Changes in plasma protein N-glycosylation, particularly in immunoglobulin G (IgG), are closely associated with human aging. However, the extent to which established human glycan biomarkers translate to commonly used rodent models remains unclear. Here, we characterized the N-glycomes of plasma IgG and non-IgG proteins in humans, rats, and mice across multiple age groups using a TiO2-PGC chip-MS platform and systematically compared age-related glycosylation changes across species. Quantitative analysis revealed several shared aging-associated trends, including increased agalactosylation and reduced digalactosylation and sialylation, although the magnitude and direction of these changes varied across species and between IgG and non-IgG fractions. Within the sex- and strain-specific rodent cohorts analyzed here, female BALB/c mice showed greater similarity to the mixed-sex human cohort in several broad age-related glycosylation traits, particularly galactosylation, whereas male Sprague-Dawley (SD) rats showed greater overlap with humans at the level of individual candidate biomarkers. Notably, the established human GlycoAgeTest showed limited translational applicability in rodents because similar ratio changes arose from distinct underlying glycan dynamics. Glycan compositions are denoted as Hex_HexNAc_Fuc_Sia, where Sia is NeuAc (A) or NeuGc (G). Using this notation, two IgG glycans emerged as exploratory candidate biomarkers under the present cohort design: 4_5_1_1(A)/(G), shared by humans and male SD rats, and 4_4_1_1(A)/(G), which showed age-associated increases across the mixed-sex human cohort, male SD rats, and female BALB/c mice. These findings define both the opportunities and the limitations of rodent models for glycan-based aging research and provide candidate markers for future mechanistic and intervention studies.
Re-amputation after transtibial or transfemoral amputation may compromise healing and rehabilitation. Inflammatory markers may aid risk stratification. To evaluate associations between preoperative inflammatory biomarkers, especially systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI), and re-amputation risk. Retrospective cohort study. We included 146 consecutive diabetic patients who underwent transtibial or transfemoral amputation for diabetic foot-related complications from January 2021 to December 2023. Patients with documented or imaging-confirmed peripheral arterial disease were excluded. Laboratory values within 48 hours before surgery were analyzed. Re-amputation was defined as additional bone resection at the same or a more proximal level. Patients without re-amputation were censored at death or last follow-up. Cox regression and receiver operating characteristic analyses were performed. Re-amputation occurred in 26 patients (17.8%). Re-amputation was associated with higher white blood cell, neutrophil, monocyte, platelet, C-reactive protein (CRP), SII, SIRI, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio values. In exploratory multivariable Cox models adjusted for age and amputation level, SIRI (hazard ratio [HR], 1.048; 95% confidence interval [CI], 1.022-1.076; p < .001), SII per 1,000-unit increase (HR, 1.066; 95% CI, 1.011-1.123; p = .018), and CRP per 10 mg/L increase (HR, 1.094; 95% CI, 1.053-1.137; p < .001) remained associated with re-amputation. CRP had the highest area under the curve (0.762), followed by SII (0.746) and SIRI (0.740). Higher preoperative systemic inflammatory burden was associated with re-amputation. CRP performed best, while SII and SIRI showed moderate complementary value. External validation is required.
PARP inhibitors (PARPi) are effective in tumors with homologous recombination repair (HRR) deficiency (HRD), typically identified by germline/tumor mutations. However, genetic testing may miss intrinsic PARPi sensitivity and resistance. We evaluated strategies to improve detection and longitudinal monitoring of HRD across >500 tumor samples and >20 paired liquid biopsies, integrating genetic, genomic, and functional readouts. HRD was more frequent in high-grade ovarian cancer (HGOC; 52%) than in metastatic breast (mBC; 12%) or prostate cancer (mPC; 20%). Assay concordance was low-to-moderate, underscoring complementarity. RAD51 testing and the genomic instability score identified HRD in tumors lacking pathogenic HRR mutations (6% and 30% in mBC, 38% and 46% in HGOC, 14% and 27% in mPC, respectively). Longitudinal ctDNA sequencing revealed BRCA1/BRCA2 reversion mutations in >30% of post-PARPi mBC samples, which were associated with poor response to subsequent platinum therapy. These findings support the use of complementary HRD biomarkers in tissue and liquid biopsy to guide PARPi use and monitor response.
Obesity is a risk factor for pancreatic cancer, but mechanisms remain unclear. We investigated how anthropometric traits, individually and combined, relate to pancreatic cancer risk and whether associations are mediated by metabolic biomarkers. We analysed 462,300 adults (40-69 years) in the UK Biobank. Principal component analysis derived three body shape phenotypes combining body mass index (BMI), height, weight, waist and hip circumference, and waist-to-hip ratio (WHR). Mediation was assessed using four-way decomposition. Over a median follow-up of 10.9 years, 1115 pancreatic cancer cases occurred. Each one-standard-deviation (SD) increase in BMI or WHR was associated with a higher incidence of pancreatic cancer, with hazard ratios (HRs) of 1.20 (confidence interval, CI: 1.12-1.28) and 1.24 (CI: 1.14-1.36), respectively. Body shape characterizing overall obesity showed a similar association (HR = 1.20; CI: 1.12-1.28 per 1-SD), with glucose and HbA1c accounting for mediated proportions (mediated interaction + pure indirect effect) of 12.2% (CI: 3.4-21.0%) and 15.0% (CI: 5.7-24.2%), respectively. For BMI, glucose accounted for 15.9% (CI: 2.8-28.9%) and HbA1c for 20.0% (CI: 6.3-33.7%) of the association. Glucose and HbA1c mediate a large proportion of the obesity-pancreatic cancer association, highlighting the important role of glycemic control in obesity-related pancreatic carcinogenesis and targeted interventions in at-risk populations.
Ischemic stroke is a major cause of disability and mortality worldwide, accounting for approximately 66% of all stroke cases. Stroke has been reported to cause approximately 6.5 million deaths annually, and the global disability-adjusted life years attributable to stroke are projected to exceed 200 million by 2030. Therefore, identifying reliable diagnostic biomarkers for acute ischemic stroke (AIS) and exploring their underlying molecular mechanisms are of great importance for early disease recognition and clinical intervention. Transglutaminases (TGases) are widely distributed in the central nervous system and play important roles in inflammatory responses, neural repair, and vascular regulation. In this study, multiple bioinformatics approaches were used to identify TGM2 as a candidate gene associated with AIS. Single-cell RNA sequencing (scRNA-seq) data were further integrated to analyze the expression pattern of TGM2 across different cell types and to explore its potential intercellular communication context. Finally, TGM2 expression was validated in peripheral blood samples from patients with AIS. This study provides new evidence supporting the potential involvement of TGM2 in AIS and its value as a candidate biomarker.
To identify serum metabolic biomarkers that distinguish corticosteroid and cyclosporin A (CS & CsA) resistant pediatric idiopathic uveitis (PIU) patients from sensitive counterparts. Serum samples were collected from 32 CS & CsA-sensitive PIU patients and 24 CS & CsA-resistant PIU patients, respectively. UHPLC-OE-MS was employed for comprehensive metabolic profiling of the serum samples. Bioinformatic analyses were performed to identify differentially expressed metabolites (DEMs) between the two patient groups. A machine learning-based classification model was constructed using the identified DEMs as predictive features. For validation purposes, an independent internal cohort of 16 CS & CsA-sensitive and 10 CS & CsA-resistant patients was recruited to evaluate the model's stability. Compared with the CS & CsA-sensitive PIU patients, serum samples from CS & CsA-resistant PIU patients displayed significant metabolic reprogramming. Among the identified differential metabolites, lipids were the most prominently dysregulated class, accounting for 72.47% of all differential metabolites. A machine learning based multivariate feature selection approach including NNET, LASSO, and XGBoost identified 4 candidate metabolite biomarkers. ROC analysis showed that three of these biomarkers (MG 15:0, PI-Cer 28:0;3O, and SPB 20:0;2O) exhibited AUC values of 0.934, 0.953, and 0.904, respectively, and were all upregulated in CS & CsA resistant patients. In contrast, N-acetylaspartic acid showed an AUC of 0.934 and was downregulated in CS & CsA resistant patients. The combined classification model incorporating these 4 metabolites achieved an AUC of 1.0. Validation in an independent internal cohort confirmed the model's excellent performance, with AUC values of 0.971 for NNET, 0.971 for LASSO, and 0.957 for XGBoost. We have established a classification model capable of effectively discriminating CS & CsA-resistant from -sensitive PIU patients. The machine learning model leveraging metabolic biomarkers demonstrates exceptional classification accuracy and generalizability, offering potential for clinical subtype classification.
Pesticide exposure during pregnancy, particularly to organophosphates and carbamates, has been associated with oxidative stress imbalance and adverse maternal and neonatal outcomes. This study aimed to evaluate cholinesterase activity and oxidative stress biomarkers in maternal peripheral blood and umbilical cord blood from mother and newborn pairs residing in an agricultural region of southern Brazil with environmental pesticide exposure. A total of 98 third-trimester pregnant women were recruited. Biomarkers of oxidative stress (MDA, GST, protein carbonyls, and 8-OHGua) and cholinesterase activity (AChE and BChE) were assessed in maternal and cord blood at delivery. Sociodemographic, clinical, and behavioral data were also collected. AChE activity was significantly reduced in cord blood (0.305 ± 0.15 μmol/min/mg) compared to maternal blood (0.612 ± 0.27 μmol/min/mg; p < 0.0001), with inhibition observed in 95% of neonatal samples. Elevated 8-OHGua levels were detected in 87% of maternal and 90% of neonatal samples. GST activity was significantly reduced in 65% of cord samples (p = 0.0001). Exploratory MCA analyses identified association patterns between biomarkers and maternal or neonatal variables. In cord blood, low birth weight showed the highest contribution in Dimension 3 (32.6%), followed by normal GST activity (20.9%). However, no significant associations were observed between biomarkers and major perinatal outcomes. The findings suggest relevant biochemical alterations in the maternal-newborn dyad, involving cholinesterase inhibition and oxidative imbalance. However, the absence of direct pesticide quantification requires cautious interpretation of the findings.
Ischemia/reperfusion injury (I/R) is common in various clinical situations. A growing body of clinical evidence suggests that renal I/R can trigger dysfunction in distant organs, underscoring the need for interventions that address both local and systemic effects. This study aimed to investigate the effect of allopurinol on distant organ damages induced by renal I/R. Twelve Sprague-Dawley rats were randomly divided into groups: (I) Sham, (II) I/R, and (III) I/R + Allopurinol. Renal I/R was induced by bilateral clamping of the renal pedicles for 60 min, followed by 24 h of reperfusion. A single dose of allopurinol (100 mg/kg, gavage) was administered 1 h before I/R. At the end of 24 h, electrocardiograms and mean blood pressure were recorded. Functional biomarkers, pathological findings, and oxidative stress levels were assessed in the tissue samples of the experimental groups. Renal I/R injury significantly increased the functional biomarkers of the kidneys (Cr and BUN), heart (LDH and cardiac troponin I), and liver enzymes (ALT, AST, and, APT). Pathological changes were significant in both the site of ischemia and the distant organs. Total oxidative status increased, while total antioxidant capacity decreased in the lung, liver, and heart tissues of the I/R group. Allopurinol pretreatment significantly restored the plasma levels of functional biomarkers and oxidative stress in the lung, heart, and liver tissues. Allopurinol pretreatment improved the pathological damage in the kidneys and distant organs. Allopurinol, a xanthine oxidase inhibitor, was able to reverse the structural damage and functional disorders of the kidney and remote organs caused by renal ischemia by modulating the oxidative stress pathway. Not applicable.
Systemic inflammatory biomarkers such as C-reactive protein (CRP) and interleukin-6 (IL-6) have been implicated in chronic pain, but their independent predictive role in older adults remains unclear. Using longitudinal data from the Health and Retirement Study (2016-2022), we analysed 1,850 older adults without chronic pain or arthritis at baseline. CRP, IL-6, IL-10, IGF-1, and the composite INFLA-score, a composite index of low-grade systemic inflammation, were assessed as exposures. Incident chronic pain was defined as the first report of pain during follow-up. Population-averaged Poisson regression models with robust standard errors were used, and sensitivity analyses were conducted in a broader cohort including participants with baseline arthritis. After multivariable adjustment, CRP remained independently associated with incident chronic pain (incidence rate ratio [IRR] 1.10, 95% CI 1.01-1.20), although the effect size was modest. Depression (IRR 1.52, 95% CI 1.23-1.88) and body mass index (IRR 1.02 per unit, 95% CI 1.01-1.04) were stronger predictors. IL-6 was not independently associated in the arthritis-free cohort but showed a small effect in the broader cohort, suggesting context-dependent associations. Incorporating CRP into a clinical prediction model yielded minimal improvement in discrimination (ΔAUC +0.009). IGF-1 showed an inverse association with chronic pain risk in participants without arthritis or obesity. Overall, systemic inflammatory biomarkers appear to contribute limited independent predictive value, while comorbidity burden and psychosocial factors dominate risk. PERSPECTIVE: This study shows that commonly used inflammatory biomarkers offer limited incremental value in predicting chronic pain among older adults. Instead, depression and metabolic factors are the dominant risk factors, supporting a shift towards clinically integrated risk stratification rather than biomarker-based screening alone.
The analysis of body fluid stains found at criminal scenes requires not only DNA profiles but also identification of the composition of these stains, particularly in mixed stains. Prior studies have demonstrated that forensic body fluids can be differentiated by their mRNA biomarkers.In this study, we screened a panel including 14 cSNPs located on four peripheral blood mRNA biomarkers (CD3G, ANK1, SPTB, and GYPA) and developed a multiplex assay for forensic blood identification using the SNaPshot method. Particularly, a one-step multiplex reverse transcription PCR (RT-PCR) strategy was used to generate cDNA amplicons from the corresponding mRNA by reverse transcription. The sensitivity, specificity, and capability of the multiplex SNaPshot assay for blood identification were systematically assessed. These results showed that the panel of 14 cSNPs successfully realized peripheral blood identification, including single-source body fluid as well as mixed samples. However, several peripheral blood cSNP markers were detected at low expression levels in menstrual blood and vaginal secretions. The detection sensitivity of the multiplex assay reached 1 ng total RNA input. Furthermore, 14 cSNP loci were genotyped across 19 peripheral blood samples, yielding 100% concordance between DNA- and RNA-derived profiles. Using allele frequencies of 14 cSNP loci in 100 unrelated Han individuals from northern China, the cumulative discrimination power of the 14 cSNPs was calculated to be 0.98303.
Mitochondrial dysfunction and neuroinflammation are critically implicated in the pathogenesis of Alzheimer's disease (AD). However, a systematic exploration of key mitochondrion-related genes (MRGs) in AD, and their specific roles in reshaping the immune microenvironment and serving as diagnostic biomarkers, remains insufficient. To address this, we conducted an integrative bioinformatics analysis. Differentially expressed MRGs were identified from public AD transcriptomic datasets. Their biological functions were elucidated through enrichment analyses. The correlations between core MRGs and ssGSEA-derived immune-cell signature enrichment scores were quantified using transcriptome-based computational analysis. Finally, machine learning models were constructed and validated to assess the diagnostic potential of identified MRG signatures. A robust set of dysregulated MRGs was identified in AD brains, showing predominant enrichment in pathways of oxidative phosphorylation and energy metabolism. Notably, the expression of key MRGs correlated significantly with altered infiltration abundances of specific immune cell types, including neutrophil-, eosinophil-, NK CD56bright cell-, and T follicular helper cell-related signatures. A diagnostic model constructed from a refined MRG signature exhibited promising predictive accuracy, with area under the curve (AUC) values reaching approximately 0.82 in the training cohort and around 0.74 in independent validation cohorts. Our study defines a novel landscape of MRGs in AD, deciphers their tight crosstalk with the immune microenvironment, and establishes a promising MRG-based signature for AD diagnosis. These findings provide fresh insights into the potential molecular interplay between mitochondrial dysfunction and neuroinflammation in AD and nominate candidate mitochondrion-related biomarkers and regulatory mechanisms that warrant further experimental and clinical validation.
Tumor-derived extracellular vesicles and particles (EVPs) represent a promising analyte class for early cancer detection. However, discrimination of tumor-derived EVPs from those shed by healthy tissues represents a major analytical challenge. This work describes the design and characterization of a novel proximity ligation-based immunoassay which targets tumor-derived EVPs in plasma displaying four colocalized surface biomarkers. We demonstrate the functionality of this approach in cancer cell line-derived EVPs and apply the method to lung adenocarcinoma (LUAD) detection. Importantly, we find that requiring four colocalized cancer-associated biomarkers reduces interference from healthy EVPs versus a three-biomarker design, resulting in strong discrimination performance for several biomarker combinations. Using this approach, we developed a prototype assay for LUAD detection which was evaluated in a case-control study composed of 92 LUAD cases and 290 non-cancer controls. The assay and trained classifier exhibited 48.5% (33/68; 95% confidence interval (CI) 37.1-60.2%) stage I sensitivity, 83.3% (15/18; 95% CI 59.8-94.8%) stage II sensitivity, and 100% (6/6; 95% CI 55.2-100%) stage III/IV sensitivity at 90% specificity. Assay signal was significantly correlated with tumor size and uncorrelated with smoking history. These preliminary results demonstrate the technical feasibility of this platform for early-stage lung cancer detection.
The aim of this work was to propose a comparative analysis of data from non-clinical studies and from pharmacovigilance evaluation collected following the COVID-19 vaccination campaign in France. Five authorized vaccines were included in the analysis: tozinameran (Comirnaty®), elasomeran (Spikevax®), ChAdOx1-S (Vaxzevria®), Ad26.COV2-S (Jcovden®), and the SARS-CoV-2-S protein with Matrix-M (Nuvaxovid®). Among the four adverse events recognized by the European Medicines Agency the following were analyzed: reactogenicity in response to vaccines, myocarditis associated with mRNA vaccines, and vaccine-induced thrombotic thrombocytopenia (VITT) associated with viral vector vaccines. A reactogenicity score was developed from PV data, based on reported symptoms and their intensity showing higher reactogenicity with viral vector vaccines. A parallel score was developed from non-clinical data (biomarkers and histopathological analysis). No correlation was evidenced between clinical outcomes and non-clinical parameters. For rare adverse events, the analysis identified clinical biomarkers such as troponin in the case of myocarditis. These rare events were not predicted by non-clinical studies as expected. While non-clinical studies currently meet regulatory safety requirements, their predictive capacity could be enhanced by integrating a reactogenicity scoring system, harmonizing biomarker monitoring, and adding targeted parameters based on clinical evidence.
Oxidative stress is a central feature of the metabolic disturbances observed in diabetes, often leading to cellular and molecular damage. Advanced oxidation protein products (AOPPs), formed during oxidative modification of plasma proteins, have been proposed as biomarkers of systemic oxidative stress. Despite increasing interest, the relationship between these markers and diabetes remains inconclusive. This study aimed to systematically evaluate and quantify differences in circulating AOPPs levels in diabetes. A systematic review and meta-analysis was performed in accordance with PRISMA guidelines. Searches were conducted across PubMed, Scopus, Embase and Web of Science databases up to March 2025 to identify relevant observational studies involving adults aged 18 years or older. Studies were included if they reported circulating levels of AOPPs in individuals with type 1 diabetes (T1DM), type 2 diabetes (T2DM), or impaired fasting glucose (IFG) or impaired glucose tolerance (IGT), compared to non-diabetic controls. Data extraction and quality assessment were conducted independently by two reviewers, and a random-effects model was used for meta-analysis. Sixteen eligible articles with twenty individual studies were identified, encompassing diverse populations and diabetes subtypes. The meta-analysis demonstrated significantly elevated circulating AOPPs in both T1DM (WMD ≈ 17.5 µmol/L) and T2DM (WMD ≈ 24.68 µmol/L) compared with controls, though heterogeneity was high. Subgroup analyses confirmed the consistent direction of effect, with stronger associations in younger populations, studies with higher baseline AOPPs, and certain designs or regions. Meta-regression showed that examined moderators (continent, study design, baseline AOPPs, age, sample size, gender, study quality, presence of overweight/ obesity, hypertension, smoking and abnormal serum lipids) explained little to none of the variability. Sensitivity analyses indicated that results were robust. Publication bias assessment revealed funnel plot asymmetry for type 2 diabetes, but trim-and-fill analysis did not materially alter the pooled estimates, supporting the stability of the findings. Elevated levels of AOPPs appear to be associated with diabetes, supporting the role of oxidative stress in its pathophysiology. Although pooled analyses demonstrated significantly higher circulating AOPP concentrations in patients with diabetes mellitus, substantial between-study heterogeneity and wide prediction intervals suggest that the magnitude and consistency of this association may vary across different populations and study settings. Also, the observational design of included studies limits the strength and generalizability of the pooled estimates. Therefore, causal inferences cannot be drawn, and further well-designed prospective research is warranted to clarify whether these biomarkers can reliably predict the onset or progression of diabetes. CRD420251275798.
Elevated maternal iron biomarkers may promote oxidative stress and inflammation, contributing to metabolic alterations that, alongside obesity, increase the risk of gestational diabetes (GDM), preeclampsia, and macrosomia. This study assessed associations of maternal hemoglobin (Hb), hematocrit (Hct), serum ferritin (SF), and soluble transferrin receptor (sTfR) with these outcomes, in a population with low anemia and high maternal obesity prevalence. This prospective cohort included 1730 pregnant women from the CHiMINCs-II study in Santiago, Chile, with Hb or Hct measured in the first or second trimester. SF and sTfR were assessed in a random subsample of 350 second-trimester samples. Multivariate logistic regression models evaluated associations between iron biomarkers and maternal and neonatal outcomes. Elevated Hb ( > 13 g/dL) occurred in 50% of women in the first trimester and 12% in the second. Anemia prevalence was <10% in the first and second, and 38% had depleted iron stores (SF concentration <15 µg/L). GDM, preeclampsia, and macrosomia prevalence were 17%, 3.3%, and 7.1%, respectively. Higher second-trimester Hb (OR 1.23, 95% CI 1.00-1.52) and Hct (OR 1.09, 95% CI 1.01-1.17) were associated with increased odds of GDM. First-trimester anemia was associated with higher odds of preeclampsia (OR 5.67, 95% CI 1.20-27.0). SF and sTfR were not significantly associated with GDM or preeclampsia. Despite a high prevalence of depleted iron stores, elevated Hb was also highly prevalent, and associated to adverse maternal outcomes. Optimizing maternal iron status during pregnancy requires addressing both deficiency and potential excess.
Autism spectrum disorder (ASD) exhibits substantial molecular heterogeneity that challenges traditional gene-centric analyses. We applied persistent homology of the graph 1-skeleton to characterize co-expression cycle structure in mutual information-based gene networks in ASD. Using transcriptomic data from brain tissue and peripheral blood, we constructed MI networks from the 500 most variable genes, computed Betti-1 numbers across 30 filtration steps, and assessed significance via 10,000-permutation testing. We note that this approach computes first homology on the 1-skeleton, not the full flag/clique complex; this distinction is made explicit throughout. ASD peripheral blood networks exhibited nominally significant topological reorganization relative to neurotypical controls, with a 20.4% reduction in the area under the Betti-1 curve. After excluding 44 redundant SNORD115-family probes to address microarray probe-redundancy concerns, the finding strengthened substantially to [Formula: see text], which we consider the primary result. This indicates that regulatory cycle structure accumulates less readily in ASD blood co-expression networks across the full filtration range. Brain cortex networks showed no significant topological differences; however, a post-hoc power analysis indicates the brain cohort has only ∼ 36% power to detect the blood-magnitude effect, so this null result should not be interpreted as evidence of preserved brain topology or tissue specificity. After SNORD115 probe exclusion, ASD hub genes form a ribosomal protein gene cluster consistent with translational dysregulation in ASD. To validate the blood finding, we applied the identical TDA pipeline to two independent blood transcriptome datasets: GSE42133 and GSE25507. Both independent cohorts exhibited the same ASD < control direction. Stouffer meta-analysis combining the z-scores from all three blood datasets, demonstrating robust directional replication of the reduced-cycle-structure signal across independent cohorts (spanning two Affymetrix platforms, Human Gene 1.0 ST and U133 Plus 2.0). These findings demonstrate that graph-filtration cycle-rank analysis detects reduced co-expression cycle structure in ASD peripheral blood, independently replicated across three blood transcriptome cohorts. The Stouffer meta-analysis substantially exceeds conventional significance thresholds and supports the potential utility of topological biomarkers in neurodevelopmental disorders.
Genetic forms of Parkinson's disease (PD) provide a unique model to investigate how distinct molecular perturbations reshape large-scale brain networks. Although most genetic variants ultimately converge on nigrostriatal dopaminergic degeneration, accumulating evidence suggests that different mutations modulate distributed motor and cognitive circuits along genotype-specific trajectories. In this narrative review, we synthesize findings from 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) and cerebral perfusion single-photon emission computed tomography (SPECT) in monogenic and high-risk forms of PD, including LRRK2, GBA1, SNCA, PRKN, PINK1, DJ-1, RAB39B, and RAB32. Spatial covariance analyses have identified reproducible disease-related metabolic networks, notably the Parkinson's disease-related motor pattern (PDRP) and the Parkinson's disease cognitive pattern (PDCP), which can be quantified at the single-subject level and tracked longitudinally. Across genotypes, mutations appear to modulate the topology, resilience, and temporal evolution of shared network architectures rather than generating distinct metabolic signatures The strongest evidence concerns severe GBA1 variants, which are associated with early posterior cortical involvement and greater PDCP expression, whereas limited case-based data suggest more diffuse cortical involvement in selected SNCA multiplication carriers. In contrast, mitochondrial- and kinase-related mutations often show metabolic alterations largely confined to subcortical motor circuits. We propose a trajectory-based framework in which genetic background shapes network vulnerability and compensatory capacity rather than defining separate metabolic entities. In the era of gene-targeted therapies, imaging-defined network phenotypes may serve as functional biomarkers for risk stratification, longitudinal monitoring, and mechanistic therapeutic trials, bridging genotype and systems-level neurodegeneration.
Per- and polyfluoroalkyl substances (PFAS) are persistent synthetic chemicals that contaminate marine environments worldwide, yet their effects on marine bivalves under realistic scenarios remain poorly understood. This study investigated the subchronic effects of PFAS mixtures on blue mussels (Mytilus spp.) over 28 days, using dietary and combined dietary-aqueous exposure pathways. The mixtures were representative of the chemical diversity found in contaminated environments, containing 36 and 7 PFAS for dietary and aqueous routes, respectively. Biological responses were assessed using analyses of gene expression, biochemical biomarkers, lipidomics and clearance rates. Unexpectedly, dietary exposure alone elicited more diverse and intense molecular responses than combined exposure. Notably, the mantle exhibited the strongest transcriptional response with several pro-apoptotic genes being upregulated, while growth/survival pathways were downregulated in the digestive gland. Dietary exposure also specifically disrupted biotransformation pathways in the gills through simultaneous cytochrome P450 downregulation and glutathione S-transferase activity induction. Conversely, combined exposure increased clearance rate, suggesting physiological compensation in mussels. Energy metabolism was also affected under both exposure conditions, as ATP production genes were specifically regulated across tissues, and limited but detectable lipidome changes occurred at the whole-organism level. Collectively, these findings indicate that PFAS mixtures can affect biotransformation, apoptosis-related and energy-related pathways in marine bivalves, although their environmental significance remains to be assessed under chronic, environmentally realistic conditions. This study underscores the importance of considering multiple exposure routes in PFAS research and supports the integration of multi-tissue approaches in future ecotoxicological assessments for bivalve biomonitoring.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a growing global health challenge. Despite decades of research dominated by the amyloid cascade hypothesis, single-target therapies aimed at Aβ or tau have largely failed, underscoring the need for a broader framework. Emerging evidence implicates neuroimmune dysfunction as a central driver of AD pathology, with the "peripheral-central immune axis" emerging as a critical node. The APOE4 allele, the strongest genetic risk factor for sporadic AD, plays a pivotal role in both central nervous system (CNS) lipid metabolism and peripheral immune homeostasis. This review synthesizes the association between APOE4 and peripheral immune dysregulation and its impact on neurodegeneration. We discuss APOE expression in CNS and peripheral immune cells, highlighting APOE4-associated alterations in monocyte/macrophage polarization, T cell subsets via IL-7/IL-7R downregulation, and gut microbiota composition. We delineate mechanisms by which APOE4 is associated with blood-brain barrier compromise, may promote conditions for immune cell trafficking, and contributes to neuroinflammation. Integrating preclinical and clinical evidence, we propose an "APOE4-associated peripheral-central immune infiltration cascade" as a unifying framework for understanding systemic AD pathogenesis. Finally, we review emerging therapeutic strategies targeting peripheral immunity and APOE, discussing multi-target approaches guided by APOE genotype and immune biomarkers, shifting from a CNS-centric toward a systemic immunomodulatory paradigm for precision medicine.
Blood-based biomarkers are increasingly recognized as promising tools for the diagnosis and monitoring of neurodegenerative diseases, offering a minimally invasive alternative to cerebrospinal fluid (CSF) testing. We evaluated the analytical performance and clinical utility of the Olink Target 48 Neurodegeneration panel, a novel multiplex proteomic platform based on the proximity extension assay (PEA) technology, in a large, clinically diverse dementia cohort. We retrospectively analyzed plasma samples from 238 patients with Alzheimer's disease (AD), dementia with Lewy bodies, frontotemporal dementia, progressive supranuclear palsy, and corticobasal degeneration, along with 65 healthy controls, quantifying 41 proteins in each sample. We assessed analytical performance using intra- and inter-assay coefficients of variation, evaluated diagnostic accuracy through receiver operating characteristic curve analysis, and investigated associations between biomarker levels, clinical severity measures, and pathology-specific CSF biomarkers for AD and Lewy body pathology (LBP) using general linear models. The platform quantified 32 proteins with variable analytical performance; nine were excluded due to poor detectability. Strong correlations were observed between PEA-based measurements and established immunoassays for plasma pTau217, NEFL, and GFAP (all p < 0.001). Plasma pTau217 demonstrated superior diagnostic accuracy for AD, achieving an area under the curve (AUC) exceeding 0.91 against all comparison groups. Novel ratios combining NEFL with markers of immune function or synaptic integrity (NEFL/ITGB2, NEFL/ITGAM, NEFL/SCG2) achieved AUCs exceeding 0.93 for discriminating patients from controls, significantly outperforming NEFL alone (all p < 0.001). Thirteen proteins, spanning markers of neuroaxonal damage, myelin-associated processes, and immune function (i.e., Abeta40, Abeta42, BMP7, CLSTN3, ENO2, KLK8, MMP10, NEFL, NPTXR, OMG, RTN4R, SCG2, SDC4, all p < 0.01) showed significant independent associations with disease stage as measured by the Clinical Dementia Rating scale. Four proteins (i.e., pTau217, GFAP, SYT1, and SDC4) were significantly associated with AD pathology, while three (ENO2, ITGAM, and ITGB2) showed significant associations with LBP (all p < 0.05). This multiplex platform provides multiplex biomarker measurements with potential utility for AD diagnosis and disease staging across neurodegenerative disorders. These findings support further validation studies for its implementation in clinical and research settings.