EBioMedicine最新文献

Background: High content imaging-based functional precision medicine approaches have been developed and successfully applied in the field of haemato-oncology. For rheumatoid arthritis (RA), treatment selection is still based on a trial-and-error principle, and biomarkers for patient stratification and drug response prediction are needed.

Methods: A high content, high throughput microscopy-based phenotyping pipeline for peripheral blood mononuclear cells (PBMCs) was developed, allowing for the quantification of cell type frequencies, cell type specific morphology and intercellular interactions from patients with RA (n = 65) and healthy controls (HC, n = 33). Samples were exposed to a curated set of RA-specific small molecules, biologicals and reference stimuli for 24 h to assess ex vivo drug effects. Data on ex vivo PBMC phenotypes were integrated with information on patients' in vivo medication and disease activity.

Findings: The unbiased data from in total 6.9e8 individual cells were collected and allowed for the identification of PBMC phenotypes specific to disease activity as well as in vivo and ex vivo treatment. The arrayed ex vivo drug perturbation enabled the systematic characterization of drug effects, clustering by mode of action and uncovered morphologic alterations associated with biologic disease-modifying anti-rheumatic drug (DMARD) treatment. Individual in vivo treatment regimens translated into altered immune cell abundances in patients with a comedication of conventional synthetic DMARDs when compared to HCs. Global integration of PBMC characteristics led to clustering of patients according to disease activity and correlation with clinical data.

Interpretation: The application of the developed screening tool demonstrates a technical proof-of-concept for feasibility of a functional precision medicine approach to the ex vivo immunophenotypic characterisation of patients with RA.

Funding: This work was supported by the Austrian Academy of Sciences, the Medical University of Vienna and a grant (RMG2235 to L.X.H.) from the European Alliance of Associations for Rheumatology (EULAR).

Background: Perivascular spaces (PVS) on brain MRI are surrogates for small parenchymal blood vessels and their perivascular compartment, and may relate to brain health. However, it is unknown whether PVS can predict dementia risk and brain atrophy trajectories in participants without dementia, as longitudinal studies on PVS are scarce and current methods for PVS assessment lack robustness and inter-scanner reproducibility.

Methods: We developed a robust algorithm to automatically assess PVS count and size on clinical MRI, and investigated 1) their relationship with dementia risk and brain atrophy in participants without dementia, 2) their longitudinal evolution, and 3) their potential use as a screening tool in simulated clinical trials. We analysed 46,478 clinical measurements of cognitive functioning and 20,845 brain MRI scans from 10,004 participants (71.1 ± 9.7 years-old, 56.6% women) from three publicly available observational studies on ageing and dementia (the Alzheimer's Disease Neuroimaging Initiative, the National Alzheimer's Coordinating Centre database, and the Open Access Series of Imaging Studies). Clinical and MRI data collected between 2004 and 2022 were analysed with consistent methods, controlling for confounding factors, and combined using mixed-effects models.

Findings: Our fully-automated method for PVS assessment showed excellent inter-scanner reproducibility (intraclass correlation coefficients >0.8). Fewer PVS and larger PVS diameter at baseline predicted higher dementia risk and accelerated brain atrophy. Longitudinal trajectories of PVS markers differed significantly in participants without dementia who converted to dementia compared with non-converters. In simulated placebo-controlled trials for treatments targeting cognitive decline, screening out participants at low risk of dementia based on our PVS markers enhanced the power of the trial independently of Alzheimer's disease biomarkers.

Interpretation: These robust cerebrovascular markers predict dementia risk and brain atrophy and may improve risk-stratification of patients, potentially reducing cost and increasing throughput of clinical trials to combat dementia.

Funding: US National Institutes of Health.

Background: Emerging findings indicate that the hypothalamus, the body's principal homeostatic centre, plays a crucial role in modulating cognition, but comprehensive population-based studies are lacking.

Methods: We used cross-sectional data from the Rhineland Study (N = 5812, 55.2 ± 13.6 years, 58% women) and the UK Biobank Imaging Study (UKB) (N = 45,076, 64.2 ± 7.7 years, 53% women), two large-scale population-based cohort studies. Volumes of hypothalamic structures were obtained from 3T structural magnetic resonance images through an automatic parcellation procedure (FastSurfer-HypVINN). The standardised cognitive domain scores were derived from extensive neuropsychological test batteries. We employed multivariable linear regression to assess associations of hypothalamic volumes with age, sex and cognitive performance.

Findings: In older individuals, volumes of total, anterior and posterior hypothalamus, and mammillary bodies were smaller, while those of medial hypothalamus and tuberal region were larger. Larger medial hypothalamus volume was related to higher cortisol levels in older individuals, providing functional validation. Volumes of all hypothalamic structures were larger in men compared to women. In both sexes, larger volumes of total, anterior and posterior hypothalamus, and mammillary bodies were associated with better domain-specific cognitive performance, whereas larger volumes of medial hypothalamus and tuberal region were associated with worse domain-specific cognitive performance.

Interpretation: We found strong age and sex effects on hypothalamic structures, as well as robust associations between these structures and domain-specific cognitive functions. Overall, these findings thus implicate specific hypothalamic subregions as potential therapeutic targets against age-associated cognitive decline.

Funding: Institutional funds, Federal Ministry of Education and Research of Germany, Alzheimer's Association.

Background: Haemodynamic changes occur in stroke and neurodegenerative diseases. Developing imaging techniques allowing the in vivo visualisation and quantification of cerebral blood flow would help better understand the underlying mechanism of these cerebrovascular diseases.

Methods: 3D ultrasound localization microscopy (ULM) is a recently developed technology that can map the microvasculature of the brain at large depth and has been mainly used until now in rodents. In this study, we tested the feasibility of 3D ULM of the nonhuman primate (NHP) brain with a single 256-channel programmable ultrasound scanner.

Findings: We achieved a highly resolved vascular map of the macaque brain at large depth (down to 3 cm) in presence of craniotomy and durectomy using an 8-MHz multiplexed matrix probe. We were able to distinguish vessels as small as 26.9 μm. We also demonstrated that transcranial imaging of the macaque brain at similar depth was feasible using a 3-MHz probe and achieved a resolution of 60 μm.

Interpretation: This work paves the way to clinical applications of 3D ULM. In particular, transcranial 3D ULM in humans could become a tool for the non-invasive study and monitoring of the brain cerebrovascular changes occurring in neurological diseases.

Funding: This work was supported by the New Frontier in Research Fund (NFRFE-2022-00590), by the Canada Foundation for Innovation under grant 38095, by the Natural Sciences and Engineering Research Council of Canada (NSERC) under discovery grant RGPIN-2020-06786, by Brain Canada under grant PSG2019, and by the Canadian Institutes of Health Research (CIHR) under grant PJT-156047 and MPI-452530. Computing support was provided by the Digital Research Alliance of Canada.

Background: Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.

Methods: This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.

Findings: We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.

Interpretation: The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.

Funding: The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).

Background: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study aims to simulate the practical implementation challenges of a recently developed ML algorithm, AI-PAL, designed for the diagnosis of acute leukaemia and report on its performance.

Methods: We conducted a detailed simulation of the AI-PAL algorithm's implementation at the University Hospital Essen. Cohort building was performed using our Fast Healthcare Interoperability Resources (FHIR) database, identifying all initially diagnosed patients with acute leukaemia and selected differential diagnoses. The algorithm's performance was assessed by reproducing the original study's results.

Findings: The AI-PAL algorithm demonstrated significantly lower performance in our simulated clinical implementation compared to prior published results. The area under the receiver operating characteristic curve for acute lymphoblastic leukaemia dropped to 0.67 (95% CI: 0.61-0.73) and for acute myeloid leukaemia to 0.71 (95% CI: 0.65-0.76). The recalibration of probability cutoffs determining confident diagnoses increased the number of confident positive diagnosis for acute leukaemia from 98 to 160, highlighting the necessity of local validation and adjustments.

Interpretation: The findings underscore the challenges of implementing ML algorithms in clinical practice. Despite robust development and validation in research settings, ML models like AI-PAL may require significant adjustments and recalibration to maintain performance in different clinical settings. Our results suggest that clinical decision support algorithms should undergo local performance validation before integration into routine care to ensure reliability and safety.

Funding: This study was supported by the DFG-cofounded UMEA Clinician Scientist Program and the Ministry of Culture and Science of the State of North Rhine-Westphalia.

Background: Cardiovascular comorbidity increases morbidity and mortality in psoriasis. Systemic treatments, particularly biologics, are effective in alleviating skin and joint inflammation. Conversely, the impact of systemic therapy on cardiovascular disease risk and mortality in psoriasis remains uncertain.

Methods: Impact of systemic treatments on all-cause mortality and cardiovascular disease risk in psoriasis patients' electronic health records (EHRs) from TriNetX was assessed. Treatment categories included apremilast, IL-17 inhibitors (IL-17i), IL23i, TNFi, and classic antipsoriatic drugs. Index event was the first prescription of each treatment, requiring a two-year continuous treatment with exclusion of other systemic antipsoriatic drugs. Propensity-score matching was used to improve comparability. Sensitivity analyses ensured study robustness.

Findings: In descriptive analysis, all-cause mortality rates were 0.61% (classic antipsoriatics, n = 7929), 0.91% (apremilast, n = 1101), 0.00% (IL17i, n = 677), 0.81% (IL23i, n = 1242), and 0.20% (TNFi, n = 6468). Major adverse cardiac events (MACE) were documented in 8.49% (classic antipsoriatics), 5.14% (apremilast), 2.99% (IL17i), 2.09% (IL23i), and 3.74% (TNFi) EHRs. Propensity-score matching showed all-cause mortality rates of 0.23% for any biologic vs. 0.49% for classic antipsoriatics or apremilast, resulting in an HR of 2.21 (95% CI 1.21-3.71, p = 0.0073). MACE risk was also higher with classic antipsoriatics or apremilast (HR 1.66, CI 1.43-1.93, p < 0.0001). The majority of findings were consistent across all four sensitivity analyses. No significant differences in all-cause mortality or MACE risk were observed among biologics.

Interpretation: Biological treatment, as opposed to classic antipsoriatic drugs or apremilast, reduces risk of death and cardiovascular disease in psoriasis. Prospective trials are required to validate these findings.

Funding: DFG: EXC 2167 and LU 877/25-1. State of Schleswig Holstein: Excellence-Chair Program.