-language screening tools in correctional settings.

To estimate the prevalence of screening-positive ADHD symptoms among incarcerated men and women in Paraguay, examine selected psychological and criminological variables, and assess the reliability and factorial structure of the Spanish version of the Symptom Check List-ADHD (SCL-ADHD).

This cross-sectional study included 836 inmates (621 men, 215 women) recruited through probabilistic sampling in three Paraguayan prisons. Screening-positive ADHD status was defined as a score ≥ 12 on the nine-item SCL-ADHD derived from the SCL-90-R. Psychiatric symptoms, suicide risk, and substance-related problems were assessed using validated measures. Multivariable logistic regression identified independent correlates of screening-positive ADHD status.

Screening-positive ADHD status was observed in 33.4% of participants, with higher prevalence among women than men (39.1% vs. 31.4%; OR = 1.40, 95% CI 1.02-1.93, p = 0.04). Screening-positive ADHD was independently associated with suicide risk (OR = 3.85, p < 0.001) and elevated SCL-90-R dimensions of hostility, anxiety, depression, and obsessive-compulsive symptoms. Sensitivity analyses using continuous symptom scores showed associations with hostility, anxiety, and obsessive-compulsive symptoms remained, whereas association with depression attenuated. No significant independent associations were observed with criminological variables. The Spanish SCL-ADHD demonstrated acceptable internal consistency (α = 0.76) and a coherent symptom structure.

Screening-positive ADHD symptoms were common among incarcerated individuals in Paraguay, particularly women, and were associated with concurrent emotional dysregulation and suicidality. These findings reflect screening-based symptom burden rather than confirmed adult ADHD diagnoses and highlight the potential utility of systematic ADHD screening within correctional mental health services.