Communications medicine最新文献

Background: With the advent of genomic technologies, pharmacogenomics has evolved significantly. Such advancement facilitates comprehensive identification of common and rare alleles crucial for psychiatry treatments, especially in context of Indian psychiatric patients who are genetically diverse and for whom data is limited.

Methods: This study explores the pharmacogenomic spectrum of CYP2C19, CYP2D6, and CYP2C9 genes in an Indian psychiatric cohort of 383 individuals (264 patients, 119 controls) using Axiom PMD Array.

Results: Beyond common phenotypes like CYP2C19 *1/*2, we identified rare functional phenotypes including CYP2C19 *1/*34, CYP2C9 *1/*11 and CYP2D6 *4/*5 that are frequently overlooked in regular screenings. Interestingly, 3% of individuals were identified as most likely non-responders to medications metabolized by these three enzymes, suggesting the importance of platforms that cover both common and rare alleles in populations with high diversity. The study observed 13.26% poor CYP2C19 metabolizers, 2.27% poor CYP2D6 metabolizers, and 3.41% poor CYP2C9 metabolizers in the psychiatric cohort.

Conclusions: The study identifies three percent of the cohort shows compromised metabolism across all three genes, emphasizing that comprehensive screening of common along with rare functional variants is essential for personalized psychiatric treatment in India.

Background: Spatial navigation impairments emerge early in Alzheimer's disease, but assessments targeting these deficits remain underutilised or impractical for cognitive screening. The Spatial Performance Assessment for Cognitive Evaluation (SPACE) is a newly developed digital tool that evaluates spatial navigation deficits associated with cognitive impairment.

Methods: We assessed spatial navigation ability using SPACE in 300 older adults recruited from memory clinics and the general community. Participants were classified across different levels of cognitive impairment using the Clinical Dementia Rating (CDR) scale. Performance in SPACE was compared with clinical diagnosis, standard cognitive assessments, and demographic models using Area Under the ROC Curve (AUC), sensitivity, and specificity.

Results: We show that SPACE reliably distinguishes CDR levels, exceeding the accuracy of demographic models and matching or surpassing most traditional neuropsychological tests. Including SPACE significantly increases the AUC for distinguishing between no dementia from mild dementia (0.76 to 0.94), no dementia from moderate dementia (0.79 to 0.95), and questionable dementia from mild dementia (0.70 to 0.91), all with consistently high sensitivity and specificity. A shortened version of SPACE, lasting less than 11 minutes, reduces administration time by 40% while maintaining high diagnostic accuracy. Cross-validation analyses confirm the reliability and robustness of these models.

Conclusions: These findings highlight the potential of digital spatial navigation assessments to advance early detection, contributing to scalable and accessible healthcare.

Background: Healthcare Artificial Intelligence (AI) offers transformative potential but often inherits biases from training data, worsening disparities. While bias mitigation has focused on structured data, mental health relies on unstructured clinical notes, where linguistic differences and data sparsity pose challenges. This study aims to detect and reduce non-biological textual bias in AI models supporting pediatric mental health screening.

Methods: We analyzed ~20,000 pediatric anxiety cases and matched controls (ages 5-15) from Cincinnati Children's Hospital records, where gender prevalence transitions from male-dominant in early childhood to female-dominant in adolescence. Anxiety prediction models were fine-tuned using a Transformer architecture optimized for computational efficiency. Classification parity across sex subgroups was evaluated, and we also verified that the model relied on clinically relevant words (using the LIME tool). Bias was mitigated through informative term filtering and systematic gender-biased text replacement.

Results: Here, we show systematic under-diagnosis of female adolescents, with 4% lower accuracy and 9% higher false-negative rates compared to male patients. Notes for male patients are on average 500 words longer, and linguistic similarity metrics reveal distinct word distributions between sexes. Applying our de-biasing framework reduces diagnostic bias by up to 27%, improving equity in model performance.

Conclusions: We develop and evaluate a data-centric de-biasing framework to address gender-based disparities in clinical text arising from non-biological differences, such as reporting practices and documentation styles. Our method selectively de-biases data by neutralizing biased language and normalizing information density while preserving clinically relevant content. Further validation across different models is essential before clinical deployment.

Background: Detailed body composition assessment, through measurement of adipose tissue and muscle distribution, is essential for understanding population health trends, guiding targeted interventions, evaluating lifestyle effects, and monitoring age-related changes such as sarcopenia. Dual X-ray absorptiometry (DXA) provides estimates of fat and lean mass from low-dose X-ray images, while magnetic resonance imaging (MRI) offers three-dimensional measurements of adipose tissue and muscle distribution. Both methods have their strengths and weaknesses, but comparison between them at scale is missing.

Methods: We assessed the accuracy of DXA compared to MRI for evaluating body composition. Using data from 32,961 participants in the UK Biobank, including 1,928 re-scanned participants after about two and a half years, we examined cross-sectional and longitudinal agreements in DXA and MRI measurements within android and gynoid regions. MRI scans were acquired using a Siemens 1.5 T MRI scanner, and DXA scans on a GE iDXA.

Results: Our results show that DXA is able to capture fat measurements, including visceral adipose tissue and fat mass, but overestimates lean mass compared to MRI, particularly in android regions for men (4.10 kg measured by DXA vs. 1.74 kg by MRI) and women (2.92 vs. 1.10 kg). Longitudinal MRI data reveal a 4-5% muscle and lean mass decrease, undetected by DXA, which shows lean mass increases in women at the follow-up visit.

Conclusions: Although DXA is practical for population-level fat assessments, it may not be as precise for lean mass assessments, especially in longitudinal studies. If economically and practically feasible, MRI remains the preferred method for detailed and precise longitudinal body composition analysis.

Background: Abdominal aortic aneurysm (AAA) rupture leads to high morbidity and mortality. Current rodent models struggle to reliably mimic infrarenal AAA rupture. Chemical treatments using pancreatic elastase (PE), papain (Pa), β-aminopropionitrile (BAPN), and angiotensin II (ANG II) are known to induce AAA in rodents. We hypothesized that combining these agents can synergistically lead to acute AAA rupture models, as well as chronic AAA models that closely resemble human pathology.

Methods: AAAs were induced in 125 male C57BL/6 mice via peri-adventitial exposure for twenty minutes using a cotton ball with either PE, Pa, or a combination of both (PE+Pa), with or without BAPN and ANG II.

Results: Two weeks post-induction, all groups exhibit significantly elevated aortic diameters, increased inflammation, elastin and collagen degradation, and matrix metallopeptidase (MMP) activity. The addition of BAPN results in large chronic AAAs (500% growth) and intraluminal thrombus (ILT) formation. Further addition of ANG II results in a 93% rupture rate in the PE+Pa group, significantly increased compared to PE and Pa alone. Compared to previous models, the PE+Pa, BAPN and ANG II combination demonstrates an increase in rupture events, inflammation, and MMP activation.

Conclusions: This murine model, using a synergistic combination of pancreatic elastase and papain, effectively replicates AAA pathophysiology and is ideal for investigating underlying mechanisms and potential therapeutic interventions.

Background: Development of therapies for CLN3 disease, a rare pediatric lysosomal storage disorder, has been hindered by the lack of etiological insights and translatable biomarkers to clinics.

Methods: We used a deep multi-omics approach to discover blood-based biomarkers using longitudinal serum samples from a porcine model of CLN3 disease. Comprehensive metabolomics was combined with a nanoparticle-based LC-MS-based proteomic profiling coupled with TMTpro 18-plex to generate quantitative data on 769 metabolites and 2634 proteins, collectively the most exhaustive multi-omics profile conducted on serum from a porcine model. This was previously impossible due to lack of efficient deep serum proteome profiling technologies compatible with model organisms.

Results: Here we show that the presymptomatic disease state is characterized by elevations in glycerophosphodiester species and lysosomal proteases, while later timepoints are enriched with species involved in immune cell activation and sphingolipid metabolism. Cathepsin S (CTSS), Cathepsin B (CTSB), glycerophosphoinositol, and glycerophosphoethanolamine captured a large portion of the genotype-correlated variation between healthy and diseased animals, suggesting that an index score based on these analytes could have great utility in the clinic.

Conclusions: This study's findings demonstrate the potential of deep multi-omics profiling for uncovering disease-specific biomarkers, providing valuable insights for understanding disease and facilitating the identification of potential drug targets, thus offering valuable insights for therapeutic interventions.

Background: The urbanization of African populations adopting Westernized lifestyles might be connected to changes in microbial exposure and immune system activity that are harmful to health and increase the risk of non-communicable diseases, such as immune-mediated diseases. The study aims to compare microbial exposure, immune system markers, and gut microbiota between rural African and Westernized Northern European children to delineate whether there are differences present in these factors in early childhood.

Methods: We compared innate immune cytokines in plasma (IL-10, IL-6, IL-1β, and TNF-α) using Luminex, gut microbiota using 16S rRNA sequencing, and microbial infections using qPCR in early childhood longitudinal sample series of children from rural Africa (participants from the iLINS-DYAD-M study conducted in Malawi) and from Northern Europe (participants from the DIPP study conducted in Finland) to identify differences which could be associated with negative health outcomes in Westernized societies.

Results: Here, we show that the levels of plasma cytokines and frequency of stool pathogen positivity are substantially higher in Malawian than in Finnish children and that some of the cytokines differ in their longitudinal pattern between the two groups. Also, the diversity and composition of gut microbiota differ between the groups at the age of 6 months and diverge more with increasing age.

Conclusions: These results highlight the early emergence of differences in the immune system and gut microbiota between children living in extremities of the microbial exposure gradient. These differences add to the existing knowledge of possible factors contributing to increasing prevalence of chronic inflammatory diseases in African societies shifting towards more Westernized lifestyle.

Background: Nutritional counselling and physiotherapy could provide synergistic effects in alleviating Post-COVID-19 Condition (PCC) symptoms. However, the feasibility of such personalised multimodal therapy (PMT) remains unclear. The aim was to examine feasibility and inform a future randomised controlled trial (RCT) by exploring preliminary clinical outcomes.

Methods: This pilot and feasibility parallel-group RCT was conducted at a tertiary hospital (ClinicalTrials.gov: NCT05254301, KCE-trials funding: LCOV-211306). Adult participants with PCC according to the WHO criteria were block-randomised into either physiotherapy (n = 33) or PMT (n = 32) for 12 weeks with 6-week follow-up. The PMT included online nutritional counselling and pacing-based physiotherapy. Outcome assessment was assessor-blinded. Feasibility outcomes included study burden, recruitment, and attrition. The 1-min sit-to-stand test (1-MSTS) was assessed as an exploratory clinical outcome. Descriptive statistics and 95% confidence intervals (95% CI) were used to estimate between-group differences. Analyses used all available data, with varying sample sizes.

Results: Here we show that study burden was higher than expected, affecting both recruitment and attrition. The PMT group attended 10 ± 4 dietitian teleconsultations and 14 ± 4 supervised physiotherapy sessions. Between-group mean differences in 1-MSTS repetitions from baseline were 0.97 [95% CI: -1.75, 3.70] at 12 weeks and 2.14 [95% CI: -1.26, 5.54] at follow-up. A definitive RCT would require 41 participants.

Conclusions: The study demonstrated overall safety and feasibility despite a high study burden. Exploratory clinical outcomes showed potential improvement in both groups, with growing estimated differences between groups over time, suggesting a longer follow-up for a definitive RCT.

Background: Major Depressive Disorder (MDD) is a leading global neuropsychiatric disorder, requiring precise diagnosis for effective intervention. Developing accurate diagnostic models for MDD remains a critical but challenging task. This study introduces a graph-based deep learning framework that addresses the issue of limited training data and facilitates robust training for identifying MDD across diverse episode patterns.

Methods: We introduce Brain Augmented-Decorrelated Network (BrainADNet), a framework designed to address data scarcity by augmenting brain signal inputs. BrainADNet builds upon the Skip-Graph Convolutional Network to aggregate informative multi-layer features, enriching its representational capacity. Recognizing the clinical relevance of demographic factors such as age, education, and gender in depression, we incorporate these attributes into the training process and examine their effect on diagnosis. To further improve feature diversity and reduce overfitting, we use a decorrelation regularizer to the model training. This encourages GCN embeddings to learn complementary, non-redundant representations from input graphs.

Results: As far as we are aware, the framework surpasses existing models in accurately identifying MDD cases across depressive stages. We present a detailed ablation study demonstrating the contribution of each component to diagnostic precision. Our study highlights the top-10 brain regions influential in diagnosing MDD in males and females, addressing a crucial gap in understanding gender-specific neural mechanisms. We also uncover distinct patterns in latent-space brain connectivity, derived from GCN embeddings, between individuals experiencing single versus multiple depression episodes.

Conclusions: This study underscores the potential of graph methods to advance diagnostic precision for MDD. By integrating gender-specific and stage-wise insights, our framework equips medical professionals and researchers to design personalized and targeted therapeutic strategies, offering transformative implications for patient care.

Background: Despite decades of control interventions in sub-Saharan Africa, morbidity associated with Onchocerca volvulus infection still exerts a substantial burden of disease, arising from cutaneous, ocular and neurological manifestations.

Methods: We developed and integrated a morbidity sub-model into our previously published individual-based, stochastic transmission model, EPIONCHO-IBM, including both reversible (severe itch, reactive skin disease (RSD)), and irreversible (skin atrophy, depigmentation, hanging groin) cutaneous sequelae, and eye disease (blindness, visual impairment). We modelled the relationship between onchocerciasis skin disease (OSD) and infection prevalence using pre-intervention data from northern Nigeria, and between onchocerciasis ocular disease (OOD) and infection intensity using data from the Onchocerciasis Control Programme in West Africa. We simulated the impact of ivermectin mass drug administration (MDA) upon OSD and OOD using data from Cameroon, Central African Republic, Nigeria, Sudan and Uganda.

Results: Modelled age-specific OSD and OOD prevalence at baseline align well with reported prevalence estimates across the simulated range of endemicity levels but underestimate irreversible OSD in older age groups. Under MDA, we capture trends in infection prevalence, severe itch and irreversible OSD but underestimate reductions in RSD and blindness prevalence.

Conclusions: Integrating morbidity outcomes into transmission dynamics modelling will help improve estimates of onchocerciasis disease burden and inform the effectiveness and cost-effectiveness of current and alternative interventions.