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Elevated Cerebrospinal Fluid Total Tau in Niemann-Pick Disease Type C1: Correlation With Clinical Severity and Response to Therapeutic Interventions.

Niemann-Pick disease, type C1 (NPC1) is an inborn error of intracellular cholesterol transport. Impaired function of NPC1 leads to endolysosomal accumulation of unesterified cholesterol, which results in progressive neurodegeneration. Although the age of onset is variable, classical NPC1 is a pediatric disease. Identification of biomarkers that correlate with clinical phenotype and respond to therapeutic interventions will be essential for developing effective therapeutic interventions. A&#x3b2; peptides and Tau protein are primary components of amyloid plaques and neurofibrillary tangles, respectively, which are major pathological features in neurodegenerative disorders. Cerebrospinal fluid (CSF) levels of total Tau, a biomarker of axonal damage, were elevated ~3-fold (p&#x2009;<&#x2009;0.0001) in 106 individuals with Niemann-Pick disease, type C1, relative to age-appropriate comparison samples. Baseline CSF total Tau levels correlated with clinical measures of disease severity. Specifically, CSF total Tau levels decreased with increased age of neurological onset (rs&#x2009;=&#x2009;-0.42, FDR adj. p&#x2009;<&#x2009;0.0001) and increased with increased Annual Severity Increment Score (rs&#x2009;=&#x2009;0.52, FDR adj. p&#x2009;<&#x2009;0.0001). Baseline CSF total Tau levels were decreased 40% (p&#x2009;=&#x2009;0.0066) in individuals being treated with miglustat, and longitudinal analysis substantiated this observation with a 40% decrease (p&#x2009;<&#x2009;0.0001, 95% CI 32%-47.4%). Longitudinal analysis also showed a significant (p&#x2009;=&#x2009;0.004) decrease of 19% (95% CI 7%-30%) in total Tau levels associated with intrathecal 2-hydroxypropyl-&#x3b2;-cyclodextrin therapy. These data show that CSF total Tau levels are significantly increased in individuals with NPC1, positively correlated with increased disease severity, and respond to therapeutic interventions.

Humans

Multiple urinary peptides are associated with hypertension: a link to molecular pathophysiology.

OBJECTIVES: Hypertension is a common condition worldwide; however, its underlying mechanisms remain largely unknown. This study aimed to identify urinary peptides associated with hypertension to further explore the relevant molecular pathophysiology. METHODS: Peptidome data from 2876 individuals without end-organ damage were retrieved from the Human Urinary Proteome Database, belonging to general population (discovery) or type 2 diabetic (validation) cohorts. Participants were divided based on systolic blood pressure (SBP) and diastolic BP (DBP) into hypertensive (SBP &#x2265;140&#x200a;mmHg and/or DBP &#x2265;90&#x200a;mmHg) and normotensive (SBP <120&#x200a;mmHg and DBP <80&#x200a;mmHg, without antihypertensive treatment) groups. Differences in peptide abundance between the two groups were confirmed using an external cohort ( n &#x200a;=&#x200a;420) of participants without end-organ damage, matched for age, BMI, eGFR, sex, and the presence of diabetes. Furthermore, the association of the peptides with BP as a continuous variable was investigated. The findings were compared with peptide biomarkers of chronic diseases and bioinformatic analyses were conducted to highlight the underlying molecular mechanisms. RESULTS: Between hypertensive and normotensive individuals, 96 (mostly COL1A1 and COL3A1) peptides were found to be significantly different in both the discovery (adjusted) and validation (nominal significance) cohorts, with consistent regulation. Of these, 83 were consistently regulated in the matched cohort. A weak, yet significant, association between their abundance and standardized BP was also observed. CONCLUSION: Hypertension is associated with an altered urinary peptide profile with evident differential regulation of collagen-derived peptides. Peptides related to vascular calcification and sodium regulation were also affected. Whether these modifications reflect the pathophysiology of hypertension and/or early subclinical organ damage requires further investigation.

Humans

Biomarkers of metastatic disease in pheochromocytoma and paraganglioma.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with variable metastatic potential. While metastatic disease occurs in approximately 10-20% of cases, its prediction remains a major clinical challenge, as no histological system has been universally validated to reliably identify aggressive tumors at diagnosis. This review aims to provide a comprehensive and updated overview of current and emerging biomarkers of metastatic risk in PPGL, encompassing histopathological scoring systems, genetic and molecular markers, biochemical phenotyping, liquid biopsy approaches, and imaging-based biomarkers. Among established markers, germline SDHB mutation status, loss of SDHB expression by immunohistochemistry, elevated plasma 3-methoxytyramine, and histopathological scoring systems, such as GAPP and COPPS, represent the most clinically validated tools for risk stratification. Emerging biomarkers - including somatic alterations in ATRX and TERT, genomic instability indices, tumor immune microenvironment characterization, circulating tumor DNA, and oncometabolite quantification - show promise in refining prognostic assessment but require prospective validation before routine clinical implementation. Accurate risk stratification in PPGL demands a multiparametric and dynamic approach, integrating clinical, genetic, biochemical, and molecular parameters. Future progress will depend on large prospective international cohorts, standardized biomarker platforms, and biomarker-driven clinical trial designs to translate emerging molecular knowledge into improved patient outcomes.

SDHB

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence

Peripheral inflammation and executive function among community samples across the lifespan: A systematic review and meta-analysis.

Higher levels of peripheral inflammatory markers are proposed to disrupt cognitive processes; however, the extent to which this relationship applies to executive functions, specifically, has yet to be systematically evaluated. The current meta-analysis synthesizes all available literature measuring the association between peripheral inflammation and executive functions among community individuals across the lifespan. This systematic review searched Web of Science, PubMed, and PsycINFO for published and unpublished studies, in the English language, that assessed the association between markers of peripheral inflammation and self-report and behavioral measures of executive function. In addition to methodological and demographic information, correlation/beta coefficients were extracted from included studies to quantify the association between inflammation and executive functions. This review included 58 studies, 41 of which were included in the random-effects meta-analysis (N&#x202f;=&#x202f;255,539). Among community individuals, higher levels of CRP and IL-6 were associated with poorer executive functioning. These results did not differ when models accounted for potential confounding variables. Meta-regressions revealed that the relationship between inflammatory biomarkers and executive function did not differ as a function of age, adiposity, or percentage of the sample who identified as female. Results of the present review indicate that higher levels of specific inflammatory markers are associated with lower performance on executive function measures; however, we observed significant heterogeneity in the study design and measurement of both inflammation and executive functioning, suggesting important considerations for future research.

Humans

CRISPR-Cas9&#x2011;based lipid nanocarriers for advanced therapy of urinary bladder cancer.

Bladder cancer (BCa) exhibits significant genetic and phenotypic variability. This variability suggests that various tumor subtypes could be influenced by several biomarkers and signaling pathways, which presents a problem for monotherapy strategies. Despite the initial effectiveness of traditional therapies, BCa's high rates of progression and recurrence, and the eventual development of drug resistance in many patients, continue to be major concerns. Because of the potential to transform the genetic modifications linked to the disease, genome editing using CRISPR/Cas9&#xa0;has become a transformative tool in medicine with noteworthy potential for BCa therapy. Although the CRISPR/Cas9 technology is incredibly effective at transforming the field of genome editing, its instability and cellular impermeability pose significant challenges to its delivery. To increase efficient delivery of CRISPR/Cas9, nanovectors may be investigated. Significant promise exists for improving the therapeutic potential of CRISPR-Cas9 technology and addressing complex cancer therapy difficulties because of the rapid development of nanotechnology-based delivery systems. Relevant articles were searched in Google Scholar, Scopus, and Web of Science covering studies published between 2007 and 2026. Along with the impact of lipid-based nanoparticles on their safe transport to cancer cells, this review emphasizes the significance of the CRISPR/Cas9 genome editing system in the treatment of BCa.

CRISPR&#x2013;Cas9

AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease around the world, affecting 33.6% of the adult population (95% CI: 28.1%-39.5%; I 2&#x2009;=&#x2009;99.9%), or roughly one in three. The extent of the liver damage is variable, from simple steatosis to metabolic dysfunction-associated steatohepatitis (MASH, formerly NASH), cirrhosis and hepatocellular carcinoma (HCC). Early diagnosis is essential to prevent serious liver damage. Traditional diagnostic techniques such as liver biopsy, imaging, and biomarker testing are all invasive, costly, reduced sensitive to early-stage disease, and they also have variability among observers. Modern diagnostic and prognostic approaches based on the principles of Artificial Intelligence (AI) and specifically on machine learning (ML) and deep learning (DL) have enabled multimodal approaches integrating clinical, imaging and molecular data. This scoping review conducted per PRISMA-ScR guidelines, synthesizes findings from 73 studies (search window 2020-2026) across three dimensions: clinical data driven models, imaging-based classifiers (ultrasound, CT and MRI), and multi-omics (genomics, transcriptomics and proteomics) techniques. Moreover, emergence of models such as U-Net and LiverNet 2.x, classification models like DeepLiverNet and BiLSTM models, as well as transformer frameworks and the identification of biomarkers models are also described. This study also investigates challenges such as data heterogeneity, data interpretability, fairness and real-world clinical application. Finally, important areas of research opportunities and future directions are highlighted to present a developing clinically applicable, explainable and ethical AI solutions to manage MASLD.

MASLD

Large-scale proteomics profiling of peripheral blood of DM1 patients identifies biomarkers for disease severity and functional capacity.

BackgroundMyotonic Dystrophy Type 1 (DM1), the most common genetic neuromuscular disorder in adults, poses significant challenges for drug development due to its multisystem nature and high clinical variability in symptoms and disease progression. With a growing number of therapies entering clinical trials, this study addresses the urgent need for biomarkers that can serve as surrogate endpoints.MethodsWe profiled 437 serum samples from adult DM1 patients collected at two timepoints of the OPTIMISTIC trial using bottom-up mass spectrometry with data-independent acquisition. Associations between protein expression, the disease-causing CTG-repeat and 25 clinical outcome measures were studied using linear mixed-effect models. All key study findings were validated in an independent cohort of 69 DM1 patients and 10 healthy controls.ResultsOf the 259 identified proteins, 161 showed significant associations with the CTG-repeat length (FDR&#x2009;<&#x2009;5%). Hypogammaglobulinemia was confirmed and shown to be worse in severely affected patients. A strong proteomic signature was associated with clinical measures of functional capacity, with the 6-Minute Walk Test showing the strongest signal (70 associations, FDR&#x2009;<&#x2009;5%). These novel associations reveal a compelling link between chronic inflammation and reduced functional capacity. A machine learning algorithm identified a minimal set of 13 proteins robustly reflecting both the underlying genetic defect and functional capacity.ConclusionsDM1 induces a broad disease fingerprint in the serum proteome, predominantly affecting proteins of the immune system. A carefully selected panel of proteins showed the greatest potential to meet the statistical criteria required for surrogate endpoints in clinical trials.

Humans

A system-level metastable model of cancer evolution: integrating replication stress, cell cycle deregulation and chromosomal instability.

INTRODUCTION: Cancer cell proliferation occurs within the context of persistent genomic instability. In this review, we propose the RS-CCD-CIN axis as a systems-level framework in which replication stress (RS), cell cycle deregulation (CCD) and chromosomal instability (CIN) form an interdependent triad that shapes tumour evolution. This axis represents a constrained metastable state in which genomic instability is tolerated and buffered. The objective of this review is to synthesize the current understanding of how the RS-CCD-CIN axis contributes to tumour heterogeneity, adaptability and therapy response. DISCUSSION: Evidence indicates that RS, CCD and CIN operate as a dynamic, interconnected network rather than as independent processes. Replication stress induces DNA damage and mutagenesis, while partial checkpoint disruption permits cells with unresolved lesions to proliferate. Chromosomal instability generates both structural and numerical alterations, contributing to intratumoural heterogeneity. Together, these processes facilitate adaptation to environmental and therapeutic pressures. Extrachromosomal DNA, micronuclei formation and cytosolic DNA signalling, including the cGAS-STING pathway, connect genomic instability to adaptive responses and immune modulation. Single-cell and spatial profiling reveal temporal and spatial variability in RS, CCD and CIN states, highlighting the limitations of static biomarkers. Therapeutically, targeting individual components often yields limited durability, whereas approaches that simultaneously perturb multiple aspects of the RS-CCD-CIN axis may improve clinical outcomes. CONCLUSIONS: This review highlights the RS-CCD-CIN axis as a fragile and metastable architecture that supports cancer evolution, while also being susceptible to collapse. A deeper understanding of this interconnected framework may inform the development of therapeutic strategies and enhance the management of resistance.

Humans

Development and Validation of a Multimodal Clinical, Pathologic, and Genomic Model for Breast Cancer Recurrence.

PURPOSE: To develop and validate a multimodal recurrence-risk model integrating histology, genomic testing, and clinical variables. METHODS: We developed AI-Path, a whole-slide image biomarker for recurrence prediction trained in CALGB 9344, and validated it in three independent cohorts: TAILORx, a multi-site Chicago cohort, and the MDX-BRCA cohort. We then integrated AI-Path with Oncotype DX Recurrence Score (RS), tumor size, and nodal status into a Cox model, PathClinRS, fit using 60% of cases from TAILORx, with the remaining 40% held out for validation. The primary end point was distant recurrence-free interval. Performance was assessed using Harrell's concordance index (C-index) and Kaplan-Meier analyses. RESULTS: A total of 12,418 patients were included. In TAILORx, AI-Path outperformed RS for distant recurrence (C-index, 0.682 vs 0.647; P = .038), driven by superior prediction of late recurrence (0.656 vs 0.567; P < .001). In node-negative disease, PathClinRS outperformed RSClin in the TAILORx fitting (0.72 vs 0.70; P = .016) and validation sets (0.74 vs 0.70; P = .004). In node-positive disease, PathClinRS outperformed RSClinN+ in Chicago (0.94 vs 0.74; P < .001) and MDX-BRCA (0.71 vs 0.66; P = .004) cohorts. Compared with NATALEE eligibility, PathClinRS identified nearly twice as many high-risk node-negative patients while maintaining a comparable 10-year distant recurrence risk (16.7% vs 16.6% per NATALEE eligibility in TAILORx fitting; 21.0% vs 19.4% in TAILORx validation). PathClinRS identified 68% of intermediate risk premenopausal patients as low-risk with no evidence of chemotherapy benefit, compared to only 36% identified as low risk by standard clinicopathologic criteria. CONCLUSION: Digital histopathology provides prognostic information complementary to genomic assays and has the potential to personalize therapy beyond existing clinicogenomic tools.

Journal Article

CS Ratio is an immune-related prognostic biomarker for cervical cancer.

BACKGROUND: The tumor microenvironment (TME) plays a crucial role in cancer progression but its complex structure significant variability among patients present considerable challenges for research. Recent studies have demonstrated that macrophage polarization states defined by the expression levels of CXCL9 SPP1 (CS Ratio) are more prognostically relevant than traditional M1/M2 markers. The CS polarization state reflects a highly coordinated network of pro-tumor anti-tumor variables offering a simplified yet effective immune response indicator for the complex TME. The CS Ratio has been shown to correlate with the abundance of anti-tumor immune cells the gene expression programs of tumor-infiltrating cells responses to immunotherapy. Cervical cancer, one of the most common gynecological malignancies, still faces limited therapeutic options. CXCL9, a member of the CXC chemokine family, plays a critical role in immune regulation, inflammation, tumor growth, angiogenesis, and metastasis. Similarly, SPP1, a cytokine, influences immune-related pathways by regulating molecules such as interferon-&#x3b3; and interleukin-12. However, no studies have systematically investigated the role of the CS Ratio in cervical cancer or its relationship with immunotherapy characteristics. Research in this area could provide critical insights into the role and clinical potential of the CS Ratio in cervical cancer and related tumors. METHODS: The expression ratio of CXCL9 to SPP1 was analyzed in cervical cancer patients using data from the Gene Expression Omnibus (GEO) database, which revealed significant differences. Data for cervical cancer patients were obtained from The Cancer Genome Atlas (TCGA) database. The optimal cutoff value for the CS Ratio was determined using the maxstat package in R, and Kaplan-Meier (KM) survival curves were constructed. Patients were categorized into High and Low groups based on the median CS Ratio. Immune scores were analyzed, and immune cell infiltration was assessed using CIBERSORT. Differences in the CS Ratio were evaluated across patients with varying pathological T stages and FIGO stages. Additionally, receiver operating characteristic (ROC) analysis was performed using the pROC package in R to calculate the area under the curve (AUC). Univariate and multivariate Cox regression analyses were performed to evaluate the potential of the CS Ratio as an independent prognostic factor in cervical cancer. A Cox regression-based nomogram integrating four key features was subsequently developed for the TCGA-CESC cohort. Nomogram performance was assessed using calibration curves and ROC analysis. RESULTS: The CS Ratio was significantly lower in cervical cancer patients compared to normal controls (P < 0.05). KM survival curves indicated that patients in the CS High group exhibited better prognoses. Immune score analysis revealed significantly higher immune scores (P < 0.05) and lower tumor purity (P < 0.05)in the CS High group compared to the Low group. CIBERSORT analysis revealed significantly higher proportions of CD8+ T cells (P < 0.05) and M1 macrophages (P < 0.05), and a significantly lower proportion of M2 macrophages (P < 0.05), in the CS High group compared to the Low group. The CS Ratio significantly decreased with advancing FIGO stage (P < 0.05). Both univariate (P < 0.05) and multivariate Cox regression analyses (P < 0.05) confirmed the CS Ratio as an independent prognostic factor. ROC analysis demonstrated that the CS Ratio had higher AUC values for predicting 1-year (AUC=0.69), 3-year (AUC=0.66), and 5-year OS (AUC=0.68) than CXCL9 or SPP1 alone. The Cox regression-based nomogram integrating four key features demonstrated predictive capability for 1-, 3-, and 5-year OS in CESC patients (Concordance Index = 0.751; 95% CI: 0.678-0.824; p = 1.50&#xcd;10-11). Significant survival differences were observed between the high-risk and low-risk groups based on the nomogram score. ROC analysis yielded high AUC values for survival prediction: 0.85 (95% CI: 0.94-0.75) at 1-year, 0.74 (95% CI:0.84-0.64) at 3-year, and 0.72 (95% CI:0.84-0.61) at 5-year. CONCLUSION: The CS Ratio may serve as a more effective prognostic biomarker for cervical cancer patients.

CXCL9

Artificial intelligence-based tumour infiltrating lymphocyte quantification in patients with triple-negative breast cancer: an independent validation study.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are a robust prognostic marker in patients with triple-negative breast cancer. Artificial intelligence (AI)-derived computational tools assessing TILs could improve efficiency, but require independent validation against clinical outcomes. We aimed to compare the prognostic performance of AI-derived TIL scores with pathologist-scored TILs in a large, prospectively collected dataset pooled from randomised controlled trials. METHODS: CATALINA was an independent, external validation study using prospectively collected long-term clinical outcome data pooled from seven randomised clinical trials conducted at multiple sites. We independently evaluated two previously validated AI pipelines that generate five computationally assessed tumour-infiltrating lymphocyte (cTIL) scores by masked, independent deployment of locked models. cTIL scores were correlated with the mean of the pathologist-scored stromal TILs (sTILs) in 220 digitised haematoxylin and eosin whole slide images in a cohort of patients with early-stage triple-negative or HER-2 positive breast cancer, previously scored by trained pathologists in a TIL-reproducibility study. Prognostic performance was assessed in a separate cohort of patients with early triple-negative breast cancer pooled from seven prospective, randomised adjuvant trials. Multivariable Cox regression models adjusted for clinicopathological factors and study heterogeneity assessed associations of cTIL score and sTIL score with invasive disease-free survival, distant disease-free survival, and overall survival. 5-year discrimination was estimated using time-dependent area under the receiver operating characteristic curve (AUC). FINDINGS: Individual data were collated from 1759 patients, of whom 1356 had complete clinicopathological data, pathologist sTIL scores, and cTIL scores available. Modest correlation (r 0&#xb7;375-0&#xb7;473) was observed between cTIL scores and the mean pathologist sTIL score. Both sTIL and cTIL were independently associated with 5-year invasive disease-free survival, distant disease-free survival, and overall survival after adjustment for clinicopathological factors (hazard ratio for invasive disease-free survival was 0&#xb7;73 [95% CI 0&#xb7;66-0&#xb7;82]; q<0&#xb7;0001, distant disease-free survival was 0&#xb7;70 [0&#xb7;61-0&#xb7;79]; q<0&#xb7;0001, and overall survival was 0&#xb7;72 [0&#xb7;63-0&#xb7;82]; q<0&#xb7;0001 for sTIL scores and 0&#xb7;80 [0&#xb7;73-0&#xb7;89]; q<0&#xb7;0001, 0&#xb7;77 [0&#xb7;69-0&#xb7;86]; q<0&#xb7;0001, and 0&#xb7;79 [0&#xb7;70-0&#xb7;88]; q=0&#xb7;0002, respectively, for percentage_lymphocyte scores). In models adjusted for clinicopathological variables and sTIL score, cTIL score did not maintain a statistically significant prognostic association. Both sTIL and cTIL scores improved the 5-year AUC over clinicopathological variables alone, while cTIL score did not significantly further improve AUC when combined with clinicopathological variables and sTIL score. INTERPRETATION: Two cTIL models deployed entirely without retraining or modification provided statistically significant prognostic information and improved risk discrimination compared with clinicopathological variables alone in this large, platform-based, independent validation study. Although cTIL score did not incrementally improve prognostication compared with models combining clinicopathological variables with sTIL score, these findings support the application of cTILs as a reproducible prognostic biomarker, particularly in settings where routine or widespread pathologist assessment is unavailable. FUNDING: Breast Cancer Research Foundation (USA).

Humans

Assessment of the Potential of Different Anthropometric Indices in Predicting the Risk of Diabetes and Associated Co-morbidities.

Diabetes, a chronic disorder, is showing a rapidly increasing trend globally. India holds the second position in the global diabetes epidemic. The present investigation is an assessment of different anthropometric measurements and their association with type 2 diabetes to determine their diagnostic potential for diabetes as well as its co-morbidities. In this cross-sectional study, we have measured anthropometric parameters and blood biomarkers in subjects with diabetes. We have presented the comparisons of cost- and time-effective anthropometric variable with costly and time-dependent biochemical variables in control and diabetic groups (n = 233/group). Correlations between anthropometric variables and biochemical measurements, as well as the diagnostic utility of anthropometric variables for diabetes, were evaluated. The diagnostic utility of anthropometric variables for diabetes was assessed through receiver operating characteristic (ROC) curves. Neck circumference, sagittal abdominal diameter (SAD), skinfold thickness, and body roundness index (BRI) displayed high specificity and diagnostic utility for diabetes, emphasizing their potential in predicting diabetes and the further development of metabolic syndrome. The study highlights the importance of cost- and time-effective anthropometric assessments in diabetes risk evaluation and calls for further research to elucidate this intricate relationship and develop personalized management strategies.

Humans

Synaptic vesicle glycoprotein 2A PET imaging in parkinsonian &#x3b1;-synucleinopathies: a systematic review.

Synaptic dysfunction is increasingly recognized as an early and biologically relevant component of &#x3b1;-synucleinopathies. However, conventional imaging biomarkers mainly assess dopaminergic dysfunction, glucose metabolism, or structural damage rather than presynaptic density itself. Synaptic vesicle glycoprotein 2A (SV2A) PET enables in vivo assessment of presynaptic terminal integrity and may provide complementary information in Parkinson's disease (PD), Parkinson's disease dementia/dementia with Lewy bodies (PDD/DLB), and multiple system atrophy (MSA). This systematic review synthesized the available evidence on SV2A-targeted PET in parkinsonian &#x3b1;-synucleinopathies, focusing on regional imaging patterns, clinical associations, longitudinal findings, and methodological determinants of interpretation. Seventeen reports were included. In PD, the most recurrent finding was reduced SV2A binding in the substantia nigra, although additional involvement of brainstem, caudate, striatal, thalamic, raphe, or cortical regions was reported in selected cohorts. In PDD/DLB, abnormalities appeared broader and more cortical, with evidence of association between cortical SV2A binding and cognitive performance. In MSA, one study suggested a distinct infratentorial and cerebellar pattern with potential relevance for phenotypic stratification. SV2A PET is a promising research biomarker for biological characterization of synucleinopathies. However, the field remains limited by small cohorts, methodological heterogeneity, variable quantification strategies, limited longitudinal evidence, and potential cohort overlap. Multicentre validation and harmonized protocols are required before clinical translation.

Humans

Precision Medicine in Transfusion-Dependent and Non-Transfusion-Dependent &#x3b2;-Thalassemia: Toward Personalized Diagnosis and Therapy.

&#x3b2;-thalassemia comprises a clinically heterogeneous group of disorders in which anemia severity, transfusion exposure, iron loading, and organ complications vary widely among individuals. This structured narrative review summarizes practical applications of precision medicine in transfusion-dependent thalassemia (TDT) and non-transfusion-dependent thalassemia (NTDT), with explicit attention to which strategies apply to each clinical category. Literature indexed in PubMed and Scopus from 2000 to 2025 was reviewed using terms related to thalassemia, precision medicine, magnetic resonance imaging (MRI), chelation tailoring, next-generation sequencing (NGS), fetal hemoglobin (HbF) modifiers, luspatercept, mitapivat, hepcidin, gene therapy, gene editing, and artificial intelligence (AI). Evidence was synthesized descriptively because interventions, outcomes, and populations were heterogeneous, and no pooled meta-analysis was performed. In TDT, precision care is centered on individualized transfusion planning, extended red-cell antigen matching, MRI-guided cardiac and hepatic iron monitoring, organ-directed chelation intensification, and selection of disease-modifying or curative approaches. In NTDT, precision care emphasizes accurate phenotype classification, MRI liver iron concentration, because serum ferritin may underestimate iron burden, selective chelation, surveillance for NTDT-specific complications, and individualized use of agents that improve anemia. Personalized chelation should include deferiprone, either alone or in combination, when cardiac iron is increased. Comprehensive molecular diagnosis should include HBB together with HBA1 and HBA2 assessment, while secondary and tertiary modifiers help explain phenotypic variability and complication risk. Hepcidin and growth differentiation factor 15 (GDF-15) are discussed as investigational biomarkers; transferrin saturation is not recommended for routine iron-overload assessment in thalassemia. AI currently has its strongest role in screening and diagnosis, whereas risk-stratification models remain exploratory. Equitable implementation requires standardized TDT/NTDT pathways, regional MRI and genomics access, longitudinal registries, and multidisciplinary interpretation.

Humans

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (&#x3b2;&#x2009;=&#x2009;0.50, 95% CI: 0.07-0.93, p&#x2009;<&#x2009;0.01), with anxiety positively correlating with depression (r&#x2009;=&#x2009;0.62, p&#x2009;=&#x2009;0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC&#x2009;=&#x2009;0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium

Clinical proteomics in inborn errors of metabolism: from biomarker discovery to implementation.

INTRODUCTION: Inborn errors of metabolism (IEMs) are rare, heterogeneous disorders traditionally diagnosed through genetic testing, enzyme assays, and metabolite measurements. However, these tools often do not fully explain phenotypic variability, organ involvement, disease progression, or treatment response. Clinical proteomics provides a complementary functional layer by capturing changes in protein abundance, proteoforms, post-translational modifications (PTM), and biological pathways, offering insights beyond genotype- and metabolite-based approaches. AREAS COVERED: This review examines the role of high-resolution mass spectrometry and computational proteomics in biomarker discovery and clinical decision-making for IEMs. It focuses on their contribution to diagnosis, variant interpretation, patient stratification, and treatment monitoring. Disease-specific applications are discussed, with the strongest evidence in lysosomal storage disorders, mitochondrial diseases, congenital disorders of glycosylation, and selected neurodegenerative or renal metabolic conditions. The literature search was performed in PubMed, Scopus, Web of Science, and Google Scholar, covering peer-reviewed articles available up to 2026, with emphasis on methodological advances and translational applications in clinical proteomics for IEMs. EXPERT OPINION: Proteomics will not replace established diagnostic tools, but it can help address clinically actionable questions in selected contexts. Translation into clinical practice will require standardized workflows, multicenter validation, clinically anchored endpoints, and integration with other omics approaches.

Humans

Inclusion of Multi-Omic Biomarkers Improves Prediction Accuracy of Response, Relapse, and Overall Survival in Acute Myeloid Leukemia Patients Receiving High-Intensity Induction Chemotherapy.

BACKGROUND: Despite advancements in genetic markers for acute myeloid leukemia (AML) risk stratification, outcome prediction remains challenging due to disease heterogeneity and dynamic genetic changes, highlighting the need for reliable biomarkers to improve AML treatment strategies and patient outcomes. To refine outcome predictions, we investigated the use of microbial-derived biomarkers to predict composite complete remission (CRc), relapse, and survival for patients on high- and low-intensity regimens, and to integrate those variables into the widely clinically utilized European Leukemia Network (ELN-2022) genetic risk classification model for high-intensity-treated patients. METHODS: We first developed machine learning models that integrate baseline fecal metabolomics, 16S rRNA-based stool microbiome features, and clinical metadata (sex, antibiotic administration, AML somatic mutations, and cytogenetics) from two cohorts of AML patients (n&#x2009;=&#x2009;83) undergoing remission induction chemotherapy. Univariate tests and sparse canonical correlation analysis were employed for variable selection and to explore fecal metabolite-microbe relationships. A robust machine learning approach using XGBoost was employed, with 100 stratified data splits (80% training, 20% testing) and coarse-to-fine hyperparameter optimization. Variable importance was aggregated across all models to select key predictors. RESULTS: For high-intensity-treated patients, XGBoost models achieved aggregated AUROC scores of 0.719, 0.729, and 0.65 for CRc, relapse, and overall survival, respectively. For low-intensity-treated patients, these models achieved aggregate AUROC scores of 0.945, 0.724, and 0.768 for these same outcomes, respectively. Integrating the biomarkers identified in the high-intensity machine-learning models with the current ELN-2022 AML risk stratification system effectively stratified patients into risk categories, which obtained higher concordance indices and likelihood ratios, demonstrating improved prognostic accuracy for each outcome compared to ELN-2022 alone. CONCLUSIONS: The inclusion of microbial-derived biomarkers serves as a robust prognostic tool to improve outcome prediction in AML patients, highlighting the potential of its integration into AML risk assessment and paving the way for personalized treatment strategies and improved patient outcomes.

Humans