Post-traumatic stress disorder (PTSD) can become a chronic and severely disabling condition resulting in a reduced quality of life and increased economic burden. The disorder is directly related to exposure to a traumatic event, e.g., a real or threatened injury, death, or sexual assault. Extensive research has been done on the neurobiological alterations underlying the disorder and its related phenotypes, revealing brain circuit disruption, neurotransmitter dysregulation, and hypothalamic–pituitary–adrenal (HPA) axis dysfunction. Psychotherapy remains the first-line treatment option for PTSD given its good efficacy, although pharmacotherapy can also be used as a stand-alone or in combination with psychotherapy. In order to reduce the prevalence and burden of the disorder, multilevel models of prevention have been developed to detect the disorder as early as possible and to reduce morbidity in those with established diseases. Despite the clinical grounds of diagnosis, attention is increasing to the discovery of reliable biomarkers that can predict susceptibility, aid diagnosis, or monitor treatment. Several potential biomarkers have been linked with pathophysiological changes related to PTSD, encouraging further research to identify actionable targets. This review highlights the current literature regarding the pathophysiology, disease development models, treatment modalities, and preventive models from a public health perspective, and discusses the current state of biomarker research.

Trauma exposure is one of the most important and prevalent risk factors for mental and physical ill-health. Prolonged or excessive stress exposure increases the risk of a wide variety of mental and physical symptoms, resulting in a condition known as post-traumatic stress disorder (PTSD). The diagnosis might be challenging due to the complex pathophysiology and co-existence with other mental disorders. The prime factor for PTSD development is exposure to a stressor, which variably, along with peritraumatic conditions, affects disease progression and severity. Additionally, many factors are thought to influence the response to the stressor, and hence reshape the natural history and course of the disease. With sufficient knowledge about the disease, preventive and intervenient methods can be implemented to improve the quality of life of the patients and to limit both the medical and economic burden of the disease. This literature review provides a highlight of up-to-date literature on traumatic stress, with a focus on causes or triggers of stress, factors that influence response to stress, disease burden, and the application of the social-ecological public health model of disease prevention. In addition, it addresses therapeutic aspects, ethnic differences in traumatic stress, and future perspectives, including potential biomarkers.

Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.

In the progressive phase of multiple sclerosis (MS), the hampered differentiation capacity of oligodendrocyte precursor cells (OPCs) eventually results in remyelination failure. We have previously shown that DNA methylation of Id2/Id4 is highly involved in OPC differentiation and remyelination. In this study, we took an unbiased approach by determining genome-wide DNA methylation patterns within chronically demyelinated MS lesions and investigated how certain epigenetic signatures relate to OPC differentiation capacity. We compared genome-wide DNA methylation and transcriptional profiles between chronically demyelinated MS lesions and matched normal-appearing white matter (NAWM), making use of post-mortem brain tissue (n = 9/group). DNA methylation differences that inversely correlated with mRNA expression of their corresponding genes were validated for their cell-type specificity in laser-captured OPCs using pyrosequencing. The CRISPR–dCas9-DNMT3a/TET1 system was used to epigenetically edit human-iPSC-derived oligodendrocytes to assess the effect on cellular differentiation. Our data show hypermethylation of CpGs within genes that cluster in gene ontologies related to myelination and axon ensheathment. Cell type-specific validation indicates a region-dependent hypermethylation of MBP, encoding for myelin basic protein, in OPCs obtained from white matter lesions compared to NAWM-derived OPCs. By altering the DNA methylation state of specific CpGs within the promotor region of MBP, using epigenetic editing, we show that cellular differentiation and myelination can be bidirectionally manipulated using the CRISPR–dCas9-DNMT3a/TET1 system in vitro. Our data indicate that OPCs within chronically demyelinated MS lesions acquire an inhibitory phenotype, which translates into hypermethylation of crucial myelination-related genes. Altering the epigenetic status of MBP can restore the differentiation capacity of OPCs and possibly boost (re)myelination.

DNA methylation (DNAm) signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimer's disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained robust across a wide range of differentially methylated positions, when upstream modeling of cell type composition was modeled with sufficient accuracy. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAM's biological relevance. The workflow is publicly available as an interactive Shiny app (https://um-dementia-systems-biology.shinyapps.io/CEAM/) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.

Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimer’s disease contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This insight underscores the need to expand molecular subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation data from three independent postmortem brain cohorts (n = 831) to identify epigenetic subtypes of late-onset Alzheimer’s disease. Unsupervised clustering approaches were employed to identify distinct DNA methylation patterns, with subsequent cross-cohort validation to ensure robustness and reproducibility. To explore the cell-type specificity of the identified epigenomic subtypes, we characterized their methylation signatures utilizing DNA methylation profiles derived from purified brain cells. Transcriptomic data from bulk and single-cell RNA sequencing were integrated to examine the functional impact of epigenetic subtypes on gene expression profiles. Finally, we performed statistical analyses to investigate associations between these DNA methylation-defined subtypes and clinical or neuropathological features, aiming to elucidate their biological significance and clinical implications. We identified two distinct epigenomic subtypes of late-onset Alzheimer’s disease, each defined by reproducible DNA methylation patterns across three cohorts. Both subtypes exhibit cell-type-specific DNA methylation profiles. Subtype 1 and subtype 2 show significant microglial methylation enrichment, with odds ratios (OR) of 1.6 and 1.3, respectively. The minimal overlap between them suggests distinct microglial states. Additionally, subtype 2 displays strong neuronal (OR = 1.6) and oligodendrocyte (OR = 3.6) enrichment. Bulk transcriptomic analyses further highlighted divergent biological mechanisms underpinning these subtypes, with subtype 1 enriched for immune-related processes, and subtype 2 characterized predominantly by neuronal and synaptic functional pathways. Single-cell transcriptional profiling of microglia revealed subtype-specific inflammatory states: subtype 1 represented a state of chronic innate immune hyperactivation with impaired resolution, while subtype 2 exhibited a more dynamic inflammatory profile balancing pro-inflammatory signaling with reparative and regulatory mechanisms. This study highlights the molecular heterogeneity of late-onset Alzheimer’s disease by identifying two epigenetic subtypes with distinct cell-type-specific DNA methylation patterns. Their alignment with previously defined molecular classifications underscores their relevance in disease pathogenesis. By linking these subtypes to inflammatory microglial activity, our findings provide a foundation for future precision medicine approaches in Alzheimer’s research and treatment.