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PubMed2026

Pilot study identifying distinct circulating proteomic profiles associated with longitudinal CT-defined fibrotic and inflammatory sarcoidosis.

INTRODUCTION: Pulmonary sarcoidosis exhibits heterogeneous clinical trajectories ranging from self-limited disease resolution to chronic progressive fibrosis, yet reliable biomarkers capable of distinguishing these disease patterns remain lacking. Whether longitudinal CT-defined sarcoidosis phenotypes are associated with distinct circulating molecular signatures remains unknown. METHODS: We performed high-throughput plasma proteomics (SomaScan 11K) in participants with pulmonary sarcoidosis classified into longitudinal chest CT-defined progressive fibrosis, progressive nodular inflammatory disease, or resolving disease trajectories, along with healthy controls. CT phenotypes were assigned based on predefined longitudinal changes in reticulation, traction bronchiectasis, nodular involvement, and mediastinal lymphadenopathy across serial CT scans. One plasma sample per participant was selected from the study visit corresponding to the CT time point at which criteria for the assigned longitudinal phenotype were met. Principal component analysis, hierarchical clustering, pathway enrichment, and correlation-based analyses linking protein expression to quantitative CT features were used to evaluate whether distinct longitudinal CT phenotypes were associated with divergent proteomic signatures. RESULTS: Principal component analysis and hierarchical clustering suggested partial segregation by CT-defined phenotype. Longitudinal CT phenotypes were associated with distinct pathway-level proteomic signatures, with progressive fibrosis enriched for epithelial-mesenchymal transition signaling, and progressive nodular inflammatory disease enriched for mTORC1, MYC, oxidative phosphorylation, adipogenesis, and fatty acid metabolism pathways. Correlation analyses showed coordinated protein-expression patterns associated with fibrotic CT features and mediastinal lymph node enlargement. DISCUSSION: These findings suggest that longitudinal CT-defined fibrotic and inflammatory sarcoidosis phenotypes are associated with distinct pathway-level proteomic signatures. This pilot study provides preliminary proof-of-concept evidence that integrating longitudinal CT imaging phenotypes with plasma proteomics may serve as a framework for future mechanistic studies and biomarker discovery in pulmonary sarcoidosis.

Humans↗

PubMed2026

Genetic heterogeneity affects the risk of incident depression, comorbidity, and response to environment: A prospective trajectory study.

BACKGROUND: Depression exhibits significant heterogeneity in its genetic underpinnings. The role of genetic components in the development of depression and its comorbidities remains insufficiently explored. METHODS: First, depression risk loci from a large-scale genome-wide meta-analysis were annotated to Gene Ontology (GO) terms by functional enrichment. GO-based polygenic risk scores (GO-PRS) were then calculated for individuals in the UK Biobank. Principal component analysis (PCA) was applied for dimensionality reduction, followed by cluster analysis to identify genetic subtypes of depression. Multistate models were applied to assess the impact of genetic patterns on the trajectory from healthy status to incident depression, and depression to 26 subsequent diseases, as well as the associations between environmental factors and disease trajectories across genetic subtypes. RESULTS: Participants were categorized into three genetic subtypes: immune-dominant, neuro-dominant, and comprehensive-risk. Significant differences in risk of depression and subsequent diseases, and susceptibility to environmental factors were observed across subtypes. Comprehensive-risk subtype showed higher risks of depression compared to immune-dominant (HR: 1.10, 95% CI: 1.05-1.15) and neuro-dominant subtype (HR: 1.12, 95% CI: 1.08-1.16). Comprehensive-risk subtype exhibited higher risks of transition from depression to subsequent diseases, such as anemia compared to immune-dominant subtype, and diseases of the digestive system compared to neuro-dominant subtype. Environmental factors were more strongly associated with the transition from depression to subsequent diseases in immune-dominant and comprehensive-risk subtypes, including cardiovascular, respiratory, and metabolic diseases. CONCLUSIONS: Our findings highlight the genetic heterogeneity of depression and comorbidities, and shed light on how genetic components modulate responses to environmental factors.

Humans↗

PubMed2025

Novel approaches and applications in identifying DNA methylation markers of cardio-kidney-metabolic disease.

Cardio-kidney-metabolic (CKM) diseases represent a major public health challenge, accounting for a large proportion of global burden of morbidity and mortality. These conditions share risk factors, including genetic predisposition, environmental exposures, and lifestyle influences, which collectively drive disease development and progression. Epigenetic modifications, particularly DNA methylation (DNAm), serve as key mediators and biomarkers between these risk factors and disease phenotypes by regulating gene expression without altering the DNA sequence. Epigenome-wide association studies have identified DNAm markers associated with CKM diseases and related phenotypes, highlighting both shared pathways and disease-specific epigenetic signatures in inflammation, metabolic dysfunction, and aging-related processes. Longitudinal studies further demonstrate the dynamic nature of DNAm changes over time, offering insights into disease trajectories. Additionally, methylation risk scores integrating multiple epigenetic markers show promise in improving disease prediction and risk stratification beyond traditional clinical factors. To synthesize the current evidence, we conducted a targeted literature search in PubMed for English-language, peer-reviewed articles published between 2014 and the present. Future research leveraging large, well-phenotyped cohorts, advanced statistical methods, and innovative study designs will be critical for uncovering novel biomarkers, refining risk prediction models, and developing targeted epigenetic therapies to mitigate the global burden.

Humans↗

PubMed2026

When Neurodevelopment Meets Autoimmunity: Pemphigus Foliaceus in Rett Syndrome Expands the Clinical Spectrum-A Case Report.

Rett syndrome (RTT, OMIM 312750) is a complex multisystem neurodevelopmental disorder. Evidence suggests that RTT may have an autoimmune component and inflammatory activation. However, the autoimmune manifestations remain poorly described. Pemphigus foliaceus is a debilitating autoimmune blistering condition caused by IgG autoantibodies that target desmoglein-1 (Dsg1), resulting in widespread skin blistering and lesions. We report a case of pemphigus foliaceus in a 20-year-old female with RTT and discuss its clinical implications. Clinical data obtained from electronic health records were extracted and reviewed. Genetic testing was performed to identify the specific methyl-CpG-binding protein 2 (MECP2) mutation and on an expanded panel of 55 genes associated with pemphigus foliaceus and related blistering disorders. The individual had pemphigus foliaceus, which required immunosuppression, intravenous immunoglobulin (IVIg) therapy, and Rituximab. The disease trajectory was complicated by infections, aspiration pneumonia, and hypoxic cardiac arrest. There was progressive functional decline, and disease control was difficult to achieve, with frequent flares. Genetic testing confirmed a heterozygous pathogenic MECP2 variant (NM_001110792.1:c.952C>T; p.(Arg318Cys)). HLA genotyping identified alleles consistent with the HLA-DRB1*04:02-HLA-DQA1*03:01-HLA-DQB1*03:02 (DR4/DQ8) haplotype. Furthermore, genetic analysis identified a heterozygous DSG1 variant rs12967407. This study reports the first case of pemphigus foliaceus in RTT, expanding the clinical spectrum of RTT beyond its neurodevelopmental phenotype. The DR4/DQ8 haplotype, previously associated with pemphigus susceptibility, supports a background of genetic susceptibility in this individual. No causal association between RTT and pemphigus foliaceus can be inferred from this single case. Rather, this case demonstrates that a rare autoimmune disorder such as pemphigus foliaceus can co-occur with a pathogenic MECP2 mutation. The coexistence of a genetic and autoimmune disease can result in a more complex clinical presentation and treatment course. The case further emphasises the need for increased vigilance in identifying new and emerging systemic pathology alongside RTT.

Humans↗

PubMed2026

Clinical Features and Outcome Measures Across Still Disease (Systemic Juvenile Idiopathic Arthritis and Adult-Onset Still Disease) Cohorts Worldwide: A Systematic Literature Review.

OBJECTIVE: Multinational research is essential to improve recognition and management of systemic juvenile idiopathic arthritis (sJIA). Current cohorts vary in the clinical variables and outcome measures collected. Adult-onset Still disease (AOSD) and sJIA are widely considered to comprise a single disease spectrum; however, classification criteria and clinical tools differ between groups. This systematic literature review aimed to identify clinical features and outcome measures collected across sJIA and AOSD cohorts worldwide to guide the development of a minimal dataset for Still disease. METHODS: A literature search was conducted from 2000 to 2024 using Ovid MEDLINE, Embase, and Wiley Cochrane Library (Trials). Included articles were in English and described sJIA or AOSD cohorts of ≥ 20 patients, reporting patient characteristics, clinical and laboratory features, and outcome measures. RESULTS: A total of 240 articles were included (95 sJIA, 134 AOSD, 11 mixed), from 37 countries, describing 23,136 patients. International League of Associations for Rheumatology classification was used in 77.9% of sJIA studies, whereas 98.5% of AOSD studies used Yamaguchi criteria. There was no clear consensus on the definition of macrophage activation syndrome. Race and ethnicity were only reported in 11.7% of articles. Cohorts evaluated aligned on the most commonly collected laboratory items for both AOSD and sJIA, with some agreement among clinical features, whereas disease outcome measures used to evaluate and follow disease trajectory were variable. CONCLUSION: Data reporting across sJIA and AOSD cohorts for clinical characteristics and outcome measures is widely heterogeneous. Consensus on the identification of a standardized minimal dataset for Still disease cohorts is needed to foster future collaboration and improve patient outcomes.

Humans↗

PubMed2026

Metabolomics in breast cancer: insights into treatment responses, disease progression, and prognostic assessment.

BACKGROUND: Alterations in metabolic pathways are a hallmark of cancer and play a pivotal role in breast cancer development and progression. The inherent metabolic heterogeneity of breast cancer contributes to differences in therapeutic response and patients' prognosis. Clinical metabolomics has emerged as a promising approach for identifying metabolic biomarkers that reflect tumor biology, treatment-related changes after diagnosis, and patients' outcomes. AIMS OF REVIEW: This review summarizes the metabolomic profiles of breast cancer patients, using various biological materials and analytical methods, to assess their potential role as biomarkers for monitoring therapeutic response, adverse treatment effects, tracking disease progression, and predicting prognosis. KEY SCIENTIFIC CONCEPT OF REVIEW: Metabolomic shifts generate unique signatures with promising potential as biomarkers for evaluating treatment response, monitoring therapeutic adverse effects, disease progression, and predicting clinical outcomes in breast cancer patients. Biological matrices, such as serum, plasma, and tumor tissue, were commonly used in both untargeted and targeted metabolomics approaches. Liquid chromatography-mass spectrometry is the most commonly used analytical method in clinical metabolomics studies. Altered metabolites were identified and linked to metabolic pathways, particularly amino acids, glucose, and fatty acids metabolism. When integrated with genomic and transcriptomic data, these metabolic fingerprints offer a multidimensional perspective on disease trajectory, thereby enhancing patient stratification and informing personalized therapeutic strategies.

Humans↗

PubMed2026

Depression and amyloid-β across CSF, PET, and plasma biomarkers: a systematic review and meta-analysis.

Alzheimer's disease is increasingly defined by biomarker evidence of amyloid-β and tau pathology, sharpening questions about whether late-life depression contributes to, or instead reflects, this pathology. We conducted a systematic review and meta-analysis of studies published between 2000 and 2025 that compared amyloid-β biomarkers in adults with and without depression, with depression defined by validated clinical diagnoses or symptom rating scales. Twenty-four studies were included, spanning three biomarker sources: cerebrospinal fluid, positron emission tomography imaging, and plasma. Across all sources, the pooled difference in amyloid-β burden between depressed and non-depressed individuals was small and clustered near zero, indicating only a weak, statistically non-significant tendency toward higher amyloid in depression. When the three sources were examined separately, each yielded a similar near-null result, although between-study heterogeneity was considerable for cerebrospinal fluid and plasma and moderate for imaging. Importantly, a prespecified subgroup analysis showed that imaging results diverged by quantification method: studies using the simpler standardized uptake value ratio clustered around zero, whereas the smaller group of studies using kinetic distribution volume ratio modelling showed a significant positive association, suggesting that methodological choices critically influence the observed relationship. Taken together, these findings indicate that depression is not consistently accompanied by greater amyloid-β burden across widely used biomarker platforms. The distribution volume ratio signal nonetheless raises the possibility of subtle associations that cruder methods may obscure, and suggests that depression may shape Alzheimer's disease trajectories more by modifying the clinical impact of amyloid than by altering its amount.

Humans↗

PubMed2026

New Evidence in Heart Failure: 2026 Update.

Heart failure (HF) remains a major cause of morbidity, mortality, impaired quality of life and healthcare expenditure worldwide. The global burden of HF continues to increase due to population aging, improved survival, and the growing prevalence of cardiovascular, renal, and metabolic comorbidities. Simultaneously, the pace of scientific progress in HF has accelerated considerably. Recent advances have refined our understanding of HF epidemiology, prognosis, and disease trajectories, including emerging concepts of HF improvement, remission, and recovery. The Second Universal Definition of HF has also updated the classification framework, moving beyond the traditional ejection fraction-based categories. HF is now broadly classified into two major phenotypes: heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF). Novel mechanistic insights highlight the role of inflammation, immune activation, metabolic dysfunction, mitochondrial biology, and multisystem interactions in HF progression. There has also been significant progress in the characterization and management of major comorbidities, including chronic kidney disease (CKD), diabetes, obesity, atrial fibrillation (AF), pulmonary hypertension, frailty, malnutrition, and cancer. Diagnostic innovations include novel biomarkers, multi-omics technologies, artificial intelligence-based approaches, advanced imaging techniques, congestion assessment tools, and emerging digital health solutions. Important advances have occurred in specific HF aetiologies, including cardiomyopathies, cardiac amyloidosis (CA), myocarditis, arrhythmia-induced cardiomyopathy (AiCM), and Chagas cardiomyopathy. Therapeutic developments continue to reshape HF management across the spectrum of left ventricular ejection fraction. Recent evidence has focused on optimization of guideline-directed medical therapy in HFrEF, expansion of evidence-based therapies in HFpEF, and growing roles for sodium-glucose cotransporter-2 inhibitors, finerenone, incretin-based therapies, and transcatheter valve interventions. Collectively, these advances support the transition from a predominantly phenotype-based approach towards a more personalized and biologically informed model of HF care, with the potential to further improve outcomes across the entire HF spectrum.

Journal Article↗

PubMed2026

Proteomics as a theranostic compass in BCR::ABL1-negative myeloproliferative neoplasms: Integrating biomarker discovery with therapeutic stratification.

Classic BCR::ABL1-negative myeloproliferative neoplasms (MPNs)-polycythaemia vera, essential thrombocythaemia, and primary myelofibrosis-are clonal haematopoietic stem cell disorders with marked heterogeneity in clinical phenotype, disease trajectory, and therapeutic response. Genomic stratification by driver and cooperating mutations only partially accounts for this variability, leaving gaps in predicting thrombotic risk, fibrotic progression, leukaemic transformation, and treatment benefit. Proteomics bridges this gap by providing function-proximal readouts of protein abundance, post-translational modifications, pathway activity, and intercellular signalling that genomics and transcriptomics cannot capture, positioning it as a theranostic platform in which the same molecular readouts simultaneously inform diagnostic stratification and therapeutic decision-making. We propose a five-stage translational framework spanning from discovery-scale mass spectrometry and affinity-based plasma profiling to targeted validation, multicentre standardisation, and machine learning-integrated clinical panels. Proteomic evidence is synthesised across the following four disease axes: clonal fitness in haematopoietic stem and progenitor cells; bone marrow microenvironmental remodelling and fibrosis; chronic inflammation and thrombosis; and leukaemic transformation. We further describe how phosphoproteomics reveals resistance mechanisms to JAK inhibitors, including AXL-MAPK bypass and PP2A-autophagy-mediated tolerance, and how protein-level biomarkers (BCL2-BCL-XL, RAS-ERK, CAMK2G, and ROCK1/2) can guide individualised therapeutic selection. Affinity-based platforms (Olink PEA and SomaScan) and spatially resolved technologies (CODEX and single-cell proteomics) complement discovery proteomics. At present, however, this evidence base is constrained by small and heterogeneous cohorts, limited cross-platform reproducibility, and a scarcity of independent external validation for candidate protein panels. Realising this vision will require multicentre standardisation, analytically validated panel assays, and prospective clinical studies that translate molecular findings into decision-grade tools for patients with MPNs.

Humans↗

PubMed2026

Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.

Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.

Humans↗

PubMed2025

High-affinity CD16A polymorphism associated with reduced risk ofsevere COVID-19.

CD16A is an activating Fc receptor on NK cells that mediates antibody-dependent cellular cytotoxicity (ADCC), a key mechanism in antiviral immunity. However, the role of NK cell-mediated ADCC in SARS-CoV-2 infection remains unclear, particularly whether it limits viral spread and disease severity or contributes to the immunopathogenesis of COVID-19. We hypothesized that the high-affinity CD16AV176 polymorphism influences these outcomes. Using an in vitro reporter system, we demonstrated that CD16AV176 is a more potent and sensitive activator than the common CD16AF176 allele. To assess its clinical relevance, we analyzed 1,027 patients hospitalized with COVID-19 from the Immunophenotyping Assessment in a COVID-19 cohort (IMPACC), a comprehensive longitudinal dataset with extensive transcriptomic, proteomic, and clinical data. The high-affinity CD16AV176 allele was associated with a significantly reduced risk of ICU admission, mechanical ventilation, and severe disease trajectories. Lower anti-SARS-CoV-2 IgG titers were correlated to CD16AV176; however, there was no difference in viral load across CD16A genotypes. Proteomic analysis revealed that participants homozygous for CD16AV176 had lower levels of inflammatory mediators. These findings suggest that CD16AV176 enhances early NK cell-mediated immune responses, limiting severe respiratory complications in COVID-19. This study identifies a protective genetic factor against severe COVID-19, informing future host-directed therapeutic strategies.

Humans↗

PubMed2026

Longitudinal Clinical, Physiological, and Molecular Profiling of Female Patients With Metastatic Cancer: Protocol and Feasibility of a Multicenter High-Definition Oncology Study.

PURPOSE: A substantial proportion of patients receiving genomically matched therapies do not achieve clinical benefit, underscoring the influence of nongenetic factors on cancer outcomes. High-Definition Oncology (HDO) proposes integrating longitudinal, multimodal patient data-spanning clinical, molecular, physiological, and behavioral domains-to enable truly individualized cancer care. This manuscript describes the HDO study design, framework, and feasibility results in women with metastatic cancer. METHODS: We initiated a prospective, multicenter observational study (HDO study; ClinicalTrials.gov identifier: NCT06590506) enrolling 300 female patients with newly diagnosed metastatic breast, lung, or colorectal cancer. Here, we report the study design, standardized workflows, prespecified feasibility criteria, and early internal pilot results. Eleven data modalities are collected longitudinally, including tumor and germline genomics, germline epigenomics, gut microbiome, blood and stool metabolomics and proteomics, exposome characterization, wearable-derived physiological monitoring, digital footprint assessment, medical imaging, and patient-reported outcomes. Standardized workflows govern clinical procedures, data acquisition, biospecimen processing, and quality control across all participating sites. RESULTS: Feasibility was evaluated in the first 30 participants (10% of planned accrual). Patients completed 100% of scheduled clinical visits, 97.4% of planned plasma collections, 80.7% of stool samples, and all tumor biopsies. Wearable devices captured activity, heart rate, sleep, and blood oxygen saturation data during 95.0%, 84.2%, 90.6%, and 70.7% of total patient-days, respectively. Biospecimens met predefined quality control metrics across all molecular modalities. Engagement with mobile applications for pain and emotion reporting exceeded 80%. CONCLUSION: The HDO study demonstrates the feasibility of comprehensive, longitudinal, multimodal data collection in women with metastatic cancer. This internal pilot establishes an integrated framework for future analyses aimed at characterizing disease trajectories, defining molecular and physiological determinants of outcomes, and developing patient-specific computational models.

Humans↗

PubMed2023

Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.

PURPOSE: To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR). METHODS: We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders. RESULTS: We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10-4, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results. CONCLUSION: Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.

Humans↗

PubMed2026

Divergent microbial preludes to necrotising enterocolitis defined by gut phages and bacterial resistomes.

BACKGROUND: Translating microbiome correlations into robust predictive features for complex gut disorders remains elusive, partly due to oversimplified models of pathogenesis and neglect of the virome, a key player in microbial ecosystems. Necrotising enterocolitis (NEC), a devastating disease of preterm infants with no reliable clinical predictors, exemplifies this challenge. OBJECTIVE: To determine the predictive potential of the gut prophageome and polymicrobial aetiologies for NEC. DESIGN: We applied integrated metagenomic and metatranscriptomic analyses and machine learning to 1825 longitudinal stool samples from 43 preterm infants who later developed NEC and 86 gestational age-matched and birthweight-matched controls across three US hospitals. We characterised gut prophageome acquisitions and their association with clinical exposures, including antibiotics, diet and pharmacotherapies. To predict NEC risk, we integrated pre-onset prophageome, antibacterial resistome and bacteriome profiles with neonatal pathology, stratifying the cohort by disease onset timing (early: ≤40 days; late: >40 days) for separate analysis. RESULTS: NEC cases exhibited distinct viral diversity trajectories before disease onset. Early-onset NEC was best predicted by phage-bacterial interaction signatures (75% accuracy, 81% sensitivity). Metatranscriptomics revealed increased phage DNA abundance with low gene expression, suggesting a lysogenic lifestyle that may stabilise pathobionts. These phages encode metabolic genes potentially enhancing pathobiont resilience. Late-onset NEC was best predicted by antibacterial resistome profiles (83% accuracy). CONCLUSION: The gut prophageome serves as both a source of pre-symptomatic predictive signals and an active modulator of NEC pathogenesis, with distinct microbial mechanisms driving early-onset and late-onset disease. These polymicrobial etiologies inform strategies for early detection, risk stratification and the development of microbiome-targeted preventive and therapeutic interventions.

BIOMARKERS↗

PubMed2025

Generative AI Models in Time-Varying Biomedical Data: Scoping Review.

BACKGROUND: Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care. OBJECTIVE: While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science. METHODS: We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality. RESULTS: We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS: We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Artificial Intelligence↗

PubMed2026

AI-Driven Precision Medicine in Alzheimer's Disease: Drug Repurposing, Digital Therapeutics and Clinical Decision Support.

Alzheimer's Disease (AD) is a neurodegenerative disease that causes significant clinical, social, and economic burden worldwide. Despite improvements in understanding its multifaceted pathogenesis, current treatments are mostly symptomatic and ineffective across varied patient populations. To overcome these constraints, AI-driven precision medicine allows tailored risk assessment, treatment selection, and disease monitoring. This review covers AI's role in AD precision medicine, focusing on drug repurposing, digital therapies and clinical decision support systems. Machine and deep learning models are used to predict medication response, integrate heterogeneous data sources such as genomics, transcriptomics, neuroimaging and electronic health records, and uncover pharmacogenomic treatment success factors. The paper covers AIenabled precision pharmacology, including tailored dosing algorithms, adaptive therapeutic monitoring, and adverse drug reaction prediction. Bioinformatics-based target identification, network pharmacology, graphbased AI models, virtual screening, and real-world and clinical data validation are emphasized in AI-driven medication repurposing. AI-powered digital treatments like personalized cognitive training platforms, wearable- derived digital biomarkers, virtual and mixed reality interventions, adherence monitoring, and digital twins for therapy optimization have been discussed. AI-based clinical decision support systems are also thoroughly assessed for clinical value, accuracy, and explainability in disease subtyping, trajectory prediction, and risk stratification in preclinical and prodromal AD. Despite these promises, data heterogeneity, algorithmic bias, legal barriers, and privacy concerns exist. Federated learning enables safe multi-center collaboration and hybrid AI-human approaches, and it represents the future. AI's ability to alter AD care opens the door to precision medicine paradigms that use repurposed medications, digital tools and intelligent decision-making to improve patient outcomes.

Alzheimer’s disease↗

PubMed2026

High-Fat Diet and a High Amyloid Load Interact to Induce PKC-α Dependent Synaptic Insulin Resistance.

A plethora of studies suggest that a high-fat diet in combination with a high amyloid load causes synaptic insulin resistance and is a risk factor for Alzheimer's disease. Our understanding of the underlying mechanisms is still fragmented. To gain new insights, we conducted integrated proteomic and phosphoproteomic profiling of hippocampal synaptosomes from WT and a transgenic mouse line with a high amyloid load (heterozygous TBA2.1 mice) that show no overt signs of neurodegeneration and dementia. Mice were fed with a regular or high-fat diet. Data-independent acquisition quantified over 5400 proteins, revealing a stable synaptic proteome across conditions. However, the combination of high amyloid load and high-fat diet triggered coordinated remodeling of lipid metabolism pathways, particularly mitochondrial and peroxisomal fatty acid catabolism. Phosphoproteomic analysis showed pronounced activation of lipid- and stress-responsive kinases, including protein kinase C-α, along with increased inhibitory phosphorylation of insulin receptor substrates (IRS1/2). In vitro experiments indicate that blocking protein kinase C-α indeed prevents synaptic insulin resistance in primary neurons. The findings suggest that this proteomic workflow, combined with kinase pathway analysis, can reveal nodal points for interventions in a complex disease state with a trajectory to Alzheimer's disease.

Animals↗

PubMed2026

Sex-specific differences in liver DNA methylation patterns and epigenetic aging in mice.

Biological sex has been shown to influence aging outcomes, contributing to distinct trajectories in disease susceptibility and lifespan. DNA methylation patterns provide a quantitative measure of biological aging. This study investigated whether aged male and female mice display distinct liver DNA methylation patterns and differences in epigenetic aging. Liver samples were collected from 17 aged c57BL/6 mice (6 males, 11 females). Genomic DNA was extracted and bisulfite-converted before targeted enrichment of 2,045 murine age-associated CpG loci. Biological age (DNAge) was estimated using a previously developed DNA methylation-based predictor generated through elastic net regression. The difference (ΔDNAge) between DNAge and chronological age was computed. Sex-specific differences were assessed by comparing site-specific methylation ratios, ΔDNAge values, and through principal component analysis (PCA) and multiple linear regression. Twelve CpG sites across six genes (Fam84b, Zswim6, Hsf4, Mn1, Qprt, and Rapgefl1) showed significant sex-associated differences in methylation. Fam84b demonstrated the largest and most consistent sex-associated effect, with all three associated CpG sites showing higher methylation in males (regression coefficients: -0.204, -0.281, and -0.294). Zswim6 exhibited consistent lower methylation ratios in females, whereas the other genes showed higher methylation in females. There were no sex differences in biological age or ΔDNAge (P = 0.596). Although the epigenetic clock did not reveal differences between sexes in aging, aged mice did exhibit sex-specific liver methylation patterns different from those reported in younger mice, suggesting that sex-dependent epigenetic changes may emerge later in life and may reflect sexual dimorphism in liver function with age.NEW & NOTEWORTHY Males and females are known to age differently and develop certain diseases at different rates. Here, we examined the livers of aged male and female mice to see if they show different DNA methylation patterns. We found that aged male and female mice had distinct DNA methylation patterns at specific genes. Interestingly, most of these methylation differences were not present in younger mice, suggesting that sex differences in the genome may change with age.

Animals↗