Search Results (853)

Fatigue in elite soccer is a multifaceted phenomenon involving physical, metabolic, psychological, and neuromuscular stressors that accumulate over training and competition. Traditional monitoring tools, while informative, are often invasive, impractical during play, or fail to provide real-time insights. This narrative review synthesizes sweat-based biomarkers linked to fatigue in elite soccer, with a focus on multi-modal domains (neuromuscular, metabolic, inflammatory, psychological). Using an integrative approach, we reviewed studies published across databases including PubMed, Scopus, and Web of Science that evaluate sweat biomarkers of different types of fatigue in soccer. We identified key candidates—lactate, sodium, cortisol, IL-6, and hypoxanthine—and evaluated their potential in non-invasive monitoring. Youth-specific and female populations remain underrepresented. We also assessed each biomarker’s technological maturity using a sweat-specific Technology Readiness Level (TRL) framework. Based on these findings, we propose a translational framework for practitioners and outline priorities for future research and validation in real-world sport settings. Full article

Ankylosing spondylitis (AS) displays wide inter-patient variability that is not accounted for by HLA-B27 alone, suggesting that additional immune and metabolic modifiers contribute to disease severity. Using a genetically matched design, we profiled peripheral blood mononuclear cells from two brother pairs discordant for AS severity and one healthy brother pair. Strand-specific RNA-seq was analyzed with a family-blocked DESeq2 model, while untargeted metabolites were quantified using gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS). Differential features were defined as follows: differentially expressed genes (DEGs) (|log₂FC| ≥ 1 and FDR < 0.05) and metabolites (VIP > 1, FC ≥ 1.2, and BH-adjusted p < 0.05). Pathway enrichment was performed with KEGG and Gene Ontology (GO). A total of 325 genes were differentially expressed. Type I interferon and neutrophil granule transcripts (e.g., IFI44L, ISG15, S100A8/A9) were markedly up-regulated, whereas mitochondrial β-oxidation genes (ACADM, CPT1A, ACOT12) were repressed. Metabolomics revealed 110 discriminant features, including 25 MS/MS-annotated metabolites. Primary bile acid intermediates were depleted, whereas oxidized fatty acid derivatives such as 12-Z-octadecadienal and palmitic amide accumulated. Spearman correlation identified two antagonistic modules (i) interferon/neutrophil genes linked to pro-oxidative lipids and (ii) lipid catabolism genes linked to bile acid species that persisted when severe and mild siblings were compared directly. Enrichment mapping associated these modules with viral defense, neutrophil degranulation, fatty acid β-oxidation, and bile acid biosynthesis pathways. This sibling-paired peripheral blood mononuclear cell (PBMC) dual-omics study delineates an interferon-driven lipid–bile acid axis that tracks AS severity, supporting composite PBMC-based biomarkers for future prospective validation and highlighting mitochondrial lipid clearance and bile acid homeostasis as potential therapeutic targets. Full article

Spinal cord injuries are often devastating and result in severe functional limitations. Our review breaks down the pathophysiology, molecular mechanisms, and omics approaches regarding spinal cord injuries. The pathophysiology can be divided into two main phases, with the secondary phase being of greater interest. Understanding the underlying mechanisms behind these phases allows for targeted approaches to be developed. Advancements in omics technologies (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) are excellent tools in creating tailored spinal cord injury treatment plans. Emerging therapeutic solutions involving ion imbalance, oxidative stress, and mitochondrial dysfunction also show promising results. Mitochondrial transplantation has shown promising initial results in maintaining cellular homeostasis and reducing inflammation. However, significant challenges remain in translating the omics and therapeutic approaches from animal models to clinical trials. Full article

Background: Circulating tumor DNA (ctDNA), shed into bodily fluids by cancer cells through apoptosis, necrosis, or active secretion, is currently used in the field of genomic investigation in clinical settings, primarily for advanced stages of non-small-cell lung cancer (NSCLC). However, its potential role in guiding the multi-omic approach to early-stage NSCLC is emerging as a promising area of investigation. Efforts are being made to integrate the genomics not only in surgery, but also in the definition of long-term prognosis after surgical or radiotherapy and for the prediction of recurrence. Methods: An extensive literature search was conducted on PubMed, covering publications from 2000 to 2024. Using the advanced search tool, titles and abstracts were filtered based on the following keywords: ctDNA, early stage, NSCLC. From this search, 20 studies that fulfilled all inclusion criteria were selected for analysis in this review. Results: This review highlights the growing body of evidence supporting the potential clinical use of ctDNA as a genomic biomarker in managing early-stage NSCLC. Baseline ctDNA levels offer valuable information about tumor molecular biology and histological characteristics. Beyond its prognostic value before treatment, liquid biopsy has proven useful for tracking minimal residual disease and forecasting recurrence following curative interventions such as surgery or radiotherapy. Future adjuvant treatment decisions may increasingly rely on predictive models that incorporate liquid biopsy findings alongside other clinical factors. Conclusions: The potential use of this analyte introduces new opportunities for the integration of genomic data in treatment, as well as relapse monitoring with more accurate and innovative than traditional methods, particularly in patients with early-stage NSCLC Full article

Precision nutrition is a tailored dietary approach that considers an individual’s genetic and metabolic profile, lifestyle factors, and specific nutritional needs to improve health and potentially modify disease progression. While research is ongoing into precision nutrition approaches for preventing dementia, there is no evidence on its targeted application to slow dementia-related disease progression and mitigate functional and cognitive decline. This narrative review addresses this gap by synthesising evidence on nutrient–gene interactions, genotype, gut microbiome, nutritional status and the interplay between metabolic pathways implicated in neuroinflammation and neurodegeneration to modify disease progression in a protective or therapeutic manner. Understanding and addressing comorbidities that share pathological mechanisms with dementia have the potential to enhance the understanding of precision nutrition to inform more effective, tailored approaches to slow dementia progression. To increase the robustness of precision nutrition trials for people with dementia, further research is needed into biomarker discovery, multi-omics technologies, and increasing mechanistic research to map the precise biological pathways underpinning the interactions between diet, gene expression, and neuroinflammation. Moreover, there is a need to evaluate the feasibility of precision nutrition for people experiencing cognitive impairment. Addressing these gaps will determine if people with dementia can benefit from precision nutrition and, subsequently, improve their quality of life and health outcomes. Full article

Congenital disorders of glycosylation (CDG) are a group of rare, multisystemic genetic diseases caused by defects in glycan biosynthesis and protein glycosylation. Their broad clinical and genetic heterogeneity often require advanced diagnostic strategies. Clinical glycomics and glycoproteomics have emerged as powerful tools for understanding and diagnosing CDG by enabling high-resolution analysis of glycan structures and glycoproteins. Advancements in high-throughput mass spectrometry (MS) and site-specific glycoproteomics have led to the identification of disease-relevant biomarkers, providing insight into underlying glycosylation defects. These technologies enable detailed analysis of glycan structures and glycoproteins, improving early diagnosis, supporting biomarker discovery, and facilitating therapy monitoring. Integration with genomic and clinical data, including the use of dried blood spot testing and isotopic tracing, further enhances diagnostic precision and reveals the functional consequences of pathogenic variants. While challenges remain in standardizing methods, ensuring accessibility, and implementing bioinformatics tools, global collaborations and harmonized guidelines are beginning to address these gaps. Future directions include the use of artificial intelligence in data analysis, the development of comprehensive diagnostic frameworks, and international efforts to standardize glycomic methods. Collectively, these advances reinforce the growing clinical value of glycomics and glycoproteomics in the diagnosis and management of CDG. Full article

The 2021 WHO classification of brain tumours revolutionised the oncological field by emphasising the role of molecular, genetic and pathogenetic advances in classifying brain tumours. In this context, incidental gliomas have been increasingly identified due to the widespread performance of standard and advanced MRI sequences and represent a diagnostic and therapeutic challenge. The impactful decision to perform a surgical procedure deeply relies on the non-invasive identification of features or parameters that may correlate with brain tumour genetic profile and grading. Therefore, it is paramount to reach an early and proper diagnosis through neuroradiological techniques, such as MRI. Standard MRI sequences are the cornerstone of diagnosis, while consolidated and emerging roles have been awarded to advanced sequences such as Diffusion-Weighted Imaging/Apparent Diffusion Coefficient (DWI/ADC), Perfusion-Weighted Imaging (PWI), Magnetic Resonance Spectroscopy (MRS), Diffusion Tensor Imaging (DTI) and functional MRI (fMRI). The current novelty relies on the application of AI in brain neuro-oncology, mainly based on radiomics and radiogenomics models, which enhance standard and advanced MRI sequences in predicting glioma genetic status by identifying the mutation of multiple key biomarkers deeply impacting patients’ diagnosis, prognosis and treatment, such as IDH, EGFR, TERT, MGMT promoter, p53, H3-K27M, ATRX, Ki67 and 1p19. AI-driven models demonstrated high accuracy in glioma detection, grading, prognostication, and pre-surgical planning and appear to be a promising frontier in the neuroradiological field. On the other hand, standardisation challenges in image acquisition, segmentation and feature extraction variability, data scarcity and single-omics analysis, model reproducibility and generalizability, the black box nature and interpretability concerns, as well as ethical and privacy challenges remain key issues to address. Future directions, rooted in enhanced standardisation and multi-institutional validation, advancements in multi-omics integration, and explainable AI and federated learning, may effectively overcome these challenges and promote efficient AI-based models in glioma management. The aims of our multidisciplinary review are to: (1) extensively present the role of standard and advanced MRI sequences in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (2) give an overview of the current and main applications of AI tools in the differential diagnosis of iLGGs as compared to HGGs (High-Grade Gliomas); (3) show the role of MRI, radiomics and radiogenomics in unravelling glioma genetic profiles. Standard and advanced MRI, radiomics and radiogenomics are key to unveiling the grading and genetic profile of gliomas and supporting the pre-operative planning, with significant impact on patients’ differential diagnosis, prognosis prediction and treatment strategies. Today, neuroradiologists are called to efficiently use AI tools for the in vivo, non-invasive, and comprehensive assessment of gliomas in the path towards patients’ personalised medicine. Full article

Obesity and related metabolic disorders are major global health challenges. Postbiotics, such as heat-inactivated probiotics, have attracted attention for their improved safety, stability, and potential metabolic benefits compared to live probiotics. However, the comparative anti-obesity effects and mechanisms of live versus heat-inactivated Lactiplantibacillus plantarum FRT4 remain unclear, so this study systematically evaluated their effects and mechanisms in high-fat-diet-induced obese mice. Mice received oral administration of live or heat-inactivated FRT4 (prepared by heating in a water bath at 80 °C for 5 min) for 16 weeks. Comprehensive analyses included metabolic profiling, histological evaluation, serum and liver biomarkers, gut microbiota composition, liver metabolomics, and transcriptomics. Both live and inactivated FRT4 significantly reduced body weight gain, adiposity, hepatic steatosis, and dyslipidemia, with inactivated FRT4 exhibiting comparable or superior efficacy. Notably, inactivated FRT4 restored gut microbiota composition, increased short-chain fatty acid production, and regulated hepatic metabolic pathways. Multi-omics analyses revealed modulation of lipid biosynthesis, amino acid metabolism, and energy utilization pathways. Specifically, the “biosynthesis of unsaturated fatty acids” pathway was downregulated in metabolomics and significantly enriched in transcriptomics, highlighting its central role in FRT4M-mediated metabolic reprogramming. These findings demonstrate that heat-inactivated Lp. plantarum FRT4 exerts systemic anti-obesity effects via gut–liver axis modulation, supporting its potential as a promising postbiotic intervention for obesity and metabolic dysfunction. Full article

Asthma has traditionally been viewed as a single disease, but recent research reveals its clinical and molecular complexity. This perspective highlights the need to shift from a traditional, uniform treatment paradigm to one that embraces the heterogeneity of asthma across individuals. Each patient presents a unique clinical story shaped by a complex interplay of genetic predispositions, developmental programming during critical early-life windows, the influence of sex and hormones, and lifelong environmental exposures. Asthma comprises multiple subtypes with distinct clinical and biological features. Furthermore, lifestyle factors such as obesity and smoking, along with highly prevalent comorbidities like allergic rhinitis and gastroesophageal reflux disease, significantly modify the disease’s course and response to treatment. This article explores how classifying the disease into clinical phenotypes (observable characteristics) and molecular endotypes (underlying mechanisms)—particularly the distinction between T2-high and T2-low inflammation—provides a crucial framework for managing this complexity. The application of this framework, guided by biomarkers, has enabled the development of targeted biologic therapies that can transform care for specific patient subgroups. Despite these advances, significant challenges remain. The pathophysiology of certain subgroups, particularly non-T2 asthma, remains poorly defined, and there is an urgent need for reliable predictive biomarkers to guide therapy and monitor outcomes. It is our opinion that future studies must adopt a systems-biology strategy, with a multi-omics approach that constructs a comprehensive molecular profile of each patient. This integrative methodology will require the use of advanced computational methods, including machine learning and artificial intelligence, to decipher the complex pathways linking genetic and environmental inputs to clinical disease. In conclusion, this article argues for a more personalized understanding of asthma, urging clinicians and researchers to consider each patient’s unique clinical presentation. Full article

Hidradenitis suppurativa (HS) is a chronic, relapsing inflammatory dermatosis of the pilosebaceous unit characterized by nodules, abscesses, and dermal tunnels. Recent transcriptomic studies have implicated dysregulation of innate and adaptive immune responses, epidermal barrier dysfunction, and systemic metabolic alterations. This review synthesizes findings from 16 studies investigating the HS transcriptome using bulk and single-cell RNA sequencing. Differential gene expression analyses revealed extensive upregulation of inflammatory cytokines and chemokines, particularly in lesional and perilesional skin. These changes were also mirrored in non-lesional skin, suggesting diffuse immune dysregulation beyond visibly affected areas. Downregulated pathways include those involved in lipid metabolism, muscle contraction, and neuronal signaling, potentially linking HS to obesity, metabolic syndrome, and neuropsychiatric comorbidities. Single-cell transcriptomics confirmed the enrichment of keratinocytes and immune cells (B cells, plasma cells, M1 macrophages, and T cells) with proinflammatory profiles in HS lesions. Keratinocyte dysfunction further implicated a compromised epidermal barrier in disease pathogenesis. While transcriptomic studies have advanced mechanistic understanding and highlighted therapeutic targets—such as the IL-1β–TH17 axis and B cell signaling pathways—methodological heterogeneity limits cross-study comparisons. Integration of multi-omics data and standardized phenotyping will be essential to identify robust biomarkers, stratify HS subtypes, and guide personalized therapeutic approaches. Full article

Homologous recombination deficiency (HRD) is a pivotal biomarker in precision oncology, driving therapeutic strategies for ovarian and breast cancers through impaired DNA double-strand break repair. This narrative review synthesizes recent advances (2021–2025) in HRD’s biological basis, prevalence, detection methods, and clinical implications, focusing on high-grade serous ovarian carcinoma (HGSOC; ~50% HRD prevalence) and triple-negative breast cancer (TNBC; 50–70% prevalence). HRD arises from genetic (BRCA1/2, RAD51C/D, PALB2) and epigenetic alterations (e.g., BRCA1 methylation), leading to genomic instability detectable via scars (LOH, TAI, LST) and mutational signatures (e.g., COSMIC SBS3). Advanced detection integrates genomic assays (Myriad myChoice CDx, Caris HRD, FoundationOne CDx), functional assays (RAD51 foci), and epigenetic profiling, with tools like HRProfiler and GIScar achieving >90% sensitivity. HRD predicts robust responses to PARP inhibitors (PARPi) and platinum therapies, extending progression-free survival by 12–36 months in HGSOC. However, resistance mechanisms (BRCA reversion, SETD1A/EME1, SOX5) and assay variability (60–70% non-BRCA concordance) pose challenges. We propose a conceptual framework in ^{Section 10}, integrating multi-omics, methylation analysis, and biallelic reporting to enhance detection and therapeutic stratification. Regional variations (e.g., Asian cohorts) and disparities in access underscore the need for standardized, cost-effective diagnostics. Future priorities include validating novel biomarkers (SBS39, miR-622) and combination therapies (PARPi with ATR inhibitors) to overcome resistance and broaden HRD’s applicability across cancers. Full article

Schizophrenia (SCZ) is a complex psychiatric disorder with heterogeneous molecular underpinnings that remain poorly resolved by conventional single-omics approaches, limiting biomarker discovery and mechanistic insights. To address this gap, we applied an artificial intelligence (AI)-driven multi-omics framework to an open access dataset comprising plasma proteomics, post-translational modifications (PTMs), and metabolomics to systematically dissect SCZ pathophysiology. In a cohort of 104 individuals, comparative analysis of 17 machine learning models revealed that multi-omics integration significantly enhanced classification performance, reaching a maximum AUC of 0.9727 (95% CI: 0.8889–1.000) using LightGBMXT, compared to 0.9636 (95% CI: 0.8636–1.0000) with CNNBiLSTM for proteomics alone. Interpretable feature prioritization identified carbamylation at immunoglobulin-constant region sites IGKC_K20 and IGHG1_K8, alongside oxidation of coagulation factor F10 at residue M8, as key discriminative molecular events. Functional analyses identified significantly enriched pathways including complement activation, platelet signaling, and gut microbiota-associated metabolism. Protein interaction networks further implicated coagulation factors F2, F10, and PLG, as well as complement regulators CFI and C9, as central molecular hubs. The clustering of these molecules highlights a potential axis linking immune activation, blood coagulation, and tissue homeostasis, biological domains increasingly recognized in psychiatric disorders. These results implicate immune–thrombotic dysregulation as a critical component of SCZ pathology, with PTMs of immune proteins serving as quantifiable disease indicators. Our work delineates a robust computational strategy for multi-omics integration into psychiatric research, offering biomarker candidates that warrant further validation for diagnostic and therapeutic applications. Full article

Circadian rhythms are intrinsic 24 h cycles that regulate metabolic processes across multiple tissues, with skeletal muscle emerging as a central node in this temporal network. Muscle clocks govern gene expression, fuel utilisation, mitochondrial function, and insulin sensitivity, thereby maintaining systemic energy homeostasis. However, circadian misalignment, whether due to behavioural disruption, nutrient excess, or metabolic disease, impairs these rhythms and contributes to insulin resistance, and the development of obesity, and type 2 diabetes mellitus. Notably, the muscle clock remains responsive to non-photic cues, particularly exercise, which can reset and amplify circadian rhythms even in metabolically impaired states. This work synthesises multi-level evidence from rodent models, human trials, and in vitro studies to elucidate the role of skeletal muscle clocks in circadian metabolic health. It explores how exercise entrains the muscle clock via molecular pathways involving AMPK, SIRT1, and PGC-1α, and highlights the time-of-day dependency of these effects. Emerging data demonstrate that optimally timed exercise enhances glucose uptake, mitochondrial biogenesis, and circadian gene expression more effectively than time-agnostic training, especially in individuals with metabolic dysfunction. Finally, findings are integrated from multi-omic approaches that have uncovered dynamic, time-dependent molecular signatures that underpin circadian regulation and its disruption in obesity. These technologies are uncovering biomarkers and signalling nodes that may inform personalised, temporally targeted interventions. By combining mechanistic insights with translational implications, this review positions skeletal muscle clocks as both regulators and therapeutic targets in metabolic disease. It offers a conceptual framework for chrono-exercise strategies and highlights the promise of multi-omics in developing precision chrono-medicine approaches aimed at restoring circadian alignment and improving metabolic health outcomes. Full article

Liquid biopsy has emerged as a valuable tool for the detection and monitoring of colorectal cancer (CRC), providing minimally invasive insights into tumor biology through circulating biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Additional biomarkers, including tumor-educated platelets (TEPs) and exosomal RNAs, offer further potential for early detection and prognostic role, although ongoing clinical validation is still needed. This review summarizes the current evidence on the diagnostic, prognostic, and predictive capabilities of liquid biopsy in both metastatic and non-metastatic CRC. In the non-metastatic setting, liquid biopsy is gaining traction in early detection through screening and in identifying minimal residual disease (MRD), potentially guiding adjuvant treatment and reducing overtreatment. In contrast, liquid biopsy is more established in metastatic CRC for monitoring treatment responses, clonal evolution, and mechanisms of resistance. The integration of ctDNA-guided treatment algorithms into clinical practice could optimize therapeutic strategies and minimize unnecessary interventions. Despite promising advances, challenges remain in assay standardization, early-stage sensitivity, and the integration of multi-omic data for comprehensive tumor profiling. Future efforts should focus on enhancing the sensitivity of liquid biopsy platforms, validating emerging biomarkers, and expanding multi-omic approaches to support more targeted and personalized treatment strategies across CRC stages. Full article

Recent studies have demonstrated that the development and progression of hypertensive kidney injury comprise not only elevated systemic blood pressure but also a complex interplay of cellular, molecular, and genetic mechanisms. In this report, we outline the key emerging pathways—ranging from dysregulated renin–angiotensin system signaling, oxidative stress, immune-mediated inflammation, and metabolic abnormalities to epigenetic alterations and genetic susceptibilities—that contribute to kidney damage in hypertensive conditions. In addition, we also discuss precision medicine approaches like biomarker-directed therapies, pharmacologically targeted therapies, and device-based innovations for modulating these pathways. This integrative review emphasizes the application of omics technologies and genetically guided interventions to better stratify patients and offer personalized care for hypertensive kidney disease. Full article