Search Results (321)

Background: Maple syrup urine disease (MSUD) is a genetic disorder caused by mutations in the branched-chain α-ketoacid dehydrogenase (BCKDH) complex, leading to toxic buildup of branched-chain amino acids (BCAAs) and their ketoacid derivatives. While newborn screening (NBS) and molecular testing are standard diagnostic tools, they face challenges such as delayed results and false positives. Untargeted metabolomics has emerged as a complementary approach, offering comprehensive metabolic profiling and potential for novel biomarker discovery. We previously applied untargeted metabolomics to neonates with MSUD, identifying distinct metabolic signatures. Objective: This follow-up study investigates metabolic changes and biomarkers in pediatric MSUD patients and explores shared dysregulated metabolites between neonatal and pediatric MSUD. Methods: Dried blood spot (DBS) samples from pediatric MSUD patients (n = 14) and matched healthy controls (n = 14) were analyzed using LC/MS-based untargeted metabolomics. Results: In pediatric MSUD, 3716 metabolites were upregulated and 4038 downregulated relative to controls. Among 1080 dysregulated endogenous metabolites, notable biomarkers included uric acid, hypoxanthine, and bilirubin diglucuronide. Affected pathways included sphingolipid, glycerophospholipid, purine, pyrimidine, nicotinate, and nicotinamide metabolism, and steroid hormone biosynthesis. Seventy-two metabolites overlapped with neonatal MSUD cases, some exhibiting inverse trends between age groups. Conclusion: Untargeted metabolomics reveals that the metabolic profiling of MCUD pediatric patients different from that of their controls. Also, there are valuable age-specific and shared metabolic alterations in MSUD, enhancing the understanding of disease progression in MSUD patients. This supports its utility in improving diagnostic precision and developing personalized treatment strategies across developmental stages. Full article

Neuronutrition to improve brain resilience to stress and human health has received considerable attention. The use of specific nutrients is effective in preventing and slowing neurodegenerative and neuropsychiatric disorders. Selective neuronutrients, including polyphenols, short-chain fatty acids (SCFAs), tryptophan, tyrosine, and sulfur metabolites, can modulate the dysregulated nuclear factor erythroid 2 (Nrf2) pathway through neuroepigenetic modifications and altered levels of neurotransmitters such as serotonin, melatonin, and dopamine. In particular, abnormal epigenetic alterations in the promoter function of the NFE2L2/Nrf2 gene may contribute to the onset and progression of various diseases by disrupting cellular homeostasis. Recent evidence has documented that polyphenols are capable of modulating Nrf2 signaling; to do this, they must reverse hypermethylation in the CpG islands of the NFE2L2 gene. This process is achieved by modifying the activity of DNA methyltransferases (DNMTs) and histone deacetylases (HDACs). Furthermore, a diverse group of polyphenolic metabolites can be identified and quantified using innovative mass spectrometry platforms in both in vitro models and human urine samples to investigate redox metabolic homeostasis under physiological and pathophysiological conditions. This review aims to deepen the current understanding of the role of nutrient-derived secondary metabolites. It highlights innovative strategies to effectively prevent, slow, or potentially reverse neuroinflammation and oxidative stress, key drivers of neuronal damage. The targeted application of these metabolites can be considered a novel, personalized neuronutritional approach to promote brain health and neuronal adaptation. Full article

Background: Childhood obesity has reached epidemic levels in developed countries and is an emerging concern in developing regions. Children with excess weight are more likely to maintain this condition over time into adulthood and face a higher risk of developing metabolic disorders such as type 2 diabetes, hypertension, metabolic dysfunction-associated liver disease, and dyslipidemia. Early identification of obesity risk is, therefore, a key public health challenge. Methods: This is an observational, prospective, single-center cohort pilot study in 66 mother–infant dyads recruited at the Gynecology and Obstetrics Service of the Virgen de la Arrixaca University Hospital (Murcia, Spain). The primary objective is to identify early-life, non-invasive biomarkers associated with increased adiposity by integrating multi-omics approaches and analyzing maternal–infant interactions. Pregnant women will be enrolled during the third trimester and will undergo a baseline visit at 38 weeks of gestation for clinical and anthropometric assessment. Buccal swabs and fecal samples will be collected at baseline and in the peripartum period for epigenetic (DNA methylation), metagenomic, and metabolomic analyses. Infants will be evaluated at birth and followed at 6 months, 1 year, 2 years, and 3 years. Each visit will include detailed anthropometric measurements, along with collection of buccal swabs and fecal samples for multi-omics profiling. Conclusions: This multidisciplinary study aims to assess how maternal factors influence infant epigenetic and microbial patterns, and their relation to adiposity development. Early identification of such biomarkers may guide personalized prevention strategies and reduce the long-term burden of obesity-related comorbidities. Full article

Multiple sclerosis (MS) is a chronic, immune-mediated neurodegenerative disorder marked by inflammation, demyelination, and neuronal loss within the central nervous system. Despite advances in diagnostics, current tools remain insufficiently sensitive and specific. Metabolomics has emerged as a promising approach to explore MS pathophysiology and discover novel biomarkers. This PRISMA-guided systematic review included 29 original studies using validated metabolomic techniques in adult patients with MS. Biological samples analyzed included serum, cerebrospinal fluid, and feces. Consistent metabolic alterations were identified across several pathways. The kynurenine pathway demonstrated a shift toward neurotoxic metabolites, alongside reductions in microbial-derived indoles, indicating inflammation and gut dysbiosis. Energy metabolism was impaired, with changes in glycolysis, tricarboxylic acid (TCA) cycle, and mitochondrial function. Lipid metabolism showed widespread dysregulation involving phospholipids, sphingolipids, endocannabinoids, and polyunsaturated fatty acids, some modulated by treatments such as ocrelizumab and interferon-β. Nitrogen metabolism was also affected, including amino acids, peptides, and nucleotides. Non-classical and xenobiotic metabolites, such as myo-inositol, further reflected host–microbiome–environment interactions. Several studies demonstrated the potential of metabolomics-based machine learning to distinguish MS subtypes. These findings highlight the value of metabolomics for biomarker discovery and support its integration into personalized therapeutic strategies in MS. Full article

Metabolic health is a dynamic equilibrium influenced by diet and lifestyle. This review synthesizes evidence on how specific dietary patterns and bioactive nutrients modulate metabolic disorders. Diets like the Mediterranean and DASH plans consistently improve cardiometabolic markers: a Mediterranean diet can halve metabolic syndrome prevalence (~52% reduction) in as little as 6 months, while the DASH diet typically lowers systolic blood pressure by ~5–7 mmHg and modestly improves lipid profiles (LDL-C by ~3–5 mg/dL). Plant-based diets (vegetarian/vegan) are associated with lower BMI, improved insulin sensitivity, and reduced inflammation. Ketogenic diets induce rapid weight loss (~12% body weight vs. 4% on control diets) and improve glycemic control (reducing HbA1c and triglycerides), though long-term effects (elevated LDL) warrant caution. Bioactive compounds present in these diets play critical roles: polyphenols improve insulin signaling and reduce oxidative stress (resveratrol supplementation reduced HOMA-IR by ~0.5 units and fasting glucose by ~0.3 mmol/L); omega-3 fatty acids (fish oil) reduce triglycerides by ~25–30% and inflammation; and probiotic interventions modestly enhance glycemic control (lowering HOMA-IR and HbA1c) and gut health. Personalized nutrition approaches, which account for genetic and microbiome differences, are emerging to maximize these benefits. In conclusion, evidence-based dietary strategies rich in fiber, unsaturated fats, and phytochemicals can substantially improve metabolic health outcomes, underscoring the potential of tailored nutrition in preventing and managing obesity-related metabolic disorders. Full article

Background: Intermittent fasting (IF) is emerging as a promising non-pharmacological intervention in oncology, with the potential to modulate key biological processes including metabolic reprogramming, inflammation, autophagy, and immune function, particularly through the PI3K/AKT/mTOR pathway. However, translating IF into clinical practice requires robust tools to monitor its biological impact and therapeutic effectiveness. Objective: This narrative review aims to present and critically evaluate current diagnostic and monitoring strategies that can support the safe and effective integration of IF into oncological care. Methods: A comprehensive literature search was conducted across PubMed/Medline, Science Direct, Scopus, Wiley Online Library, and Google Scholar using a combination of free-text and MeSH terms related to intermittent fasting, oncology, biomarkers, immunophenotyping, metabolic pathways, gut microbiome, and diagnostic imaging. Results: Two principal categories of monitoring objectives were identified. The first—mechanistic monitoring—focuses on elucidating IF-induced biological effects, including modulation of insulin/IGF-1 signaling, oxidative stress reduction, autophagy activation, immune reprogramming, and microbiome alterations. Advanced research tools such as single-cell RNA sequencing, proteomics, metabolomics, and circulating tumor DNA (ctDNA) assays offer high-resolution insights but currently remain limited to preclinical or translational settings due to cost and complexity. The second—clinical response monitoring—assesses IF’s impact on treatment outcomes, including chemotherapy and immunotherapy response, toxicity reduction, tumor dynamics, and maintenance of nutritional and functional status. This requires clinically validated, accessible, and interpretable diagnostic tools. Conclusions: A dual-layered monitoring framework that integrates both mechanistic insights and clinical applicability is essential for the personalized implementation of IF in oncology. Although preliminary findings are promising, large-scale randomized trials with standardized protocols are necessary to confirm the efficacy, safety, and feasibility of IF in routine oncological care. The integration of IF with modern diagnostics may ultimately contribute to a more individualized, biologically informed cancer treatment paradigm. Full article

Background: The ketogenic diet (KD), a high-fat and low-carbohydrate dietary approach, has been used therapeutically in drug-resistant epilepsy and other neurological and metabolic disorders. Recent interest has shifted toward understanding its broader metabolic effects through metabolomics. This review aims to summarize current knowledge on the biochemical mechanisms and therapeutic implications of the KD, with a particular focus on metabolomic profiling and neurological health. Methods: This narrative review synthesizes findings from the last five years of metabolomic studies investigating the biochemical consequences of the KD and its variants, including the classical KD, modified Atkins diet (MAD), medium-chain triglyceride diet (MCT), and low glycemic index treatment (LGIT). The review integrates data on analytical techniques, such as liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS), and evaluates alterations in key metabolic pathways. Results: The KD significantly modulates energy metabolism, shifting adenosine triphosphate (ATP) production from glycolysis to fatty acid oxidation and ketone body utilization. It affects mitochondrial function, one-carbon metabolism, redox balance, neurotransmitter regulation, and gut–brain axis signaling. Metabolomic profiling has identified β-hydroxybutyrate (βHB) as a key regulatory metabolite influencing mitochondrial respiration. Long-term KD use may impact renal and hepatic function, necessitating clinical caution and individualized nutritional monitoring. Conclusions: Metabolomic analysis provides critical insights into the multifaceted effects of the KD, supporting its role as a targeted metabolic therapy in neurological diseases. However, potential risks linked to prolonged ketosis warrant further investigation. Future studies should focus on personalized applications and long-term safety profiles of KD variants across patient populations. Full article

Treatment-resistant schizophrenia (TRS) affects up to one in three individuals with schizophrenia and is associated with a significant clinical, social, and economic burden. Different from treatment-responsive forms, TRS appears to involve other biological mechanisms extending beyond dopaminergic dysfunctions. This review outlines current knowledge on the molecular and cellular basis of TRS, focusing on alterations in glutamate signaling, imbalances between excitatory and inhibitory activity, disruptions in D-amino acid metabolism, and evidence of neuroinflammation, oxidative stress, and mitochondrial or endoplasmic reticulum dysfunction. Data from genomics, proteomics, metabolomics, preclinical models, and postmortem studies suggest that TRS may have a peculiar neurobiological substrate. Further, multimodal brain imaging studies reveal differences in brain structure, white matter integrity, and network connectivity when compared to treatment-responsive individuals. Altogether, these findings support a shift from the traditional dopamine hypothesis toward a more comprehensive model that includes multiple immune, metabolic, and synaptic factors. Understanding the possible interplay of these complex mechanisms may lead to the identification of potential biomarkers that may help to predict antipsychotic response, as well as the development of more targeted treatments. Early recognition and a deeper biological insight into TRS are essential for improving care and guiding personalized therapeutic strategies. Full article

Human engagement in extreme activities, from spaceflight to deep-sea diving and extreme sports, presents unique physiological challenges. Understanding the molecular mechanisms underlying adaptations to these demands is crucial for developing strategies to enhance human performance and resilience in such environments. This review integrates multi-omics data across a range of extreme phenotypes, including astronauts, scuba divers, acute alcohol consumers, long-haul flight passengers, bodybuilders, and simulation racers. We analyze current literature in genomic, transcriptomic, proteomic, metabolomic, and metagenomic studies to identify common and phenotype-specific adaptations, highlighting potential biomarkers and pathways associated with resilience in harsh conditions. This integrated approach offers insights into human adaptability and provides a foundation for developing personalized strategies to mitigate risks and enhance performance in extreme environments, with particular relevance to extended spaceflight. Full article

Background/Objectives: Atherosclerosis is a major contributor to ischemic cardiovascular diseases (CVDs) such as myocardial infarction and stroke, which are leading causes of mortality and morbidity. The management of atherosclerosis through personalized nutrition has gained importance in recent years due to advancements in nutrigenomics, gut microbiome evaluation, and metabolomics. However, no systematic review has comprehensively evaluated the impact of personalized nutrition interventions on atherosclerotic plaque progression and clinical outcomes in humans. Methods: We adopted a systematic approach based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Key databases like PubMed, Cochrane, Google Scholar, and MEDLINE via EBSCOhost were searched using predefined terms related to personalized nutrition, atherosclerosis, nutrigenomics, and clinical outcomes. Results: Evidence evaluation using the framework of Boffetta et al. for cumulative evidence on the joint effects of genes and environments strongly suggested significant diet–gene interactions. Polymorphisms in the apolipoprotein A-II (APOA2) gene have been shown to influence body mass index and lipid levels. Furthermore, studies have demonstrated that omega-3 polyunsaturated fatty acids (PUFAs) can modulate microRNA expression, thereby impacting lipid metabolism. Epigenetic studies showed that dietary components can modify histone acetylation and non-coding RNA activity, which ultimately influence gene expression related to inflammation and lipid metabolism, improving clinical outcomes in atherosclerosis management. Conclusions: Integrating personalized nutrition into clinical practice promises to enhance atherosclerosis outcomes through targeted dietary interventions. Advancements in personalized nutrition offer a promising pathway toward more effective and personalized approaches to cardiovascular health. Full article

Introduction: Osteoarthritis (OA) is a chronic degenerative joint disease with limited treatment options focused primarily on symptom management. Emerging evidence suggests that dietary interventions may influence inflammation and pain through modulation of the gut microbiome and metabolome. Methods: We conducted a 4-week open-label pilot trial evaluating the effects of an anti-inflammatory dietary intervention (ITIS diet) in 20 patients with knee OA (ClinicalTrials.gov ID: NCT05559463, registered prior to enrollment; sponsor: University of California, San Diego; responsible party: Monica Guma; study start date: 1 October 2021). The following were assessed before and after the intervention: (1) clinical outcomes; (2) gut and salivary microbiomes; and (3) salivary, stool, and plasma metabolomes. Responders were defined as patients achieving ≥30% reduction in Western Ontario and McMaster Universities Arthritis Index (WOMAC) pain scores. Results: The ITIS diet was well-tolerated, with good adherence (66.2%) and a significant improvement in clinical outcomes, including reduced pain and improved overall health measured with the visual analog scale (VAS). Responders (n = 8) showed distinct gut microbiome and metabolome profiles compared to non-responders (n = 12). Notably, taxa within the Lachnospiraceae family exhibited dynamic, bidirectional shifts post-intervention: Anaerostipes and Limivivens were enriched among responders and negatively correlated with pain scores, while Oliverpabstia and Fusicatenibacter were depleted following dietary intervention. These taxa also showed strong correlations with anti-inflammatory metabolites, including hydroxydecanoic acid derivatives and pyridoxine. Furthermore, subsequent network analysis revealed more structured and selective microbiome–metabolome interactions in responders, specifically post-intervention. Conclusions: This pilot study shows that a short-term anti-inflammatory dietary intervention was associated with meaningful changes in the gut microbiome and metabolome. Members of the Lachnospiraceae family emerged as key taxa associated with pain reduction and anti-inflammatory metabolite production. Our findings suggest that specific microbial responses—rather than global diversity changes—may underlie dietary responsiveness in OA. Although exploratory and limited by sample size, our results support further investigation into personalized, microbiome-informed nutritional strategies for OA management. Full article

Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies—such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets—can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Akkermansia muciniphila abundance, SCFA production). In parallel, digital health tools—including mobile health applications, wearable devices, and AI-supported platforms—enhance self-monitoring, adherence, and dynamic feedback in real-world settings. Mechanistic pathways such as gut–brain axis regulation, microbial fermentation, gene–diet interactions, and anti-inflammatory responses are explored to explain inter-individual differences in dietary outcomes. However, challenges such as cost, accessibility, and patient motivation remain and should be addressed to ensure the effective implementation of these integrated strategies in real-world settings. Collectively, these insights underscore the pivotal role of precision nutrition as a cornerstone for personalized, scalable, and sustainable obesity interventions. Full article

Cardiovascular diseases (CVDs) are the leading global cause of death, with long-term hospitalization becoming increasingly frequent in advanced or chronic cases. In this context, the interplay between systemic factors such as lipid metabolism, circulating metabolites, gut microbiota, and oral health is gaining attention for its potential role in influencing inflammation, cardiometabolic risk, and long-term outcomes. Despite their apparent independence, these domains are increasingly recognized as interconnected and influential in cardiovascular pathophysiology. Methods: This narrative review was conducted by analyzing studies published between 2015 and 2024 from databases including PubMed, Scopus, and Web of Science. Keywords such as “lipid profile,” “metabolomics,” “gut microbiota,” “oral health,” and “cardiovascular disease” were used. Original research, meta-analyses, and reviews relevant to hospitalized cardiac patients were included. A critical integrative approach was applied to highlight cross-domain connections. Results and Discussion: Evidence reveals significant interrelations between altered lipid profiles, gut dysbiosis (including increased TMAO levels), metabolic imbalances, and oral inflammation. Each component contributes to a systemic pro-inflammatory state that worsens cardiovascular prognosis, particularly in long-term hospitalized patients. Despite isolated research in each domain, there is a paucity of studies integrating all four. The need for interdisciplinary diagnostic models and preventive strategies is emphasized, especially in populations with frailty or immobilization. Conclusions: Monitoring lipid metabolism, metabolomic shifts, gut microbial balance, and oral status should be considered part of comprehensive cardiovascular care. Gut microbiota exerts a dual role in cardiac health: when balanced, it supports anti-inflammatory and metabolic homeostasis; when dysbiotic, it contributes to systemic inflammation and worsened cardiac outcomes. Future research should aim to develop integrative screening tools and personalized interventions that address the multifactorial burden of disease. A systemic approach may improve both short- and long-term outcomes in this complex and vulnerable patient population. Full article

Male infertility is a multifactorial condition often associated with disruptions in sperm metabolism and mitochondrial function, yet traditional semen analysis provides limited insight into these molecular mechanisms. Understanding sperm bioenergetics and metabolic dysfunctions is crucial for improving the diagnosis and treatment of conditions such as asthenozoospermia and azoospermia. This systematic review synthesizes recent literature, focusing on advanced tools and techniques—including omics technologies, advanced imaging, spectroscopy, and functional assays—that enable comprehensive molecular assessment of sperm metabolism and development. The reviewed studies highlight the effectiveness of metabolomics, proteomics, and transcriptomics in identifying metabolic biomarkers linked to male infertility. Non-invasive imaging modalities such as Raman and magnetic resonance spectroscopy offer real-time metabolic profiling, while the seminal microbiome is increasingly recognized for its role in modulating sperm metabolic health. Despite these advances, challenges remain in clinical validation and implementation of these techniques in routine infertility diagnostics. Integrating molecular metabolic assessments with conventional semen analysis promises enhanced diagnostic precision and personalized therapeutic approaches, ultimately improving reproductive outcomes. Continued research is needed to standardize biomarkers and validate clinical utility. Furthermore, these metabolic tools hold significant potential to elucidate the underlying causes of previously misunderstood and unexplained infertility cases, offering new avenues for diagnosis and treatment. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article