INTRODUCTION:MethylCog is a 29-CpG blood DNA methylation (DNAm) proxy for general cognitive ability (g). Its incremental association with blood biomarkers of Alzheimer's disease and related dementias (ADRD) and prospective cognitive ability remains unclear. METHODS:In the held-out test set from the original MethylCog study, we tested whether MethylCog explained baseline g beyond four ADRD blood biomarkers, and whether it predicted six-year follow-up g beyond baseline g and biomarkers. RESULTS:MethylCog showed a stronger age-adjusted association with baseline g than individual biomarkers (r=.368 vs absolute r=.083-.162). MethylCog added 10.0% variance beyond all four biomarkers cross-sectionally (p<.001) and predicted six-year follow-up g in the biomarker-adjusted model (β=.108, p=.002). No individual ADRD biomarker independently predicted follow-up g. DISCUSSION:MethylCog may provide cognition-related DNAm information complementary to blood-based ADRD biomarkers.

Multi-omics studies are widely used across many areas of biomedical research. In many diseases, some signals are shared across data types, while others are strongest in a single omics layer. Current multi-omics clustering methods often either merge all data types into a single representation, which can blur biology that is strong in one layer, or rely on linear structure that may miss more complex relationships across data types. We introduce multiRF, a random-forest-based method that handles complex data types and separates shared and modality-specific structure for multi-omics data. multiRF learns sample similarities across omics layers from multivariate random forests, combines them across data types, and uses the resulting weights to estimate the part of each omics layer that is predictable from the others. The remaining residual is treated as modality-specific signal, allowing shared and modality-specific similarities to be clustered separately. In simulations, multiRF recovered shared clusters as well as or better than established integrative methods while more reliably separating modality-specific signal under nonlinear data structures. In TCGA head and neck squamous cell carcinoma, the shared component aligned with the main subtype structure across established reference classifications, while gene- and miRNA-specific components revealed additional immune and developmental biology. In the ADNI cohort with matched blood DNA methylation and structural MRI, the shared cross-modal aging signal was associated with future conversion to mild cognitive impairment or Alzheimer's disease, and a DNAm-specific residual signal showed exploratory additional information. These results show that multiRF can recover a common disease axis while retaining biologically meaningful signals specific to one data type. multiRF is available as an open-source R package at https://github.com/novawz/multiRF.

The tumor necrosis factor (TNF) receptor superfamily member, transmembrane activator and CAML interactor (TACI) encoded by TNFRSF13B, are extensively involved in immune responses. In our previous work, TNFRSF13B exon 2 variants were recurrently identified in chronic active Epstein-Barr virus disease (CAEBV). Here we aim to reveal the roles of TNFRSF13B variants in CAEBV, and investigate the feasibility of targeting TNFRSF13B/TACI as a new approach to control EBV infection. The lymphoblastoid cell lines (LCL) models carrying homozygous TNFRSF13B exon 2 frameshift mutations were constructed using CRISPR/Cas9. Immunological assays, transcriptomic analysis, and gene silencing experiments were performed on LCL models to measure the effect of TNFRSF13B exon 2 variants and explore the underlying mechanisms. TACI ligands and a TLR9 agonist were applied to modulate TACI signaling and EBV activities. Frameshift mutations in exon 2 of TNFRSF13B significantly up-regulated the short isoforms of TACI (TACI-S) at the expense of its long isoforms (TACI-L) in LCLs. The up-regulated TACI-S induced more intense activation of NF-κB, MAPK, and Rho signaling pathways, leading to the switch of EBV activities to lytic reactivation. The subsequent increased viral load and viral IL-10 provide a rational for the susceptibility of variant carriers to CAEBV. The BAFF trimer, an indirect TACI-signaling inhibitor, also significantly suppressed the EBV lytic program. Gene silencing experiments indicated that XBP-1 might be involved in the TACI-mediated regulation of EBV lytic activities in EBV-immortalized B cells. This study underscores the impact of TNFRSF13B variants on EBV infection and host immune responses, offering insights into CAEBV pathogenesis and potential therapeutic strategies.

Background:Cognitive reserve (CR) refers to differences in the adaptability of cognitive processes that modify the impact of Alzheimer's disease (AD) pathology on cognitive performance. Currently there are no established blood-based biomarkers of CR in prodromal AD. In this study, we operationalize CR as memory reserve, defined as moderation (attenuation) of the CSF pTau181-memory association. DNA methylation (DNAm) integrates genetic and environmental influences and may capture biological processes that mitigate the impact of AD pathology on memory. We aimed to identify blood DNAm loci that moderate the association between cerebrospinal fluid (CSF) phosphorylated tau (pTau181) and memory in mild cognitive impairment (MCI). We also sought to determine if a DNAm-based signature of memory reserve predicts future memory decline. Methods:We analyzed 92 amyloid positive MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with blood DNAm, CSF pTau181, and memory scores (PHC_MEM) collected at the same visit. We first regressed memory scores on covariates (age, sex, number of APOE4 alleles, estimated major immune cell type proportions) and used the residuals as covariate-adjusted memory scores. At each CpG, we then fitted linear models of memory on DNAm, pTau181, and their interaction. Inflations were corrected using the bacon method. We identified differentially methylated regions (DMRs), assessed pathway enrichment, and performed integrative analyses incorporating external resources including expression quantitative trait methylation (eQTM), methylation quantitative trait loci (mQTL) databases, AD genome-wide association study summary statistics, and blood-brain DNAm correlations. A methylation score was constructed and evaluated in linear mixed-effects models of longitudinal memory in 88 participants with follow-up information. Results:After removing CpGs with low variability, we identified 6 CpGs with suggestive significance for DNAm×pTau181 interaction (P-value < 1× 10-5) and 11 DMRs that passed multiple comparisons correction. These loci mapped to genes involved in synaptic function, vascular and blood-brain barrier integrity, amyloid clearance, immune and metabolic regulation. Almost all showed no strong marginal associations with pTau181 or memory, supporting a moderating rather than mediating role. Pathway analysis revealed enrichment of adipocytokine signaling and adipose metabolic pathways, and a number of CpGs associated with mQTLs overlapped with AD genetic risk loci. A higher baseline MRS attenuated the pTau-memory association and significantly associated with slower future memory decline, independent of age, sex, education, APOE ε4, and baseline pTau181. Conclusions:Blood DNAm patterns that moderate the pTau-memory relationship capture biology underlying memory reserve involving synaptic, vascular, immune, and metabolic pathways, and can be summarized into an MRS that predicts longitudinal memory trajectories in MCI. These findings support blood DNAm as a promising, non-invasive biomarker of cognitive resilience to AD pathology.

Background Pulmonary function after elective resection has been reported in asymptomatic infants and older children; however, evidence regarding early postoperative lung function in symptomatic neonates requiring urgent surgery remains scarce. We characterized the early postoperative pulmonary function in symptomatic neonates with congenital lung malformations (CLMs) requiring urgent surgery, and explored whether disproportionate airway parenchymal growth (dysanapsis) may underlie the observed functional limitations. Methods This retrospective cohort study included children with CLMs who underwent surgical resection, categorized into a neonatal urgent surgery group—comprising symptomatic infants with respiratory distress—and an elective reference group—comprising asymptomatic infants undergoing scheduled resection. Postoperative pulmonary function was assessed within 6 months post-surgery using tidal breathing flow volume loop (TBFV) technology. The primary inferential analysis focused on factors associated with moderate to severe obstructive dysfunction within the neonatal subgroup. Results Abnormal postoperative pulmonary function was present in all neonates in the neonatal urgent surgery group, compared with 64.2% in the elective reference group (p < 0.001). Obstructive ventilatory dysfunction predominated in both groups and was more common among neonates (p = 0.033). Moderate to severe obstruction (time to peak tidal expiratory flow as a proportion of expiratory time < 23%) was also more frequent in neonates (57.9% vs. 22.6%, p = 0.002). Within the neonatal subgroup, exploratory multivariable analysis identified lobectomy, preoperative invasive mechanical ventilation), and prenatal maximum congenital pulmonary airway malformation volume ratio > 1.6 as factors associated with more severe dysfunction. Conclusions Symptomatic neonates requiring urgent CLM resection have a uniformly high incidence of early postoperative pulmonary dysfunction, predominantly obstructive. Notably, the pattern of preserved tidal volume but reduced expiratory flow ratios is consistent with dysanapsis—a disproportionate growth of airways relative to lung parenchyma. These findings support structured respiratory follow up and highlight the need for research on long term airway development after neonatal CLM surgery.

Background Weight loss improves metabolic health, but the DNA methylation (DNAm) changes induced by the lifestyle intervention and their relevance to dementia outcomes remain unclear. We studied longitudinal blood DNAm changes during an 18-month weight-loss intervention in the CENTRAL clinical trial and evaluated their relevance to dementia progression in an external cohort. Methods We analyzed paired baseline and 18-month blood DNAm data from 103 male CENTRAL participants, including 47 in diet-only and 56 in diet plus physical activity groups. CpG-level methylation M -value changes between baseline and follow up were tested using linear mixed-effects models adjusted for dietary group, age, smoking score, leukocyte proportions, and random participant effects. Differentially methylated regions were identified using comb-p, and pathway enrichment was assessed using methylGSA software. Weight-loss-associated CpGs and regions were compared with cardiometabolic and dementia epigenome-wide association studies (EWAS) findings. In 117 Alzheimer’s Disease Neuroimaging Initiative participants with repeated DNAm and diagnostic follow-up, we tested whether DNAm changes at baseline resembling the reverse of the CENTRAL weight-loss profile predicted dementia progression. Results At a 5% false discovery rate, 51 CpGs and three differentially methylated regions (DMRs) were identified in the diet-only analysis, and 49 CpGs and one DMR were identified in the diet plus physical activity analysis, that were significantly associated with the weight loss interventions. Enriched pathways included DNA double-strand break response and ATM-mediated phosphorylation in the diet-only analysis, as well as energy metabolism and insulin secretion in the diet plus physical activity analysis. Weight-loss-associated DNAm signals overlapped with cardiometabolic, inflammatory, and dementia-related EWAS findings in expected directions. In ADNI, 28 participants progressed clinically. Between-visit DNAm changes at baseline in converters showed higher correlation scores with the reversed diet-only DNAm profile than non-converters (mean correlation score, 0.131 vs. 0.037; P -value = 0.026), and higher correlation scores were associated with increased progression risk in a Cox model adjusted for age, sex, APOE ε4, baseline diagnosis, education, and smoking history (hazard ratio = 1.49 per standard deviation, P -value = 0.041). The diet plus physical activity profile showed a similar but weaker association. Conclusions Weight-loss intervention was associated with blood DNAm changes enriched in genomic maintenance and metabolic pathways. External EWAS comparisons and ADNI validation suggest that weight-loss-responsive DNAm profiles may capture biological processes connecting lifestyle-related metabolic change with dementia progression risk.

INTRODUCTION:Heterogeneity in cognitive ability increases with age and predicts mild cognitive impairment (MCI) and dementia, but scalable blood-based biomarkers are lacking. We developed and validated MethylCog, a parsimonious DNA methylation (DNAm) marker of general cognitive ability (g). METHODS:MethylCog was developed using elastic net regression on principal components analysis (PCA) -derived g in a population-based cohort (n = 2,069; training/test split) externally validated (n = 112). Criterion validity, MCI discrimination, and specificity relative to GrimAge and Alzheimer's disease (AD) biomarkers were assessed. RESULTS:MethylCog (29 CpGs) predicted g in the test set (R2 = 0.17) and external cohort (R2 = 0.13), explaining ∼11% of variance beyond age and sex. MethylCog improved MCI discrimination beyond demographics (ΔAUC = 0.03-0.07) and outperformed GrimAge but did not add value beyond cognitive screeners. Exploratory analyses showed no significant associations with AD plasma biomarkers or MRI measures. DISCUSSION:MethylCog provides initial evidence that parsimonious DNAm scores can index individual differences in cognitive ability, with potential utility where direct assessment is unavailable.

MethylCog is a 29-CpG blood DNA methylation (DNAm) score developed to index general cognitive ability (g). Whether it captures cognition-related information associated with later cognitive performance that is not fully represented by blood-based biomarkers of Alzheimer's disease and related dementias (ADRD) remains unclear. Using the held-out Health and Retirement Study Harmonized Cognitive Assessment Protocol (HRS-HCAP) test set from the original MethylCog study (N = 605), we examined associations with baseline g after adjustment for age, sex, education, apolipoprotein E (APOE) ε4 carrier status, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated tau 181 (p-tau181), and amyloid-β 42/40 ratio (Aβ42/40). Prospective associations with six-year follow-up g were examined after adjustment for baseline g, age, sex, education, and APOE ε4 carrier status, with additional adjustment for the ADRD biomarker panel. MethylCog remained associated with baseline g in the fully adjusted cross-sectional model (standardized β=.178, 95% CI: .111-.244, p<.001; ΔAdjR²=.025). MethylCog was also associated with six-year follow-up g after adjustment for baseline g and all covariates and biomarkers (standardized β=.101, 95% CI: .033-.170, p=.004; N = 331; ΔAdjR²=.007). Findings were similar among participants without baseline cognitive impairment. These findings provide preliminary evidence that a cognition-trained DNAm signature captures variation in later cognitive performance not fully represented by baseline cognition or available ADRD blood biomarkers.

Aging is the strongest risk factor for Alzheimer’s disease (AD), yet the role of age-associated DNA methylation (DNAm) changes in blood and their relevance to AD remains poorly understood. We performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65 years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We adjusted for sex and immune cell-type proportions and corrected batch effects and genomic inflation. Integrative analyses included pathway enrichment, mQTL analysis, colocalization with Alzheimer’s disease and related dementia (ADRD) GWAS summary statistics, brain-blood DNAm correlations, and comparison to independent AD methylation studies. We identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with chronological age in both cohorts at a 5

Abstract INTRODUCTION Cognitive reserve (CR) reflects variability in cognitive adaptability that modifies the impact of Alzheimer's disease (AD) pathology on cognition. However, blood‐based biomarkers of CR have not been established in prodromal AD. We operationalized CR as memory reserve, defined by the attenuation of the cerebrospinal fluid (CSF) phosphorylated tau threonine 181 (pTau181)–memory association and aimed to identify blood DNA methylation (DNAm) loci involved in memory reserve. METHODS We studied 92 amyloid‐positive participants with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with blood DNAm, CSF pTau181, and memory (PHC_MEM) measured at the same visit. Memory was residualized after adjustment for age, sex, APOE 𝜀4 allele count, and estimated immune cell‐type proportions. For each CpG, linear models tested DNAm, pTau181, and DNAm×pTau181 interaction; inflation was corrected using the bacon method. In addition, we also identified differentially methylated regions (DMRs). Moreover, we constructed a methylation reserve score (MRS) from loci identified in this cohort at baseline and tested its associations with longitudinal memory using linear mixed‐effects models in 88 participants with follow‐up information. RESULTS After removing low‐variability CpGs, we identified six CpGs with suggestive DNAm×pTau181 interaction (p value < 1 × 10−5, none passed a 5% false discovery rate) and 11 DMRs passing multiple‐comparisons correction. The suggestive CpGs and significant DMRs mapped to genes implicating synaptic function, vascular/blood–brain barrier integrity, and immune regulation, with minimal marginal associations with pTau181 or memory, consistent with a moderation model rather than mediation. In this cohort, higher baseline MRS was associated with attenuation of the pTau181–memory association and with slower subsequent memory decline, independent of age, sex, education, APOE ε4, and baseline pTau181. DISCUSSION Blood DNAm that moderates the pTau181–memory association may reflect epigenetic correlates of memory reserve (i.e., differential susceptibility to tau‐related memory impairment), rather than reflecting variations in pTau181 levels. These DNAm patterns can be summarized as a MRS that, in this cohort, was associated with longitudinal memory trajectories in MCI. Further validation in independent cohorts is warranted.

Abstract Background Aging is the strongest risk factor for Alzheimer’s disease (AD), but the molecular connections between aging and AD remain unclear. DNA methylation (DNAm) is implicated in both processes. Methods We conducted a meta-analysis of DNAm in prefrontal cortex from two independent postmortem cohorts: the Religious Orders Study and Memory and Aging Project (ROSMAP) and Brains for Dementia Research (BDR). Age-associated CpG sites were identified using cohort-specific linear models adjusted for neuronal proportion, sex, and batch, followed by meta-analysis. We computed epigenetic age acceleration in brain samples as delta-age (DNAmAge − chronological age), and compared clinically diagnosed AD with cognitively unimpaired participants. Functional analyses included genomic feature enrichment, pathway analysis, brain-blood DNAm correlation, and colocalization with genome-wide association study (GWAS) loci. Prognostic relevance of age-associated CpGs was tested in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Cox proportional hazards models for disease progression. Results We identified 3264 CpG sites associated with aging; most were hypermethylated and enriched in promoters and CpG islands, and involved genes related to immune regulation and metabolism. Comparison with AD neuropathology-associated methylation showed substantial overlap, with nearly all shared CpGs and regions showing concordant directional changes. Cortical epigenetic age acceleration was higher in ROSMAP participants with clinical AD than in cognitively unimpaired individuals after covariates adjustment, and this association persisted when the cortical clock was restricted to the aging-associated CpGs identified here, suggesting that acceleration in AD is attributable to age-related CpGs. Several CpGs showed significant brain-blood methylation correlations or were linked to AD GWAS risk loci through colocalization analyses. In ADNI, among 33 candidate CpGs selected for concordant aging- and AD-associated changes in cortex and significant brain-blood methylation correlations, baseline methylation at one CpG (cg10752406 in AZU1 promoter) was associated with progression at a 5% false discovery rate after covariate adjustment. Conclusions Aging-associated DNAm changes in prefrontal cortex overlap with AD neuropathology-related changes and are involved in accelerated epigenetic aging in clinical AD. Our study provides valuable insights into the epigenetic landscape of aging and its implications for AD.

Aging is a major risk factor for Alzheimer's disease (AD), but the molecular processes linking aging to AD remain unclear. Epigenetic modifications, particularly DNA methylation (DNAm), play a crucial role in understanding aging and AD. We studied brain DNA methylation (DNAm) changes in normal aging versus AD in late life. We performed a comprehensive meta-analysis of two large cohorts of postmortem prefrontal cortex samples from subjects over 65 years old. Our analysis adjusted estimated cell-type proportions (i.e., the proportion of neurons), sex, and batch effects, and corrected for inflation and multiple testing. We identified numerous DNAm differences consistently associated with aging in both cohorts, highlighting key genes such as ELOVL2 , ISM1 , and KLF14 , which are implicated in various aging processes. These DNAm differences are predominantly hypermethylated, enriched in promoter regions, and associated with genes involved in immune processes and metabolic functions. Our results also revealed significant overlaps between aging-associated DNAm differences and those involved in AD, supporting the hypothesis that aging and AD are interconnected at the molecular level. Intriguingly, nearly all DNAm differences significantly associated with both age (at death) and AD Braak stage showed concordant effect sizes in the same direction. Our study provides valuable insights into the aging-associated epigenetic landscape and its potential implications for AD. As aging and AD are intertwined, targeting age-related epigenetic modifications may offer new therapeutic strategies for AD.

BACKGROUND:High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating multiple omics layers measured on the same samples can reveal cross-layer molecular hubs that single-layer analyses miss. However, many existing integrative methods rely on linear assumptions or univariate feature importance, limiting their ability to capture nonlinear and interaction-driven dependencies across data modalities. RESULTS:We present an unsupervised, multivariate random forest (MRF) framework with an inverse minimal depth (IMD) importance to prioritize shared biomarkers across omics. In each forest, one layer serves as a multivariate response and the other as predictors; IMD summarizes how early a predictor (or response maximal splitting response variable) appears across trees, yielding interpretable, cross-layer feature rankings. We provide two IMD-based selection strategies and introduce an optional IMD power transform to enhance sensitivity to interaction signals. In extensive simulations spanning linear, nonlinear, and interaction regimes, our method matches sparse partial least squares/canonical correlation analysis under linear settings and outperforms them as nonlinearity increases, while adapted univariate ensemble learners (random forest, gradient boosting machine, XGBoost) underperform in the multivariate, unsupervised context. Applied to breast invasive carcinoma and colon adenocarcinoma in The Cancer Genome Atlas (TCGA), MRF-IMD identifies genes, CpGs, and microRNAs enriched for cancer-relevant pathways and yields more robust survival stratification than linear integrators with matched model sizes. In a TCGA pan-cancer analysis, MRF-IMD features achieve a higher Adjusted Rand Index than alternatives and recover coherent tumor-type clusters; in the Alzheimer's Disease Neuroimaging Initiative (ADNI), the integrative signature improves dementia progression stratification over a published methylation risk score. CONCLUSIONS:MRF-IMD provides a scalable and interpretable framework for multiomics integration that reliably identifies cross-layer biomarkers when nonlinear and interaction-driven dependencies are present. This approach advances robust biomarker discovery beyond the limits of linear integrative methods.

Cognitive resilience (CR) contributes to the variability in risk for developing and progressing in Alzheimer's disease (AD) among individuals. Beyond genetics, recent studies highlight the critical role of lifestyle factors in enhancing CR and delaying cognitive decline. DNA methylation (DNAm), an epigenetic mechanism influenced by both genetic and environmental factors, including CR-related lifestyle factors, offers a promising pathway for understanding the biology of CR. We studied DNAm changes associated with the Resilience Index (RI), a composite measure of lifestyle factors, using blood samples from the Healthy Brain Initiative (HBI) cohort. After corrections for multiple comparisons, our analysis identified 19 CpGs and 24 differentially methylated regions significantly associated with the RI, adjusting for covariates age, sex, APOE ε4, and immune cell composition. The RI-associated methylation changes are significantly enriched in pathways related to lipid metabolism, synaptic plasticity, and neuroinflammation, and highlight the connection between cardiovascular health and cognitive function. By identifying RI-associated DNAm, our study provided an alternative approach to discovering future targets and treatment strategies for AD, complementary to the traditional approach of identifying disease-associated variants directly. Furthermore, we developed a Methylation-based Resilience Score (MRS) that successfully predicted future cognitive decline in an external dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), even after accounting for age, sex, APOE ε4, years of education, baseline diagnosis, and baseline MMSE score. Our findings are particularly relevant for a better understanding of epigenetic architecture underlying cognitive resilience. Importantly, the significant association between baseline MRS and future cognitive decline demonstrated that DNAm could be a predictive marker for AD, laying the foundation for future studies on personalized AD prevention.

Sex is an important factor that contributes to both clinical and biological heterogeneity in Alzheimer’s disease (AD), but the regulatory mechanisms underlying sex differences in AD are still not well understood. DNA methylation (DNAm) is an epigenetic modification that regulates gene transcription and is known to be involved in AD. However, due to analytical and biological complexity, few previous DNAm studies analyzed the X chromosome, where many genes influencing cognitive abilities and immune functions are located. We performed a sex-specific X chromosome-wide analysis of the DNAm data generated by the longitudinal Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. We used mixed effects logistic regression models with AD status as the outcome, adjusted for age, sex, batch, and immune cell-type proportions, random subject effects, and corrected for inflation. Our analysis included 632 female DNAm samples (188 cases, 444 controls) and 652 male DNAm samples (239 cases, 413 controls), measured on blood samples of 179 and 219 independent subjects with ages older than 65 years. Given our modest sample size, we considered CpGs with suggestive significance at the prespecified significance threshold of P < 1×10 -5 . In females, we identified 2 significant CpGs (cg04150893 and cg16580361), mapped to the intergenic region and gene body of the HMGN5 gene. No significant CpGs were identified in male samples. Interestingly, blood DNAm at cg16580361 is significantly associated with DNAm in the prefrontal cortex (Blood Brain DNA methylation Comparison Tool: r = 0.599, P = 1.74×10 -8 ). Consistent with our observed hypermethylation at cg16580361 in ADNI data (OR = 1.11, P = 6.14×10 -6 ), the HMGN5 gene is also significantly upregulated in the frontal cortex of female AD subjects (Agora database https://agora.adknowledgeportal.org : OR = 2 0.227 = 1.17, adjusted P = 5.7×10 -6 ). The HMGN5 gene is involved in the metabolism of the brain antioxidant glutathione. Decreased levels of glutathione have been implicated in both AD onset and progression. Our analysis of the X chromosome in the ADNI study dataset nominated cg16580361 located on the HMGN5 as a plausible biomarker for AD. Future studies that validate our findings in larger and more diverse community-based cohorts are needed.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) pose significant challenges to public health and underscore the need for accurate and early diagnostic tools. Structural magnetic resonance imaging (sMRI) combined with advanced analytical techniques like convolutional neural networks (CNNs) seemed to offer a promising avenue for the diagnosis of these conditions. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of CNN algorithms applied to sMRI data in differentiating between AD, MCI, and normal cognition (NC). Following the PRISMA-DTA guidelines, a comprehensive literature search was carried out in PubMed and Web of Science databases for studies published between 2018 and 2024. Studies were included if they employed CNNs for the diagnostic classification of sMRI data from participants with AD, MCI, or NC. The methodological quality of the included studies was assessed using the QUADAS-2 and METRICS tools. Data extraction and statistical analysis were performed to calculate pooled diagnostic accuracy metrics. A total of 21 studies were included in the study, comprising 16,139 participants in the analysis. The pooled sensitivity and specificity of CNN algorithms for differentiating AD from NC were 0.92 and 0.91, respectively. For distinguishing MCI from NC, the pooled sensitivity and specificity were 0.74 and 0.79, respectively. The algorithms also showed a moderate ability to differentiate AD from MCI, with a pooled sensitivity and specificity of 0.73 and 0.79, respectively. In the pMCI versus sMCI classification, a pooled sensitivity was 0.69 and a specificity was 0.81. Heterogeneity across studies was significant, as indicated by meta-regression results. CNN algorithms demonstrated promising diagnostic performance in differentiating AD, MCI, and NC using sMRI data. The highest accuracy was observed in distinguishing AD from NC and the lowest accuracy observed in distinguishing pMCI from sMCI. These findings suggest that CNN-based radiomics has the potential to serve as a valuable tool in the diagnostic armamentarium for neurodegenerative diseases. However, the heterogeneity among studies indicates a need for further methodological refinement and validation. This systematic review was registered in PROSPERO (Registration ID: CRD42022295408).

As dementia cases continue to rise, effective prevention strategies are urgently needed. However, objective biomarkers that directly reflect lifestyle factors remain limited. Life’s Essential 8 (LE8) is a composite of modifiable cardiovascular health metrics, and lower LE8 has been consistently associated with increased risk of dementia. In this study, we aimed to identify DNA methylation biomarkers associated with LE8 scores and investigate their relevance for dementia risk. We performed an epigenome-wide association study of 273 stroke-free, self-identified Hispanic adults aged 40 and older from the Northern Manhattan Study (NOMAS), a community-based urban cohort study. DNA methylation (DNAm) was assessed using Illumina MethylationEPIC arrays. Robust linear models identified CpGs associated with LE8 score, a composite score on eight health metrics including diet quality, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. Differentially methylated regions were identified by combining P-values in sliding windows while accounting for spatial correlations across the genome. We also performed functional annotation, pathway analyses, and integrative analyses with gene expression, genetic variants, brain-blood correlations, and comparisons with previous dementia studies to identify the most biologically meaningful DNAm sites. After adjusting for age, sex, APOE ε4, immune cell composition, and ancestry, we found 11 CpGs with suggestive evidence of association with LE8 (P-value < 1 × 10–5) and 37 differentially methylated regions that passed multiple-testing correction. These LE8-associated loci mapped to genes and pathways that support vascular integrity and regulate inflammation, key biological processes relevant to both cardiovascular disease and dementia. Integrative analyses highlighted several CpGs in the HOXA5 gene promoter with converging evidence supporting their potential as dementia biomarkers, including strong blood–brain DNAm correlations, association with gene expression and genetic variants, and prior associations with Alzheimer’s disease neuropathology. Our comparison with published results showed that a number of LE8-associated DNA methylation sites are associated with dementia, highlighting the possible connection between cardiovascular health and dementia risk and pointing to potential actionable targets for dementia prevention. Moreover, DNAm biomarkers have clinical potential as objective measures to identify individuals at elevated risk, stratify participants based on biologically informed risk profiles, and monitor epigenetic responses to lifestyle interventions in dementia prevention trials. Future studies in larger and more diverse cohorts are needed to validate and refine these methylation biomarkers for clinical applications.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare, aggressive hematologic cancer, characterized by frequent cutaneous involvement and dismal prognosis. While skin manifestations often precede systemic spread, diagnostic delays persist due to clinical mimicry of benign dermatologic conditions. Current knowledge gaps exist regarding its clinical behaviour in Chinese populations. This single-center retrospective case series was conducted to analyze the clinical characteristics and survival outcomes of BPDCN patients in China. A total of 18 patients (mean age 45.6 years; 77.8% male) were included. Cutaneous lesions were observed in 94.4% of cases, with most showing disseminated skin involvement at diagnosis. Leukemic infiltration was the most common extracutaneous manifestation, while no central nervous system involvement was detected among patients who underwent lumbar puncture. Systemic chemotherapy achieved an objective response in 85.7% of patients, but 75% experienced relapse or disease progression. The median overall survival was 13 months, with 1- and 2-year survival rates of 53.3% and 26.7%, respectively. Survival outcomes were not significantly associated with clinical features, genetic profiles, or treatment regimens. This study demonstrates the clinical diversity of BPDCN and highlights its uniformly poor prognosis regardless of therapeutic approach. The cohort had a younger age of onset than previously reported. While some patients initially presented with solitary skin lesions, most showed disseminated skin and multi-organ involvement by diagnosis. No CNS involvement was observed, and no factors associated with favorable prognosis were identified. Enhanced diagnostic strategies and the development of novel targeted therapies are urgently needed for this understudied population.

Aging is the strongest risk factor for Alzheimer's disease (AD), yet the role of age-associated DNA methylation (DNAm) changes in blood and their relevance to AD remains poorly understood. In this study, we performed a meta-analysis of blood DNAm samples from 475 dementia-free subjects aged over 65 years across two independent cohorts, the Framingham Heart Study (FHS) at Exam 9 and the Alzheimer's Disease Neuroimaging Initiative (ADNI). After adjusting for age, sex, and immune cell type proportions, and correcting for batch effects and genomic inflation, we identified 3758 CpGs and 556 differentially methylated regions (DMRs) consistently associated with aging in both cohorts at a 5% false discovery rate. Our pathway enrichment analyses highlighted immune response, metabolic regulation, and synaptic plasticity, all of which are key biological processes implicated in AD. Moreover, our colocalization analysis revealed 32 genomic regions where shared genetic variants influenced both DNAm and dementia risk. Adjusting for age and other covariate variables, we found roughly one-third of aging-associated CpGs are also associated with AD or AD neuropathology in independent studies external to the ADNI and FHS datasets. Finally, we prioritized 9 aging-associated CpGs, located in promoter regions of PDE1B, ELOVL2, PODXL2, and other genomic regions, that showed strong positive blood-to-brain methylation concordance, as well as association with AD or AD neuropathology in independent studies, after adjusting for age and other covariates. Our findings provided insights into the functional overlap between the aging processes and AD, and nominated promising blood-based biomarkers for future AD research.

INTRODUCTION:Distinguishing between molecular changes that precede dementia onset and those resulting from the disease is challenging with cross-sectional studies. METHODS:We studied blood DNA methylation (DNAm) differences and incident dementia in two large longitudinal cohorts: the Offspring cohort of the Framingham Heart Study (FHS) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We analyzed blood DNAm samples from > 1000 cognitively unimpaired subjects. RESULTS:Meta-analysis identified 44 CpGs and 44 differentially methylated regions consistently associated with time to dementia in both cohorts. Our integrative analysis identified early processes in dementia, such as immune responses and metabolic dysfunction. Furthermore, we developed a methylation-based risk score, which successfully predicted future cognitive decline in an independent validation set, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score. DISCUSSION:DNAm offers a promising source as a biomarker for dementia risk assessment. HIGHLIGHTS:Blood DNA methylation (DNAm) differences at individual CpGs and differentially methylated regions are significantly associated with incident dementia. Pathway analysis revealed DNAm differences associated with incident dementia are significantly enriched in biological pathways involved in immune responses and metabolic processes. Out-of-sample validation analysis demonstrated that a methylation-based risk score successfully predicted future cognitive decline in an independent dataset, even after accounting for age, sex, apolipoprotein E ε4, years of education, baseline diagnosis, and baseline Mini-Mental State Examination score.